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Автор: Sedgewick Robert Wayne Kevin
Теги: programming computer science
ISBN: 978-0-13-407642-3
Год: 2017
Текст
Computer
Science
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Computer
Science
An Interdisciplinary Approach
Robert Sedgewick
Kevin Wayne
Princeton University
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ISBN-13: 978-0-13-407642-3
ISBN-10: 0-13-407642-7
2
16
______________________________
To Adam, Andrew, Brett, Robbie,
Henry, Iona, Rose, Peter,
and especially Linda
______________________________
______________________________
To Jackie, Alex, and Michael
______________________________
Contents
Preface . . . . . . . . . . . . . . . . . . . . xiii
1—Elements of Programming . . . . . . . . . . . . 1
1.1 Your First Program
2
1.2 Built-in Types of Data
14
1.3 Conditionals and Loops
50
1.4 Arrays
90
1.5 Input and Output
126
1.6 Case Study: Random Web Surfer
170
2—Functions and Modules . . . . . . . . . . . .
2.1 Defining Functions
192
2.2 Libraries and Clients
226
2.3 Recursion
262
2.4 Case Study: Percolation
300
191
3—Object-Oriented Programming . . . . . . . . .
3.1 Using Data Types
330
3.2 Creating Data Types
382
3.3 Designing Data Types
428
3.4 Case Study: N-Body Simulation
478
329
4—Algorithms and Data Structures . . . . . . . . . 493
4.1 Performance
494
4.2 Sorting and Searching
532
4.3 Stacks and Queues
566
4.4 Symbol Tables
624
4.5 Case Study: Small-World Phenomenon
670
vi
5—Theory of Computing . . . . . . . . . . . . . 715
5.1 Formal Languages
718
5.2 Turing Machines
766
5.3 Universality
786
5.4 Computability
806
5.5 Intractability
822
6—A Computing Machine. . . . . . . . . . . . . 873
6.1 Representing Information
874
6.2 TOY Machine
906
6.3 Machine-Language Programming
930
6.4 TOY Virtual Machine
958
7—Building a Computing Device . . . . . . . . . . 985
7.1 Boolean Logic
986
7.2 Basic Circuit Model
1002
7.3 Combinational Circuits
1012
7.4 Sequential Circuits
1048
7.5 Digital Devices
1070
Context. . . . . . . . . . . . . . . . . . . 1093
Glossary . . . . . . . . . . . . . . . . . .
1097
Index . . . . . . . . . . . . . . . . . . . . 1107
APIs . . . . . . . . . . . . . . . . . . . . 1139
vii
Programs
Elements of Programming
Functions and Modules
Your First Program
Defining Functions
1.1.1 Hello, World. . . . . . . . . . . 4
1.1.2 Using a command-line argument . 7
Built-in Types of Data
1.2.1
1.2.2
1.2.3
1.2.4
1.2.5
String concatenation . . . . . . . 20
Integer multiplication and division.23
Quadratic formula. . . . . . . . 25
Leap year . . . . . . . . . . . 28
Casting to get a random integer. . 34
Conditionals and Loops
1.3.1
1.3.2
1.3.3
1.3.4
1.3.5
1.3.6
1.3.7
1.3.8
1.3.9
Flipping a fair coin . . . . . . . 53
Your first while loop. . . . . . . 55
Computing powers of 2 . . . . . 57
Your first nested loops. . . . . . 63
Harmonic numbers. . . . . . . 65
Newton’s method. . . . . . . . 66
Converting to binary. . . . . . . 68
Gambler’s ruin simulation . . . . 71
Factoring integers. . . . . . . . 73
Arrays
1.4.1
1.4.2
1.4.3
1.4.4
Generating a random sequence . 128
Interactive user input. . . . . . 136
Averaging a stream of numbers . 138
A simple filter. . . . . . . . . 140
Standard input-to-drawing filter. 147
Bouncing ball. . . . . . . . . 153
Digital signal processing . . . . . 158
Case Study: Random Web Surfer
viii
Harmonic numbers (revisited) . 194
Gaussian functions. . . . . . . 203
Coupon collector (revisited) . . . 206
Play that tune (revisited). . . . . 213
Libraries and Clients
2.2.1
2.2.2
2.2.3
2.2.4
2.2.5
2.2.6
Random number library. . . . . 234
Array I/O library. . . . . . . . 238
Iterated function systems. . . . . 241
Data analysis library. . . . . . . 245
Plotting data values in an array . 247
Bernoulli trials . . . . . . . . . 250
Recursion
2.3.1
2.3.2
2.3.3
2.3.4
2.3.5
2.3.6
Euclid’s algorithm. . . . . . . 267
Towers of Hanoi . . . . . . . . 270
Gray code . . . . . . . . . . . 275
Recursive graphics. . . . . . . 277
Brownian bridge. . . . . . . . 279
Longest common subsequence . 287
Case Study: Percolation
Sampling without replacement. . 98
Coupon collector simulation . . . 102
Sieve of Eratosthenes. . . . . . 104
Self-avoiding random walks. . . 113
Input and Output
1.5.1
1.5.2
1.5.3
1.5.4
1.5.5
1.5.6
1.5.7
2.1.1
2.1.2
2.1.3
2.1.4
1.6.1 Computing the transition matrix. 173
1.6.2 Simulating a random surfer . . . 175
1.6.3 Mixing a Markov chain. . . . . 182
2.4.1
2.4.2
2.4.3
2.4.4
2.4.5
2.4.6
Percolation scaffolding. . . . . 304
Vertical percolation detection. . . 306
Visualization client . . . . . . . 309
Percolation probability estimate .311
Percolation detection . . . . . . 313
Adaptive plot client . . . . . . . 316
Object-Oriented Programming
Algorithms and Data Structures
Using Data Types
Performance
3.1.1 Identifying a potential gene . . . 337
3.1.2 Albers squares . . . . . . . . . 342
3.1.3 Luminance library . . . . . . . 345
3.1.4 Converting color to grayscale. . . 348
3.1.5 Image scaling . . . . . . . . . 350
3.1.6 Fade effect. . . . . . . . . . . 352
3.1.7 Concatenating files. . . . . . . 356
3.1.8 Screen scraping for stock quotes . 359
3.1.9 Splitting a file . . . . . . . . . 360
Creating Data Types
3.2.1
3.2.2
3.2.3
3.2.4
3.2.5
3.2.6
3.2.7
3.2.8
Charged particle . . . . . . . . 387
Stopwatch. . . . . . . . . . . 391
Histogram. . . . . . . . . . . 393
Turtle graphics. . . . . . . . . 396
Spira mirabilis. . . . . . . . . 399
Complex number. . . . . . . . 405
Mandelbrot set. . . . . . . . . 409
Stock account. . . . . . . . . 413
Designing Data Types
3.3.1
3.3.2
3.3.3
3.3.4
3.3.5
Complex number (alternate) . . . 434
Counter. . . . . . . . . . . . 437
Spatial vectors . . . . . . . . . 444
Document sketch. . . . . . . . 461
Similarity detection. . . . . . . 463
Case Study: N-Body Simulation
3.4.1
3.4.2
Gravitational body . . . . . . . 482
N-body simulation. . . . . . . 485
4.1.1 3-sum problem. . . . . . . . . 497
4.1.2 Validating a doubling hypothesis. 499
Sorting and Searching
4.2.1
4.2.2
4.2.3
4.2.4
4.2.5
4.2.6
4.2.7
Binary search (20 questions). . . 534
Bisection search . . . . . . . . 537
Binary search (sorted array). . . 539
Insertion sort . . . . . . . . . 547
Doubling test for insertion sort .549
Mergesort . . . . . . . . . . . 552
Frequency counts. . . . . . . . 557
Stacks and Queues
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.3.7
4.3.8
Stack of strings (array). . . . . 570
Stack of strings (linked list). . . . 575
Stack of strings (resizing array) . 579
Generic stack . . . . . . . . . 584
Expression evaluation . . . . . . 588
Generic FIFO queue (linked list). 594
M/M/1 queue simulation . . . . 599
Load balancing simulation . . . . 607
Symbol Tables
4.4.1
4.4.2
4.4.3
4.4.4
4.4.5
Dictionary lookup. . . . . . . 631
Indexing. . . . . . . . . . . 633
Hash table. . . . . . . . . . . 638
Binary search tree. . . . . . . . 646
Dedup filter. . . . . . . . . . 653
Case Study: Small-World Phenomenon
4.5.1
4.5.2
4.5.3
4.5.4
4.5.5
4.5.6
Graph data type . . . . . . . . 677
Using a graph to invert an index . 681
Shortest-paths client . . . . . . 685
Shortest-paths implementation .691
Small-world test. . . . . . . . 696
Performer–performer graph . . . 698
ix
Theory of Computing
A Computing Machine
Formal Languages
Representing Information
5.1.1 RE recognition. . . . . . . . . 729
5.1.2 Generalized RE pattern match . 736
5.1.3 Universal virtual DFA . . . . . . 743
Turing Machines
5.2.1 Virtual Turing machine tape . . . 776
5.2.2 Universal virtual TM. . . . . . 777
Universality
Computability
Intractability
5.5.1 SAT solver . . . . . . . . . . 855
6.1.1
6.1.2
Number conversion. . . . . . . 881
Floating-point components . . . 893
TOY Machine
6.2.1 Your first TOY program. . . . . 915
6.2.2 Conditionals and loops. . . . . 921
6.2.3 Self-modifying code. . . . . . . 923
Machine-Language Programming
6.3.1 Calling a function . . . . . . . 933
6.3.2 Standard output. . . . . . . . 935
6.3.3 Standard input. . . . . . . . . 937
6.3.4 Array processing . . . . . . . . 939
6.3.5 Linked structures. . . . . . . . 943
TOY Virtual Machine
6.4.1
x
TOY virtual machine. . . . . . 967
Circuits
Building a Computing Device
Boolean Logic
Basic Circuit Model
Combinational Circuits
Basic logic gates. . . . . . . . . . . 1014
Selection multiplexer. . . . . . . . 1024
Decoder. . . . . . . . . . . . . . 1021
Demultiplexer. . . . . . . . . . . 1022
Multiplexer . . . . . . . . . . . . 1023
XOR . . . . . . . . . . . . . . . 1024
Majority. . . . . . . . . . . . . . 1025
Odd parity. . . . . . . . . . . . . 1026
Adder. . . . . . . . . . . . . . . 1029
ALU . . . . . . . . . . . . . . . 1033
Bus multiplexer. . . . . . . . . . . 1036
Sequential Circuits
SR flip-flop . . . . . . . . . . . . 1050
Register bit. . . . . . . . . . . . . 1051
Register. . . . . . . . . . . . . . 1052
Memory bit. . . . . . . . . . . . 1056
Memory. . . . . . . . . . . . . . 1057
Clock . . . . . . . . . . . . . . . 1061
Digital Devices
Program counter. . . . . . . . . . 1074
Control. . . . . . . . . . . . . . 1081
CPU . . . . . . . . . . . . . . . 1086
xi
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Preface
T
he basis for education in the last millennium was “reading, writing, and arithmetic”; now it is reading, writing, and computing. Learning to program is an
essential part of the education of every student in the sciences and engineering.
Beyond direct applications, it is the first step in understanding the nature of computer science’s undeniable impact on the modern world. This book aims to teach
programming to those who need or want to learn it, in a scientific context.
Our primary goal is to empower students by supplying the experience and
basic tools necessary to use computation effectively. Our approach is to teach students that composing a program is a natural, satisfying, and creative experience.
We progressively introduce essential concepts, embrace classic applications from
applied mathematics and the sciences to illustrate the concepts, and provide opportunities for students to write programs to solve engaging problems. We seek
also to demystify computation for students and to build awareness about the substantial intellectual underpinnings of the field of computer science.
We use the Java programming language for all of the programs in this book.
The first part of the book teaches basic skills for computational problem solving
that are applicable in many modern computing environments, and it is a selfcontained treatment intended for people with no previous experience in programming. It is about fundamental concepts in programming, not Java per se. The second
part of the book demonstrates that there is much more to computer science than
programming, but we do often use Java programs to help communicate the main
ideas.
This book is an interdisciplinary approach to the traditional CS1 curriculum,
in that we highlight the role of computing in other disciplines, from materials science to genomics to astrophysics to network systems. This approach reinforces for
students the essential idea that mathematics, science, engineering, and computing
are intertwined in the modern world. While it is a CS1 textbook designed for any
first-year college student, the book also can be used for self-study.
xiii
Preface
xiv
Coverage
The first part of the book is organized around three stages of learning
to program: basic elements, functions, object-oriented programming, and algorithms. We provide the basic information that readers need to build confidence
in composing programs at each level before moving to the next level. An essential
feature of our approach is the use of example programs that solve intriguing problems, supported with exercises ranging from self-study drills to challenging problems that call for creative solutions.
Elements of programming include variables, assignment statements, built-in
types of data, flow of control, arrays, and input/output, including graphics and
sound.
Functions and modules are the students’ first exposure to modular programming. We build upon students’ familiarity with mathematical functions to
introduce Java functions, and then consider the implications of programming
with functions, including libraries of functions and recursion. We stress the fundamental idea of dividing a program into components that can be independently
debugged, maintained, and reused.
Object-oriented programming is our introduction to data abstraction. We
emphasize the concept of a data type and its implementation using Java’s class
mechanism. We teach students how to use, create, and design data types. Modularity, encapsulation, and other modern programming paradigms are the central
concepts of this stage.
The second part of the book introduces advanced topics in computer science:
algorithms and data structures, theory of computing, and machine architecture.
Algorithms and data structures combine these modern programming paradigms with classic methods of organizing and processing data that remain effective
for modern applications. We provide an introduction to classical algorithms for
sorting and searching as well as fundamental data structures and their application,
emphasizing the use of the scientific method to understand performance characteristics of implementations.
Theory of computing helps us address basic questions about computation,
using simple abstract models of computers. Not only are the insights gained invaluable, but many of the ideas are also directly useful and relevant in practical
computing applications.
Machine architecture provides a path to understanding what computation
actually looks like in the real world—a link between the abstract machines of the
theory of computing and the real computers that we use. Moreover, the study of
Preface
machine architecture provides a link to the past, as the microprocessors found in
today’s computers and mobile devices are not so different from the first computers
that were developed in the middle of the 20th century.
Applications in science and engineering are a key feature of the text. We motivate each programming concept that we address by examining its impact on
specific applications. We draw examples from applied mathematics, the physical
and biological sciences, and computer science itself, and include simulation of
physical systems, numerical methods, data visualization, sound synthesis, image
processing, financial simulation, and information technology. Specific examples
include a treatment in the first chapter of Markov chains for web page ranks and
case studies that address the percolation problem, n-body simulation, and the
small-world phenomenon. These applications are an integral part of the text. They
engage students in the material, illustrate the importance of the programming concepts, and provide persuasive evidence of the critical role played by computation in
modern science and engineering.
Historical context is emphasized in the later chapters. The fascinating story of
the development and application of fundamental ideas about computation by Alan
Turing, John von Neumann, and many others is an important subtext.
Our primary goal is to teach the specific mechanisms and skills that are
needed to develop effective solutions to any programming problem. We work with
complete Java programs and encourage readers to use them. We focus on programming by individuals, not programming in the large.
Use in the Curriculum
This book is intended for a first-year college course
aimed at teaching computer science to novices in the context of scientific applications. When such a course is taught from this book, college student will learn to
program in a familiar context. Students completing a course based on this book
will be well prepared to apply their skills in later courses in their chosen major and
to recognize when further education in computer science might be beneficial.
Prospective computer science majors, in particular, can benefit from learning
to program in the context of scientific applications. A computer scientist needs the
same basic background in the scientific method and the same exposure to the role
of computation in science as does a biologist, an engineer, or a physicist.
Indeed, our interdisciplinary approach enables colleges and universities to
teach prospective computer science majors and prospective majors in other fields
in the same course. We cover the material prescribed by CS1, but our focus on
xv
Preface
xvi
applications brings life to the concepts and motivates students to learn them. Our
interdisciplinary approach exposes students to problems in many different disciplines, helping them to choose a major more wisely.
Whatever the specific mechanism, the use of this book is best positioned early
in the curriculum. First, this positioning allows us to leverage familiar material
in high school mathematics and science. Second, students who learn to program
early in their college curriculum will then be able to use computers more effectively
when moving on to courses in their specialty. Like reading and writing, programming is certain to be an essential skill for any scientist or engineer. Students who
have grasped the concepts in this book will continually develop that skill throughout their lifetimes, reaping the benefits of exploiting computation to solve or to
better understand the problems and projects that arise in their chosen field.
Prerequisites
This book is suitable for typical first-year college students. That
is, we do not expect preparation beyond what is typically required for other entrylevel science and mathematics courses.
Mathematical maturity is important. While we do not dwell on mathematical
material, we do refer to the mathematics curriculum that students have taken in
high school, including algebra, geometry, and trigonometry. Most students in our
target audience automatically meet these requirements. Indeed, we take advantage
of their familiarity with the basic curriculum to introduce basic programming
concepts.
Scientific curiosity is also an essential ingredient. Science and engineering students bring with them a sense of fascination with the ability of scientific inquiry to
help explain what goes on in nature. We leverage this predilection with examples
of simple programs that speak volumes about the natural world. We do not assume
any specific knowledge beyond that provided by typical high school courses in
mathematics, physics, biology, or chemistry.
Programming experience is not necessary, but also is not harmful. Teaching
programming is one of our primary goals, so we assume no prior programming
experience. But composing a program to solve a new problem is a challenging intellectual task, so students who have written numerous programs in high school
can benefit from taking an introductory programming course based on this book.
The book can support teaching students with varying backgrounds because the
applications appeal to both novices and experts alike.
Preface
Experience using a computer is not necessary, but also is not a problem. College students use computers regularly—for example, to communicate with friends
and relatives, listen to music, process photos, and as part of many other activities.
The realization that they can harness the power of their own computer in interesting and important ways is an exciting and lasting lesson.
In summary, virtually all college students are prepared to take a course based
on this book as a part of their first-semester curriculum.
Goals What can instructors of upper-level courses in science and engineering
expect of students who have completed a course based on this book?
We cover the CS1 curriculum, but anyone who has taught an introductory
programming course knows that expectations of instructors in later courses are
typically high: each instructor expects all students to be familiar with the computing
environment and approach that he or she wants to use. A physics professor might
expect some students to design a program over the weekend to run a simulation;
an engineering professor might expect other students to use a particular package
to numerically solve differential equations; or a computer science professor might
expect knowledge of the details of a particular programming environment. Is it
realistic for a single entry-level course to meet such diverse expectations? Should
there be a different introductory course for each set of students?
Colleges and universities have been wrestling with such questions since computers came into widespread use in the latter part of the 20th century. Our answer
to them is found in this common introductory treatment of programming, which
is analogous to commonly accepted introductory courses in mathematics, physics,
biology, and chemistry. Computer Science strives to provide the basic preparation
needed by all students in science and engineering, while sending the clear message
that there is much more to understand about computer science than programming.
Instructors teaching students who have studied from this book can expect that they
will have the knowledge and experience necessary to enable those students to adapt
to new computational environments and to effectively exploit computers in diverse
applications.
What can students who have completed a course based on this book expect to
accomplish in later courses?
Our message is that programming is not difficult to learn and that harnessing the power of the computer is rewarding. Students who master the material in
this book are prepared to address computational challenges wherever they might
xvii
Preface
xviii
appear later in their careers. They learn that modern programming environments,
such as the one provided by Java, help open the door to any computational problem they might encounter later, and they gain the confidence to learn, evaluate, and
use other computational tools. Students interested in computer science will be well
prepared to pursue that interest; students in science and engineering will be ready
to integrate computation into their studies.
Online lectures A complete set of studio-produced videos that can be used in
conjunction with this text are available at
http://www.informit.com/store/computer-science-videolectures-20-part-lecture-series-9780134493831
These lectures are fully coordinated with the text, but also include examples and
other materials that supplement the text and are intended to bring the subject to
life. As with traditional live lectures, the purpose of these videos is to inform and
inspire, motivating students to study and learn from the text. Our experience is
that student engagement with the material is significantly better with videos than
with live lectures because of the ability to play the lectures at a chosen speed and to
replay and review the lectures at any time.
Booksite An extensive amount of other information that supplements this text
may be found on the web at
http://introcs.cs.princeton.edu/java
For economy, we refer to this site as the booksite throughout. It contains material
for instructors, students, and casual readers of the book. We briefly describe this
material here, though, as all web users know, it is best surveyed by browsing. With
a few exceptions to support testing, the material is all publicly available.
One of the most important implications of the booksite is that it empowers
teachers, students, and casual readers to use their own computers to teach and
learn the material. Anyone with a computer and a browser can delve into the study
of computer science by following a few instructions on the booksite.
For teachers, the booksite is a rich source of enrichment materials and material
for quizzes, examinations, programming assignments, and other assessments. Together with the studio-produced videos (and the book), it represents resources for
teaching that are sufficiently flexible to support many of the models for teaching
that are emerging as teachers embrace technology in the 21st century. For example,
at Princeton, our teaching style was for many years based on offering two lectures
Preface
xix
per week to a large audience, supplemented by “precepts” each week where students met in small groups with instructors or teaching assistants. More recently, we
have evolved to a model where students watch lectures online and we hold class
meetings in addition to the precepts. These class meetings generally involve exams,
exam preparation sessions, tutorials, and other activities that previously had to be
scheduled outside of class time. Other teachers may work completely online. Still
others may use a “flipped” model involving enrichment of each lecture after students watch it.
For students, the booksite contains quick access to much of the material in the
book, including source code, plus extra material to encourage self-learning. Solutions are provided for many of the book’s exercises, including complete program
code and test data. There is a wealth of information associated with programming
assignments, including suggested approaches, checklists, FAQs, and test data.
For casual readers, the booksite is a resource for accessing all manner of extra information associated with the book’s content. All of the booksite content provides
web links and other routes to pursue more information about the topic under consideration. There is far more information accessible than any individual could fully
digest, but our goal is to provide enough to whet any reader’s appetite for more
information about the book’s content.
Our goal in creating these materials is to provide complementary approaches to
the ideas. People may learn a concept through careful study in the book, an engaging example in an online lectures, browsing on the booksite, or perhaps all three.
Acknowledgments This project has been under development since 1992, so far
too many people have contributed to its success for us to acknowledge them all here.
Special thanks are due to Anne Rogers, for helping to start the ball rolling; to Dave
Hanson, Andrew Appel, and Chris van Wyk, for their patience in explaining data
abstraction; to Lisa Worthington and Donna Gabai, for being the first to truly relish the challenge of teaching this material to first-year students; and to Doug Clark
for his patience as we learned about building Turing machines and circuits. We also
gratefully acknowledge the efforts of /dev/126; the faculty, graduate students, and
teaching staff who have dedicated themselves to teaching this material over the past
25 years here at Princeton University; and the thousands of undergraduates who
have dedicated themselves to learning it.
Robert Sedgewick and Kevin Wayne
Princeton, NJ, December 2016
Chapter One
Elements of Programming
1.1 Your First Program . . . . . . . . . . . . 2
1.2 Built-in Types of Data . . . . . . . . . 14
1.3 Conditionals and Loops . . . . . . . . 50
1.4 Arrays . . . . . . . . . . . . . . . . . . . 90
1.5 Input and Output . . . . . . . . . . . .126
1.6 Case Study: Random Web Surfer . . .170
O
ur goal in this chapter is to convince you that writing a program is easier than
writing a piece of text, such as a paragraph or essay. Writing prose is difficult:
we spend many years in school to learn how to do it. By contrast, just a few building blocks suffice to enable us to write programs that can help solve all sorts of
fascinating, but otherwise unapproachable, problems. In this chapter, we take you
through these building blocks, get you started on programming in Java, and study
a variety of interesting programs. You will be able to express yourself (by writing
programs) within just a few weeks. Like the ability to write prose, the ability to program is a lifetime skill that you can continually refine well into the future.
In this book, you will learn the Java programming language. This task will be
much easier for you than, for example, learning a foreign language. Indeed, programming languages are characterized by only a few dozen vocabulary words and
rules of grammar. Much of the material that we cover in this book could be expressed in the Python or C++ languages, or any of several other modern programming languages. We describe everything specifically in Java so that you can get
started creating and running programs right away. On the one hand, we will focus
on learning to program, as opposed to learning details about Java. On the other
hand, part of the challenge of programming is knowing which details are relevant
in a given situation. Java is widely used, so learning to program in this language
will enable you to write programs on many computers (your own, for example).
Also, learning to program in Java will make it easy for you to learn other languages,
including lower-level languages such as C and specialized languages such as Matlab.
1
Elements of Programming
1.1 Your First Program
In this section, our plan is to lead you into the world of Java programming by taking you through the basic steps required to get a simple program running. The
Java platform (hereafter abbreviated Java) is a collection of applications, not unlike
many of the other applications that you
are accustomed to using (such as your
1.1.1 Hello, World . . . . . . . . . . . . . . 4
word processor, email program, and web
1.1.2 Using a command-line argument . . . 7
browser). As with any application, you
Programs in this section
need to be sure that Java is properly installed on your computer. It comes preloaded on many computers, or you can download it easily. You also need a text
editor and a terminal application. Your first task is to find the instructions for installing such a Java programming environment on your computer by visiting
http://introcs.cs.princeton.edu/java
We refer to this site as the booksite. It contains an extensive amount of supplementary information about the material in this book for your reference and use while
programming.
Programming in Java To introduce you to developing Java programs, we
break the process down into three steps. To program in Java, you need to:
• Create a program by typing it into a file named, say, MyProgram.java.
• Compile it by typing javac MyProgram.java in a terminal window.
• Execute (or run) it by typing java MyProgram in the terminal window.
In the first step, you start with a blank screen and end with a sequence of typed
characters on the screen, just as when you compose an email message or an essay.
Programmers use the term code to refer to program text and the term coding to refer to the act of creating and editing the code. In the second step, you use a system
application that compiles your program (translates it into a form more suitable for
the computer) and puts the result in a file named MyProgram.class. In the third
step, you transfer control of the computer from the system to your program (which
returns control back to the system when finished). Many systems have several different ways to create, compile, and execute programs. We choose the sequence given here because it is the simplest to describe and use for small programs.
1.1 Your First Program
3
Creating a program. A Java program is nothing more than a sequence of characters, like a paragraph or a poem, stored in a file with a .java extension. To create
one, therefore, you need simply define that sequence of characters, in the same way
as you do for email or any other computer application. You can use any text editor
for this task, or you can use one of the more sophisticated integrated development
environments described on the booksite. Such environments are overkill for the
sorts of programs we consider in this book, but they are not difficult to use, have
many useful features, and are widely used by professionals.
Compiling a program. At first, it might seem that Java is designed to be best understood by the computer. To the contrary, the language is designed to be best
understood by the programmer—that’s you. The computer’s language is far more
primitive than Java. A compiler is an application that translates a program from the
Java language to a language more suitable for execution on the computer. The compiler takes a file with a .java extension as input (your program) and produces a
file with the same name but with a .class extension (the computer-language version). To use your Java compiler, type in a terminal window the javac command
followed by the file name of the program you want to compile.
Executing (running) a program. Once you compile the program, you can execute (or run) it. This is the exciting part, where your program takes control of your
computer (within the constraints of what Java allows). It is perhaps more accurate
to say that your computer follows your instructions. It is even more accurate to say
that a part of Java known as the Java Virtual Machine (JVM, for short) directs your
computer to follow your instructions. To use the JVM to execute your program,
type the java command followed by the program name in a terminal window.
use any text editor to
create your program
editor
type javac HelloWorld.java
to compile your program
type java HelloWorld
to execute your program
compiler
JVM
HelloWorld.java
your program
(a text file)
HelloWorld.class
computer-language
version of your program
Developing a Java program
"Hello, World"
output
Elements of Programming
4
Program 1.1.1
Hello, World
public class HelloWorld
{
public static void main(String[] args)
{
// Prints "Hello, World" in the terminal window.
System.out.println("Hello, World");
}
}
This code is a Java program that accomplishes a simple task. It is traditionally a beginner’s first
program. The box below shows what happens when you compile and execute the program. The
terminal application gives a command prompt (% in this book) and executes the commands
that you type (javac and then java in the example below). Our convention is to highlight in
boldface the text that you type and display the results in regular face. In this case, the result is
that the program prints the message Hello, World in the terminal window.
% javac HelloWorld.java
% java HelloWorld
Hello, World
Program 1.1.1 is an example of a complete Java program. Its name is
means that its code resides in a file named HelloWorld.java
(by convention in Java). The program’s sole action is to print a message to the terminal window. For continuity, we will use some standard Java terms to describe the
program, but we will not define them until later in the book: Program 1.1.1 consists of a single class named HelloWorld that has a single method named main().
(When referring to a method in the text, we use () after the name to distinguish it
from other kinds of names.) Until Section 2.1, all of our classes will have this same
structure. For the time being, you can think of “class” as meaning “program.”
HelloWorld, which
1.1 Your First Program
5
The first line of a method specifies its name and other information; the rest
is a sequence of statements enclosed in curly braces, with each statement typically followed by a semicolon. For the time being, you can think of “programming”
as meaning “specifying a class name and a sequence of statements for its main()
method,” with the heart of the program consisting of the sequence of statements in
the main() method (its body). Program 1.1.1 contains two such statements:
• The first statement is a comment, which serves to document the program.
In Java a single-line comment begins with two '/' characters and extends to
the end of the line. In this book, we display comments in gray. Java ignores
comments—they are present only for human readers of the program.
• The second statement is a print statement. It calls the method named
System.out.println() to print a text message—the one specified between the matching double quotes—to the terminal window.
In the next two sections, you will learn about many different kinds of statements
that you can use to make programs. For the moment, we will use only comments
and print statements, like the ones in HelloWorld.
When you type java followed by a class name in your terminal window, the
system calls the main() method that you defined in that class, and executes its
statements in order, one by one. Thus, typing java HelloWorld causes the system
to call the main() method in Program 1.1.1 and execute its two statements. The
first statement is a comment, which Java ignores. The second statement prints the
specified message to the terminal window.
text file named HelloWorld.java
name
main() method
public class HelloWorld
{
public static void main(String[] args)
{
// Prints "Hello, World" in the terminal window.
System.out.print("Hello, World");
}
}
statements
body
Anatomy of a program
6
Elements of Programming
Since the 1970s, it has been a tradition that a beginning programmer’s first
program should print Hello, World. So, you should type the code in Program
1.1.1 into a file, compile it, and execute it. By doing so, you will be following in the
footsteps of countless others who have learned how to program. Also, you will be
checking that you have a usable editor and terminal application. At first, accomplishing the task of printing something out in a terminal window might not seem
very interesting; upon reflection, however, you will see that one of the most basic
functions that we need from a program is its ability to tell us what it is doing.
For the time being, all our program code will be just like Program 1.1.1, except with a different sequence of statements in main(). Thus, you do not need to
start with a blank page to write a program. Instead, you can
• Copy HelloWorld.java into a new file having a new program name of
your choice, followed by .java.
• Replace HelloWorld on the first line with the new program name.
• Replace the comment and print statements with a different sequence of
statements.
Your program is characterized by its sequence of statements and its name. Each
Java program must reside in a file whose name matches the one after the word
class on the first line, and it also must have a .java extension.
Errors. It is easy to blur the distinctions among editing, compiling, and executing
programs. You should keep these processes separate in your mind when you are
learning to program, to better understand the effects of the errors that inevitably
arise.
You can fix or avoid most errors by carefully examining the program as you
create it, the same way you fix spelling and grammatical errors when you compose
an email message. Some errors, known as compile-time errors, are identified when
you compile the program, because they prevent the compiler from doing the translation. Other errors, known as run-time errors, do not show up until you execute
the program.
In general, errors in programs, also commonly known as bugs, are the bane of
a programmer’s existence: the error messages can be confusing or misleading, and
the source of the error can be very hard to find. One of the first skills that you will
learn is to identify errors; you will also learn to be sufficiently careful when coding,
to avoid making many of them in the first place. You can find several examples of
errors in the Q&A at the end of this section.
1.1 Your First Program
Program 1.1.2
Using a command-line argument
public class UseArgument
{
public static void main(String[] args)
{
System.out.print("Hi, ");
System.out.print(args[0]);
System.out.println(". How are you?");
}
}
This program shows the way in which we can control the actions of our programs: by providing
an argument on the command line. Doing so allows us to tailor the behavior of our programs.
% javac UseArgument.java
% java UseArgument Alice
Hi, Alice. How are you?
% java UseArgument Bob
Hi, Bob. How are you?
Input and output Typically, we want to provide input to our programs—that
is, data that they can process to produce a result. The simplest way to provide input data is illustrated in UseArgument (Program 1.1.2). Whenever you execute the
program UseArgument, it accepts the command-line argument that you type after
the program name and prints it back out to the terminal window as part of the
message. The result of executing this program depends on what you type after the
program name. By executing the program with different command-line arguments,
you produce different printed results. We will discuss in more detail the mechanism
that we use to pass command-line arguments to our programs later, in Section 2.1.
For now it is sufficient to understand that args[0] is the first command-line argument that you type after the program name, args[1] is the second, and so forth.
Thus, you can use args[0] within your program’s body to represent the first string
that you type on the command line when it is executed, as in UseArgument.
7
Elements of Programming
8
In addition to the System.out.println() method, UseArgument calls the
method. This method is just like System.out.println(),
but prints just the specified string (and not a newline character).
Again, accomplishing the task of getting a program to print back out what we
type in to it may not seem interesting at first, but upon reflection you will realize
that another basic function of a program is its ability to respond to basic information from the user to control what the program does. The simple model that
UseArgument represents will suffice to allow us to consider Java’s basic programming mechanism and to address all sorts of interesting computational problems.
Stepping back, we can see that UseArgument does neither more nor less than
implement a function that maps a string of characters (the command-line argument) into another string of characters (the message printed back to the terminal
window). When using it, we might think of our Java program as a black box that
converts our input string to some output string.
This model is attractive because it is not only
input string
Alice
simple but also sufficiently general to allow completion, in principle, of any computational task. For
example, the Java compiler itself is nothing more
black box
than a program that takes one string of characters as
input (a .java file) and produces another string of
output string
characters as output (the corresponding .class file).
Hi, Alice. How are you?
Later, you will be able to write programs that accomplish a variety of interesting tasks (though we stop
short of programs as complicated as a compiler). For A bird’s-eye view of a Java program
the moment, we will live with various limitations on
the size and type of the input and output to our programs; in Section 1.5, you will
see how to incorporate more sophisticated mechanisms for program input and
output. In particular, you will see that we can work with arbitrarily long input and
output strings and other types of data such as sound and pictures.
System.out.print()
1.1 Your First Program
Q&A
Q. Why Java?
A. The programs that we are writing are very similar to their counterparts in several other languages, so our choice of language is not crucial. We use Java because
it is widely available, embraces a full set of modern abstractions, and has a variety
of automatic checks for mistakes in programs, so it is suitable for learning to program. There is no perfect language, and you certainly will be programming in other
languages in the future.
Q. Do I really have to type in the programs in the book to try them out? I believe
that you ran them and that they produce the indicated output.
A. Everyone should type in and run
HelloWorld. Your understanding will be
you also run UseArgument, try it on various inputs, and modi-
greatly magnified if
fy it to test different ideas of your own. To save some typing, you can find all of the
code in this book (and much more) on the booksite. This site also has information
about installing and running Java on your computer, answers to selected exercises,
web links, and other extra information that you may find useful while programming.
Q. What is the meaning of the words public, static, and void?
A. These keywords specify certain properties of main() that you will learn about
later in the book. For the moment, we just include these keywords in the code (because they are required) but do not refer to them in the text.
Q. What is the meaning of the //, /*, and */ character sequences in the code?
A. They denote comments, which are ignored by the compiler. A comment is either
text in between /* and */ or at the end of a line after //. Comments are indispensable because they help other programmers to understand your code and even
can help you to understand your own code in retrospect. The constraints of the
book format demand that we use comments sparingly in our programs; instead
we describe each program thoroughly in the accompanying text and figures. The
programs on the booksite are commented to a more realistic degree.
9
Elements of Programming
10
Q. What are Java’s rules regarding tabs, spaces, and newline characters?
A. Such characters are known as whitespace characters. Java compilers consider all whitespace in program text to be equivalent. For example, we could write
HelloWorld as follows:
public class HelloWorld { public static void main ( String
[] args) { System.out.println("Hello, World")
; } }
But we do normally adhere to spacing and indenting conventions when we write
Java programs, just as we indent paragraphs and lines consistently when we write
prose or poetry.
Q. What are the rules regarding quotation marks?
A. Material inside double quotation marks is an exception to the rule defined in
the previous question: typically, characters within quotes are taken literally so that
you can precisely specify what gets printed. If you put any number of successive
spaces within the quotes, you get that number of spaces in the output. If you accidentally omit a quotation mark, the compiler may get very confused, because it
needs that mark to distinguish between characters in the string and other parts of
the program.
Q. What happens when you omit a curly brace or misspell one of the words, such
as public or static or void or main?
A. It depends upon precisely what you do. Such errors are called syntax errors and
are usually caught by the compiler. For example, if you make a program Bad that is
exactly the same as HelloWorld except that you omit the line containing the first
left curly brace (and change the program name from HelloWorld to Bad), you get
the following helpful message:
% javac Bad.java
Bad.java:1: error: '{' expected
public class Bad
^
1 error
1.1 Your First Program
From this message, you might correctly surmise that you need to insert a left curly
brace. But the compiler may not be able to tell you exactly which mistake you made,
so the error message may be hard to understand. For example, if you omit the second left curly brace instead of the first one, you get the following message:
% javac Bad.java
Bad.java:3: error: ';' expected
public static void main(String[] args)
^
Bad.java:7: error: class, interface, or enum expected
}
^
2 errors
One way to get used to such messages is to intentionally introduce mistakes into a
simple program and then see what happens. Whatever the error message says, you
should treat the compiler as a friend, because it is just trying to tell you that something is wrong with your program.
Q. Which Java methods are available for me to use?
A. There are thousands of them. We introduce them to you in a deliberate fashion
(starting in the next section) to avoid overwhelming you with choices.
Q. When I ran UseArgument, I got a strange error message. What’s the problem?
A. Most likely, you forgot to include a command-line argument:
% java UseArgument
Hi, Exception in thread “main”
java.lang.ArrayIndexOutOfBoundsException: 0
at UseArgument.main(UseArgument.java:6)
Java is complaining that you ran the program but did not type a command-line argument as promised. You will learn more details about array indices in Section 1.4.
Remember this error message—you are likely to see it again. Even experienced programmers forget to type command-line arguments on occasion.
11
Elements of Programming
12
Exercises
1.1.1 Write a program that prints the Hello, World message 10 times.
1.1.2 Describe what happens if you omit the following in HelloWorld.java:
a.
b.
c.
d.
public
static
void
args
1.1.3 Describe what happens if you misspell (by, say, omitting the second letter)
the following in HelloWorld.java:
a. public
b. static
c. void
d. args
1.1.4 Describe what happens if you put the double quotes in the print statement
of HelloWorld.java on different lines, as in this code fragment:
System.out.println("Hello,
World");
1.1.5 Describe what happens if you try to execute UseArgument with each of the
following command lines:
a. java UseArgument java
b. java UseArgument @!&^%
c. java UseArgument 1234
d. java UseArgument.java Bob
e. java UseArgument Alice Bob
1.1.6 Modify UseArgument.java to make a program UseThree.java that takes
three names as command-line arguments and prints a proper sentence with the
names in the reverse of the order given, so that, for example, java UseThree Alice
Bob Carol prints Hi Carol, Bob, and Alice.
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Elements of Programming
1.2 Built-in Types of Data
When programming in Java, you must always be aware of the type of data that your
program is processing. The programs in Section 1.1 process strings of characters,
many of the programs in this section process numbers, and we consider numerous other types later in the book. Understanding the distinctions among them is
1.2.1 String concatenation . . . . . . . . . 20
so important that we formally define the
1.2.2 Integer multiplication and division. 23
1.2.3 Quadratic formula. . . . . . . . . . . 25
idea: a data type is a set of values and a set
1.2.4 Leap year. . . . . . . . . . . . . . . . 28
of operations defined on those values. You
1.2.5 Casting to get a random integer . . . 34
are familiar with various types of numPrograms in this section
bers, such as integers and real numbers,
and with operations defined on them,
such as addition and multiplication. In
mathematics, we are accustomed to thinking of sets of numbers as being infinite;
in computer programs we have to work with a finite number of possibilities. Each
operation that we perform is well defined only for the finite set of values in an associated data type.
There are eight primitive types of data in Java, mostly for different kinds of
numbers. Of the eight primitive types, we most often use these: int for integers;
double for real numbers; and boolean for true–false values. Other data types are
available in Java libraries: for example, the programs in Section 1.1 use the type
String for strings of characters. Java treats the String type differently from other
types because its usage for input and output is essential. Accordingly, it shares some
characteristics of the primitive types; for example, some of its operations are built
into the Java language. For clarity, we refer to primitive types and String collectively as built-in types. For the time being, we concentrate on programs that are
based on computing with built-in types. Later, you will learn about Java library
data types and building your own data types. Indeed, programming in Java often
centers on building data types, as you shall see in Chapter 3.
After defining basic terms, we consider several sample programs and code
fragments that illustrate the use of different types of data. These code fragments
do not do much real computing, but you will soon see similar code in longer programs. Understanding data types (values and operations on them) is an essential
step in beginning to program. It sets the stage for us to begin working with more
intricate programs in the next section. Every program that you write will use code
like the tiny fragments shown in this section.
1.2 Built-in Types of Data
15
type
set of values
common operators
sample literal values
int
integers
+ - * / %
99 12 2147483647
double
floating-point numbers
+ - * /
3.14 2.5 6.022e23
boolean
boolean values
&& || !
char
characters
String
sequences of characters
true false
'A' '1' '%' '\n'
+
"AB" "Hello" "2.5"
Basic built-in data types
Terminology To talk about data types, we need to introduce some terminology.
To do so, we start with the following code fragment:
int
a =
b =
c =
a, b, c;
1234;
99;
a + b;
The first line is a declaration statement that declares the names of three variables
using the identifiers a, b, and c and their type to be int. The next three lines are
assignment statements that change the values of the variables, using the literals 1234
and 99, and the expression a + b, with the end result that c has the value 1333.
Literals. A literal is a Java-code representation of a data-type value. We use sequences of digits such as 1234 or 99 to represent values of type int; we add a decimal point, as in 3.14159 or 2.71828, to represent values of type double; we use the
keywords true or false to represent the two values of type boolean; and we use
sequences of characters enclosed in matching quotes, such as "Hello, World", to
represent values of type String.
Operators. An operator is a Java-code representation of a data-type operation.
Java uses + and * to represent addition and multiplication for integers and floatingpoint numbers; Java uses &&, ||, and ! to represent boolean operations; and so
forth. We will describe the most commonly used operators on built-in types later
in this section.
Identifiers. An identifier is a Java-code representation of a name (such as for a
variable). Each identifier is a sequence of letters, digits, underscores, and currency
symbols, the first of which is not a digit. For example, the sequences of characters
Elements of Programming
16
abc, Ab$, abc123, and a_b
are all legal Java identifiers, but Ab*, 1abc, and a+b are
not. Identifiers are case sensitive, so Ab, ab, and AB are all different names. Certain
reserved words—such as public, static, int, double, String, true, false, and
null—are special, and you cannot use them as identifiers.
Variables. A variable is an entity that holds a data-type value, which we can refer
to by name. In Java, each variable has a specific type and stores one of the possible
values from that type. For example, an int variable can store either the value 99
or 1234 but not 3.14159 or "Hello, World". Different variables of the same type
may store the same value. Also, as the name suggests, the value of a variable may
change as a computation unfolds. For example, we use a variable named sum in several programs in this book to keep the running sum of a sequence of numbers. We
create variables using declaration statements and compute with them in expressions,
as described next.
Declaration statements. To create a variable in Java, you use
type
variable name
a declaration statement, or just declaration for short A declaration includes a type followed by a variable name. Java reserves
double total;
enough memory to store a data-type value of the specified
type, and associates the variable name with that area of memdeclaration statement
ory, so that it can access the value when you use the variable in
later code. For economy, you can declare several variables of Anatomy of a declaration
the same type in a single declaration statement.
Variable naming conventions. Programmers typically follow stylistic conventions when naming things. In this book, our convention is to give each variable
a meaningful name that consists of a lowercase letter followed by lowercase letters, uppercase letters, and digits. We use uppercase letters to mark the words of
a multi-word variable name. For example, we use the variable names i, x, y, sum,
isLeapYear, and outDegrees, among many others. Programmers refer to this
naming style as camel case.
Constant variables. We use the oxymoronic term constant variable to describe a
variable whose value does not change during the execution of a program (or from
one execution of the program to the next). In this book, our convention is to give
each constant variable a name that consists of an uppercase letter followed by uppercase letters, digits, and underscores. For example, we might use the constant
variable names SPEED_OF_LIGHT and DARK_RED.
1.2 Built-in Types of Data
17
Expressions. An expression is a combination of literals, variables,
operands
and operations that Java evaluates to produce a value. For primi(and expressions)
tive types, expressions often look just like mathematical formulas,
using operators to specify data-type operations to be performed on
4 * ( x - 3 )
one more operands. Most of the operators that we use are binary
operators that take exactly two operands, such as x - 3 or 5 * x.
operator
Each operand can be any expression, perhaps within parentheses. Anatomy of an expression
For example, we can write 4 * (x - 3) or 5 * x - 6 and Java will
understand what we mean. An expression is a directive to perform
a sequence of operations; the expression is a representation of the resulting value.
Operator precedence. An expression is shorthand for a sequence of operations:
in which order should the operators be applied? Java has natural and well defined
precedence rules that fully specify this order. For arithmetic operations, multiplication and division are performed before addition and subtraction, so that a - b * c
and a - (b * c) represent the same sequence of operations. When arithmetic operators have the same precedence, the order is determined by left associativity, so that
a - b - c and (a - b) - c represent the same sequence of operations. You can use
parentheses to override the rules, so you can write a - (b - c) if that is what you
want. You might encounter in the future some Java code that depends subtly on
precedence rules, but we use parentheses to avoid such code in this book. If you are
interested, you can find full details on the rules on the booksite.
Assignment statements. An assignment statement associates a data-type value
with a variable. When we write c = a + b in Java, we are not expressing mathematical equality, but are instead expressing an action: set the
declaration statement
value of the variable c to be the value of a plus the value
of b. It is true that the value of c is mathematically equal
int a, b;
literal
to the value of a + b immediately after the assignment variable name
a = 1234 ;
statement has been executed, but the point of the stateassignment
b = 99;
statement
ment is to change (or initialize) the value of c. The leftint c = a + b;
hand side of an assignment statement must be a single
variable; the right-hand side can be any expression that inline initialization
statement
produces a value of a compatible type. So, for example,
both 1234 = a; and a + b = b + a; are invalid statements
Using a primitive data type
in Java. In short, the meaning of = is decidedly not the
same as in mathematical equations.
Elements of Programming
18
Inline initialization. Before you can use a variable in an expression, you must first
declare the variable and assign to it an initial value. Failure to do either results in a
compile-time error. For economy, you can combine a declaration statement with
an assignment statement in a construct known as an inline initialization statement.
For example, the following code declares two variables a and b, and initializes them
to the values 1234 and 99, respectively:
int a = 1234;
int b = 99;
Most often, we declare and initialize a variable in this manner at the point of its first
use in our program.
Tracing changes in variable values. As a final check on your understanding of
the purpose of assignment statements, convince yourself that the following code
exchanges the values of a and b (assume that a and
b are int variables):
a
b
int t = a;
a = b;
b = t;
To do so, use a time-honored method of examining program behavior: study a table of the variable
values after each statement (such a table is known
as a trace).
int
a =
b =
int
a =
b =
t
a, b; undefined undefined
1234;
1234 undefined
99;
1234
99
t = a; 1234
99
1234
b;
99
99
1234
t;
99
1234 1234
Your first trace
Type safety. Java requires you to declare the type of every variable. This enables
Java to check for type mismatch errors at compile time and alert you to potential
bugs in your program. For example, you cannot assign a double value to an int
variable, multiply a String with a boolean, or use an uninitialized variable within
an expression. This situation is analogous to making sure that quantities have the
proper units in a scientific application (for example, it does not make sense to add
a quantity measured in inches to another measured in pounds).
Next, we consider these details for the basic built-in types that you will use most
often (strings, integers, floating-point numbers, and true–false values), along with
sample code illustrating their use. To understand how to use a data type, you need
to know not just its defined set of values, but also which operations you can perform, the language mechanism for invoking the operations, and the conventions
for specifying literals.
1.2 Built-in Types of Data
Characters and strings The char type represents individ-
19
values
characters
ual alphanumeric characters or symbols, like the ones that you
typical
'a'
type. There are 216 different possible char values, but we usu'\n'
literals
ally restrict attention to the ones that represent letters, numbers,
Java’s built-in char data type
symbols, and whitespace characters such as tab and newline.
You can specify a char literal by enclosing a character within
single quotes; for example, 'a' represents the letter a. For tab, newline, backslash,
single quote, and double quote, we use the special escape sequences \t, \n, \\, \',
and \", respectively. The characters are encoded as 16-bit integers using an encoding scheme known as Unicode, and there are also escape sequences for specifying
special characters not found on your keyboard (see the booksite). We usually do
not perform any operations directly on characters other than assigning values to
variables.
The String type represents sequences of characters.
values
sequences of characters
You can specify a String literal by enclosing a sequence of
typical
"Hello, World"
characters within double quotes, such as "Hello, World".
" * "
literals
The String data type is not a primitive type, but Java some- operation
concatenate
times treats it like one. For example, the concatenation opoperator
+
erator (+) takes two String operands and produces a third
Java’s built-in String data type
String that is formed by appending the characters of the
second operand to the characters of the first operand.
The concatenation operation (along with the ability
to declare String variables and to use them in expressions and assignment statements) is sufficiently powerful to allow us to attack some nontrivial computing
tasks. As an example, Ruler (Program 1.2.1) computes a table of values of the ruler
function that describes the relative lengths of the marks on a ruler. One noteworthy
feature of this computation is that it illustrates how easy it is to craft a short program that produces a huge amount of output. If you extend this program in the
obvious way to print five lines, six lines, seven lines, and so forth, you will see that
each time you add two statements to this
expression
value
program, you double the size of the output.
"Hi, " + "Bob"
"Hi, Bob"
Specifically, if the program prints n lines, the
"1" + " 2 " + "1"
"1 2 1"
nth line contains 2n1 numbers. For exam"1234" + " + " + "99"
"1234 + 99"
ple, if you were to add statements in this way
so that the program prints 30 lines, it would
"1234" + "99"
"123499"
print more than 1 billion numbers.
Typical String expressions
Elements of Programming
20
Program 1.2.1
String concatenation
public class Ruler
{
public static void main(String[] args)
{
String ruler1 = "1";
String ruler2 = ruler1 + " 2 " + ruler1;
String ruler3 = ruler2 + " 3 " + ruler2;
String ruler4 = ruler3 + " 4 " + ruler3;
System.out.println(ruler1);
System.out.println(ruler2);
System.out.println(ruler3);
System.out.println(ruler4);
}
}
This program prints the relative lengths of the subdivisions on a ruler. The nth line of output
is the relative lengths of the marks on a ruler subdivided in intervals of 1/2 n of an inch. For
example, the fourth line of output gives the relative lengths of the marks that indicate intervals
of one-sixteenth of an inch on a ruler.
%
%
1
1
1
1
javac Ruler.java
java Ruler
2 1
2 1 3 1 2 1
2 1 3 1 2 1 4 1 2 1 3 1 2 1
1 2 1 3 1 2 1 4 1 2 1 3 1 2 1
The ruler function for n = 4
Our most frequent use (by far) of the concatenation operation is to put together results of computation for output with System.out.println(). For example, we could simplify UseArgument (Program 1.1.2) by replacing its three statements in main() with this single statement:
System.out.println("Hi, " + args[0] + ". How are you?");
1.2 Built-in Types of Data
21
We have considered the String type first precisely because we need it for output (and command-line arguments) in programs that process not only strings but
other types of data as well. Next we consider two convenient mechanisms in Java
for converting numbers to strings and strings to numbers.
Converting numbers to strings for output. As mentioned at the beginning of this
section, Java’s built-in String type obeys special rules. One of these special rules is
that you can easily convert a value of any type to a String value: whenever we use
the + operator with a String as one of its operands, Java automatically converts
the other operand to a String, producing as a result the String formed from the
characters of the first operand followed by the characters of the second operand.
For example, the result of these two code fragments
String a = "1234";
String b = "99";
String c = a + b;
String a = "1234";
int b = 99;
String c = a + b;
are both the same: they assign to c the value "123499". We use this automatic
conversion liberally to form String values for use with System.out.print() and
System.out.println(). For example, we can write statements like this one:
System.out.println(a + " + " + b + " = " + c);
If a, b, and c are int variables with the values 1234, 99, and 1333, respectively, then
this statement prints the string 1234 + 99 = 1333.
Converting strings to numbers for input. Java also provides library methods that convert the strings that we type as command-line arguments
into numeric values for primitive types. We use the Java library methods
Integer.parseInt() and Double.parseDouble() for this purpose. For example,
typing Integer.parseInt("123") in program text is equivalent to typing the int
literal 123. If the user types 123 as the first command-line argument, then the code
Integer.parseInt(args[0]) converts the String value "123" into the int value
123. You will see several examples of this usage in the programs in this section.
With these mechanisms, our view of each Java program as a black box that takes
string arguments and produces string results is still valid, but we can now interpret
those strings as numbers and use them as the basis for meaningful computations.
Elements of Programming
22
Integers
The int type represents integers (natural numbers) between
–2147483648 (2 31) and 2147483647 (2 311). These bounds derive from the fact
that integers are represented in binary with 32 binary digits; there are 232 possible
values. (The term binary digit is omnipresent in computer science, and we nearly
always use the abbreviation bit : a bit is either 0 or 1.) The range of possible int
values is asymmetric because zero is included with the positive values. You can see
the Q&A at the end of this section for more details about number representation,
but in the present context it suffices to know that
expression
value
comment
an int is one of the finite set of values in the
99
99
integer literal
range just given. You can specify an int literal
+99
99
with a sequence of the decimal digits 0 through
positive sign
9 (that, when interpreted as decimal numbers,
-99
-99
negative sign
fall within the defined range). We use ints fre5 + 3
8
addition
quently because they naturally arise when we are
5 - 3
2
subtraction
implementing programs.
5 * 3
15
multiplication
Standard arithmetic operators for addi5 / 3
1
no fractional part
tion/subtraction (+ and -), multiplication (*),
5 % 3
2
remainder
division (/), and remainder (%) for the int data
1 / 0
run-time error
type are built into Java. These operators take two
3 * 5 - 2
13
* has precedence
int operands and produce an int result, with
one significant exception—division or remain3 + 5 / 2
5
/ has precedence
der by zero is not allowed. These operations are
3 - 5 - 2
-4
left associative
defined as in grade school (keeping in mind that
( 3 - 5 ) - 2
-4
better style
all results must be integers): given two int val- 3 - ( 5 - 2 ) 0
unambiguous
ues a and b, the value of a / b is the number of
Typical int expressions
times b goes into a with the fractional part discarded, and the value of a % b is the remainder
that you get when you divide a by b. For example, the value of 17 / 3 is 5, and the
value of 17 % 3 is 2. The int results that we get from arithmetic operations are just
what we expect, except that if the result is too large to fit into int’s 32-bit representation, then it will be truncated in a well-defined manner. This situation is known
integers between 2 31 and 2 311
values
1234
typical literals
99
0
1000000
operations
sign
add
subtract
multiply
divide
remainder
operators
+ -
+
-
*
/
%
Java’s built-in int data type
1.2 Built-in Types of Data
Program 1.2.2
23
Integer multiplication and division
public class IntOps
{
public static void main(String[] args)
{
int a = Integer.parseInt(args[0]);
int b = Integer.parseInt(args[1]);
int p = a * b;
int q = a / b;
int r = a % b;
System.out.println(a + " * " + b + "
System.out.println(a + " / " + b + "
System.out.println(a + " % " + b + "
System.out.println(a + " = " + q + "
}
}
=
=
=
*
"
"
"
"
+
+
+
+
p);
q);
r);
b + " + " + r);
Arithmetic for integers is built into Java. Most of this code is devoted to the task of getting the
values in and out; the actual arithmetic is in the simple statements in the middle of the program
that assign values to p, q, and r.
% javac IntOps.java
% java IntOps 1234 99
1234 * 99 = 122166
1234 / 99 = 12
1234 % 99 = 46
1234 = 12 * 99 + 46
as overflow. In general, we have to take care that such a result is not misinterpreted
by our code. For the moment, we will be computing with small numbers, so you do
not have to worry about these boundary conditions.
Program 1.2.2 illustrates three basic operations (multiplication, division, and remainder) for manipulating integers,. It also demonstrates the use of
Integer.parseInt() to convert String values on the command line to int values, as well as the use of automatic type conversion to convert int values to String
values for output.
Elements of Programming
24
Three other built-in types are different representations of integers in Java.
The long, short, and byte types are the same as int except that they use 64, 16,
and 8 bits respectively, so the range of allowed values is accordingly different. Programmers use long when working with huge integers, and the other types to save
space. You can find a table with the maximum and minimum values for each type
on the booksite, or you can figure them out for yourself from the numbers of bits.
Floating-point numbers The double type represents floating-point numbers,
for use in scientific and commercial applications. The internal representation is
like scientific notation, so that we can compute with numbers in a huge range.
We use floating-point numbers to represent real numbers, but they are decidedly
not the same as real numbers! There are infinitely many real numbers, but we can
represent only a finite number of floatingexpression
value
point numbers in any digital computer
3.141 + 2.0
5.141
representation. Floating-point numbers
3.141
2.0
1.111
do approximate real numbers sufficiently
3.141 / 2.0
1.5705
well that we can use them in applications,
5.0 / 3.0
1.6666666666666667
but we often need to cope with the fact that
we cannot always do exact computations.
10.0 % 3.141
0.577
You can specify a double literal with
1.0 / 0.0
Infinity
a sequence of digits with a decimal point.
Math.sqrt(2.0)
1.4142135623730951
For example, the literal 3.14159 represents
Math.sqrt(-1.0)
NaN
a six-digit approximation to . AlternaTypical double expressions
tively, you specify a double literal with a
notation like scientific notation: the literal
6.022e23 represents the number 6.022 1023. As with integers, you can use these
conventions to type floating-point literals in your programs or to provide floatingpoint numbers as string arguments on the command line.
The arithmetic operators +, -, *, and / are defined for double. Beyond these
built-in operators, the Java Math library defines the square root function, trigonometric functions, logarithm/exponential functions, and other common functions
for floating-point numbers. To use one of these functions in an expression, you
type the name of the function followed by its argument in parentheses. For exvalues
typical literals
real numbers (specified by IEEE 754 standard)
3.14159
6.022e23
2.0
1.4142135623730951
operations
add
subtract
multiply
divide
operators
+
-
*
/
Java’s built-in double data type
1.2 Built-in Types of Data
Program 1.2.3
25
Quadratic formula
public class Quadratic
{
public static void main(String[] args)
{
double b = Double.parseDouble(args[0]);
double c = Double.parseDouble(args[1]);
double discriminant = b*b - 4.0*c;
double d = Math.sqrt(discriminant);
System.out.println((-b + d) / 2.0);
System.out.println((-b - d) / 2.0);
}
}
This program prints the roots of the polynomial x2 + bx + c, using the quadratic formula. For
example, the roots of x2 – 3x + 2 are 1 and 2 since we can factor the equation as (x – 1)(x – 2);
the roots of x2 – x – 1 are and 1 – , where is the golden ratio; and the roots of x2 + x + 1
are not real numbers.
% javac Quadratic.java
% java Quadratic -3.0 2.0
2.0
1.0
% java Quadratic -1.0 -1.0
1.618033988749895
-0.6180339887498949
% java Quadratic 1.0 1.0
NaN
NaN
ample, the code Math.sqrt(2.0) evaluates to a double value that is approximately
the square root of 2. We discuss the mechanism behind this arrangement in more
detail in Section 2.1 and more details about the Math library at the end of this section.
When working with floating-point numbers, one of the first things that you
will encounter is the issue of precision. For example, printing 5.0/2.0 results in
2.5 as expected, but printing 5.0/3.0 results in 1.6666666666666667. In Section
1.5, you will learn Java’s mechanism for controlling the number of significant digits
that you see in output. Until then, we will work with the Java default output format.
Elements of Programming
26
The result of a calculation can be one of the special values Infinity (if the
number is too large to be represented) or NaN (if the result of the calculation is
undefined). Though there are myriad details to consider when calculations involve
these values, you can use double in a natural way and begin to write Java programs
instead of using a calculator for all kinds of calculations. For example, Program
1.2.3 shows the use of double values in computing the roots of a quadratic equation using the quadratic formula. Several of the exercises at the end of this section
further illustrate this point.
As with long, short, and byte for integers, there is another representation
for real numbers called float. Programmers sometimes use float to save space
when precision is a secondary consideration. The double type is useful for about
15 significant digits; the float type is good for only about 7 digits. We do not use
float in this book.
Booleans The
type represents truth valvalues
true or false
ues from logic. It has just two values: true and false.
literals
true false
These are also the two possible boolean literals. Every
operations
and
or
not
boolean variable has one of these two values, and evoperators
&&
||
!
ery boolean operation has operands and a result that
takes on just one of these two values. This simplicity Java’s built-in boolean data type
is deceiving—boolean values lie at the foundation of
computer science.
The most important operations defined for booleans are and (&&), or (||),
and not (!), which have familiar definitions:
• a && b is true if both operands are true, and false if either is false.
• a || b is false if both operands are false, and true if either is true.
• !a is true if a is false, and false if a is true.
Despite the intuitive nature of these definitions, it is worthwhile to fully specify
each possibility for each operation in tables known as truth tables. The not function
has only one operand: its value for each of the two possible values of the operand is
boolean
a
!a
a
b
a && b
a || b
true
false
false
false
false
false
false
true
false
true
false
true
true
false
false
true
true
true
true
true
Truth-table definitions of
boolean operations
1.2 Built-in Types of Data
27
specified in the second column. The and and or functions each have two operands:
there are four different possibilities for operand values, and the values of the functions for each possibility are specified in the right two columns.
We can use these operators with parentheses to develop arbitrarily complex
expressions, each of which specifies a well-defined boolean function. Often the
same function appears in different guises. For example, the expressions (a && b)
and !(!a || !b) are equivalent.
a
b
a && b
!a
!b
!a || !b
!(!a || !b)
false
false
false
true
true
true
false
false
true
false
true
false
true
false
true
false
false
false
true
true
false
true
true
true
false
false
false
true
Truth-table proof that a
&& b
and
!(!a || !b) are identical
The study of manipulating expressions of this kind is known as Boolean logic.
This field of mathematics is fundamental to computing: it plays an essential role in
the design and operation of computer hardware itself, and it is also a starting point
for the theoretical foundations of computation. In the present context, we are interested in boolean expressions because we use them to control the behavior of
our programs. Typically, a particular condition of interest is specified as a boolean
expression, and a piece of program code is written to execute one set of statements
if that expression is true and a different set of statements if the expression is false.
The mechanics of doing so are the topic of Section 1.3.
Comparisons
Some mixed-type operators take operands of one type and produce a result of another type. The most important operators of this kind are the
comparison operators ==, !=, <, <=, >, and >=, which all are defined for each primitive numeric type and produce a boolean result. Since operations are defined only
with respect to data types, each of these symbols stands for many operations, one
for each data type. It is required that both operands be of the same type.
non-negative discriminant?
(b*b - 4.0*a*c) >= 0.0
beginning of a century?
(year % 100) == 0
legal month?
(month >= 1) && (month <= 12)
Typical comparison expressions
Elements of Programming
28
Program 1.2.4
Leap year
public class LeapYear
{
public static void main(String[] args)
{
int year = Integer.parseInt(args[0]);
boolean isLeapYear;
isLeapYear = (year % 4 == 0);
isLeapYear = isLeapYear && (year % 100 != 0);
isLeapYear = isLeapYear || (year % 400 == 0);
System.out.println(isLeapYear);
}
}
This program tests whether an integer corresponds to a leap year in the Gregorian calendar. A
year is a leap year if it is divisible by 4 (2004), unless it is divisible by 100 in which case it is not
(1900), unless it is divisible by 400 in which case it is (2000).
% javac LeapYear.java
% java LeapYear 2004
true
% java LeapYear 1900
false
% java LeapYear 2000
true
Even without going into the details of number representation, it is clear that
the operations for the various types are quite different. For example, it is one thing
to compare two ints to check that (2 <= 2) is true, but quite another to compare two doubles to check whether (2.0 <= 0.002e3) is true. Still, these operations are well defined and useful to write code that tests for conditions such as
(b*b - 4.0*a*c) >= 0.0, which is frequently needed, as you will see.
1.2 Built-in Types of Data
29
The comparison operations have lower precedence than arithmetic operators
and higher precedence than boolean operators, so you do not need the parentheses
in an expression such as (b*b - 4.0*a*c) >= 0.0, and you could write an expression such as month >= 1 && month <= 12 without parentheses to test whether
the value of the int variable month is between 1 and 12. (It is better style to use the
parentheses, however.)
true
meaning
false
Comparison operations, to- operator
gether with boolean logic, provide
==
2 == 2
2 == 3
equal
the basis for decision making in Java
3 != 2
2 != 2
!=
not equal
programs. Program 1.2.4 is an ex2 < 13
2 < 2
<
less than
ample of their use, and you can find
<=
2 <= 2
3 <= 2
less than or equal
other examples in the exercises at the
13 > 2
2 > 13
>
greater than
end of this section. More importantly,
3
>=
2
2 >= 3
>=
greater than or equal
in Section 1.3 we will see the role that
boolean expressions play in more soComparisons with int operands and a boolean result
phisticated programs.
Library methods and APIs As we have seen, many programming tasks involve using Java library methods in addition to the built-in operators. The number
of available library methods is vast. As you learn to program, you will learn to use
more and more library methods, but it is best at the beginning to restrict your attention to a relatively small set of methods. In this chapter, you have already used
some of Java’s methods for printing, for converting data from one type to another,
and for computing mathematical functions (the Java Math library). In later chapters, you will learn not just how to use other methods, but how to create and use
your own methods.
For convenience, we will consistently summarize the library methods that
you need to know how to use in tables like this one:
void System.out.print(String s)
void System.out.println(String s)
void System.out.println()
print s
print s, followed by a newline
print a newline
Note: Any type of data can be used as argument (and will be automatically converted to String).
Java library methods for printing strings to the terminal
Elements of Programming
30
Such a table is known as an application programming interface (API ). Each method is described by a line in the API
public class Math
that specifies the information you need to know to use the
. . .
method name
method. The code in the tables is not the code that you type
signature
to use the method; it is known as the method’s signature.
double sqrt(double a)
The signature specifies the type of the arguments, the methreturn type
argument type
od name, and the type of the result that the method computes (the return value).
. . .
In your code, you can call a method by typing its name
Anatomy of a method signature
followed by arguments, enclosed in parentheses and separated by commas. When Java executes your program, we
say that it calls (or evaluates) the method with the given arguments and that the
method returns a value. A method call is an
expression, so you can use a method call in
library name
method name
the same way that you use variables and literdouble d = Math.sqrt(b*b - 4.0*a*c);
als to build up more complicated expressions.
return type
argument
For example, you can write expressions like
Math.sin(x) * Math.cos(y) and so on. An
Using a library method
argument is also an expression, so you can
write code like Math.sqrt(b*b - 4.0*a*c)
and Java knows what you mean—it evaluates the argument expression and passes
the resulting value to the method.
The API tables on the facing page show some of the commonly used methods
in Java’s Math library, along with the Java methods we have seen for printing text to
the terminal window and for converting strings to primitive types. The following
table shows several examples of calls that use these library methods:
library name
method call
library
return type
value
Integer.parseInt("123")
Integer
int
123
Double.parseDouble("1.5")
Double
double
1.5
Math.sqrt(5.0*5.0 - 4.0*4.0)
Math
double
3.0
Math.log(Math.E)
Math
double
1.0
Math.random()
Math
double
random in [0, 1)
Math.round(3.14159)
Math
long
3
Math.max(1.0, 9.0)
Math
double
9.0
Typical calls to Java library methods
1.2 Built-in Types of Data
31
public class Math
double abs(double a)
absolute value of a
double max(double a, double b) maximum of a and b
double min(double a, double b) minimum of a and b
Note 1: abs(), max(), and min() are defined also for int, long, and float.
double sin(double theta)
sine of theta
double cos(double theta)
cosine of theta
double tan(double theta)
tangent of theta
Note 2: Angles are expressed in radians. Use toDegrees() and toRadians() to convert.
Note 3: Use asin(), acos(), and atan() for inverse functions.
double exp(double a)
exponential (e a)
double log(double a)
natural log (loge a, or ln a)
double pow(double a, double b) raise a to the bth power (ab )
long round(double a)
round a to the nearest integer
double random()
random number in [0, 1)
double sqrt(double a)
square root of a
double E
value of e (constant)
double PI
value of (constant)
See booksite for other available functions.
Excerpts from Java’s Math library
void System.out.print(String s)
void System.out.println(String s)
print s
print s, followed by a newline
void System.out.println()
print a newline
Java library methods for printing strings to the terminal
int Integer.parseInt(String s)
double
Double.parseDouble(String s)
long Long.parseLong(String s)
convert s to an int value
convert s to a double value
convert s to a long value
Java library methods for converting strings to primitive types
Elements of Programming
32
With three exceptions, the methods on the previous page are pure—given
the same arguments, they always return the same value, without producing any
observable side effect. The method Math.random() is impure because it returns potentially a different value each time it is called; the methods System.out.print()
and System.out.println() are impure because they produce side effects—printing strings to the terminal. In APIs, we use a verb phrase to describe the behavior
of a method that produces side effects; otherwise, we use a noun phrase to describe
the return value. The keyword void designates a method that does not return a
value (and whose main purpose is to produce side effects).
The Math library also defines the constant values Math.PI (for ) and
Math.E (for e), which you can use in your programs. For example, the value of
Math.sin(Math.PI/2) is 1.0 and the value of Math.log(Math.E) is 1.0 (because
Math.sin() takes its argument in radians and Math.log() implements the natural logarithm function).
These APIs are typical of the online documentation that is the standard in modern
programming. The extensive online documentation of the Java APIs is routinely
used by professional programmers, and it is available to you (if you are interested)
directly from the Java website or through our booksite. You do not need to go to
the online documentation to understand the code in this book or to write similar
code, because we present and explain in the text all of the library methods that we
use in APIs like these and summarize them in the endpapers. More important, in
Chapters 2 and 3 you will learn in this book how to develop your own APIs and to
implement methods for your own use.
Type conversion
One of the primary rules of modern programming is that you
should always be aware of the type of data that your program is processing. Only by
knowing the type can you know precisely which set of values each variable can have,
which literals you can use, and which operations you can perform. For example,
suppose that you wish to compute the average of the four integers 1, 2, 3, and 4.
Naturally, the expression (1 + 2 + 3 + 4) / 4 comes to mind, but it produces
the int value 2 instead of the double value 2.5 because of type conversion conventions. The problem stems from the fact that the operands are int values but it is
natural to expect a double value for the result, so conversion from int to double
is necessary at some point. There are several ways to do so in Java.
1.2 Built-in Types of Data
33
Implicit type conversion. You can use an int value wherever a double value is
expected, because Java automatically converts integers to doubles when appropriate. For example, 11*0.25 evaluates to 2.75 because 0.25 is a double and both
operands need to be of the same type; thus, 11 is converted to a double and then
the result of dividing two doubles is a double. As another example, Math.sqrt(4)
evaluates to 2.0 because 4 is converted to a double, as expected by Math.sqrt(),
which then returns a double value. This kind of conversion is called automatic
promotion or coercion. Automatic promotion is appropriate because your intent is
clear and it can be done with no loss of information. In contrast, a conversion that
might involve loss of information (for example, assigning a double value to an int
variable) leads to a compile-time error.
Explicit cast. Java has some built-in type conversion conventions for primitive
types that you can take advantage of when you are aware that you might lose information. You have to make your intention to do so explicit by using a device
expression expression
expression
type
value
called a cast. You cast an expression
from one primitive type to another
(1 + 2 + 3 + 4) / 4.0
double
2.5
by prepending the desired type name
Math.sqrt(4)
double
2.0
within parentheses. For example, the
"1234" + 99
String
"123499"
expression (int) 2.71828 is a cast
11 * 0.25
double
2.75
from double to int that produces
(int) 11 * 0.25
double
2.75
an int with value 2. The conversion
11 * (int) 0.25
int
0
methods defined for casts throw away
(int) (11 * 0.25)
int
2
information in a reasonable way (for a
(int) 2.71828
int
2
full list, see the booksite). For example,
Math.round(2.71828)
long
3
casting a floating-point number to
(int)
Math.round(2.71828)
int
3
an integer discards the fractional part
Integer.parseInt("1234")
int
1234
by rounding toward zero. RandomInt
Typical type conversions
(Program 1.2.5) is an example that
uses a cast for a practical computation.
Casting has higher precedence than arithmetic operations—any cast
is applied to the value that immediately follows it. For example, if we write
int value = (int) 11 * 0.25, the cast is no help: the literal 11 is already an
integer, so the cast (int) has no effect. In this example, the compiler produces
a possible loss of precision error message because there would be a loss
Elements of Programming
34
Program 1.2.5
Casting to get a random integer
public class RandomInt
{
public static void main(String[] args)
{
int n = Integer.parseInt(args[0]);
double r = Math.random();
// uniform between 0.0 and 1.0
int value = (int) (r * n); // uniform between 0 and n-1
System.out.println(value);
}
}
This program uses the Java method Math.random() to generate a random number r between
0.0 (inclusive) and 1.0 (exclusive); then multiplies r by the command-line argument n to get
a random number greater than or equal to 0 and less than n; then uses a cast to truncate the
result to be an integer value between 0 and n-1.
% javac RandomInt.java
% java RandomInt 1000
548
% java RandomInt 1000
141
% java RandomInt 1000000
135032
of precision in converting the resulting value (2.75) to an int for assignment to
value. The error is helpful because the intended computation for this code is likely
(int) (11 * 0.25), which has the value 2, not 2.75.
Explicit type conversion. You can use a method that takes an argument of one
type (the value to be converted) and produces a result of another type. We have
already used the Integer.parseInt() and Double.parseDouble() library methods to convert String values to int and double values, respectively. Many other
methods are available for conversion among other types. For example, the library
1.2 Built-in Types of Data
35
method Math.round() takes a double argument and returns a long result: the
nearest integer to the argument. Thus, for example, Math.round(3.14159) and
Math.round(2.71828) are both of type long and have the same value (3). If you
want to convert the result of Math.round() to an int, you must use an explicit cast.
Beginning programmers tend to find type conversion to be an
annoyance, but experienced programmers know that paying
careful attention to data types is a key to success in programming. It may also be a key to avoiding failure: in a famous incident in 1985, a French rocket exploded in midair because of
a type-conversion problem. While a bug in your program may
not cause an explosion, it is well worth your while to take the
time to understand what type conversion is all about. After you
have written just a few programs, you will see that an understanding of data types will help you not only compose compact
code but also make your intentions explicit and avoid subtle
bugs in your programs.
Summary
Photo: ESA
Explosion of Ariane 5 rocket
A data type is a set of values and a set of operations on those values.
Java has eight primitive data types: boolean, char, byte, short, int, long, float,
and double. In Java code, we use operators and expressions like those in familiar
mathematical expressions to invoke the operations associated with each type. The
boolean type is used for computing with the logical values true and false; the
char type is the set of character values that we type; and the other six numeric
types are used for computing with numbers. In this book, we most often use boolean, int, and double; we do not use short or float. Another data type that we
use frequently, String, is not primitive, but Java has some built-in facilities for
Strings that are like those for primitive types.
When programming in Java, we have to be aware that every operation is defined only in the context of its data type (so we may need type conversions) and
that all types can have only a finite number of values (so we may need to live with
imprecise results).
The boolean type and its operations—&&, ||, and !—are the basis for logical
decision making in Java programs, when used in conjunction with the mixed-type
comparison operators ==, !=, <, >, <=, and >=. Specifically, we use boolean expressions to control Java’s conditional (if) and loop (for and while) constructs, which
we will study in detail in the next section.
36
Elements of Programming
The numeric types and Java’s libraries give us the ability to use Java as an extensive mathematical calculator. We write arithmetic expressions using the built-in
operators +, -, *, /, and % along with Java methods from the Math library.
Although the programs in this section are quite rudimentary by the standards
of what we will be able to do after the next section, this class of programs is quite
useful in its own right. You will use primitive types and basic mathematical functions extensively in Java programming, so the effort that you spend now in understanding them will certainly be worthwhile.
1.2 Built-in Types of Data
Q&A (Strings)
Q. How does Java store strings internally?
A. Strings are sequences of characters that are encoded with Unicode, a modern
standard for encoding text. Unicode supports more than 100,000 different characters, including more than 100 different languages plus mathematical and musical
symbols.
Q. Can you use < and > to compare String values?
A. No. Those operators are defined only for primitive-type values.
Q. How about == and != ?
A. Yes, but the result may not be what you expect, because of the meanings these
operators have for nonprimitive types. For example, there is a distinction between
a String and its value. The expression "abc" == "ab" + x is false when x is a
String with value "c" because the two operands are stored in different places in
memory (even though they have the same value). This distinction is essential, as
you will learn when we discuss it in more detail in Section 3.1.
Q. How can I compare two strings like words in a book index or dictionary?
A. We defer discussion of the
String data type and associated methods until
Section 3.1, where we introduce object-oriented programming. Until then, the
string concatenation operation suffices.
Q. How can I specify a string literal that is too long to fit on a single line?
A. You can’t. Instead, divide the string literal into independent string literals and
concatenate them together, as in the following example:
String dna = "ATGCGCCCACAGCTGCGTCTAAACCGGACTCTG" +
"AAGTCCGGAAATTACACCTGTTAG";
37
Elements of Programming
38
Q&A (Integers)
Q. How does Java store integers internally?
A. The simplest representation is for small positive integers, where the binary
number system is used to represent each integer with a fixed amount of computer
memory.
Q. What’s the binary number system?
A. In the binary number system, we represent an integer as a sequence of bits. A bit
is a single binary (base 2) digit—either 0 or 1—and is the basis for representing
information in computers. In this case the bits are coefficients of powers of 2. Specifically, the sequence of bits bnbn–1…b2b1b0 represents the integer
bn2n + bn–12n–1 + … + b222 + b121 + b020
For example, 1100011 represents the integer
99 = 1· 64 + 1· 32 + 0· 16 + 0· 8 + 0· 4 + 1· 2 +1· 1
The more familiar decimal number system is the same except that the digits are
between 0 and 9 and we use powers of 10. Converting a number to binary is an
interesting computational problem that we will consider in the next section. Java
uses 32 bits to represent int values. For example, the decimal integer 99 might be
represented with the 32 bits 00000000000000000000000001100011.
Q. How about negative numbers?
A. Negative numbers are handled with a convention known as two’s complement,
which we need not consider in detail. This is why the range of int values in Java
is –2147483648 (–231) to 2147483647 (231 – 1). One surprising consequence of
this representation is that int values can become negative when they get large and
overflow (exceed 2147483647). If you have not experienced this phenomenon, see
Exercise 1.2.10. A safe strategy is to use the int type when you know the integer
values will be fewer than ten digits and the long type when you think the integer
values might get to be ten digits or more.
Q. It seems wrong that Java should just let
ints
Shouldn’t Java automatically check for overflow?
overflow and give bad values.
1.2 Built-in Types of Data
39
A. Yes, this issue is a contentious one among programmers. The short answer for
now is that the lack of such checking is one reason such types are called primitive
data types. A little knowledge can go a long way in avoiding such problems. Again,
it is fine to use the int type for small numbers, but when values run into the billions, you cannot.
Q. What is the value of Math.abs(-2147483648)?
A.
-2147483648. This strange (but true) result is a typical example of the effects of
integer overflow and two’s complement representation.
Q. What do the expressions 1
/ 0 and 1 % 0
evaluate to in Java?
A. Each generates a run-time exception, for division by zero.
Q. What is the result of division and remainder for negative integers?
A. The quotient a / b rounds toward 0; the remainder a % b is defined such that
(a / b) * b + a % b is always equal to a. For example, -14 / 3 and 14 / -3 are both
-4, but -14 % 3 is -2 and 14 % -3 is 2. Some other languages (including Python)
have different conventions when dividing by negative integers.
Q. Why is the value of 10
^ 6 not 1000000 but 12?
A. The ^ operator is not an exponentiation operator, which you must have been
thinking. Instead, it is the bitwise exclusive or operator, which is seldom what you
want. Instead, you can use the literal 1e6. You could also use Math.pow(10, 6) but
doing so is wasteful if you are raising 10 to a known power.
Elements of Programming
40
Q&A (Floating-Point Numbers)
Q. Why is the type for real numbers named double?
A. The decimal point can “float” across the digits that make up the real number. In
contrast, with integers the (implicit) decimal point is fixed after the least significant
digit.
Q. How does Java store floating-point numbers internally?
A. Java follows the IEEE 754 standard, which supported in hardware by most
modern computer systems. The standard specifies that a floating-point number
is stored using three fields: sign, mantissa, and exponent. If you are interested,
see the booksite for more details. The IEEE 754 standard also specifies how special floating-point values—positive zero, negative zero, positive infinity, negative
infinity, and NaN (not a number)—should be handled. In particular, floatingpoint arithmetic never leads to a run-time exception. For example, the expression
-0.0/3.0 evaluates to -0.0, the expression 1.0/0.0 evaluates to positive infinity,
and Math.sqrt(-2.0) evaluates to NaN.
Q. Fifteen digits for floating-point numbers certainly seems enough to me. Do I
really need to worry much about precision?
A. Yes, because you are used to mathematics based on real numbers with infinite
precision, whereas the computer always deals with finite approximations. For example, the expression (0.1 + 0.1 == 0.2) evaluates to true but the expression
(0.1 + 0.1 + 0.1 == 0.3) evaluates to false! Pitfalls like this are not at all unusual in scientific computing. Novice programmers should avoid comparing two
floating-point numbers for equality.
Q. How can I initialize a double variable to NaN or infinity?
A. Java has built-in constants available for this purpose:
Double.NaN,
Double.POSITIVE_INFINITY, and Double.NEGATIVE_INFINITY.
Q. Are there functions in Java’s
Math library for other trigonometric functions,
such as cosecant, secant, and cotangent?
1.2 Built-in Types of Data
A. No, but you could use Math.sin(), Math.cos(), and Math.tan() to compute
them. Choosing which functions to include in an API is a tradeoff between the
convenience of having every function that you need and the annoyance of having to find one of the few that you need in a long list. No choice will satisfy all
users, and the Java designers have many users to satisfy. Note that there are plenty
of redundancies even in the APIs that we have listed. For example, you could use
Math.sin(x)/Math.cos(x) instead of Math.tan(x).
Q. It is annoying to see all those digits when printing a double. Can we arrange
System.out.println() to print just two or three digits after the decimal point?
A. That sort of task involves a closer look at the method used to convert from
to String. The Java library function System.out.printf() is one way
to do the job, and it is similar to the basic printing method in the C programming
language and many modern languages, as discussed in Section 1.5. Until then, we
will live with the extra digits (which is not all bad, since doing so helps us to get
used to the different primitive types of numbers).
double
41
Elements of Programming
42
Q&A (Variables and Expressions)
Q. What happens if I forget to declare a variable?
A. The compiler complains when you refer to that variable in an expression. For
example, IntOpsBad is the same as Program 1.2.2 except that the variable p is not
declared (to be of type int).
% javac IntOpsBad.java
IntOpsBad.java:7: error: cannot find symbol
p = a * b;
^
symbol:
variable p
location: class IntOpsBad
IntOpsBad.java:10: error: cannot find symbol
System.out.println(a + " * " + b + " = " + p);
^
symbol:
variable p
location: class IntOpsBad
2 errors
The compiler says that there are two errors, but there is really just one: the declaration of p is missing. If you forget to declare a variable that you use often, you will
get quite a few error messages. A good strategy is to correct the first error and check
that correction before addressing later ones.
Q. What happens if I forget to initialize a variable?
A. The compiler checks for this condition and will give you a variable
might
error message if you try to use the variable in an
expression before you have initialized it.
not have been initialized
Q. Is there a difference between the = and == operators?
A. Yes, they are quite different! The first is an assignment operator that changes
the value of a variable, and the second is a comparison operator that produces a
boolean result. Your ability to understand this answer is a sure test of whether you
understood the material in this section. Think about how you might explain the
difference to a friend.
1.2 Built-in Types of Data
Q. Can you compare a double to an int?
A. Not without doing a type conversion, but remember that Java usually does the
requisite type conversion automatically. For example, if x is an int with the value
3, then the expression (x < 3.1) is true—Java converts x to double (because 3.1
is a double literal) before performing the comparison.
Q. Will the statement a = b = c = 17; assign the value 17 to the three integer
variables a, b, and c?
A. Yes. It works because an assignment statement in Java is also an expression (that
evaluates to its right-hand side) and the assignment operator is right associative. As
a matter of style, we do not use such chained assignments in this book.
Q. Will the expression (a
test whether the values of three integer variables a, b, and c are in strictly ascending order?
< b < c)
A. No, it will not compile because the expression a
< b produces a boolean value,
which would then be compared to an int value. Java does not support chained
comparisons. Instead, you need to write (a < b && b < c).
Q. Why do we write (a
A. Java also has an
&
&& b) and not (a & b)?
operator that you may encounter if you pursue advanced
programming courses.
Q. What is the value of Math.round(6.022e23)?
A. You should get in the habit of typing in a tiny Java program to answer such
questions yourself (and trying to understand why your program produces the result that it does).
Q. I’ve heard Java referred to as a statically typed language. What does this mean?
A. Static typing means that the type of every variable and expression is known at
compile time. Java also verifies and enforces type constraints at compile time; for
example, your program will not compile if you attempt to store a value of type
double in a variable of type int or call Math.sqrt() with a String argument.
43
Elements of Programming
44
Exercises
1.2.1 Suppose that a and b are int variables. What does the following sequence
of statements do?
int t = a; b = t; a = b;
1.2.2 Write a program that uses Math.sin() and Math.cos() to check that the
value of cos2 + sin2 is approximately 1 for any entered as a command-line argument. Just print the value. Why are the values not always exactly 1?
1.2.3 Suppose that a and b are boolean variables. Show that the expression
(!(a && b) && (a || b)) || ((a && b) || !(a || b))
evaluates to true.
1.2.4 Suppose that a and b
(!(a < b) && !(a > b)).
are int variables. Simplify the following expression:
1.2.5 The exclusive or operator ^ for boolean operands is defined to be true if
they are different, false if they are the same. Give a truth table for this function.
1.2.6 Why does 10/3 give 3 and not 3.333333333?
Solution. Since both 10 and 3 are integer literals, Java sees no need for type conversion and uses integer division. You should write 10.0/3.0 if you mean the numbers
to be double literals. If you write 10/3.0 or 10.0/3, Java does implicit conversion
to get the same result.
1.2.7 What does each of the following print?
a. System.out.println(2 + "bc");
b. System.out.println(2 + 3 + "bc");
c. System.out.println((2+3) + "bc");
d. System.out.println("bc" + (2+3));
e. System.out.println("bc" + 2 + 3);
Explain each outcome.
1.2.8 Explain how to use Program 1.2.3 to find the square root of a number.
1.2 Built-in Types of Data
45
1.2.9 What does each of the following print?
a. System.out.println('b');
b. System.out.println('b' + 'c');
c. System.out.println((char) ('a' + 4));
Explain each outcome.
1.2.10 Suppose that a variable a is declared as int
a = 2147483647
(or equiva-
lently, Integer.MAX_VALUE). What does each of the following print?
a. System.out.println(a);
b. System.out.println(a+1);
c. System.out.println(2-a);
d. System.out.println(-2-a);
e. System.out.println(2*a);
f. System.out.println(4*a);
Explain each outcome.
1.2.11 Suppose that a variable a is declared as double
a = 3.14159. What does
each of the following print?
a. System.out.println(a);
b. System.out.println(a+1);
c. System.out.println(8/(int) a);
d. System.out.println(8/a);
e. System.out.println((int) (8/a));
Explain each outcome.
1.2.12 Describe what happens if you write sqrt instead of Math.sqrt in Program
1.2.3.
1.2.13 Evaluate the expression (Math.sqrt(2) * Math.sqrt(2) == 2).
1.2.14 Write a program that takes two positive integers as command-line
arguments and prints true if either evenly divides the other.
Elements of Programming
46
1.2.15 Write a program that takes three positive integers as command-line
arguments and prints false if any one of them is greater than or equal to the sum
of the other two and true otherwise. (Note : This computation tests whether the
three numbers could be the lengths of the sides of some triangle.)
1.2.16 A physics student gets unexpected results when using the code
double force = G * mass1 * mass2 / r * r;
to compute values according to the formula F = Gm1m2 r 2. Explain the problem
and correct the code.
1.2.17 Give the value of the variable a after the execution of each of the following
sequences of statements:
int
a =
a =
a =
a
a
a
a
=
+
+
+
1;
a;
a;
a;
boolean a = true;
a = !a;
a = !a;
a = !a;
int
a =
a =
a =
a
a
a
a
=
*
*
*
2;
a;
a;
a;
1.2.18 Write a program that takes two integer command-line arguments x and y
and prints the Euclidean distance from the point (x, y) to the origin (0, 0).
1.2.19 Write a program that takes two integer command-line arguments a and b
and prints a random integer between a and b, inclusive.
1.2.20 Write a program that prints the sum of two random integers between 1 and
6 (such as you might get when rolling dice).
1.2.21 Write a program that takes a double command-line argument t and prints
the value of sin(2t) sin(3t).
1.2.22 Write a program that takes three double command-line arguments x0, v0,
and t and prints the value of x0 v0t − g t 2 2, where g is the constant 9.80665. (Note :
This value is the displacement in meters after t seconds when an object is thrown
straight up from initial position x0 at velocity v0 meters per second.)
1.2.23 Write a program that takes two integer command-line arguments
d and prints true if
m and
day d of month m is between 3/20 and 6/20, false otherwise.
1.2 Built-in Types of Data
47
Creative Exercises
1.2.24 Continuously compounded interest. Write a program that calculates and
prints the amount of money you would have after t years if you invested P dollars
at an annual interest rate r (compounded continuously). The desired value is given
by the formula Pe rt.
1.2.25 Wind chill. Given the temperature T (in degrees Fahrenheit) and the wind
speed v (in miles per hour), the National Weather Service defines the effective temperature (the wind chill) as follows:
w = 35.74 0.6215 T (0.4275 T 35.75) v 0.16
Write a program that takes two double command-line arguments temperature
and velocity and prints the wind chill. Use Math.pow(a, b) to compute ab. Note :
The formula is not valid if T is larger than 50 in absolute value or if v is larger than
120 or less than 3 (you may assume that the values you get are in that range).
1.2.26 Polar coordinates. Write a program that converts from Cartesian
to polar coordinates. Your program should accept two double commandline arguments x and y and print the polar coordinates r and . Use the
method Math.atan2(y, x) to compute the arctangent value of y/x that is
in the range from to .
1.2.27 Gaussian random numbers. Write a program
x
r
Polar coordinates
RandomGaussian
that prints a random number r drawn from the Gaussian distribution. One way to
do so is to use the Box–Muller formula
r = sin(2 v) (2 ln u)1/2
where u and v are real numbers between 0 and 1 generated by the Math.random()
method.
1.2.28 Order check. Write a program that takes three
command-line
arguments x, y, and z and prints true if the values are strictly ascending or descending ( x < y < z or x > y > z ), and false otherwise.
double
y
Elements of Programming
48
1.2.29 Day of the week. Write a program that takes a date as input and prints the
day of the week that date falls on. Your program should accept three int commandline arguments: m (month), d (day), and y (year). For m, use 1 for January, 2 for
February, and so forth. For output, print 0 for Sunday, 1 for Monday, 2 for Tuesday,
and so forth. Use the following formulas, for the Gregorian calendar:
y0 = y (14 m) / 12
x = y0 y0 / 4 y0 / 100 y0 / 400
m0 = m 12 ((14 m) / 12) 2
d0 = (d x (31 m0) / 12) % 7
Example:
On which day of the week did February 14, 2000 fall?
y0 =
x =
m0 =
d0 =
Answer :
2000 1 = 1999
1999 1999 / 4 1999 / 100 1999 / 400 = 2483
2 12 1 2 = 12
(14 2483 (31 12) / 12) % 7 = 2500 % 7 = 1
Monday.
1.2.30 Uniform random numbers. Write a program that prints five uniform random numbers between 0 and 1, their average value, and their minimum and maximum values. Use Math.random(), Math.min(), and Math.max().
1.2.31 Mercator projection. The Mercator projection is a conformal (anglepreserving) projection that maps latitude and longitude to rectangular coordinates (x, y). It is widely used—for example, in nautical charts and in the maps that
you print from the web. The projection is defined by the equations x 0 and
y 1/2 ln ((1 sin ) (1 sin )), where 0 is the longitude of the point in the
center of the map. Write a program that takes 0 and the latitude and longitude of
a point from the command line and prints its projection.
1.2.32 Color conversion. Several different formats are used to represent color. For
example, the primary format for LCD displays, digital cameras, and web pages,
known as the RGB format, specifies the level of red (R), green (G), and blue (B)
on an integer scale from 0 to 255. The primary format for publishing books and
magazines, known as the CMYK format, specifies the level of cyan (C), magenta
1.2 Built-in Types of Data
49
(M), yellow (Y), and black (K) on a real scale from 0.0 to 1.0. Write a program
RGBtoCMYK that converts RGB to CMYK. Take three integers—r, g, and b—from
the command line and print the equivalent CMYK values. If the RGB values are all
0, then the CMY values are all 0 and the K value is 1; otherwise, use these formulas:
w max ( r / 255, g / 255, b / 255 )
c (w ( r / 255)) w
m (w ( g / 255)) w
y (w ( b / 255)) w
k 1 w
1.2.33 Great circle. Write a program
GreatCircle that takes four double
command-line arguments—x1, y1, x2, and y2—(the latitude and longitude, in de-
grees, of two points on the earth) and prints the great-circle distance between them.
The great-circle distance (in nautical miles) is given by the following equation:
d = 60 arccos(sin(x1) sin(x2) cos(x1) cos(x2) cos(y1 y2))
Note that this equation uses degrees, whereas Java’s trigonometric functions use radians. Use Math.toRadians() and Math.toDegrees() to convert between the two.
Use your program to compute the great-circle distance between Paris (48.87° N
and 2.33° W) and San Francisco (37.8° N and 122.4° W).
1.2.34 Three-sort. Write a program that takes three integer command-line arguments and prints them in ascending order. Use Math.min() and Math.max().
1.2.35 Dragon curves. Write a program to print the instructions for drawing the
dragon curves of order 0 through 5. The instructions are strings of F, L, and R
characters, where F means “draw line while moving 1 unit
F
forward,” L means “turn left,” and R means “turn right.” A
FLF
dragon curve of order n is formed when you fold a strip
of paper in half n times, then unfold to right angles. The
key to solving this problem is to note that a curve of order
FLFLFRF
n is a curve of order n1 followed by an L followed by a
curve of order n1 traversed in reverse order, and then
FLFLFRFLFLFRFRF
to figure out a similar description for the reverse curve.
Dragon curves of order 0, 1, 2, and 3
Elements of Programming
1.3 Conditionals and Loops
In the programs that we have examined to this point, each of the statements in the
program is executed once, in the order given. Most programs are more complicated
because the sequence of statements and the number of times each is executed can
vary. We use the term control flow to refer to statement sequencing in a program.
1.3.1 Flipping a fair coin. . . . . . . . . . . 53
In this section, we introduce statements
1.3.2 Your first while loop. . . . . . . . . . 55
that allow us to change the control flow,
1.3.3 Computing powers of 2. . . . . . . . 57
using logic about the values of program
1.3.4 Your first nested loops. . . . . . . . . 63
1.3.5 Harmonic numbers . . . . . . . . . . 65
variables. This feature is an essential
1.3.6 Newton’s method . . . . . . . . . . . 66
component of programming.
1.3.7 Converting to binary . . . . . . . . . 68
Specifically, we consider Java state1.3.8 Gambler’s ruin simulation . . . . . . 71
ments that implement conditionals,
1.3.9 Factoring integers . . . . . . . . . . . 73
where some other statements may or may
Programs in this section
not be executed depending on certain
conditions, and loops, where some other
statements may be executed multiple times, again depending on certain conditions.
As you will see in this section, conditionals and loops truly harness the power of the
computer and will equip you to write programs to accomplish a broad variety of
tasks that you could not contemplate attempting without a computer.
If statements
Most computations require different actions for different inputs.
One way to express these differences in Java is the if statement:
if (<boolean expression>) { <statements> }
This description introduces a formal notation known as a template that we will
use to specify the format of Java constructs. We put within angle brackets (< >)
a construct that we have already defined, to indicate that we can use any instance
of that construct where specified. In this case, <boolean expression> represents
an expression that evaluates to a boolean value, such as one involving a comparison operation, and <statements> represents a statement block (a sequence of Java
statements). This latter construct is familiar to you: the body of main() is such a sequence. If the sequence is a single statement, the curly braces are optional. It is possible to make formal definitions of <boolean expression> and <statements>,
but we refrain from going into that level of detail. The meaning of an if statement
1.3 Conditionals and Loops
51
is self-explanatory: the statement(s) in the sequence are to be executed if and only
if the expression is true.
As a simple example, suppose that you want to compute the absolute value of
an int value x. This statement does the job:
boolean
expression
if (x < 0) x = -x;
(More precisely, it replaces x with the absolute value of x.) As a
second simple example, consider the following statement:
if ( x > y )
{
if (x > y)
{
int t = x;
x = y;
y = t;
}
int t = x;
x = y;
y = t;
sequence
of
statements
}
Anatomy of an if statement
This code puts the smaller of the two int values in x and the larger of the two values in y, by exchanging the values in the two variables if necessary.
You can also add an else clause to an if statement, to express the concept of
executing either one statement (or sequence of statements) or another, depending
on whether the boolean expression is true or false, as in the following template:
if (<boolean expression>) <statements T>
else
<statements F>
As a simple example of the need for an else clause, consider the following code,
which assigns the maximum of two int values to the variable max:
if (x > y) max = x;
else
max = y;
One way to understand control flow is to visualize it with a diagram called a
flowchart. Paths through the flowchart correspond to flow-of-control paths in the
if (x > y) max = x;
else
max = y;
if (x < 0) x = -x;
yes
x < 0 ?
x = -x;
no
yes
x > y ?
max = x;
Flowchart examples (if statements)
no
max = y;
Elements of Programming
52
program. In the early days of computing, when programmers used low-level languages and difficult-to-understand flows of control, flowcharts were an essential
part of programming. With modern languages, we use flowcharts just to understand basic building blocks like the if statement.
The accompanying table contains some examples of the use of if and ifelse statements. These examples are typical of simple calculations you might need
in programs that you write. Conditional statements are an essential part of programming. Since the semantics (meaning) of statements like these is similar to their
meanings as natural-language phrases, you will quickly grow used to them.
Program 1.3.1 is another example of the use of the if-else statement, in
this case for the task of simulating a fair coin flip. The body of the program is a
single statement, like the ones in the table, but it is worth special attention because
it introduces an interesting philosophical issue that is worth contemplating: can a
computer program produce random values? Certainly not, but a program can produce numbers that have many of the properties of random numbers.
absolute value
if (x < 0) x = -x;
put the smaller
value in x
and the larger
value in y
if (x > y)
{
int t = x;
x = y;
y = t;
}
maximum of
x and y
error check
for division
operation
error check
for quadratic
formula
if (x > y) max = x;
else
max = y;
if (den == 0) System.out.println("Division by zero");
else
System.out.println("Quotient = " + num/den);
double discriminant = b*b
if (discriminant < 0.0)
{
System.out.println("No
}
else
{
System.out.println((-b
System.out.println((-b
}
- 4.0*c;
real roots");
+ Math.sqrt(discriminant))/2.0);
- Math.sqrt(discriminant))/2.0);
Typical examples of using if and if-else statements
1.3 Conditionals and Loops
Program 1.3.1
Flipping a fair coin
public class Flip
{
public static void main(String[] args)
{ // Simulate a fair coin flip.
if (Math.random() < 0.5) System.out.println("Heads");
else
System.out.println("Tails");
}
}
This program uses Math.random() to simulate a fair coin flip. Each time you run it, it prints
either Heads or Tails. A sequence of flips will have many of the same properties as a sequence
that you would get by flipping a fair coin, but it is not a truly random sequence.
% java Flip
Heads
% java Flip
Tails
% java Flip
Tails
While loops Many computations are inherently repetitive. The basic Java construct for handling such computations has the following format:
while (<boolean expression>) { <statements> }
The while statement has the same form as the if statement (the only difference
being the use of the keyword while instead of if), but the meaning is quite different. It is an instruction to the computer to behave as follows: if the boolean expression is false, do nothing; if the boolean expression is true, execute the sequence
of statements (just as with an if statement) but then check the expression again,
execute the sequence of statements again if the expression is true, and continue as
long as the expression is true. We refer to the statement block in a loop as the body
of the loop. As with the if statement, the curly braces are optional if a while loop
body has just one statement. The while statement is equivalent to a sequence of
identical if statements:
53
Elements of Programming
54
if (<boolean expression>) { <statements> }
if (<boolean expression>) { <statements> }
if (<boolean expression>) { <statements> }
...
At some point, the code in one of the statements must change something (such as
the value of some variable in the boolean expression) to make the boolean expression false, and then the sequence is broken.
A common programming paradigm involves maintaining an integer value
that keeps track of the number of times a loop iterates. We start at some initial
value, and then increment the value by 1 each time
initialization is a
loopthrough the loop, testing whether it exceeds a pre- separate
statement
continuation
condition
determined maximum before deciding to continue.
int power = 1;
TenHellos (Program 1.3.2) is a simple example of
while ( power <= n/2 )
this paradigm that uses a while statement. The key braces are {
optional
to the computation is the statement
power = 2*power;
when body
i = i + 1;
is a single
statement
}
body
As a mathematical equation, this statement is nonAnatomy of a while loop
sense, but as a Java assignment statement it makes
perfect sense: it says to compute the value i + 1
and then assign the result to the variable i. If the value of i was 4 before the statement, it becomes 5 afterward; if it was 5, it becomes 6; and so forth. With the initial
condition in TenHellos that the value of i starts at 4, the statement block is executed seven times until the sequence is broken,
int i = 4;
when
the value of i becomes 11.
while (i <= 10)
{
Using the while loop is barely worthSystem.out.println(i + "th Hello"); while for this simple task, but you will soon be
i = i + 1;
addressing tasks where you will need to specify
}
that statements be repeated far too many times
i = 4;
to contemplate doing it without loops. There is
a profound difference between programs with
no
i <= 10 ?
while statements and programs without them,
yes
because while statements allow us to specify
System.out.println(i + "th Hello");
a potentially unlimited number of statements
to
be executed in a program. In particular, the
i = i + 1;
while statement allows us to specify lengthy
Flowchart example (while statement)
1.3 Conditionals and Loops
Program 1.3.2
55
Your first while loop
public class TenHellos
{
public static void main(String[] args)
{ // Print 10 Hellos.
System.out.println("1st Hello");
System.out.println("2nd Hello");
System.out.println("3rd Hello");
int i = 4;
while (i <= 10)
{ // Print the ith Hello.
System.out.println(i + "th Hello");
i = i + 1;
}
}
}
This program uses a while loop for the simple, repetitive task of printing the output shown
below. After the third line, the lines to be printed differ only in the value of the index counting
the line printed, so we define a variable i to contain that index. After initializing the value of
i to 4, we enter into a while loop where we use the value of i in the System.out.println()
statement and increment it each time through the loop. After printing 10th Hello, the value
of i becomes 11 and the loop terminates.
% java TenHellos
1st Hello
2nd Hello
3rd Hello
4th Hello
5th Hello
6th Hello
7th Hello
8th Hello
9th Hello
10th Hello
i
i <= 10
output
4
true
4th Hello
5
true
5th Hello
6
true
6th Hello
7
true
7th Hello
8
true
8th Hello
9
true
9th Hello
10
true
10th Hello
11
false
Trace of java
TenHellos
56
Elements of Programming
computations in short programs. This ability opens the
door to writing programs for tasks that we could not
contemplate addressing without a computer. But there
is also a price to pay: as your programs become more
sophisticated, they become more difficult to understand.
PowersOfTwo (Program 1.3.3) uses a while loop to
print out a table of the powers of 2. Beyond the loop control counter i, it maintains a variable power that holds
the powers of 2 as it computes them. The loop body contains three statements: one to print the current power of
2, one to compute the next (multiply the current one by
2), and one to increment the loop control counter.
There are many situations in computer science
where it is useful to be familiar with powers of 2. You
should know at least the first 10 values in this table and
you should note that 210 is about 1 thousand, 220 is about
1 million, and 230 is about 1 billion.
PowersOfTwo is the prototype for many useful
computations. By varying the computations that change
the accumulated value and the way that the loop control
variable is incremented, we can print out tables of a variety of functions (see Exercise 1.3.12).
It is worthwhile to carefully examine the behavior of programs that use loops by studying a trace of
the program. For example, a trace of the operation of
PowersOfTwo should show the value of each variable
before each iteration of the loop and the value of the
boolean expression that controls the loop. Tracing the
operation of a loop can be very tedious, but it is often
worthwhile to run a trace because it clearly exposes what
a program is doing.
PowersOfTwo is nearly a self-tracing program,
because it prints the values of its variables each time
through the loop. Clearly, you can make any program produce a trace of itself by adding appropriate
System.out.println() statements. Modern program-
i
power
i <= n
0
1
true
1
2
true
2
4
true
3
8
true
4
16
true
5
32
true
6
64
true
7
128
true
8
256
true
9
512
true
10
1024
true
11
2048
true
12
4096
true
13
8192
true
14
16384
true
15
32768
true
16
65536
true
17
131072
true
18
262144
true
19
524288
true
20
1048576
true
21
2097152
true
22
4194304
true
23
8388608
true
24
16777216
true
25
33554432
true
26
67108864
true
27
134217728
true
28
268435456
true
29
536870912
true
30
1073741824
false
Trace of java
PowersOfTwo 29
1.3 Conditionals and Loops
Program 1.3.3
57
Computing powers of 2
public class PowersOfTwo
{
public static void main(String[] args)
{ // Print the first n powers of 2.
int n = Integer.parseInt(args[0]);
int power = 1;
int i = 0;
while (i <= n)
{ // Print ith power of 2.
System.out.println(i + " " + power);
power = 2 * power;
i = i + 1;
}
}
}
n
i
power
loop termination value
loop control counter
current power of 2
This program takes an integer command-line argument n and prints a table of the powers of 2
that are less than or equal to 2 n. Each time through the loop, it increments the value of i and
doubles the value of power. We show only the first three and the last three lines of the table; the
program prints n+1 lines.
%
0
1
2
3
4
5
java PowersOfTwo 5
1
2
4
8
16
32
% java PowersOfTwo 29
0 1
1 2
2 4
...
27 134217728
28 268435456
29 536870912
ming environments provide sophisticated tools for tracing, but this tried-and-true
method is simple and effective. You certainly should add print statements to the
first few loops that you write, to be sure that they are doing precisely what you
expect.
Elements of Programming
58
There is a hidden trap in PowersOfTwo, because the largest integer in Java’s
data type is 231 – 1 and the program does not test for that possibility. If you
invoke it with java PowersOfTwo 31, you may be surprised by the last line of
output printed:
int
...
1073741824
-2147483648
The variable power becomes too large and takes on a negative value because of the
way Java represents integers. The maximum value of an int is available for us to
use as Integer.MAX_VALUE. A better version of Program 1.3.3 would use this value
to test for overflow and print an error message if the user types too large a value,
though getting such a program to work properly for all inputs is trickier than you
might think. (For a similar challenge, see Exercise 1.3.16.)
As a more complicated example, suppose that we
want to compute the largest power of 2 that is less than
or equal to a given positive integer n. If n is 13, we want
int power = 1;
the result 8; if n is 1000, we want the result 512; if n is 64,
we want the result 64; and so forth. This computation is
power <= n/2 ?
simple to perform with a while loop:
int power = 1;
while (power <= n/2)
power = 2*power;
no
yes
power = 2*power;
It takes some thought to convince yourself that this simple piece of code produces the desired result. You can do
so by making these observations:
Flowchart for the statements
• power is always a power of 2.
int power = 1;
• power is never greater than n.
while (power <= n/2)
power = 2*power;
• power increases each time through the loop, so the
loop must terminate.
• After the loop terminates, 2*power is greater than n.
Reasoning of this sort is often important in understanding how while loops work.
Even though many of the loops you will write will be much simpler than this one,
you should be sure to convince yourself that each loop you write will behave as you
expect.
The logic behind such arguments is the same whether the loop iterates just a
few times, as in TenHellos, or dozens of times, as in PowersOfTwo, or millions of
1.3 Conditionals and Loops
59
times, as in several examples that we will soon consider. That leap from a few tiny
cases to a huge computation is profound. When writing loops, understanding how
the values of the variables change each time through the loop (and checking that
understanding by adding statements to trace their values and running for a small
number of iterations) is essential. Having done so, you can confidently remove
those training wheels and truly unleash the power of the computer.
For loops
As you will see, the
loop allows us to write prodeclare and initialize
another a loop control variable
grams for all manner of applica- initialize
loopvariable in a
continuation
separate
tions. Before considering more
increment
condition
statement
int power = 1;
examples, we will look at an alterfor (int i = 0; i <= n; i++ )
nate Java construct that allows us
{
even more flexibility when writing
System.out.println(i + " " + power);
programs with loops. This alterpower = 2*power;
nate notation is not fundamental}
body
ly different from the basic while
Anatomy of a for loop (that prints powers of 2)
loop, but it is widely used because
it often allows us to write more
compact and more readable programs than if we used only while statements.
while
For notation. Many loops follow this scheme: initialize an index variable to some
value and then use a while loop to test a loop-continuation condition involving
the index variable, where the last statement in the while loop increments the index
variable. You can express such loops directly with Java’s for notation:
for (<initialize>; <boolean expression>; <increment>)
{
<statements>
}
This code is, with only a few exceptions, equivalent to
<initialize>;
while (<boolean expression>)
{
<statements>
<increment>;
}
Elements of Programming
60
Your Java compiler might even produce identical results for the two loops. In truth,
and <increment> can be more complicated statements, but we
nearly always use for loops to support this typical initialize-and-increment programming idiom. For example, the following two lines of code are equivalent to the
corresponding lines of code in TenHellos (Program 1.3.2):
<initialize>
for (int i = 4; i <= 10; i = i + 1)
System.out.println(i + "th Hello");
Typically, we work with a slightly more compact version of this code, using the
shorthand notation discussed next.
Compound assignment idioms. Modifying the value of a variable is something
that we do so often in programming that Java provides a variety of shorthand notations for the purpose. For example, the following four statements all increment
the value of i by 1:
i = i+1;
i++;
++i;
i += 1;
You can also say i-- or --i or i -= 1 or i = i-1 to decrement that value of i by
1. Most programmers use i++ or i-- in for loops, though any of the others would
do. The ++ and -- constructs are normally used for integers, but the compound assignment constructs are useful operations for any arithmetic operator in any primitive numeric type. For example, you can say power *= 2 or power += power instead
of power = 2*power. All of these idioms are provided for notational convenience,
nothing more. These shortcuts came into widespread use with the C programming
language in the 1970s and have become standard. They have survived the test of
time because they lead to compact, elegant, and easily understood programs. When
you learn to write (and to read) programs that use them, you will be able to transfer
that skill to programming in numerous modern languages, not just Java.
Scope. The scope of a variable is the part of the program that can refer to that
variable by name. Generally the scope of a variable comprises the statements that
follow the declaration in the same block as the declaration. For this purpose, the
code in the for loop header is considered to be in the same block as the for loop
body. Therefore, the while and for formulations of loops are not quite equivalent:
in a typical for loop, the incrementing variable is not available for use in later statements; in the corresponding while loop, it is. This distinction is often a reason to
use a while loop instead of a for loop.
1.3 Conditionals and Loops
Choosing among different formulations of the same computation is a matter of
each programmer’s taste, as when a writer picks from among synonyms or chooses
between using active and passive voice when composing a sentence. You will not
find good hard-and-fast rules on how to write a program any more than you will
find such rules on how to compose a paragraph. Your goal should be to find a style
that suits you, gets the computation done, and can be appreciated by others.
The accompanying table includes several code fragments with typical examples of loops used in Java code. Some of these relate to code that you have already
seen; others are new code for straightforward computations. To cement your understanding of loops in Java, write some loops for similar computations of your
own invention, or do some of the early exercises at the end of this section. There
is no substitute for the experience gained by running code that you create yourself,
and it is imperative that you develop an understanding of how to write Java code
that uses loops.
compute the largest
power of 2
less than or equal to n
compute a finite sum
(1 + 2 + … + n)
compute a finite product
(n ! = 1 × 2 × … × n)
int power = 1;
while (power <= n/2)
power = 2*power;
System.out.println(power);
int sum = 0;
for (int i = 1; i <= n; i++)
sum += i;
System.out.println(sum);
int product = 1;
for (int i = 1; i <= n; i++)
product *= i;
System.out.println(product);
for (int i = 0; i <= n; i++)
System.out.println(i + " " + 2*Math.PI*i/n);
print a table of
function values
compute the ruler function
(see Program 1.2.1)
String ruler = "1";
for (int i = 2; i <= n; i++)
ruler = ruler + " " + i + " " + ruler;
System.out.println(ruler);
Typical examples of using for and while loops
61
Elements of Programming
62
Nesting The if, while, and for statements have the same status as assignment
statements or any other statements in Java; that is, we can use them wherever a
statement is called for. In particular, we can use one or more of them in the body
of another statement to make compound statements. As a first example, DivisorPattern (Program 1.3.4) has a for loop whose body contains a for loop (whose
body is an if-else statement) and a print statement. It prints a pattern of asterisks
where the ith row has an asterisk in each position corresponding to divisors of i
(the same holds true for the columns).
To emphasize the nesting, we use indentation in the program code. We refer
to the i loop as the outer loop and the j loop as the inner loop. The inner loop iterates all the way through for each iteration of the outer loop. As usual, the best way
to understand a new programming construct like this is to study a trace.
DivisorPattern has a complicated control flow, as you can see from its flowchart. A diagram like this illustrates the importance of using a limited number of
simple control flow structures in programming. With nesting, you can compose
loops and conditionals to build programs that are easy to understand even though
they may have a complicated control flow. A great many useful computations can
be accomplished with just one or two levels of nesting. For example, many programs in this book have the same general structure as DivisorPattern.
i = 1;
i++;
i <= n ?
no
yes
j = 1;
j++;
yes
j <= n ?
yes
no
(i % j == 0) || (j % i == 0) ?
System.out.print("* ");
no
System.out.print("
System.out.println(i);
Flowchart for DivisorPattern
");
1.3 Conditionals and Loops
Program 1.3.4
63
Your first nested loops
public class DivisorPattern
{
public static void main(String[] args)
{ // Print a square that visualizes divisors.
int n = Integer.parseInt(args[0]);
for (int i = 1; i <= n; i++)
{ // Print the ith line.
for (int j = 1; j <= n; j++)
{ // Print the jth element in the ith line.
if ((i % j == 0) || (j % i == 0))
System.out.print("* ");
else
System.out.print(" ");
}
System.out.println(i);
}
}
}
n
i
j
number of rows
and columns
row index
column index
This program takes an integer command-line argument n and uses nested for loops to print
an n-by-n table with an asterisk in row i and column j if either i divides j or j divides i. The
loop control variables i and j control the computation.
% java DivisorPattern 3
* * * 1
* *
2
*
* 3
%
*
*
*
*
*
*
*
*
*
*
*
*
java DivisorPattern
* * * * * * * * * *
*
*
*
*
*
*
*
*
*
*
*
*
*
* *
*
*
*
*
*
*
*
*
*
*
*
* * *
*
12
* 1
* 2
* 3
* 4
5
* 6
7
8
9
10
11
* 12
i
j
i % j
j % i
output
1
1
0
0
*
1
2
1
0
*
1
3
1
0
*
1
2
1
0
1
*
2
2
0
0
*
2
3
2
1
2
3
1
0
1
3
2
1
2
3
3
0
0
*
*
3
Trace of java
DivisorPattern 3
Elements of Programming
64
As a second example of nesting, consider the following program fragment,
which a tax preparation program might use to compute income tax rates:
if
else
else
else
else
else
else
else
if
if
if
if
if
if
(income
(income
(income
(income
(income
(income
(income
<
0) rate =
<
8925) rate =
< 36250) rate =
< 87850) rate =
< 183250) rate =
< 398350) rate =
< 400000) rate =
rate =
0.00;
0.10;
0.15;
0.23;
0.28;
0.33;
0.35;
0.396;
In this case, a number of if statements are nested to test from among a number
of mutually exclusive possibilities. This construct is a special one that we use often.
Otherwise, it is best to use curly braces to resolve ambiguities when nesting if
statements. This issue and more examples are addressed in the Q&A and exercises.
Applications The ability to program with loops immediately opens up the full
world of computation. To emphasize this fact, we next consider a variety of examples. These examples all involve working with the types of data that we considered in Section 1.2, but rest assured that the same mechanisms serve us well for
any computational application. The sample programs are carefully crafted, and by
studying them, you will be prepared to write your own programs containing loops.
The examples that we consider here involve computing with numbers. Several of our examples are tied to problems faced by mathematicians and scientists
throughout the past several centuries. While computers have existed for only 70
years or so, many of the computational methods that we use are based on a rich
mathematical tradition tracing back to antiquity.
Finite sum. The computational paradigm used by
PowersOfTwo is one that
you will use frequently. It uses two variables—one as an index that controls
a loop and the other to accumulate a computational result. HarmonicNumber
(Program 1.3.5) uses the same paradigm to evaluate the finite
1
sum Hn = 1 + 1/2 + 1/3 + ... + 1/n . These numbers, which are
known as the harmonic numbers, arise frequently in discrete
mathematics. Harmonic numbers are the discrete analog of
the logarithm. They also approximate the area under the curve
y = 1/x. You can use Program 1.3.5 as a model for computing the
values of other finite sums (see Exercise 1.3.18).
1/2
1/3
1/4
1/5
1.3 Conditionals and Loops
Program 1.3.5
65
Harmonic numbers
public class HarmonicNumber
{
public static void main(String[] args)
{ // Compute the nth harmonic number.
int n = Integer.parseInt(args[0]);
double sum = 0.0;
for (int i = 1; i <= n; i++)
{ // Add the ith term to the sum.
sum += 1.0/i;
}
System.out.println(sum);
}
}
n
number of terms in sum
loop index
sum cumulated sum
i
This program takes an integer command-line argument n and computes the value of the nth
harmonic number. The value is known from mathematical analysis to be about ln(n) + 0.57721
for large n. Note that ln(1,000,000) + 0.57721 14.39272.
% java HarmonicNumber 2
1.5
% java HarmonicNumber 10000
7.485470860550343
% java HarmonicNumber 10
2.9289682539682538
% java HarmonicNumber 1000000
14.392726722864989
Computing the square root. How are functions in Java’s Math library, such as Math.sqrt(), implemented? Sqrt (Program 1.3.6)
illustrates one technique. To compute the square root of a positive
number, it uses an iterative computation that was known to the
Babylonians more than 4,000 years ago. It is also a special case
of a general computational technique that was developed in the
17th century by Isaac Newton and Joseph Raphson and is widely
known as Newton’s method. Under generous conditions on a given
function f (x), Newton’s method is an effective way to find roots
y = f(x)
root
ti+2
ti+1
ti
Newton’s method
Elements of Programming
66
Program 1.3.6
Newton’s method
public class Sqrt
{
public static void main(String[] args)
{
double c = Double.parseDouble(args[0]);
double EPSILON = 1e-15;
double t = c;
while (Math.abs(t - c/t) > EPSILON * t)
{ // Replace t by the average of t and c/t.
t = (c/t + t) / 2.0;
}
System.out.println(t);
}
}
c
EPSILON
t
argument
error tolerance
estimate of
square root of c
This program takes a positive floating-point number c as a command-line argument and computes the square root of c to 15 decimal places of accuracy, using Newton’s method (see text).
% java Sqrt 2.0
1.414213562373095
% java Sqrt 2544545
1595.1630010754388
iteration
t
c/t
2.0000000000000000
1.0
1
1.5000000000000000
1.3333333333333333
2
1.4166666666666665
1.4117647058823530
3
1.4142156862745097
1.4142114384748700
4
1.4142135623746899
1.4142135623715002
5
1.4142135623730950
1.4142135623730951
Trace of java
Sqrt 2.0
(values of x for which the function is 0). Start with an initial estimate, t0. Given the
estimate ti , compute a new estimate by drawing a line tangent to the curve y = f (x)
at the point (ti , f (ti)) and set ti+1 to the x-coordinate of the point where that line hits
the x-axis. Iterating this process, we get closer to the root.
1.3 Conditionals and Loops
Computing the square root of a positive number c
is equivalent to finding the positive root of the function
f (x) = x 2 – c. For this special case, Newton’s method
amounts to the process implemented in Sqrt (see Exercise 1.3.19). Start with the estimate t = c. If t is equal
to c / t, then t is equal to the square root of c, so the
computation is complete. If not, refine the estimate by
replacing t with the average of t and c / t. With Newton’s
method, we get the value of the square root of 2 accurate to 15 decimal places in just 5 iterations of the loop.
Newton’s method is important in scientific computing because the same iterative approach is effective for finding the roots of a broad class of functions,
including many for which analytic solutions are not
known (and for which the Java Math library is no help).
Nowadays, we take for granted that we can find whatever values we need of mathematical functions; before
computers, scientists and engineers had to use tables
or compute values by hand. Computational techniques
that were developed to enable calculations by hand
needed to be very efficient, so it is not surprising that
many of those same techniques are effective when we
use computers. Newton’s method is a classic example of
this phenomenon. Another useful approach for evaluating mathematical functions is to use Taylor series expansions (see Exercise 1.3.37 and Exercise 1.3.38).
67
greater than 16
1????
16
>16
less than 16 + 8
10???
<24
16 8
less than 16 4
100??
<20
16
4
greater than 16 2
1001?
16
2
>18
Number conversion.
Binary (Program 1.3.7) prints
the binary (base 2) representation of the decimal number typed as the command-line argument. It is based
on decomposing a number into a sum of powers of 2.
For example, the binary representation of 19 is 10011,
which is the same as saying that 19 = 16 + 2 + 1. To
compute the binary representation of n, we consider
the powers of 2 less than or equal to n in decreasing order to determine which belong in the binary decomposition (and therefore correspond to a 1 bit in the binary
equal to 16 + 2 + 1
10011
16
21
10000+10+1
=19
=
10011
Scale analog to binary conversion
Elements of Programming
68
Program 1.3.7
Converting to binary
public class Binary
{
public static void main(String[] args)
{ // Print binary representation of n.
int n = Integer.parseInt(args[0]);
int power = 1;
while (power <= n/2)
power *= 2;
// Now power is the largest power of 2 <= n.
n
power
integer to convert
current power of 2
while (power > 0)
{ // Cast out powers of 2 in decreasing order.
if (n < power) { System.out.print(0);
}
else
{ System.out.print(1); n -= power; }
power /= 2;
}
System.out.println();
}
}
This program takes a positive integer n as a command-line argument and prints the binary
representation of n, by casting out powers of 2 in decreasing order (see text).
% java Binary 19
10011
% java Binary 100000000
101111101011110000100000000
representation). The process corresponds precisely to using a balance scale to
weigh an object, using weights whose values are powers of 2. First, we find the largest weight not heavier than the object. Then, considering the weights in decreasing
order, we add each weight to test whether the object is lighter. If so, we remove the
weight; if not, we leave the weight and try the next one. Each weight corresponds to
1.3 Conditionals and Loops
n
binary
representation
power
19
10011
3
0011
3
3
1
0
69
power > 0
binary
representation
n < power
output
16
true
10000
false
1
8
true
1000
true
0
011
4
true
100
true
0
01
2
true
10
false
1
1
1
true
1
false
1
0
false
Trace of casting-out-powers-of-2 loop for java
Binary 19
a bit in the binary representation of the weight of the object; leaving a weight corresponds to a 1 bit in the binary representation of the object’s weight, and removing
a weight corresponds to a 0 bit in the binary representation of the object’s weight.
In Binary, the variable power corresponds to the current weight being tested,
and the variable n accounts for the excess (unknown) part of the object’s weight (to
simulate leaving a weight on the balance, we just subtract that weight from n). The
value of power decreases through the powers of 2. When it is larger than n, Binary
prints 0; otherwise, it prints 1 and subtracts power from n. As usual, a trace (of the
values of n, power, n < power, and the output bit for each loop iteration) can be
very useful in helping you to understand the program. Read from top to bottom in
the rightmost column of the trace, the output is 10011, the binary representation
of 19.
Converting data from one representation to another is a frequent theme in
writing computer programs. Thinking about conversion emphasizes the distinction between an abstraction (an integer like the number of hours in a day) and a
representation of that abstraction (24 or 11000). The irony here is that the computer’s representation of an integer is actually based on its binary representation.
Simulation. Our next example is different in character from the ones we have
been considering, but it is representative of a common situation where we use computers to simulate what might happen in the real world so that we can make informed decisions. The specific example that we consider now is from a thoroughly
studied class of problems known as gambler’s ruin. Suppose that a gambler makes
a series of fair $1 bets, starting with some given initial stake. The gambler always
goes broke eventually, but when we set other limits on the game, various questions
Elements of Programming
70
goal
stake
0
goal
stake
0
arise. For example, suppose that the gambler decides
ahead of time to walk away after reaching a certain
goal. What are the chances that the gambler will win?
How many bets might be needed to win or lose the
game? What is the maximum amount of money that
the gambler will have during the course of the game?
Gambler (Program 1.3.8) is a simulation that
can help answer these questions. It does a sequence
of trials, using Math.random() to simulate the sequence of bets, continuing until either the gambler
is broke or the goal is reached, and keeping track of
the number of times the gambler reaches the goal
Gambler simulation sequences
and the number of bets. After running the experiment for the specified number of trials, it averages and prints the results. You might
wish to run this program for various values of the command-line arguments, not
necessarily just to plan your next trip to the casino, but to help you think about the
following questions: Is the simulation an accurate reflection of what would happen in real life? How many trials are needed to get an accurate answer? What are
the computational limits on performing such a simulation? Simulations are widely
used in applications in economics, science, and engineering, and questions of this
sort are important in any simulation.
In the case of Gambler, we are verifying classical results from probability theory, which say the probability of success is the ratio of the stake to the goal and that
the expected number of bets is the product of the stake and the desired gain (the difference between the goal and the stake). For example, if you go to Monte Carlo to try
to turn $500 into $2,500, you have a reasonable (20%) chance of success, but you
should expect to make a million $1 bets! If you try to turn $1 into $1,000, you have
a 0.1% chance and can expect to be done (ruined, most likely) in about 999 bets.
Simulation and analysis go hand-in-hand, each validating the other. In practice, the value of simulation is that it can suggest answers to questions that might
be too difficult to resolve with analysis. For example, suppose that our gambler,
recognizing that there will never be enough time to make a million bets, decides
ahead of time to set an upper limit on the number of bets. How much money can
the gambler expect to take home in that case? You can address this question with
an easy change to Program 1.3.8 (see Exercise 1.3.26), but addressing it with mathematical analysis is not so easy.
win
1.3 Conditionals and Loops
Program 1.3.8
71
Gambler’s ruin simulation
public class Gambler
stake
{
goal
public static void main(String[] args)
trials
{ // Run trials experiments that start with
bets
// $stake and terminate on $0 or $goal.
wins
int stake = Integer.parseInt(args[0]);
cash
int goal
= Integer.parseInt(args[1]);
int trials = Integer.parseInt(args[2]);
int bets = 0;
int wins = 0;
for (int t = 0; t < trials; t++)
{ // Run one experiment.
int cash = stake;
while (cash > 0 && cash < goal)
{ // Simulate one bet.
bets++;
if (Math.random() < 0.5) cash++;
else
cash--;
} // Cash is either 0 (ruin) or $goal (win).
if (cash == goal) wins++;
}
System.out.println(100*wins/trials + "% wins");
System.out.println("Avg # bets: " + bets/trials);
}
}
initial stake
walkaway goal
number of trials
bet count
win count
cash on hand
This program takes three integers command-line arguments stake, goal, and trials. The
inner while loop in this program simulates a gambler with $stake who makes a series of $1
bets, continuing until going broke or reaching $goal. The running time of this program is proportional to trials times the average number of bets. For example, the third command below
causes nearly 100 million random numbers to be generated.
% java Gambler 10 20 1000
50% wins
Avg # bets: 100
% java Gambler 50 250 100
19% wins
Avg # bets: 11050
% java Gambler 10 20 1000
51% wins
Avg # bets: 98
% java Gambler 500 2500 100
21% wins
Avg # bets: 998071
Elements of Programming
72
Factoring. A prime number is an integer greater than 1 whose only positive divi-
factor
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
sors are 1 and itself. The prime factorization of an integer is the multiset of primes
whose product is the integer. For example, 3,757,208 = 2 2 2 7 13 13 397.
Factors (Program 1.3.9) computes the prime factorization of any given positive
integer. In contrast to many of the other programs that we have seen (which we
could do in a few minutes with a calculator or even a pencil and paper), this computation would not be feasible without a computer. How would you go about trying to find the factors of a number like 287994837222311? You might find the
factor 17 quickly, but even with a calculator it would take you quite a while to find
1739347.
Although Factors is compact, it certainly will take some thought to convince
yourself that it produces the desired result for any given integer. As usual, following a trace that shows the values of the variables at the beginning of each iteration
of the outer for loop is a good way to understand the computation. For the case
where the initial value of n is 3757208, the inner while loop iterates three times
when factor is 2, to remove the three factors of 2; then zero
n
output
times when factor is 3, 4, 5, and 6, since none of those
numbers divides 469651; and so forth. Tracing the program
3757208
2 2 2
for a few example inputs reveals its basic operation. To con469651
vince
ourselves that the program will behave as expected for
469651
all
inputs,
we reason about what we expect each of the loops
469651
to do. The while loop prints and removes from n all factors
469651
of factor, but the key to understanding the program is to
469651
7
see that the following fact holds at the beginning of each
67093
iteration of the for loop: n has no factors between 2 and
67093
factor-1. Thus, if factor is not prime, it will not divide
67093
n; if factor is prime, the while loop will do its job. Once
67093
we know that n has no divisors less than or equal to factor,
67093
we also know that it has no factors greater than n/factor,
67093
13 13
so we need look no further when factor is greater than n/
397
factor.
397
In a more naïve implementation, we might simply
have used the condition (factor < n) to terminate the for
397
loop. Even given the blinding speed of modern computers,
397
such a decision would have a dramatic effect on the size of
397
the numbers that we could factor. Exercise 1.3.28 encour397
ages you to experiment with the program to learn the ef397
397
Trace of java
Factors 3757208
1.3 Conditionals and Loops
Program 1.3.9
73
Factoring integers
public class Factors
n
{
factor
public static void main(String[] args)
{ // Print the prime factorization of n.
long n = Long.parseLong(args[0]);
for (long factor = 2; factor <= n/factor; factor++)
{ // Test potential factor.
while (n % factor == 0)
{ // Cast out and print factor.
n /= factor;
System.out.print(factor + " ");
} // Any factor of n must be greater than factor.
}
if (n > 1) System.out.print(n);
System.out.println();
}
}
unfactored part
potential factor
This program takes a positive integer n as a command-line argument and prints the prime
factorization of n. The code is simple, but it takes some thought to convince yourself that it is
correct (see text).
% java Factors 3757208
2 2 2 7 13 13 397
% java Factors 287994837222311
17 1739347 9739789
fectiveness of this simple change. On a computer that can do billions of operations
per second, we could factor numbers on the order of 109 in a few seconds; with
the (factor <= n/factor) test, we can factor numbers on the order of 1018 in a
comparable amount of time. Loops give us the ability to solve difficult problems,
but they also give us the ability to construct simple programs that run slowly, so we
must always be cognizant of performance.
In modern applications in cryptography, there are important situations where
we wish to factor truly huge numbers (with, say, hundreds or thousands of digits).
Such a computation is prohibitively difficult even with the use of a computer.
74
Elements of Programming
Other conditional and loop constructs To more fully cover the Java language, we consider here four more control-flow constructs. You need not think
about using these constructs for every program that you write, because you are
likely to encounter them much less frequently than the if, while, and for statements. You certainly do not need to worry about using these constructs until you
are comfortable using if, while, and for. You might encounter one of them in a
program in a book or on the web, but many programmers do not use them at all
and we rarely use any of them outside this section.
Break statements. In some situations, we want to immediately exit a loop without
letting it run to completion. Java provides the break statement for this purpose.
For example, the following code is an effective way to test whether a given integer
n > 1 is prime:
int factor;
for (factor = 2; factor <= n/factor; factor++)
if (n % factor == 0) break;
if (factor > n/factor)
System.out.println(n + " is prime");
There are two different ways to leave this loop: either the break statement is executed (because factor divides n, so n is not prime) or the loop-continuation condition is not satisfied (because no factor with factor <= n/factor was found
that divides n, which implies that n is prime). Note that we have to declare factor
outside the for loop instead of in the initialization statement so that its scope extends beyond the loop.
Continue statements. Java also provides a way to skip to the next iteration of a
loop: the continue statement. When a continue statement is executed within the
body of a for loop, the flow of control transfers directly to the increment statement
for the next iteration of the loop.
Switch statements. The if and if-else statements allow one or two alternatives
in directing the flow of control. Sometimes, a computation naturally suggests more
than two mutually exclusive alternatives. We could use a sequence or a chain of ifelse statements (as in the tax rate calculation discussed earlier in this section), but
the Java switch statement provides a more direct solution. Let us move right to a
typical example. Rather than printing an int variable day in a program that works
with days of the weeks (such as a solution to Exercise 1.2.29), it is easier to use a
switch statement, as follows:
1.3 Conditionals and Loops
switch (day)
{
case 0: System.out.println("Sun");
case 1: System.out.println("Mon");
case 2: System.out.println("Tue");
case 3: System.out.println("Wed");
case 4: System.out.println("Thu");
case 5: System.out.println("Fri");
case 6: System.out.println("Sat");
}
75
break;
break;
break;
break;
break;
break;
break;
When you have a program that seems to have a long and regular sequence of if
statements, you might consider consulting the booksite and using a switch statement, or using an alternate approach described in Section 1.4.
Do–while loops. Another way to write a loop is to use the template
do { <statements> } while (<boolean expression>);
The meaning of this statement is the same as
while (<boolean expression>) { <statements> }
except that the first test of the boolean condition is omitted. If the boolean condition initially holds, there is no difference. For an example in which do-while is
useful, consider the problem of generating points that are randomly distributed in
the unit disk. We can use Math.random() to generate x- and y-coordinates independently to get points that are randomly distributed in the 2-by-2 square centered
on the origin. Most points fall within the unit disk, so we just reject those that do
not. We always want to generate at least one point, so a do-while loop is ideal for
this computation. The following code sets x and y such that the point (x, y) is randomly distributed in the unit disk:
do
{ // Scale x and y to be random in (-1, 1).
x = 2.0*Math.random() - 1.0;
y = 2.0*Math.random() - 1.0;
} while (x*x + y*y > 1.0);
Since the area of the disk is and the area of the square is 4, the expected number of times the loop is iterated is 4/ (about 1.27).
y
(1, 1)
in
x
(0, 0)
out
Elements of Programming
76
Infinite loops
Before you write programs that use loops, you need to think
about the following issue: what if the loop-continuation condition in a while loop
is always satisfied? With the statements that you have learned so far, one of two bad
things could happen, both of which you need to learn to cope with.
First, suppose that such a loop calls System.out.println(). For example, if
the loop-continuation condition in TenHellos were (i > 3) instead of (i <= 10),
it would always be true. What happens? Nowadays, we use print as an abstraction
to mean display in a terminal window and the result of attempting to display an
unlimited number of lines in a terminal window is dependent on operating-system
conventions. If your system is set up to have print mean print characters on a piece of
paper, you might run out of paper or have to unplug the printer.
In a terminal window, you need a stop printing operation. Bepublic class BadHellos
fore running programs with loops on your own, you make sure
...
int i = 4;
that you know what to do to “pull the plug” on an infinite loop
while (i > 3)
of System.out.println() calls and then test out the strategy
{
by making the change to TenHellos indicated above and trying
System.out.println
to stop it. On most systems, <Ctrl-C> means stop the current
(i + "th Hello");
program, and should do the job.
i = i + 1;
Second, nothing might happen. If your program has an
}
infinite
loop that does not produce any output, it will spin
...
through the loop and you will see no results at all. When you
find yourself in such a situation, you can inspect the loops to
% java BadHellos
make sure that the loop exit condition always happens, but the
1st Hello
2nd Hello
problem may not be easy to identify. One way to locate such
3rd Hello
a bug is to insert calls to System.out.println() to produce
5th Hello
a trace. If these calls fall within an infinite loop, this strategy
6th Hello
reduces the problem to the case discussed in the previous para7th Hello
graph, but the output might give you a clue about what to do.
...
You might not know (or it might not matter) whether a
An infinite loop
loop is infinite or just very long. Even BadHellos eventually
would terminate after printing more than 1 billion lines because of integer overflow. If you invoke Program 1.3.8 with arguments such as java
Gambler 100000 200000 100, you may not want to wait for the answer. You will
learn to be aware of and to estimate the running time of your programs.
Why not have Java detect infinite loops and warn us about them? You might
be surprised to know that it is not possible to do so, in general. This counterintuitive fact is one of the fundamental results of theoretical computer science.
1.3 Conditionals and Loops
Summary
77
For reference, the accompanying table lists the programs that we
have considered in this section. They are representative of the kinds of tasks we can
address with short programs composed of if, while, and for statements processing built-in types of data. These types of computations are an appropriate way to
become familiar with the basic Java flow-of-control constructs.
To learn how to use conditionals and loops, you must practice writing and
debugging programs with if, while, and for statements. The exercises at the end
of this section provide many opportunities for you to begin this process. For each
exercise, you will write a Java program, then run and test it. All programmers know
that it is unusual to have a program
program
description
work as planned the first time it is run,
so you will want to have an understandFlip
simulate a coin flip
ing of your program and an expectaTenHellos
your first loop
tion of what it should do, step by step.
PowersOfTwo
compute and print a table of values
At first, use explicit traces to check your
DivisorPattern
your first nested loop
understanding and expectation. As you
Harmonic
compute finite sum
gain experience, you will find yourself
thinking in terms of what a trace might
Sqrt
classic iterative algorithm
produce as you compose your loops.
Binary
basic number conversion
Ask yourself the following kinds of
Gambler
simulation with nested loops
questions: What will be the values of the
Factors
while loop within a for loop
variables after the loop iterates the first
time? The second time? The final time?
Summary of programs in this section
Is there any way this program could get
stuck in an infinite loop?
Loops and conditionals are a giant step in our ability to compute: if, while,
and for statements take us from simple straight-line programs to arbitrarily complicated flow of control. In the next several chapters, we will take more giant steps
that will allow us to process large amounts of input data and allow us to define
and process types of data other than simple numeric types. The if, while, and
for statements of this section will play an essential role in the programs that we
consider as we take these steps.
Elements of Programming
78
Q&A
Q. What is the difference between = and ==?
A. We repeat this question here to remind you to be sure not to use = when you
mean == in a boolean expression. The expression (x = y) assigns the value of y to
x, whereas the expression (x == y) tests whether the two variables currently have
the same values. In some programming languages, this difference can wreak havoc
in a program and be difficult to detect, but Java’s type safety usually will come to
the rescue. For example, if we make the mistake of typing (cash = goal) instead
of (cash == goal) in Program 1.3.8, the compiler finds the bug for us:
javac Gambler.java
Gambler.java:18: incompatible types
found : int
required: boolean
if (cash = goal) wins++;
^
1 error
Be careful about writing if (x = y) when x and y are boolean variables, since this
will be treated as an assignment statement, which assigns the value of y to x and
evaluates to the truth value of y. For example, you should write if (!isPrime)
instead of if (isPrime = false).
Q. So I need to pay attention to using == instead of = when writing loops and conditionals. Is there something else in particular that I should watch out for?
A. Another common mistake is to forget the braces in a loop or conditional with a
multi-statement body. For example, consider this version of the code in Gambler:
for (int t = 0; t < trials; t++)
for (cash = stake; cash > 0 && cash < goal; bets++)
if (Math.random() < 0.5) cash++;
else
cash--;
if (cash == goal) wins++;
The code appears correct, but it is dysfunctional because the second if is outside
both for loops and gets executed just once. Many programmers always use braces
to delimit the body of a loop or conditional precisely to avoid such insidious bugs.
1.3 Conditionals and Loops
Q. Anything else?
A. The third classic pitfall is ambiguity in nested if statements:
if <expr1> if <expr2> <stmntA> else <stmntB>
In Java, this is equivalent to
if <expr1> { if <expr2> <stmntA> else <stmntB> }
even if you might have been thinking
if <expr1> { if <expr2> <stmntA> } else <stmntB>
Again, using explicit braces to delimit the body is a good way to avoid this pitfall.
Q. Are there cases where I must use a for loop but not a while, or vice versa?
A. No. Generally, you should use a for loop when you have an initialization, an
increment, and a loop continuation test (if you do not need the loop control variable outside the loop). But the equivalent while loop still might be fine.
Q. What are the rules on where we declare the loop-control variables?
A. Opinions differ. In older programming languages, it was required that all variables be declared at the beginning of a block, so many programmers are in this
habit and a lot of code follows this convention. But it makes a great deal of sense
to declare variables where they are first used, particularly in for loops, when it is
normally the case that the variable is not needed outside the loop. However, it is
not uncommon to need to test (and therefore declare) the loop-control variable
outside the loop, as in the primality-testing code we considered as an example of
the break statement.
Q. What is the difference between ++i and i++?
A. As statements, there is no difference. In expressions, both increment i, but ++i
has the value after the increment and i++ the value before the increment. In this
book, we avoid statements like x = ++i that have the side effect of changing variable values. So, it is safe to not worry much about this distinction and just use i++
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80
in for loops and as a statement. When we do use ++i in this book, we will call attention to it and say why we are using it.
Q. In a for loop, <initialize> and <increment> can be statements more complicated than declaring, initializing, and updating a loop-control variable. How can
I take advantage of this ability?
A. The <initialize> and <increment> can be sequences of statements, separated
by commas. This notation allows for code that initializes and modifies other variables besides the loop-control variable. In some cases, this ability leads to compact
code. For example, the following two lines of code could replace the last eight lines
in the body of the main() method in PowersOfTwo (Program 1.3.3):
for (int i = 0, power = 1; i <= n; i++, power *= 2)
System.out.println(i + " " + power);
Such code is rarely necessary and better avoided, particularly by beginners.
Q Can I use a double variable as a loop-control variable in a for loop?
A It is legal, but generally bad practice to do so. Consider the following loop:
for (double x = 0.0; x <= 1.0; x += 0.1)
System.out.println(x + " " + Math.sin(x));
How many times does it iterate? The number of iterations depends on an equality
test between double values, which may not always give the result that you expect
because of floating-point precision.
Q. Anything else tricky about loops?
A. Not all parts of a
for loop need to be filled in with code. The initialization
statement, the boolean expression, the increment statement, and the loop body can
each be omitted. It is generally better style to use a while statement int power = 1;
while (power <= n/2)
empty increment
than null statements in a for loop.
power *= 2;
statement
In the code in this book, we avoid
for (int power = 1; power <= n/2; )
such empty statements.
power *= 2;
for (int power = 1; power <= n/2; power *= 2)
;
empty loop body
Three equivalent loops
1.3 Conditionals and Loops
81
Exercises
1.3.1 Write a program that takes three integer command-line arguments and
prints equal if all three are equal, and not equal otherwise.
1.3.2 Write a more general and more robust version of Quadratic (Program
1.2.3) that prints the roots of the polynomial ax2 + bx + c, prints an appropriate
message if the discriminant is negative, and behaves appropriately (avoiding division by zero) if a is zero.
1.3.3 What (if anything) is wrong with each of the following statements?
a.
b.
c.
d.
if (a > b) then c = 0;
if a > b { c = 0; }
if (a > b) c = 0;
if (a > b) c = 0 else b = 0;
1.3.4 Write a code fragment that prints true if the double variables x and y are
both strictly between 0 and 1, and false otherwise.
1.3.5 Write a program RollLoadedDie that prints the result of rolling a loaded
die such that the probability of getting a 1, 2, 3, 4, or 5 is 1/8 and the probability of
getting a 6 is 3/8.
1.3.6 Improve your solution to Exercise 1.2.25 by adding code to check that the
values of the command-line arguments fall within the ranges of validity of the formula, and by also adding code to print out an error message if that is not the case.
1.3.7 Suppose that i and j are both of type int. What is the value of j after each
of the following statements is executed?
a. for (i = 0, j = 0; i < 10;
b. for (i = 0, j = 1; i < 10;
c. for (j = 0; j < 10; j++) j
d. for (i = 0, j = 0; i < 10;
1.3.8 Rewrite
i++)
j += i;
i++)
j += j;
+= j;
i++) j += j++;
to make a program Hellos that takes the number of
lines to print as a command-line argument. You may assume that the argument is
less than 1000. Hint: Use i % 10 and i % 100 to determine when to use st, nd, rd, or
th for printing the ith Hello.
TenHellos
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1.3.9 Write a program that, using one for loop and one if statement, prints the
integers from 1,000 to 2,000 with five integers per line. Hint: Use the % operation.
1.3.10 Write a program that takes an integer command-line argument n, uses
Math.random() to print n uniform random values between 0 and 1, and then
prints their average value (see Exercise 1.2.30).
1.3.11 Describe what happens when you try to print a ruler function (see the table
on page 57) with a value of n that is too large, such as 100.
1.3.12 Write a program FunctionGrowth that prints a table of the values log n, n,
n loge n, n 2, n 3, and 2 n for n = 16, 32, 64, ... , 2,048. Use tabs (\t characters) to align
columns.
1.3.13 What are the values of m and n after executing the following code?
int n = 123456789;
int m = 0;
while (n != 0)
{
m = (10 * m) + (n % 10);
n = n / 10;
}
1.3.14 What does the following code fragment print?
int f = 0, g = 1;
for (int i = 0; i <= 15; i++)
{
System.out.println(f);
f = f + g;
g = f - g;
}
Solution. Even an expert programmer will tell you that the only way to understand a program like this is to trace it. When you do, you will find that it prints the
values 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 134, 233, 377, and 610. These numbers are
the first sixteen of the famous Fibonacci sequence, which are defined by the following formulas: F0 = 0, F1 = 1, and Fn = Fn-1 + Fn-2 for n > 1.
1.3 Conditionals and Loops
83
1.3.15 How many lines of output does the following code fragment produce?
for (int i = 0; i < 999; i++);
{ System.out.println("Hello");
}
Solution. One. Note the spurious semicolon at the end of the first line.
1.3.16 Write a program that takes an integer command-line argument
n and
prints all the positive powers of 2 less than or equal to n. Make sure that your program works properly for all values of n.
1.3.17 Expand your solution to Exercise 1.2.24 to print a table giving the total
amount of money you would have after t years for t = 0 to 25.
1.3.18 Unlike the harmonic numbers, the sum 1/12 + 1/22 + ... + 1/n2 does converge to a constant as n grows to infinity. (Indeed, the constant is 2/6, so this
formula can be used to estimate the value of .) Which of the following for loops
computes this sum? Assume that n is an int variable initialized to 1000000 and sum
is a double variable initialized to 0.0.
a. for (int i = 1; i <= n; i++) sum += 1 / (i*i);
b. for (int i = 1; i <= n; i++) sum += 1.0 / i*i;
c. for (int i = 1; i <= n; i++) sum += 1.0 / (i*i);
d. for (int i = 1; i <= n; i++) sum += 1 / (1.0*i*i);
1.3.19 Show that Program 1.3.6 implements Newton’s method for finding the
square root of c. Hint : Use the fact that the slope of the tangent to a (differentiable)
function f (x) at x = t is f (t) to find the equation of the tangent line, and then use
that equation to find the point where the tangent line intersects the x-axis to show
that you can use Newton’s method to find a root of any function as follows: at each
iteration, replace the estimate t by t f (t) / f (t).
1.3.20 Using Newton’s method, develop a program that takes two integer command-line arguments n and k and prints the kth root of n (Hint : See Exercise 1.3.19).
1.3.21 Modify
Binary to get a program Kary that takes two integer commandline arguments i and k and converts i to base k. Assume that i is an integer in Java’s
long data type and that k is an integer between 2 and 16. For bases greater than 10,
use the letters A through F to represent the 11th through 16th digits, respectively.
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84
1.3.22 Write a code fragment that puts the binary representation of a positive
integer n into a String variable s.
Solution. Java has a built-in method Integer.toBinaryString(n) for this job,
but the point of the exercise is to see how such a method might be implemented.
Working from Program 1.3.7, we get the solution
String s = "";
int power = 1;
while (power <= n/2) power *= 2;
while (power > 0)
{
if (n < power) { s += 0;
}
else
{ s += 1; n -= power; }
power /= 2;
}
A simpler option is to work from right to left:
String s = "";
for (int i = n; i > 0; i /= 2)
s = (i % 2) + s;
Both of these methods are worthy of careful study.
1.3.23 Write a version of Gambler that uses two nested while loops or two nested
for loops instead of
a while loop inside a for loop.
1.3.24 Write a program GamblerPlot that traces a gambler’s ruin simulation by
printing a line after each bet in which one asterisk corresponds to each dollar held
by the gambler.
1.3.25 Modify Gambler to take an extra command-line argument that specifies
the (fixed) probability that the gambler wins each bet. Use your program to try to
learn how this probability affects the chance of winning and the expected number
of bets. Try a value of p close to 0.5 (say, 0.48).
1.3.26 Modify Gambler to take an extra command-line argument that specifies
the number of bets the gambler is willing to make, so that there are three possible
1.3 Conditionals and Loops
ways for the game to end: the gambler wins, loses, or runs out of time. Add to the
output to give the expected amount of money the gambler will have when the game
ends. Extra credit : Use your program to plan your next trip to Monte Carlo.
1.3.27 Modify Factors to print just one copy each of the prime divisors.
1.3.28 Run quick experiments to determine the impact of using the termination
condition (factor <= n/factor) instead of (factor < n) in Factors in Program
1.3.9. For each method, find the largest n such that when you type in an n-digit
number, the program is sure to finish within 10 seconds.
1.3.29 Write a program Checkerboard that takes an integer command-line argument n and uses a loop nested within a loop to print out a two-dimensional n-by-n
checkerboard pattern with alternating spaces and asterisks.
1.3.30 Write a program GreatestCommonDivisor that finds the greatest common
divisor (gcd) of two integers using Euclid’s algorithm, which is an iterative computation based on the following observation: if x is greater than y, then if y divides x,
the gcd of x and y is y; otherwise, the gcd of x and y is the same as the gcd of x % y
and y.
1.3.31 Write a program
RelativelyPrime that takes an integer command-line
argument n and prints an n-by-n table such that there is an * in row i and column
j if the gcd of i and j is 1 (i and j are relatively prime) and a space in that position
otherwise.
1.3.32 Write a program PowersOfK that takes an integer command-line argument
and prints all the positive powers of k in the Java long data type. Note : The constant Long.MAX_VALUE is the value of the largest integer in long.
k
1.3.33 Write a program that prints the coordinates of a random point (a, b, c) on
the surface of a sphere. To generate such a point, use Marsaglia’s method: Start by
picking a random point (x, y) in the unit disk using the method described at the
end of this section. Then, set a to 2 x 1 – x2 – y2 , b to 2 1 – x2 – y2 , and c to
1– 2 (x2 + y2).
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Creative Exercises
1.3.34 Ramanujan’s taxi. Srinivasa Ramanujan was an Indian mathematician
who became famous for his intuition for numbers. When the English mathematician G. H. Hardy came to visit him one day, Hardy remarked that the number of
his taxi was 1729, a rather dull number. To which Ramanujan replied, “No, Hardy!
No, Hardy! It is a very interesting number. It is the smallest number expressible as
the sum of two cubes in two different ways.” Verify this claim by writing a program
that takes an integer command-line argument n and prints all integers less than or
equal to n that can be expressed as the sum of two cubes in two different ways. In
other words, find distinct positive integers a, b, c, and d such that a3 + b3 = c3 + d3.
Use four nested for loops.
1.3.35 Checksum. The International Standard Book Number (ISBN) is a 10-digit
code that uniquely specifies a book. The rightmost digit is a checksum digit that
can be uniquely determined from the other 9 digits, from the condition that
d1 + 2d2 +3d3 + ... + 10d10 must be a multiple of 11 (here di denotes the ith digit
from the right). The checksum digit d1 can be any value from 0 to 10. The ISBN
convention is to use the character 'X' to denote 10. As an example, the checksum
digit corresponding to 020131452 is 5 since 5 is the only value of x between 0 and
10 for which
10·0 + 9·2 + 8·0 + 7·1 + 6·3 + 5·1 +4·4 +3·5 + 2·2 + 1·x
is a multiple of 11. Write a program that takes a 9-digit integer as a command-line
argument, computes the checksum, and prints the ISBN number.
1.3.36 Counting primes. Write a program
PrimeCounter that takes an integer
command-line argument n and finds the number of primes less than or equal to n.
Use it to print out the number of primes less than or equal to 10 million. Note : If
you are not careful, your program may not finish in a reasonable amount of time!
1.3.37 2D random walk. A two-dimensional random walk simulates the behavior
of a particle moving in a grid of points. At each step, the random walker moves
north, south, east, or west with probability equal to 1/4, independent of previous
moves. Write a program RandomWalker that takes an integer command-line argument n and estimates how long it will take a random walker to hit the boundary of
a 2n-by-2n square centered at the starting point.
1.3 Conditionals and Loops
1.3.38 Exponential function. Assume that x is a positive variable of type double.
Write a code fragment that uses the Taylor series expansion to set the value of sum
to e x = 1 + x + x2/2! + x3/3! + . . . .
Solution. The purpose of this exercise is to get you to think about how a library
function like Math.exp() might be implemented in terms of elementary operators.
Try solving it, then compare your solution with the one developed here.
We start by considering the problem of computing one term. Suppose that x
and term are variables of type double and n is a variable of type int. The following code fragment sets term to x n / n ! using the direct method of having one loop
for the numerator and another loop for the denominator, then dividing the results:
double num = 1.0, den = 1.0;
for (int i = 1; i <= n; i++) num *= x;
for (int i = 1; i <= n; i++) den *= i;
double term = num/den;
A better approach is to use just a single for loop:
double term = 1.0;
for (i = 1; i <= n; i++) term *= x/i;
Besides being more compact and elegant, the latter solution is preferable because
it avoids inaccuracies caused by computing with huge numbers. For example, the
two-loop approach breaks down for values like x = 10 and n = 100 because 100! is
too large to represent as a double.
To compute ex , we nest this for loop within another for loop:
double term = 1.0;
double sum = 0.0;
for (int n = 1; sum != sum + term; n++)
{
sum += term;
term = 1.0;
for (int i = 1; i <= n; i++) term *= x/i;
}
The number of times the loop iterates depends on the relative values of the next
term and the accumulated sum. Once the value of the sum stops changing, we leave
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the loop. (This strategy is more efficient than using the loop-continuation condition (term > 0) because it avoids a significant number of iterations that do not
change the value of the sum.) This code is effective, but it is inefficient because the
inner for loop recomputes all the values it computed on the previous iteration of
the outer for loop. Instead, we can make use of the term that was added in on the
previous loop iteration and solve the problem with a single for loop:
double term = 1.0;
double sum = 0.0;
for (int n = 1; sum != sum + term; n++)
{
sum += term;
term *= x/n;
}
1.3.39 Trigonometric functions. Write two programs,
and Cos, that
compute the sine and cosine functions using their Taylor series expansions
sin x = x x 3/3! + x 5/5! ... and cos x = 1 x 2/2! + x 4/4! . . . .
Sin
1.3.40 Experimental analysis. Run experiments to determine the relative costs of
and the methods from Exercise 1.3.38 for computing e x : the direct
method with nested for loops, the improvement with a single for loop, and the
latter with the loop-continuation condition (term > 0). Use trial-and-error with
a command-line argument to determine how many times your computer can perform each computation in 10 seconds.
Math.exp()
1.3.41 Pepys problem. In 1693 Samuel Pepys asked Isaac Newton which is more
likely: getting 1 at least once when rolling a fair die six times or getting 1 at least
twice when rolling it 12 times. Write a program that could have provided Newton
with a quick answer.
1.3.42 Game simulation. In the game show Let’s Make a Deal, a contestant is presented with three doors. Behind one of them is a valuable prize. After the contestant
chooses a door, the host opens one of the other two doors (never revealing the prize,
of course). The contestant is then given the opportunity to switch to the other
unopened door. Should the contestant do so? Intuitively, it might seem that the
1.3 Conditionals and Loops
contestant’s initial choice door and the other unopened door are equally likely to
contain the prize, so there would be no incentive to switch. Write a program MonteHall to test this intuition by simulation. Your program should take a commandline argument n, play the game n times using each of the two strategies (switch or
do not switch), and print the chance of success for each of the two strategies.
1.3.43 Median-of-5. Write a program that takes five distinct integers as commandline arguments and prints the median value (the value such that two of the other
integers are smaller and two are larger). Extra credit : Solve the problem with a
program that compares values fewer than 7 times for any given input.
1.3.44 Sorting three numbers. Suppose that the variables a, b, c, and t are all of the
type int. Explain why the following code puts a, b, and c in ascending order:
if (a > b) { t = a; a = b; b = t; }
if (a > c) { t = a; a = c; c = t; }
if (b > c) { t = b; b = c; c = t; }
1.3.45 Chaos. Write a program to study the following simple model for population growth, which might be applied to study fish in a pond, bacteria in a test tube,
or any of a host of similar situations. We suppose that the population ranges from
0 (extinct) to 1 (maximum population that can be sustained). If the population at
time t is x, then we suppose the population at time t + 1 to be r x (1x), where the
argument r, known as the fecundity parameter, controls the rate of growth. Start
with a small population—say, x = 0.01—and study the result of iterating the model, for various values of r. For which values of r does the population stabilize at
x = 1 1/r ? Can you say anything about the population when r is 3.5? 3.8? 5?
1.3.46 Euler’s sum-of-powers conjecture. In 1769 Leonhard Euler formulated a
generalized version of Fermat’s Last Theorem, conjecturing that at least n nth powers are needed to obtain a sum that is itself an nth power, for n > 2. Write a program
to disprove Euler’s conjecture (which stood until 1967), using a quintuply nested
loop to find four positive integers whose 5th power sums to the 5th power of another positive integer. That is, find a, b, c, d, and e such that a 5 b 5 c 5 d 5 e 5.
Use the long data type.
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Elements of Programming
1.4 Arrays
In this section, we introduce you to the idea of a data structure and to your first
data structure, the array. The primary purpose of an array is to facilitate storing
and manipulating large quantities of data.
Arrays play an essential role in many data
1.4.1 Sampling without replacement. . . . 98
processing tasks. They also correspond
1.4.2 Coupon collector simulation. . . . 102
to vectors and matrices, which are widely
1.4.3 Sieve of Eratosthenes . . . . . . . . 104
used in science and in scientific program1.4.4 Self-avoiding random walks . . . . 113
ming. We will consider basic properties
Programs in this section
of arrays in Java, with many examples illustrating why they are useful.
A data structure is a way to organize data in a computer (usually to save time
or space). Data structures play an essential role in computer programming—indeed, Chapter 4 of this book is devoted to the study of classic data structures of all
sorts.
A one-dimensional array (or array) is a data structure that stores a se- a
a[0]
quence of values, all of the same type. We refer to the components of an ara[1]
ray as its elements. We use indexing to refer to the array elements: If we have
a[2]
n elements in an array, we think of the elements as being numbered from
a[3]
0 to n-1 so that we can unambiguously specify an element with an integer
a[4]
index in this range.
a[5]
A two-dimensional array is an array of one-dimensional arrays. Wherea[6]
as the elements of a one-dimensional array are indexed by a single integer,
a[7]
the elements of a two-dimensional array are indexed by a pair of integers:
the first index specifies the row, and the second index specifies the column.
An array
Often, when we have a large amount of data to process, we first put all
of the data into one or more arrays. Then we use indexing to refer to individual elements and to process the data. We might have exam scores, stock prices,
nucleotides in a DNA strand, or characters in a book. Each of these examples involves a large number of values that are all of the same type. We consider such applications when we discuss input/output in Section 1.5 and in the case study that
is the subject of Section 1.6. In this section, we expose the basic properties of arrays by considering examples where our programs first populate arrays with values
computed from experimental studies and then process them.
1.4 Arrays
91
Arrays in Java Making an array in a Java program involves three distinct steps:
• Declare the array.
• Create the array.
• Initialize the array elements.
To declare an array, you need to specify a name and the type of data it will contain.
To create it, you need to specify its length (the number of elements). To initialize it,
you need to assign a value to each of its elements. For example, the following code
makes an array of n elements, each of type double and initialized to 0.0:
double[] a;
a = new double[n];
for (int i = 0; i < n; i++)
a[i] = 0.0;
// declare the array
// create the array
// initialize the array
The first statement is the array declaration. It is just like a declaration of a variable
of the corresponding primitive type except for the square brackets following the
type name, which specify that we are declaring an array. The second statement
creates the array; it uses the keyword new to allocate memory to store the specified
number of elements. This action is unnecessary for variables of a primitive type,
but it is needed for all other types of data in Java (see Section 3.1). The for loop
assigns the value 0.0 to each of the n array elements. We refer to an array element
by putting its index in square brackets after the array name: the code a[i] refers to
element i of array a[]. (In the text, we use the notation a[] to indicate that variable a is an array, but we do not use a[] in Java code.)
The obvious advantage of using arrays is to define many variables without
explicitly naming them. For example, if you wanted to process eight variables of
type double, you could declare them with
double a0, a1, a2, a3, a4, a5, a6, a7;
and then refer to them as a0, a1, a2, and so forth. Naming dozens of individual variables in this way is cumbersome and naming millions is untenable. Instead, with arrays, you can declare n variables with the statement double[] a = new double[n]
and refer to them as a[0], a[1], a[2], and so forth. Now, it is easy to define
dozens or millions of variables. Moreover, since you can use a variable (or other expression computed at run time) as an array index, you can process arbitrarily many
elements in a single loop, as we do above. You should think of each array element as
an individual variable, which you can use in an expression or as the left-hand side
of an assignment statement.
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92
As our first example, we use arrays to represent vectors. We consider vectors in
detail in Section 3.3; for the moment, think of a vector as a sequence of real numbers. The dot product of two vectors (of the same length) is the sum of the products
of their corresponding elements. The dot product of two vectors that are represented as one-dimensional arrays x[] and y[], each of length 3, is the expression
x[0]*y[0] + x[1]*y[1] + x[2]*y[2]. More generally, if each array is of length
n, then the following code computes their
dot product:
i
x[i]
y[i]
x[i]*y[i]
sum
double sum = 0.0;
for (int i = 0; i < n; i++)
sum += x[i]*y[i];
The simplicity of coding such computations makes the use of arrays the natural
choice for all kinds of applications.
0.00
0
0.30
0.50
0.15
0.15
1
0.60
0.10
0.06
0.21
2
0.10
0.40
0.04
0.25
0.25
Trace of dot product computation
The table on the facing page has many examples of array-processing code, and we
will consider even more examples later in the book, because arrays play a central
role in processing data in many applications. Before considering more sophisticated examples, we describe a number of important characteristics of programming
with arrays.
Zero-based indexing. The first element of an array a[] is a[0], the second element is a[1], and so forth. It might seem more natural to you to refer to the first
element as a[1], the second element as a[2], and so forth, but starting the indexing with 0 has some advantages and has emerged as the convention used in most
modern programming languages. Misunderstanding this convention often leads
to off-by one-errors that are notoriously difficult to avoid and debug, so be careful!
Array length. Once you create an array in Java, its length is fixed. One reason that
you need to explicitly create arrays at run time is that the Java compiler cannot
always know how much space to reserve for the array at compile time (because its
length may not be known until run time). You do not need to explicitly allocate
memory for variables of type int or double because their size is fixed, and known
at compile time. You can use the code a.length to refer to the length of an array
a[]. Note that the last element of an array a[] is always a[a.length-1]. For convenience, we often keep the array length in an integer variable n.
1.4 Arrays
create an array
with random values
93
double[] a = new double[n];
for (int i = 0; i < n; i++)
a[i] = Math.random();
print the array values,
one per line
for (int i = 0; i < n; i++)
System.out.println(a[i]);
find the maximum of
the array values
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < n; i++)
if (a[i] > max) max = a[i];
compute the average of
the array values
reverse the values
within an array
copy a sequence of
values to another array
double sum = 0.0;
for (int i = 0; i < n; i++)
sum += a[i];
double average = sum / n;
for (int i = 0; i < n/2; i++)
{
double temp = a[i];
a[i] = a[n-1-i];
a[n-i-1] = temp;
}
double[] b = new double[n];
for (int i = 0; i < n; i++)
b[i] = a[i];
Typical array-processing code (for an array a[] of n double values)
Default array initialization. For economy in code, we often take advantage of
Java’s default array initialization to declare, create, and initialize an array in a single
statement. For example, the following statement is equivalent to the code at the top
of page 91:
double[] a = new double[n];
The code to the left of the equals sign constitutes the declaration; the code to the
right constitutes the creation. The for loop is unnecessary in this case because Java
automatically initializes array elements of any primitive type to zero (for numeric
types) or false (for the type boolean). Java automatically initializes array elements of type String (and other nonprimitive types) to null, a special value that
you will learn about in Chapter 3.
Elements of Programming
94
Memory representation. Arrays are fundamental data struc-
tures in that they have a direct correspondence with memory 000
systems on virtually all computers. The elements of an array are
stored consecutively in memory, so that it is easy to quickly access any array value. Indeed, we can view memory itself as a giant
a
array. On modern computers, memory is implemented in hard123
523
ware as a sequence of memory locations, each of which can be
124
8
quickly accessed with an appropriate index. When referring to
computer memory, we normally refer to a location’s index as its
a.length
address. It is convenient to think of the name of the array—say,
a—as storing the memory address of the first element of the array a[0]. For the purposes of illustration, suppose that the com- 523
a[0]
524
a[1]
puter’s memory is organized as 1,000 values, with addresses from
a[2]
000 to 999. (This simplified model bypasses the fact that array el- 525
a[3]
ements can occupy differing amounts of memory depending on 526
527
a[4]
their type, but you can ignore such details for the moment.) Now,
528
a[5]
suppose that an array of eight elements is stored in memory loca- 529
a[6]
tions 523 through 530. In such a situation, Java would store the
530
a[7]
memory address (index) of the first array element somewhere
else in memory, along with the array length. We refer to the address as a pointer and think of it as pointing to the referenced
memory location. When we specify a[i], the compiler generates
code that accesses the desired value by adding the index i to the
999
memory address of the array a[]. For example, the Java code
a[4] would generate machine code that finds the value at memory location 523 + 4 = 527. Accessing element i of an array is an Memory representation
efficient operation because it simply requires adding two integers
and then referencing memory—just two elementary operations.
Memory allocation. When you use the keyword
new to create an array, Java reserves sufficient space in memory to store the specified number of elements. This
process is called memory allocation. The same process is required for all variables
that you use in a program (but you do not use the keyword new with variables of
primitive types because Java knows how much memory to allocate). We call attention to it now because it is your responsibility to create an array before accessing
any of its elements. If you fail to adhere to this rule, you will get an uninitialized
variable error at compile time.
1.4 Arrays
Bounds checking. As already indicated, you must be careful when programming with arrays. It is your responsibility to use valid indices when referring
to an array element. If you have created an array of length n and use an index
whose value is less than 0 or greater than n-1, your program will terminate with
an ArrayIndexOutOfBoundsException at run time. (In many programming
languages, such buffer overflow conditions are not checked by the system. Such unchecked errors can and do lead to debugging nightmares, but it is also not uncommon for such an error to go unnoticed and remain in a finished program. You
might be surprised to know that such a mistake can be exploited by a hacker to
take control of a system, even your personal computer, to spread viruses, steal personal information, or wreak other malicious havoc.) The error messages provided
by Java may seem annoying to you at first, but they are small price to pay to have a
more secure program.
Setting array values at compile time. When we have a small number of values
that we want to keep in array, we can declare, create, and initialize the array by listing the values between curly braces, separated by commas. For example, we might
use the following code in a program that processes playing cards:
String[] SUITS = { "Clubs", "Diamonds", "Hearts", "Spades" };
String[] RANKS =
{
"2", "3", "4", "5", "6", "7", "8", "9", "10",
"Jack", "Queen", "King", "Ace"
};
Now, we can use the two arrays to print a random card name, such as Queen of
Clubs, as follows:
int i = (int) (Math.random() * RANKS.length);
int j = (int) (Math.random() * SUITS.length);
System.out.println(RANKS[i] + " of " + SUITS[j]);
This code uses the idiom introduced in Section 1.2 to generate random indices and
then uses the indices to pick strings out of the two arrays. Whenever the values of
all array elements are known (and the length of the array is not too large), it makes
sense to use this method of initializing the array—just put all the values in curly
braces on the right-hand side of the equals sign in the array declaration. Doing so
implies array creation, so the new keyword is not needed.
95
Elements of Programming
96
Setting array values at run time. A more typical situation is when we wish to
compute the values to be stored in an array. In this case, we can use an array name
with indices in the same way we use a variable names on the left-hand side of an
assignment statement. For example, we might use the following code to initialize
an array of length 52 that represents a deck of playing cards, using the two arrays
just defined:
String[] deck = new String[RANKS.length * SUITS.length];
for (int i = 0; i < RANKS.length; i++)
for (int j = 0; j < SUITS.length; j++)
deck[SUITS.length*i + j] = RANKS[i] + " of " + SUITS[j];
After this code has been executed, if you were to print the contents of deck[] in
order from deck[0] through deck[51], you would get
2 of Clubs
2 of Diamonds
2 of Hearts
2 of Spades
3 of Clubs
3 of Diamonds
...
Ace of Hearts
Ace of Spades
Exchanging two values in an array. Frequently, we wish to exchange the values of
two elements in an array. Continuing our example with playing cards, the following code exchanges the cards at indices i and j using the same idiom that we traced
as our first example of the use of assignment statements in Section 1.2:
String temp = deck[i];
deck[i] = deck[j];
deck[j] = temp;
For example, if we were to use this code with i equal to 1 and j equal to 4 in the
deck[] array of the previous example, it would leave 3 of Clubs in deck[1] and
2 of Diamonds in deck[4]. You can also verify that the code leaves the array unchanged when i and j are equal. So, when we use this code, we are assured that we
are perhaps changing the order of the values in the array but not the set of values
in the array.
1.4 Arrays
97
Shuffling an array. The following code shuffles the values in our deck of cards:
int n = deck.length;
for (int i = 0; i < n; i++)
{
int r = i + (int) (Math.random() * (n-i));
String temp = deck[i];
deck[i] = deck[r];
deck[r] = temp;
}
Proceeding from left to right, we pick a random card from deck[i] through
(each card equally likely) and exchange it with deck[i]. This code is
more sophisticated than it might seem: First, we ensure that the cards in the deck
after the shuffle are the same as the cards in the deck before the shuffle by using
the exchange idiom. Second, we ensure that the shuffle is random by choosing uniformly from the cards not yet chosen.
deck[n-1]
Sampling without replacement. In many situations, we want to draw a random
sample from a set such that each member of the set appears at most once in the
sample. Drawing numbered ping-pong balls from a basket for a lottery is an example of this kind of sample, as is dealing a hand from a deck of cards. Sample
(Program 1.4.1) illustrates how to sample, using the basic operation underlying
shuffling. It takes two command-line arguments m and n and creates a permutation
of length n (a rearrangement of the integers from 0 to n-1) whose first m elements
i
r
0
9
1
2
3
perm[]
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
9
1
2
3
4
5
6
7
8
0 10 11 12 13 14 15
5
9
5
2
3
4
1
6
7
8
0 10 11 12 13 14 15
13
9
5 13
3
4
1
6
7
8
0 10 11 12
2 14 15
5
9
5 13
1
4
3
6
7
8
0 10 11 12
2 14 15
4
11
9
5 13
1 11
3
6
7
8
0 10
4 12
2 14 15
5
8
9
5 13
1 11
8
6
7
3
0 10
4 12
2 14 15
9
5 13
1 11
8
6
7
3
0 10
4 12
2 14 15
Trace of java
Sample 6 16
Elements of Programming
98
Program 1.4.1
Sampling without replacement
public class Sample
{
public static void main(String[] args)
{ // Print a random sample of m integers
// from 0 ... n-1 (no duplicates).
int m = Integer.parseInt(args[0]);
int n = Integer.parseInt(args[1]);
int[] perm = new int[n];
// Initialize perm[].
for (int j = 0; j < n; j++)
perm[j] = j;
m
sample size
range
perm[] permutation of 0 to n-1
n
// Take sample.
for (int i = 0; i < m; i++)
{ // Exchange perm[i] with a random element to its right.
int r = i + (int) (Math.random() * (n-i));
int t = perm[r];
perm[r] = perm[i];
perm[i] = t;
}
// Print sample.
for (int i = 0; i < m; i++)
System.out.print(perm[i] + " ");
System.out.println();
}
}
This program takes two command-line arguments m and n and produces a sample of m of the
integers from 0 to n-1. This process is useful not just in state and local lotteries, but in scientific applications of all sorts. If the first argument is equal to the second, the result is a random
permutation of the integers from 0 to n-1. If the first argument is greater than the second, the
program will terminate with an ArrayOutOfBoundsException.
% java Sample 6 16
9 5 13 1 11 8
% java Sample 10 1000
656 488 298 534 811 97 813 156 424 109
% java Sample 20 20
6 12 9 8 13 19 0 2 4 5 18 1 14 16 17 3 7 11 10 15
1.4 Arrays
comprise a random sample. The accompanying trace of the contents of the perm[]
array at the end of each iteration of the main loop (for a run where the values of m
and n are 6 and 16, respectively) illustrates the process.
If the values of r are chosen such that each value in the given range is equally
likely, then the elements perm[0] through perm[m-1] are a uniformly random
sample at the end of the process (even though some values might move multiple
times) because each element in the sample is assigned a value uniformly at random
from those values not yet sampled. One important reason to explicitly compute
the permutation is that we can use it to print a random sample of any array by using the elements of the permutation as indices into the array. Doing so is often an
attractive alternative to actually rearranging the array because it may need to be in
order for some other reason (for instance, a company might wish to draw a random sample from a list of customers that is kept in alphabetical order).
To see how this trick works, suppose that we wish to draw a random poker
hand from our deck[] array, constructed as just described. We use the code in
Sample with n = 52 and m = 5 and replace perm[i] with deck[perm[i]] in the
System.out.print() statement (and change it to println()), resulting in output such as the following:
3 of Clubs
Jack of Hearts
6 of Spades
Ace of Clubs
10 of Diamonds
Sampling like this is widely used as the basis for statistical studies in polling, scientific research, and many other applications, whenever we want to draw conclusions
about a large population by analyzing a small random sample.
Precomputed values. One simple application of arrays is to save values that you
have computed for later use. As an example, suppose that you are writing a program that performs calculations using small values of the harmonic numbers (see
Program 1.3.5). An efficient approach is to save the values in an array, as follows:
double[] harmonic = new double[n];
for (int i = 1; i < n; i++)
harmonic[i] = harmonic[i-1] + 1.0/i;
99
Elements of Programming
100
Then you can just use the code harmonic[i] to refer to the ith harmonic number.
Precomputing values in this way is an example of a space–time tradeoff: by investing in space (to save the values), we save time (since we do not need to recompute
them). This method is not effective if we need values for huge n, but it is very effective if we need values for small n many different times.
Simplifying repetitive code. As an example of another simple application of arrays, consider the following code fragment, which prints the name of a month
given its number (1 for January, 2 for February, and so forth):
if
else
else
else
else
else
else
else
else
else
else
else
if
if
if
if
if
if
if
if
if
if
if
(m
(m
(m
(m
(m
(m
(m
(m
(m
(m
(m
(m
== 1) System.out.println("Jan");
== 2) System.out.println("Feb");
== 3) System.out.println("Mar");
== 4) System.out.println("Apr");
== 5) System.out.println("May");
== 6) System.out.println("Jun");
== 7) System.out.println("Jul");
== 8) System.out.println("Aug");
== 9) System.out.println("Sep");
== 10) System.out.println("Oct");
== 11) System.out.println("Nov");
== 12) System.out.println("Dec");
We could also use a switch statement, but a much more compact alternative is to
use an array of strings, consisting of the names of each month:
String[] MONTHS =
{
"", "Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
};
System.out.println(MONTHS[m]);
This technique would be especially useful if you needed to access the name of a
month by its number in several different places in your program. Note that we intentionally waste one slot in the array (element 0) to make MONTHS[1] correspond
to January, as required.
With these basic definitions and examples out of the way, we can now consider two
applications that both address interesting classical problems and illustrate the fundamental importance of arrays in efficient computation. In both cases, the idea of
using an expression to index into an array plays a central role and enables a computation that would not otherwise be feasible.
1.4 Arrays
101
Coupon collector Suppose that you have a deck of cards and you turn
♣ ♠ ♣
♥ ♥ ♣
♠ ♦
up cards uniformly at random (with replacement) one by one. How many
cards do you need to turn up before you have seen one of each suit? How
many cards do you need to turn up before seeing one of each value? These
Coupon collection
are examples of the famous coupon collector problem. In general, suppose
that a trading card company issues trading cards with n different possible
cards: how many do you have to collect before you have all n possibilities, assuming
that each possibility is equally likely for each card that you collect?
Coupon collecting is no toy problem. For example, scientists often want to
know whether a sequence that arises in nature has the same characteristics as a
random sequence. If so, that fact might be of interest; if not, further investigation may be warranted to look for patterns that might be of importance. For example, such tests are used by scientists to decide which parts of genomes are worth
studying. One effective test for whether a sequence is truly random is the coupon
collector test : compare the number of elements that need to be examined before
all values are found against the corresponding number for a uniformly random
sequence. CouponCollector (Program 1.4.2) is an example program that simulates this process and illustrates the utility of arrays. It takes a command-line argument n and generates a sequence of random integers between 0 and n-1 using the
code (int) (Math.random() * n)—see Program 1.2.5. Each integer represents a
card: for each card, we want to know if we have seen that card before. To maintain that knowledge, we use an array isCollected[], which uses the card as an
index; isCollected[i] is true if we have seen
isCollected[]
a card i and false if we have not. When we
r
distinct
count
0 1 2 3 4 5
get a new card that is represented by the integer
F F F F F F
0
0
r, we check whether we have seen it before by
2
F
F
T
F
F
F
1
1
accessing isCollected[r]. The computation
0
T F T F F F
2
2
consists of keeping count of the number of distinct cards seen and the number of cards gen4
T F T F T F
3
3
erated, and printing the latter when the former
0
T F T F T F
3
4
reaches n.
1
T T T F T F
4
5
As usual, the best way to understand a
2
T T T F T F
4
6
program is to consider a trace of the values
5
T T T F T T
5
7
of its variables for a typical run. It is easy to
add code to CouponCollector that produces a
0
T T T F T T
5
8
trace that gives the values of the variables at the
1
T T T F T T
5
9
3
T T T T T T
6
Trace for a typical run of
java CouponCollector 6
10
Elements of Programming
102
Program 1.4.2
Coupon collector simulation
public class CouponCollector
{
public static void main(String[] args)
{
// Generate random values in [0..n) until finding each one.
int n = Integer.parseInt(args[0]);
boolean[] isCollected = new boolean[n];
int count = 0;
int distinct = 0;
while (distinct < n)
{
// Generate another coupon.
int r = (int) (Math.random() * n);
count++;
if (!isCollected[r])
{
distinct++;
isCollected[r] = true;
}
} // n distinct coupons found.
System.out.println(count);
n
isCollected[i]
count
distinct
r
# coupon values
(0 to n-1)
has coupon i
been collected?
# coupons
# distinct coupons
random coupon
}
}
This program takes an integer command-line argument n and simulates coupon collection by
generating random numbers between 0 and n-1 until getting every possible value.
% java CouponCollector 1000
6583
% java CouponCollector 1000
6477
% java CouponCollector 1000000
12782673
1.4 Arrays
end of the while loop. In the accompanying figure, we use F for the value false
and T for the value true to make the trace easier to follow. Tracing programs that
use large arrays can be a challenge: when you have an array of length n in your program, it represents n variables, so you have to list them all. Tracing programs that
use Math.random() also can be a challenge because you get a different trace every
time you run the program. Accordingly, we check relationships among variables
carefully. Here, note that distinct always is equal to the number of true values
in isCollected[].
Without arrays, we could not contemplate simulating the coupon collector
process for huge n; with arrays, it is easy to do so. We will see many examples of
such processes throughout the book.
Sieve of Eratosthenes Prime numbers play an important role in mathematics
and computation, including cryptography. A prime number is an integer greater
than 1 whose only positive divisors are 1 and itself. The prime counting function
(n) is the number of primes less than or equal to n. For example, (25) = 9 since
the first nine primes are 2, 3, 5, 7, 11, 13, 17, 19, and 23. This function plays a central
role in number theory.
One approach to counting primes is to use a program like Factors (Program
1.3.9). Specifically, we could modify the code in Factors to set a boolean variable
to true if a given number is prime and false otherwise (instead of printing out
factors), then enclose that code in a loop that increments a counter for each prime
number. This approach is effective for small n, but becomes too slow as n grows.
PrimeSieve (Program 1.4.3) takes a command-line integer n and computes
the prime count using a technique known as the Sieve of Eratosthenes. The program
uses a boolean array isPrime[] to record which integers are prime. The goal is
to set isPrime[i] to true if the integer i is prime, and to false otherwise. The
sieve works as follows: Initially, set all array elements to true, indicating that no
factors of any integer have yet been found. Then, repeat the following steps as long
as i <= n/i:
• Find the next smallest integer i for which no factors have been found.
• Leave isPrime[i] as true since i has no smaller factors.
• Set the isPrime[] elements for all multiples of i to false.
When the nested for loop ends, isPrime[i] is true if and only if integer i is prime.
With one more pass through the array, we can count the number of primes less
than or equal to n.
103
Elements of Programming
104
Program 1.4.3
Sieve of Eratosthenes
public class PrimeSieve
{
public static void main(String[] args)
{ // Print the number of primes <= n.
int n = Integer.parseInt(args[0]);
boolean[] isPrime = new boolean[n+1];
for (int i = 2; i <= n; i++)
isPrime[i] = true;
n
isPrime[i]
primes
argument
is i prime?
prime counter
for (int i = 2; i <= n/i; i++)
{ if (isPrime[i])
{ // Mark multiples of i as nonprime.
for (int j = i; j <= n/i; j++)
isPrime[i * j] = false;
}
}
// Count the primes.
int primes = 0;
for (int i = 2; i <= n; i++)
if (isPrime[i]) primes++;
System.out.println(primes);
}
}
This program takes an integer command-line argument n and computes the number of primes
less than or equal to n. To do so, it computes a boolean array with isPrime[i] set to true if
i is prime, and to false otherwise. First, it sets to true all array elements to indicate that no
numbers are initially known to be nonprime. Then it sets to false array elements corresponding to indices that are known to be nonprime (multiples of known primes). If a[i] is still true
after all multiples of smaller primes have been set to false, then we know i to be prime. The
termination test in the second for loop is i <= n/i instead of the naive i <= n because any
number with no factor less than n/i has no factor greater than n/i, so we do not have to look
for such factors. This improvement makes it possible to run the program for large n.
% java PrimeSieve 25
9
% java PrimeSieve 100
25
% java PrimeSieve 1000000000
50847534
1.4 Arrays
105
As usual, it is easy to add code to print a trace. For programs such as
PrimeSieve, you have to be a bit careful—it contains a nested for-if-for, so you
have to pay attention to the curly braces to put the print code in the correct place.
Note that we stop when i > n/i, as we did for Factors.
i
isPrime[]
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
2
T
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
3
T
T
F
T
F
T
F
F
F
T
F
T
F
F
F
T
F
T
F
F
F
T
F
T
5
T
T
F
T
F
T
F
F
F
T
F
T
F
F
F
T
F
T
F
F
F
T
F
F
T
T
F
T
F
T
F
F
F
T
F
T
F
F
F
T
F
T
F
F
F
T
F
F
Trace of java
PrimeSieve 25
With PrimeSieve, we can compute (n) for large n, limited primarily by the
maximum array length allowed by Java. This is another example of a space–time
tradeoff. Programs like PrimeSieve play an important role in helping mathematicians to develop the theory of numbers, which has many important applications.
Elements of Programming
106
Two-dimensional arrays In many applications, a convenient way to store information is to use a table of numbers organized in a rectangle and refer to rows and
columns in the table. For example, a teacher might need to maintain a table with
rows corresponding to students and columns corresponding to exams, a scientist
might need to maintain a table of experimental data with rows corresponding to
experiments and columns corresponding to various outcomes,
or a programmer might want to prepare an image for display
a[1][2]
by setting a table of pixels to various grayscale values or colors.
99 85 98
The mathematical abstraction corresponding to such
98 57 78
row 1
tables is a matrix; the corresponding Java construct is a two92 77 76
dimensional array. You are likely to have already encountered
94 32 11
many applications of matrices and two-dimensional arrays,
99 34 22
90 46 54
and you will certainly encounter many others in science, engi76 59 88
neering, and computing applications, as we will demonstrate
92 66 89
with examples throughout this book. As with vectors and one97 71 24
dimensional arrays, many of the most important applications
89 29 38
involve processing large amounts of data, and we defer considcolumn 2
ering those applications until we introduce input and output,
in Section 1.5.
Anatomy of a
Extending Java array constructs to handle two-dimen- two-dimensional array
sional arrays is straightforward. To refer to the element in row
i and column j of a two-dimensional array a[][], we use
the notation a[i][j]; to declare a two-dimensional array, we add another pair of
square brackets; and to create the array, we specify the number of rows followed
by the number of columns after the type name (both within square brackets), as
follows:
double[][] a = new double[m][n];
We refer to such an array as an m-by-n array. By convention, the first dimension
is the number of rows and the second is the number of columns. As with onedimensional arrays, Java initializes all elements in arrays of numbers to zero and in
boolean arrays to false.
Default initialization. Default initialization of two-dimensional arrays is useful
because it masks more code than for one-dimensional arrays. The following code
is equivalent to the single-line create-and-initialize idiom that we just considered:
1.4 Arrays
107
double[][] a;
a = new double[m][n];
for (int i = 0; i < m; i++)
{ // Initialize the ith row.
for (int j = 0; j < n; j++)
a[i][j] = 0.0;
}
This code is superfluous when initializing the elements of a two-dimensional array
to zero, but the nested for loops are needed to initialize the elements to some other
value(s). As you will see, this code is a model for the code that we use to access or
modify each element of a two-dimensional array.
Output. We use nested for loops for many two-dimensional array-processing operations. For example, to print an m-by-n array in the tabular format, we can use
the following code:
for (int i = 0; i < m; i++)
{ // Print the ith row.
for (int j = 0; j < n; j++)
System.out.print(a[i][j] + " ");
System.out.println();
}
If desired, we could add code to embellish the output
with row and column indices (see Exercise 1.4.6). Java
programmers typically tabulate two-dimensional arrays with row indices running top to bottom from 0
and column indices running left to right from 0.
a[][]
a[0][0] a[0][1] a[0][2]
a[1][0] a[1][1] a[1][2]
a[2][0] a[2][1] a[2][2]
a[3][0] a[3][1] a[3][2]
Memory representation. Java represents a two-dimensional array as an array of arrays. That is, a two-dimensional array with m rows and n columns is actually
an array of length m, each element of which is a onedimensional array of length n. In a two-dimensional
Java array a[][], you can use the code a[i] to refer to
row i (which is a one-dimensional array), but there is
no corresponding way to refer to column j.
a[4][0] a[4][1] a[4][2]
a[5]
a[5][0] a[5][1] a[5][2]
a[6][0] a[6][1] a[6][2]
a[7][0] a[7][1] a[7][2]
a[8][0] a[8][1] a[8][2]
a[9][0] a[9][1] a[9][2]
A 10-by-3 array
Elements of Programming
108
Setting values at compile time. The Java method
for initializing an array of values at compile time
follows immediately from the representation. A
two-dimensional array is an array of rows, each row
initialized as a one-dimensional array. To initialize a
two-dimensional array, we enclose in curly braces a
list of terms to initialize the rows, separated by commas. Each term in the list is itself a list: the values
for the array elements in the row, enclosed in curly
braces and separated by commas.
Spreadsheets. One familiar use of arrays is a spread-
double[][]
{
{ 99.0,
{ 98.0,
{ 92.0,
{ 94.0,
{ 99.0,
{ 80.0,
{ 76.0,
{ 92.0,
{ 97.0,
{ 89.0,
{ 0.0,
};
a =
85.0,
57.0,
77.0,
62.0,
94.0,
76.5,
58.5,
66.0,
70.5,
89.5,
0.0,
98.0,
79.0,
74.0,
81.0,
92.0,
67.0,
90.5,
91.0,
66.5,
81.0,
0.0,
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
},
},
},
},
},
},
},
},
},
},
}
sheet for maintaining a table of numbers. For examCompile-time initialization of a
ple, a teacher with m students and n test grades for
of an 11-by-4 double array
each student might maintain an (m +1)-by-(n +1)
array, reserving the last column for each student’s
average grade and the last row for the average test grades. Even though we typically
do such computations within specialized applications, it is worthwhile to study the
underlying code as an introduction to array processing. To compute the average
grade for each student (average values for each row), sum the elements for each
row and divide by n. The row-by-row order in which this code processes the matrix
elements is known as row-major order. Similarly, to compute the average test grade
(average values for each column), sum the elements for each column and divide by
m. The column-by-column order in which this code processes the matrix elements
is known as column-major order.
n=3
m = 10
99.0
98.0
92.0
94.0
99.0
80.0
76.0
92.0
97.0
89.0
91.6
85.0
57.0
77.0
62.0
94.0
76.5
58.5
66.0
70.5
89.5
73.6
98.0
79.0
74.0
81.0
92.0
67.0
90.5
91.0
66.5
81.0
82.0
85 + 57 + ... + 89.5
10
row averages
in column n
94.0 92 + 77 + 74
3
78.0
81.0
79.0
95.0
74.5
75.0
83.0
78.0
86.5
column
averages
in row m
Compute row averages
for (int i = 0; i < m; i++)
{ // Compute average for row i
double sum = 0.0;
for (int j = 0; j < n; j++)
sum += a[i][j];
a[i][n] = sum / n;
}
Compute column averages
for (int j = 0; j < n; j++)
{ // Compute average for column j
double sum = 0.0;
for (int i = 0; i < m; i++)
sum += a[i][j];
a[m][j] = sum / m;
}
Typical spreadsheet calculations
1.4 Arrays
109
Matrix operations. Typical applications in science and engineering involve representing matrices as two-dimensional arrays and
then implementing various mathematical operations with matrix
operands. Again, even though such processing is often done within
specialized applications, it is worthwhile for you to understand the
underlying computation. For example, you can add two n-by-n matrices as follows:
double[][] c = new double[n][n];
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
c[i][j] = a[i][j] + b[i][j];
a[][]
.70 .20 .10
.30 .60 .10
.50 .10 .40
a[1][2]
b[][]
b[1][2]
.20 .30 .50
.10 .20 .10
.10 .30 .40
c[][]
.90 .50 .60
.40 .80 .20
.60 .40 .80
c[1][2]
Similarly, you can multiply two matrices. You may have
Matrix addition
learned matrix multiplication, but if you do not recall or are not
familiar with it, the Java code below for multiplying two square matrices is essentially the same as the mathematical definition. Each element c[i][j]
in the product of a[][] and b[][] is computed by taking the dot product of row i
of a[][] with column j of b[][].
double[][] c = new double[n][n];
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
// Dot product of row i and column j.
for (int k = 0; k < n; k++)
c[i][j] += a[i][k]*b[k][j];
}
}
b[][]
a[][]
.70 .20 .10
.30 .60 .10
.50 .10 .40
row 1
column 2
.20 .30 .50
.10 .20 .10
.10 .30 .40
c[1][2] =
+
c[][]
+
.17 .28 .41
=
.13 .24 .25
.15 .29 .42
Matrix multiplication
0.3 * 0.5
0.6 * 0.1
0.1 * 0.4
0.25
Elements of Programming
110
Special cases of matrix multiplication. Two special cases of matrix multiplication
are important. These special cases occur when one of the dimensions of one of the
matrices is 1, so it may be viewed as a vector. We have matrix–vector multiplication,
where we multiply an m-by-n matrix by a column vector (an n-by-1 matrix) to
get an m-by-1 column vector result (each element in the result is the dot product
of the corresponding row in the Matrix–vector multiplication a[][]*x[] = b[]
matrix with the operand vector).
for (int i = 0; i < m; i++)
The second case is vector–matrix
{ // Dot product of row i and x[].
for (int j = 0; j < n; j++)
multiplication, where we multiply
b[i] += a[i][j]*x[j];
a row vector (a 1-by-m matrix) by
}
an m-by-n matrix to get a 1-by-n
a[][]
b[]
row vector result (each element
94
99 85 98
in the result is the dot product of
77
98 57 78
81
92 77 76
x[]
the operand vector with the cor45
94 32 11
responding column in the matrix).
.33
51
99 34 22
row
.33
63
averages
90 46 54
These operations provide a
.33
74
76 59 88
succinct way to express numerous
82
92 66 89
64
97 71 24
matrix calculations. For example,
52
89 29 38
the row-average computation for
such a spreadsheet with m rows
and n columns is equivalent to Vector–matrix multiplication y[]*a[][] = c[]
a matrix–vector multiplication
for (int j = 0; j < n; j++)
where the column vector has n el{ // Dot product of y[] and column j.
for (int i = 0; i < m; i++)
ements all equal to 1/n. Similarly,
c[j] += y[i]*a[i][j];
the column-average computation
}
in such a spreadsheet is equivalent
y[] [ .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 ]
to a vector–matrix multiplication
a[][] 99 85 98
where the row vector has m ele98 57 78
92 77 76
ments all equal to 1/m. We return
94 32 11
to vector–matrix multiplication in
99 34 22
90 46 54
the context of an important appli76 59 88
cation at the end of this chapter.
92 66 89
c[]
97
89
71
29
24
38
[92
55
57]
column
averages
Matrix–vector and vector–matrix multiplication
1.4 Arrays
Ragged arrays. There is actually no requirement that all rows in a two-dimensional array have the same length—an array with rows of nonuniform length is known
as a ragged array (see Exercise 1.4.34 for an example application). The possibility of
ragged arrays creates the need for more care in crafting array-processing code. For
example, this code prints the contents of a ragged array:
for (int i = 0; i < a.length; i++)
{
for (int j = 0; j < a[i].length; j++)
System.out.print(a[i][j] + " ");
System.out.println();
}
This code tests your understanding of Java arrays, so you should take the time to
study it. In this book, we normally use square or rectangular arrays, whose dimension are given by the variable m or n. Code that uses a[i].length in this way is a
clear signal to you that an array is ragged.
Multidimensional arrays. The same notation extends to allow us to write code
using arrays that have any number of dimensions. For instance, we can declare and
initialize a three-dimensional array with the code
double[][][] a = new double[n][n][n];
and then refer to an element with code like a[i][j][k], and so forth.
Two-dimensional arrays provide a natural representation for matrices, which are
omnipresent in science, mathematics, and engineering. They also provide a natural way to organize large amounts of data—a key component in spreadsheets and
many other computing applications. Through Cartesian coordinates, two- and
three-dimensional arrays also provide the basis for models of the physical world.
We consider their use in all three arenas throughout this book.
111
Elements of Programming
112
Example: self-avoiding random walks
Suppose that you leave your dog in
the middle of a large city whose streets form a familiar grid pattern. We
dead end
assume that there are n north–south streets and n east–west streets all
regularly spaced and fully intersecting in a pattern known as a lattice.
Trying to escape the city, the dog makes a random choice of which way
to go at each intersection, but knows by scent to avoid visiting any place
previously visited. But it is possible for the dog to get stuck in a dead
end
where there is no choice but to revisit some intersection. What is the
escape
chance that this will happen? This amusing problem is a simple example
of a famous model known as the self-avoiding random walk, which has
important scientific applications in the study of polymers and in statistical mechanics, among many others. For example, you can see that
this process models a chain of material growing a bit at a time, until no
growth is possible. To better understand such processes, scientists seek to
Self-avoiding walks
understand the properties of self-avoiding walks.
The dog’s escape probability is certainly dependent on the size of
the city. In a tiny 5-by-5 city, it is easy to convince yourself that the dog is certain
to escape. But what are the chances of escape when the city is large? We are also
interested in other parameters. For example, how long is the dog’s path, on the average? How often does the dog come within one block of escaping? These sorts of
properties are important in the various applications just mentioned.
SelfAvoidingWalk (Program 1.4.4) is a simulation of this situation that uses
a two-dimensional boolean array, where each element represents an intersection.
The value true indicates that the dog has visited the intersection; false indicates
that the dog has not visited the intersection. The path starts in the center and takes
random steps to places not yet visited until getting stuck or escaping at a boundary. For simplicity, the code is written so that if a random choice is made to go to a
spot that has already been visited, it takes no action, trusting that some subsequent
random choice will find a new place (which is assured because the code explicitly
tests for a dead end and terminates the loop in that case).
Note that the code depends on Java initializing all of the array elements to
false for each experiment. It also exhibits an important programming technique
where we code the loop exit test in the while statement as a guard against an illegal
statement in the body of the loop. In this case, the while loop-continuation condition serves as a guard against an out-of-bounds array access within the loop. This
corresponds to checking whether the dog has escaped. Within the loop, a successful
dead-end test results in a break out of the loop.
1.4 Arrays
113
Program 1.4.4
Self-avoiding random walks
public class SelfAvoidingWalk
{
public static void main(String[] args)
{
n
lattice size
# trials
# trials resulting in
deadEnds
a dead end
a[][]
intersections visited
x, y
current position
r
random number in (0, 1)
trials
// Do trials random self-avoiding
// walks in an n-by-n lattice.
int n = Integer.parseInt(args[0]);
int trials = Integer.parseInt(args[1]);
int deadEnds = 0;
for (int t = 0; t < trials; t++)
{
boolean[][] a = new boolean[n][n];
int x = n/2, y = n/2;
while (x > 0 && x < n-1 && y > 0 && y < n-1)
{ // Check for dead end and make a random move.
a[x][y] = true;
if (a[x-1][y] && a[x+1][y] && a[x][y-1] && a[x][y+1])
{ deadEnds++; break; }
double r = Math.random();
if
(r < 0.25) { if (!a[x+1][y]) x++; }
else if (r < 0.50) { if (!a[x-1][y]) x--; }
else if (r < 0.75) { if (!a[x][y+1]) y++; }
else if (r < 1.00) { if (!a[x][y-1]) y--; }
}
}
System.out.println(100*deadEnds/trials + "% dead ends");
}
}
This program takes command-line arguments n and trials and computes trials self-avoiding
walks in an n-by-n lattice. For each walk, it creates a boolean array, starts the walk in the center,
and continues until either a dead end or a boundary is reached. The result of the computation
is the percentage of dead ends. Increasing the number of experiments increases the precision.
% java SelfAvoidingWalk 5 100
0% dead ends
% java SelfAvoidingWalk 5 1000
0% dead ends
% java SelfAvoidingWalk 20 100
36% dead ends
% java SelfAvoidingWalk 20 1000
32% dead ends
% java SelfAvoidingWalk 40 100
80% dead ends
% java SelfAvoidingWalk 40 1000
70% dead ends
% java SelfAvoidingWalk 80 100
98% dead ends
% java SelfAvoidingWalk 80 1000
95% dead ends
114
Elements of Programming
Self-avoiding random walks in a 21-by-21 grid
1.4 Arrays
As you can see from the sample runs on the facing page, the unfortunate truth
is that your dog is nearly certain to get trapped in a dead end in a large city. If you
are interested in learning more about self-avoiding walks, you can find several suggestions in the exercises. For example, the dog is virtually certain to escape in the
three-dimensional version of the problem. While this is an intuitive result that is
confirmed by our tests, the development of a mathematical model that explains
the behavior of self-avoiding walks is a famous open problem; despite extensive research, no one knows a succinct mathematical expression for the escape probability,
the average length of the path, or any other important parameter.
Summary
Arrays are the fourth basic element (after assignments, conditionals,
and loops) found in virtually every programming language, completing our coverage of basic Java constructs. As you have seen with the sample programs that we
have presented, you can write programs that can solve all sorts of problems using
just these constructs.
Arrays are prominent in many of the programs that we consider, and the basic operations that we have discussed here will serve you well in addressing many
programming tasks. When you are not using arrays explicitly (and you are sure to
do so frequently), you will be using them implicitly, because all computers have a
memory that is conceptually equivalent to an array.
The fundamental ingredient that arrays add to our programs is a potentially
huge increase in the size of a program’s state. The state of a program can be defined
as the information you need to know to understand what a program is doing. In a
program without arrays, if you know the values of the variables and which statement is the next to be executed, you can normally determine what the program
will do next. When we trace a program, we are essentially tracking its state. When
a program uses arrays, however, there can be too huge a number of values (each of
which might be changed in each statement) for us to effectively track them all. This
difference makes writing programs with arrays more of a challenge than writing
programs without them.
Arrays directly represent vectors and matrices, so they are of direct use in
computations associated with many basic problems in science and engineering. Arrays also provide a succinct notation for manipulating a potentially huge amount
of data in a uniform way, so they play a critical role in any application that involves
processing large amounts of data, as you will see throughout this book.
115
Elements of Programming
116
Q&A
Q. Some Java programmers use int a[] instead of int[] a to declare arrays.
What’s the difference?
A. In Java, both are legal and essentially equivalent. The former is how arrays are
declared in C. The latter is the preferred style in Java since the type of the variable
integers.
int[] more clearly indicates that it is an array of
Q. Why do array indices start at 0 instead of 1?
A. This convention originated with machine-language programming, where the
address of an array element would be computed by adding the index to the address
of the beginning of an array. Starting indices at 1 would entail either a waste of
space at the beginning of the array or a waste of time to subtract the 1.
Q. What happens if I use a negative integer to index an array?
A. The same thing as when you use an index that is too large. Whenever a program
attempts to index an array with an index that is not between 0 and the array length
minus 1, Java will issue an ArrayIndexOutOfBoundsException.
Q. Must the entries in an array initializer be literals?
A. No. The entries in an array initializer can be arbitrary expressions (of the specified type), even if their values are not known at compile time. For example, the
following code fragment initializes a two-dimensional array using a command-line
argument theta:
double theta = Double.parseDouble(args[0]);
double[][] rotation =
{
{ Math.cos(theta), -Math.sin(theta) },
{ Math.sin(theta), Math.cos(theta) },
};
Q. Is there a difference between an array of characters and a String?
A. Yes. For example, you can change the individual characters in a char[] but not
in a String. We will consider strings in detail in Section 3.1.
1.4 Arrays
Q. What happens when I compare two arrays with (a
117
== b)?
A. The expression evaluates to true if and only if a[] and b[] refer to the same
array (memory address), not if they store the same sequence of values. Unfortunately, this is rarely what you want. Instead, you can use a loop to compare the corresponding elements.
Q. When happens when I use an array in an assignment statement like a
= b?
A. The assignment statement makes the variable a refer to the same array as b—it
does not copy the values from the array b to the array a, as you might expect. For
example, consider the following code fragment:
int[] a = { 1, 2, 3, 4 };
int[] b = { 5, 6, 7, 8 };
a = b;
a[0] = 9;
After the assignment statement a = b, we have a[0] equal to 5, a[1] equal to 6, and
so forth, as expected. That is, the arrays correspond to the same sequence of values.
However, they are not independent arrays. For example, after the last statement, not
only is a[0] equal to 9, but b[0] is equal to 9 as well. This is one of the key differences between primitive types (such as int and double) and nonprimitive types
(such as arrays). We will revisit this subtle (but fundamental) distinction in more
detail when we consider passing arrays to functions in Section 2.1 and reference
types in Section 3.1.
Q. If
a[] is an array, why does System.out.println(a)
@f62373, instead of the sequence of values in the array?
print something like
A. Good question. It prints the memory address of the array (as a hexadecimal
integer), which, unfortunately, is rarely what you want.
Q. Which other pitfalls should I watch out for when using arrays?
A. It is very important to remember that Java automatically initializes arrays when
you create them, so that creating an array takes time proportional to its length.
Elements of Programming
118
Exercises
1.4.1 Write a program that declares, creates, and initializes an array a[] of length
1000 and accesses a[1000]. Does your program compile? What happens when you
run it?
1.4.2 Describe and explain what happens when you try to compile a program with
the following statement:
int n = 1000;
int[] a = new int[n*n*n*n];
1.4.3 Given two vectors of length
that are represented with one-dimensional
arrays, write a code fragment that computes the Euclidean distance between them
(the square root of the sums of the squares of the differences between corresponding elements).
n
1.4.4 Write a code fragment that reverses the order of the values in a onedimensional string array. Do not create another array to hold the result. Hint : Use
the code in the text for exchanging the values of two elements.
1.4.5 What is wrong with the following code fragment?
int[] a;
for (int i = 0; i < 10; i++)
a[i] = i * i;
1.4.6 Write a code fragment that prints the contents of a two-dimensional boolean array, using * to represent true and a space to represent false. Include row
and column indices.
1.4.7 What does the following code fragment print?
int[] a = new int[10];
for (int i = 0; i < 10; i++)
a[i] = 9 - i;
for (int i = 0; i < 10; i++)
a[i] = a[a[i]];
for (int i = 0; i < 10; i++)
System.out.println(a[i]);
1.4 Arrays
119
1.4.8 Which values does the following code put in the array a[]?
int n = 10;
int[] a = new int[n];
a[0] = 1;
a[1] = 1;
for (int i = 2; i < n; i++)
a[i] = a[i-1] + a[i-2];
1.4.9 What does the following code fragment print?
int[] a = { 1, 2, 3 };
int[] b = { 1, 2, 3 };
System.out.println(a == b);
1.4.10 Write a program Deal that takes an integer command-line argument n and
prints n poker hands (five cards each) from a shuffled deck, separated by blank lines.
1.4.11 Write a program HowMany that takes a variable number of command-line
arguments and prints how many there are.
1.4.12 Write a program DiscreteDistribution that takes a variable number of
integer command-line arguments and prints the integer i with probability proportional to the ith command-line argument.
1.4.13 Write code fragments to create a two-dimensional array
that is a
copy of an existing two-dimensional array a[][], under each of the following assumptions:
a. a[][] is square
b. a[][] is rectangular
c. a[][] may be ragged
Your solution to b should work for a, and your solution to c should work for both b
and a, and your code should get progressively more complicated.
b[][]
Elements of Programming
120
1.4.14 Write a code fragment to print the transposition (rows and columns exchanged) of a square two-dimensional array. For the example spreadsheet array in
the text, you code would print the following:
99
85
98
98
57
78
92
77
76
94
32
11
99
34
22
90
46
54
76
59
88
92
66
89
97
71
24
89
29
38
1.4.15 Write a code fragment to transpose a square two-dimensional array in place
without creating a second array.
1.4.16 Write a program that takes an integer command-line argument n and creates an n-by-n boolean array a[][] such that a[i][j] is true if i and j are relatively prime (have no common factors), and false otherwise. Use your solution to
Exercise 1.4.6 to print the array. Hint: Use sieving.
1.4.17 Modify the spreadsheet code fragment in the text to compute a weighted
average of the rows, where the weights of each exam score are in a one-dimensional
array weights[]. For example, to assign the last of the three exams in our example
to be twice the weight of the first two, you would use
double[] weights = { 0.25, 0.25, 0.50 };
Note that the weights should sum to 1.
1.4.18 Write a code fragment to multiply two rectangular matrices that are not
necessarily square. Note: For the dot product to be well defined, the number of columns in the first matrix must be equal to the number of rows in the second matrix.
Print an error message if the dimensions do not satisfy this condition.
1.4.19 Write a program that multiplies two square boolean matrices, using the or
operation instead of + and the and operation instead of *.
1.4.20 Modify SelfAvoidingWalk (Program 1.4.4) to calculate and print the average length of the paths as well as the dead-end probability. Keep separate the
average lengths of escape paths and dead-end paths.
1.4.21 Modify SelfAvoidingWalk to calculate and print the average area of the
smallest axis-aligned rectangle that encloses the dead-end paths.
1.4 Arrays
Creative Exercises
1.4.22 Dice simulation. The following code computes the exact probability distribution for the sum of two dice:
int[] frequencies = new int[13];
for (int i = 1; i <= 6; i++)
for (int j = 1; j <= 6; j++)
frequencies[i+j]++;
double[] probabilities = new double[13];
for (int k = 1; k <= 12; k++)
probabilities[k] = frequencies[k] / 36.0;
The value probabilities[k] is the probability that the dice sum to k. Run experiments that validate this calculation by simulating n dice throws, keeping track of
the frequencies of occurrence of each value when you compute the sum of two
uniformly random integers between 1 and 6. How large does n have to be before
your empirical results match the exact results to three decimal places?
1.4.23 Longest plateau. Given an array of integers, find the length and location
of the longest contiguous sequence of equal values for which the values of the elements just before and just after this sequence are smaller.
1.4.24 Empirical shuffle check. Run computational experiments to check that our
shuffling code works as advertised. Write a program ShuffleTest that takes two
integer command-line arguments m and n, does n shuffles of an array of length m
that is initialized with a[i] = i before each shuffle, and prints an m-by-m table such
that row i gives the number of times i wound up in position j for all j. All values
in the resulting array should be close to n / m.
1.4.25 Bad shuffling. Suppose that you choose a random integer between 0 and
n-1 in our shuffling code instead of one between i and n-1. Show that the resulting
order is not equally likely to be one of the n! possibilities. Run the test of the previ-
ous exercise for this version.
1.4.26 Music shuffling. You set your music player to shuffle mode. It plays each of
the n songs before repeating any. Write a program to estimate the likelihood that
you will not hear any sequential pair of songs (that is, song 3 does not follow song
2, song 10 does not follow song 9, and so on).
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1.4.27 Minima in permutations. Write a program that takes an integer commandline argument n, generates a random permutation, prints the permutation, and
prints the number of left-to-right minima in the permutation (the number of
times an element is the smallest seen so far). Then write a program that takes two
integer command-line arguments m and n, generates m random permutations of
length n, and prints the average number of left-to-right minima in the permutations generated. Extra credit : Formulate a hypothesis about the number of left-toright minima in a permutation of length n, as a function of n.
1.4.28 Inverse permutation. Write a program that reads in a permutation of the
integers 0 to n-1 from n command-line arguments and prints the inverse permutation. (If the permutation is in an array a[], its inverse is the array b[] such that
a[b[i]] = b[a[i]] = i.) Be sure to check that the input is a valid permutation.
1.4.29 Hadamard matrix. The n-by-n Hadamard matrix H(n) is a boolean matrix
with the remarkable property that any two rows differ in exactly n / 2 values. (This
property makes it useful for designing error-correcting codes.) H(1) is a 1-by-1
matrix with the single element true, and for n > 1, H(2n) is obtained by aligning
four copies of H(n) in a large square, and then inverting all of the values in the lower
right n-by-n copy, as shown in the following examples (with T representing true
and F representing false, as usual).
H(1)
H(2)
H(4)
T
T T
T T T T
T F
T F T F
T T F F
T F F T
Write a program that takes an integer command-line argument n and prints H(n).
Assume that n is a power of 2.
1.4 Arrays
123
1.4.30 Rumors. Alice is throwing a party with n other guests, including Bob. Bob
starts a rumor about Alice by telling it to one of the other guests. A person hearing this rumor for the first time will immediately tell it to one other guest, chosen
uniformly at random from all the people at the party except Alice and the person
from whom they heard it. If a person (including Bob) hears the rumor for a second
time, he or she will not propagate it further. Write a program to estimate the probability that everyone at the party (except Alice) will hear the rumor before it stops
propagating. Also calculate an estimate of the expected number of people to hear
the rumor.
1.4.31 Counting primes. Compare PrimeSieve with the method that we used to
demonstrate the break statement, at the end of Section 1.3. This is a classic example of a space–time tradeoff: PrimeSieve is fast, but requires a boolean array
of length n; the other approach uses only two integer variables, but is substantially
slower. Estimate the magnitude of this difference by finding the value of n for which
this second approach can complete the computation in about the same time as
java PrimeSeive 1000000.
1.4.32 Minesweeper. Write a program that takes three command-line arguments
m, n, and p and produces an m-by-n boolean array where each element is occupied
with probability p. In the minesweeper game, occupied cells represent bombs and
empty cells represent safe cells. Print out the array using an asterisk for bombs
and a period for safe cells. Then, create an integer two-dimensional array with the
number of neighboring bombs (above, below, left, right, or diagonal).
* * . . .
. . . . .
. * . . .
* * 1 0 0
3 3 2 0 0
1 * 1 0 0
Write your code so that you have as few special cases as possible to deal with, by
using an (m2)-by-(n2) boolean array.
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1.4.33 Find a duplicate. Given an integer array of length n, with each value between 1 and n, write a code fragment to determine whether there are any duplicate
values. You may not use an extra array (but you do not need to preserve the contents of the given array.)
1.4.34 Self-avoiding walk length. Suppose that there is no limit on the size of the
grid. Run experiments to estimate the average path length.
1.4.35 Three-dimensional self-avoiding walks. Run experiments to verify that the
dead-end probability is 0 for a three-dimensional self-avoiding walk and to compute the average path length for various values of n.
1.4.36 Random walkers. Suppose that n random walkers, starting in the center
of an n-by-n grid, move one step at a time, choosing to go left, right, up, or down
with equal probability at each step. Write a program to help formulate and test a
hypothesis about the number of steps taken before all cells are touched.
1.4.37 Bridge hands. In the game of bridge, four players are dealt hands of 13
cards each. An important statistic is the distribution of the number of cards in each
suit in a hand. Which is the most likely, 5–3-3–2, 4–4-3–2, or 4–3–3–3?
1.4.38 Birthday problem. Suppose that people enter an empty room until a pair
of people share a birthday. On average, how many people will have to enter before
there is a match? Run experiments to estimate the value of this quantity. Assume
birthdays to be uniform random integers between 0 and 364.
1.4.39 Coupon collector. Run experiments to validate the classical mathematical
result that the expected number of coupons needed to collect n values is approximately n Hn, where Hn in the nth harmonic number. For example, if you are observing the cards carefully at the blackjack table (and the dealer has enough decks
randomly shuffled together), you will wait until approximately 235 cards are dealt,
on average, before seeing every card value.
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125
1.4.40 Riffle shuffle. Compose a program to rearrange a deck of n cards using the
Gilbert–Shannon–Reeds model of a riffle shuffle. First, generate a random integer r
according to a binomial distribution: flip a fair coin n times and let r be the number
of heads. Now, divide the deck into two piles: the first r cards and the remaining
n r cards. To complete the shuffle, repeatedly take the top card from one of the
two piles and put it on the bottom of a new pile. If there are n1 cards remaining in
the first pile and n2 cards remaining in the second pile, choose the next card from
the first pile with probability n1 / (n1 + n2) and from the second pile with probability
n2 / (n1 + n2). Investigate how many riffle shuffles you need to apply to a deck of 52
cards to produce a (nearly) uniformly shuffled deck.
1.4.41 Binomial distribution. Write a program that takes an integer commandline argument n and creates a two-dimensional ragged array a[][] such that a[n]
[k] contains the probability that you get exactly k heads when you toss a fair coin n
times. These numbers are known as the binomial distribution: if you multiply each
element in row i by 2 n, you get the binomial coefficients—the coefficients of x k in
(x+1)n—arranged in Pascal’s triangle. To compute them, start with a[n][0] = 0.0
for all n and a[1][1] = 1.0, then compute values in successive rows, left to right,
with a[n][k] = (a[n-1][k] + a[n-1][k-1]) / 2.0.
Pascal’s triangle
binomial distribution
1
1
1 1
1/2
1/2
1 2 1
1/4
1/2
1/4
1 3 3 1
1/8
3/8
3/8
1/8
1 4 6 4 1
1/16 1/4
3/8
1/4
1/16
Elements of Programming
1.5 Input and Output
In this section we extend the set of simple abstractions (command-line arguments
and standard output) that we have been using as the interface between our Java
programs and the outside world to include standard input, standard draw1.5.1 Generating a random sequence. . . 128
ing, and standard audio. Standard input
1.5.2 Interactive user input . . . . . . . . 136
1.5.3 Averaging a stream of numbers. . . 138
makes it convenient for us to write pro1.5.4 A simple filter . . . . . . . . . . . . 140
grams that process arbitrary amounts of
1.5.5 Standard input-to-drawing filter. . 147
input and to interact with our programs;
1.5.6 Bouncing ball . . . . . . . . . . . . 153
standard drawing makes it possible for us
1.5.7 Digital signal processing . . . . . . 158
to work with graphical representations of
Programs in this section
images, freeing us from having to encode
everything as text; and standard audio
adds sound. These extensions are easy to use, and you will find that they bring you
to yet another new world of programming.
The abbreviation I/O is universally understood to mean input/output, a collective term that refers to the mechanisms by which programs communicate with
the outside world. Your computer’s operating system controls the physical devices
that are connected to your computer. To implement the standard I/O abstractions,
we use libraries of methods that interface to the operating system.
You have already been accepting arguments from the command line and
printing strings in a terminal window; the purpose of this section is to provide
you with a much richer set of tools for processing and presenting data. Like the
System.out.print() and System.out.println() methods that you have been
using, these methods do not implement pure mathematical functions—their purpose is to cause some side effect, either on an input device or an output device.
Our prime concern is using such devices to get information into and out of our
programs.
An essential feature of standard I/O mechanisms is that there is no limit on
the amount of input or output, from the point of view of the program. Your programs can consume input or produce output indefinitely.
One use of standard I/O mechanisms is to connect your programs to files on
your computer’s external storage. It is easy to connect standard input, standard
output, standard drawing, and standard audio to files. Such connections make it
easy to have your Java programs save or load results to files for archival purposes or
for later reference by other programs or other applications.
1.5 Input and Output
Bird’s-eye view
The conventional model that we have been using for Java programming has served us since Section 1.1. To build context, we begin by briefly
reviewing the model.
A Java program takes input strings from the command line and prints a string
of characters as output. By default, both command-line arguments and standard
output are associated with the application that takes commands (the one in which
you have been typing the java and javac commands). We use the generic term
terminal window to refer to this application. This model has proved to be a convenient and direct way for us to interact with our programs and data.
Command-line arguments. This mechanism, which we have been using to provide input values to our programs, is a standard part of Java programming. All of
our classes have a main() method that takes a String array args[] as its argument.
That array is the sequence of command-line arguments that we type, provided to
Java by the operating system. By convention, both Java and the operating system
process the arguments as strings, so if we intend for an argument to be a number,
we use a method such as Integer.parseInt() or Double.parseDouble() to convert it from String to the appropriate type.
Standard output. To print output values in our programs, we have been using the
system methods System.out.println() and System.out.print(). Java puts the
results of a program’s sequence of these method calls into the form of an abstract
stream of characters known as standard output. By default, the operating system
connects standard output to the terminal window. All of the output in our programs so far has been appearing in the terminal window.
For reference, and as a starting point, RandomSeq (Program 1.5.1) is a program
that uses this model. It takes a command-line argument n and produces an output
sequence of n random numbers between 0 and 1.
Now we are going to complement command-line arguments and standard output with three additional mechanisms that address their limitations and provide
us with a far more useful programming model. These mechanisms give us a new
bird’s-eye view of a Java program in which the program converts a standard input
stream and a sequence of command-line arguments into a standard output stream,
a standard drawing, and a standard audio stream.
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128
Program 1.5.1
Generating a random sequence
public class RandomSeq
{
public static void main(String[] args)
{ // Print a random sequence of n real values in [0, 1)
int n = Integer.parseInt(args[0]);
for (int i = 0; i < n; i++)
System.out.println(Math.random());
}
}
This program illustrates the conventional model that we have been using so far for Java programming. It takes a command-line argument n and prints n random numbers between 0.0
and 1.0. From the program’s point of view, there is no limit on the length of the output sequence.
% java RandomSeq 1000000
0.2498362534343327
0.5578468691774513
0.5702167639727175
0.32191774192688727
0.6865902823177537
...
Standard input. Our class
StdIn is a library that implements a standard input
abstraction to complement the standard output abstraction. Just as you can print a
value to standard output at any time during the execution of your program, so you
can read a value from a standard input stream at any time.
Standard drawing. Our class StdDraw allows you to create drawings with your
programs. It uses a simple graphics model that allows you to create drawings consisting of points and lines in a window on your computer. StdDraw also includes
facilities for text, color, and animation.
Standard audio. Our class StdAudio allows you to create sound with your programs. It uses a standard format to convert arrays of numbers into sound.
1.5 Input and Output
129
To use both command-line arguments and standard output, you have been using
built-in Java facilities. Java also has built-in facilities that support abstractions like
standard input, standard drawing, and standard audio, but they are somewhat more
complicated to use, so we have developed a simpler interface to them in our StdIn,
StdDraw, and StdAudio libraries. To logically complete our programming model,
we also include a StdOut library. To use these libraries, you must make StdIn.java,
StdOut.java, StdDraw.java, and StdAudio.java available to Java (see the Q&A
at the end of this section for details).
The standard input and standard output
command-line
standard input
arguments
abstractions date back to the development of
the Unix operating system in the 1970s and are
found in some form on all modern systems. Although they are primitive by comparison to varistandard output
ous mechanisms developed since then, modern
programmers still depend on them as a reliable
way to connect data to programs. We have destandard audio
veloped for this book standard drawing and
standard audio in the same spirit as these earlier
standard drawing
abstractions to provide you with an easy way to
A bird’s-eye view of a Java program (revisited)
produce visual and aural output.
Standard output
Java’s System.out.print() and System.out.println()
methods implement the basic standard output abstraction that we need. Nevertheless, to treat standard input and standard output in a uniform manner (and
to provide a few technical improvements), starting in this section and continuing
through the rest of the book, we use similar methods that are defined in our StdOut
library. StdOut.print() and StdOut.println() are nearly the same as the Java
methods that you have been using (see the booksite for a discussion of the differences, which need not concern you now). The StdOut.printf() method is a main
topic of this section and will be of interest to you now because it gives you more
control over the appearance of the output. It was a feature of the C language of the
early 1970s that still survives in modern languages because it is so useful.
Since the first time that we printed double values, we have been distracted by excessive precision in the printed output. For example, when we use
System.out.print(Math.PI) we get the output 3.141592653589793, even
though we might prefer to see 3.14 or 3.14159. The print() and println()
Elements of Programming
130
public class StdOut
void print(String s)
print s to standard output
void println(String s)
print s and a newline to standard output
void println()
print a newline to standard output
void printf(String format, ... )
print the arguments to standard output,
as specified by the format string format
API for our library of static methods for standard output
methods present each number to up to 15 decimal places even when we would
be happy with only a few. The printf() method is more flexible. For example, it
allows us to specify the number of decimal places when converting floating-point
numbers to strings for output. We can write StdOut.printf("%7.5f", Math.PI)
to get 3.14159, and we can replace System.out.print(t) with
StdOut.printf("The square root of %.1f is %.6f", c, t);
in Newton (Program 1.3.6) to get output like
The square root of 2.0 is 1.414214
Next, we describe the meaning and operation of these statements, along with extensions to handle the other built-in types of data.
Formatted printing basics. In its simplest form, printf() takes two arguments.
The first argument is called the format string. It contains a conversion specification
that describes how the second argument is to be converted to a string for output. A
conversion specification has the form %w.pc, where w and p are integers and c is a
character, to be interpreted as follows:
• w is the field width, the number of characters that should be written. If the
number of characters to be written exceeds (or equals) the field width, then
the field width is ignored; otherwise, the output is padded with spaces on
the left. A negative field width indicates that the output instead should be
padded with spaces on the right.
• .p is the precision. For floating-point numbers, the precision is the number
of digits that should be written after the decimal point; for strings, it is the
number of characters of the string that should be printed. The precision is
not used with integers.
1.5 Input and Output
131
• c is the conversion code. The conversion codes
format
string
number to print
that we use most frequently are d (for decimal
values from Java’s integer types), f (for floatStdOut.printf(" % 7 . 5 f " , Math.PI)
ing-point values), e (for floating-point values
conversion
field width
using scientific notation), s (for string values),
precision specification
and b (for boolean values).
The field width and precision can be omitted, but
Anatomy of a formatted print statement
every specification must have a conversion code.
The most important thing to remember about using printf() is that the
conversion code and the type of the corresponding argument must match. That is, Java
must be able to convert from the type of the argument to the type required by the
conversion code. Every type of data can be converted to String, but if you write
StdOut.printf("%12d", Math.PI) or StdOut.printf("%4.2f", 512), you will
get an IllegalFormatConversionException run-time error.
Format string. The format string can contain characters in addition to those for
the conversion specification. The conversion specification is replaced by the argument value (converted to a string as specified) and all remaining characters are
passed through to the output. For example, the statement
StdOut.printf("PI is approximately %.2f.\n", Math.PI);
prints the line
PI is approximately 3.14.
Note that we need to explicitly include the newline character \n in the format string
to print a new line with printf().
Multiple arguments. The printf() method can take more than two arguments.
In this case, the format string will have an additional conversion specification for
each additional argument, perhaps separated by other characters to pass through
to the output. For example, if you were making payments on a loan, you might use
code whose inner loop contains the statements
String formats = "%3s $%6.2f
$%7.2f
$%5.2f\n";
StdOut.printf(formats, month[i], pay, balance, interest);
to print the second and subsequent lines in a table like this (see Exercise 1.5.13):
Elements of Programming
132
Jan
Feb
Mar
...
payment
$299.00
$299.00
$299.00
balance
$9742.67
$9484.26
$9224.78
interest
$41.67
$40.59
$39.52
Formatted printing is convenient because this sort of code is much more compact
than the string-concatenation code that we have been using to create output strings.
We have described only the basic options; see the booksite for more details.
type
code
typical
literal
int
d
512
double
f
e
String
boolean
sample
format strings
converted string
values for output
"%14d"
"%-14d"
"
"512
512"
"
1595.1680010754388
"%14.2f"
"%.7f"
"%14.4e"
"
1595.17"
"1595.1680011"
"
1.5952e+03"
s
"Hello, World"
"%14s"
"%-14s"
"%-14.5s"
" Hello, World"
"Hello, World "
"Hello
"
b
true
"%b"
"true"
Format conventions for printf() (see the booksite for many other options)
Standard input Our StdIn library takes data from a standard input stream that
may be empty or may contain a sequence of values separated by whitespace (spaces,
tabs, newline characters, and the like). Each value is a string or a value from one of
Java’s primitive types. One of the key features of the standard input stream is that
your program consumes values when it reads them. Once your program has read a
value, it cannot back up and read it again. This assumption is restrictive, but it reflects the physical characteristics of some input devices. The API for StdIn appears
on the facing page. The methods fall into one of four categories:
• Those for reading individual values, one at a time
• Those for reading lines, one at a time
• Those for reading characters, one at a time
• Those for reading a sequence of values of the same type
1.5 Input and Output
133
Generally, it is best not to mix functions from the different categories in the same
program. These methods are largely self-documenting (the names describe their
effect), but their precise operation is worthy of careful consideration, so we will
consider several examples in detail.
public class StdIn
methods for reading individual tokens from standard input
boolean isEmpty()
int readInt()
is standard input empty (or only whitespace)?
read a token, convert it to an int, and return it
double readDouble()
read a token, convert it to a double, and return it
boolean readBoolean()
read a token, convert it to a boolean, and return it
String readString()
read a token and return it as a String
methods for reading characters from standard input
boolean hasNextChar()
does standard input have any remaining characters?
char readChar()
read a character from standard input and return it
methods for reading lines from standard input
boolean hasNextLine()
String readLine()
does standard input have a next line?
read the rest of the line and return it as a String
methods for reading the rest of standard input
int[] readAllInts()
read all remaining tokens and return them as an int array
double[] readAllDoubles()
read all remaining tokens and return them as a double array
boolean[] readAllBooleans()
read all remaining tokens and return them as a boolean array
String[] readAllStrings()
read all remaining tokens and return them as a String array
String[] readAllLines()
read all remaining lines and return them as a String array
String readAll()
read the rest of the input and return it as a String
Note 1: A token is a maximal sequence of non-whitespace characters.
Note 2: Before reading a token, any leading whitespace is discarded.
Note 3: There are analogous methods for reading values of type byte, short, long, and float.
Note 4: Each method that reads input throws a run-time exception if it cannot read in the next value,
either because there is no more input or because the input does not match the expected type.
API for our library of static methods for standard input
Elements of Programming
134
Typing input. When you use the java command to invoke a Java program from
the command line, you actually are doing three things: (1) issuing a command to
start executing your program, (2) specifying the command-line arguments, and (3)
beginning to define the standard input stream. The string of characters that you
type in the terminal window after the command line is the standard input stream.
When you type characters, you are interacting with your program. The program
waits for you to type characters in the terminal window.
For example, consider the program AddInts, which takes a command-line
argument n, then reads n numbers from standard input, adds them, and prints the
result to standard output. When you type java AddInts 4, after the program
takes the command-line argument, it calls the method StdIn.readInt() and
waits for you to type an integer. Suppose that you want 144 to be the first value. As
you type 1, then 4, and then 4, nothing happens, because StdIn does not know that
you are done typing the integer. But when you then type <Return> to signify the
end of your integer, StdIn.readInt() immediately returns the value 144, which
your program adds to sum and then calls StdIn.readInt() again. Again, nothing happens until you type the second value: if you type 2, then 3, then 3, and
then <Return> to end the number, StdIn.readInt() returns the value 233, which
your program again adds to sum. After you have typed four numbers in this way,
AddInts expects no more input and prints the sum, as desired.
public class AddInts
{
public static void main(String[] args)
{
int n = Integer.parseInt(args[0]);
int sum = 0;
parse commandfor (int i = 0; i < n; i++)
line argument
{
int value = StdIn.readInt();
read from
sum += value;
standard input stream
}
StdOut.println("Sum is " + sum);
}
print to
standard output stream
}
command-line
argument
command line
% java AddInts 4
144
233
377
standard input stream
1024
Sum is 1778
standard output stream
Anatomy of a command
1.5 Input and Output
Input format. If you type abc or 12.2 or true when StdIn.readInt() is expecting an int, it will respond with an InputMismatchException. The format for each
type is essentially the same as you have been using to specify literals within Java
programs. For convenience, StdIn treats strings of consecutive whitespace characters as identical to one space and allows you to delimit your numbers with such
strings. It does not matter how many spaces you put between numbers, or whether
you enter numbers on one line or separate them with tab characters or spread them
out over several lines, (except that your terminal application processes standard
input one line at a time, so it will wait until you type <Return> before sending all of
the numbers on that line to standard input). You can mix values of different types
in an input stream, but whenever the program expects a value of a particular type,
the input stream must have a value of that type.
Interactive user input.
TwentyQuestions (Program 1.5.2) is a simple example
of a program that interacts with its user. The program generates a random integer
and then gives clues to a user trying to guess the number. (As a side note, by using binary search, you can always get to the answer in at most 20 questions. See
Section 4.2.) The fundamental difference between this program and others that
we have written is that the user has the ability to change the control flow while the
program is executing. This capability was very important in early applications of
computing, but we rarely write such programs nowadays because modern applications typically take such input through the graphical user interface, as discussed in
Chapter 3. Even a simple program like TwentyQuestions illustrates that writing
programs that support user interaction is potentially very difficult because you
have to plan for all possible user inputs.
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Elements of Programming
136
Program 1.5.2
Interactive user input
public class TwentyQuestions
secret secret value
{
guess user’s guess
public static void main(String[] args)
{ // Generate a number and answer questions
// while the user tries to guess the value.
int secret = 1 + (int) (Math.random() * 1000000);
StdOut.print("I'm thinking of a number ");
StdOut.println("between 1 and 1,000,000");
int guess = 0;
while (guess != secret)
{ // Solicit one guess and provide one answer.
StdOut.print("What's your guess? ");
guess = StdIn.readInt();
if (guess == secret) StdOut.println("You win!");
if (guess < secret) StdOut.println("Too low ");
if (guess > secret) StdOut.println("Too high");
}
}
}
This program plays a simple guessing game. You type numbers, each of which is an implicit
question (“Is this the number?”) and the program tells you whether your guess is too high or
too low. You can always get it to print You win! with fewer than 20 questions. To use this
program, you StdIn and StdOut must be available to Java (see the first Q&A at the end of
this section).
% java TwentyQuestions
I’m thinking of a number between 1 and 1,000,000
What’s your guess? 500000
Too high
What’s your guess? 250000
Too low
What’s your guess? 375000
Too high
What’s your guess? 312500
Too high
What’s your guess? 300500
Too low
...
1.5 Input and Output
Processing an arbitrary-size input stream. Typically, input streams are finite:
your program marches through the input stream, consuming values until the
stream is empty. But there is no restriction of the size of the input stream, and some
programs simply process all the input presented to them. Average (Program 1.5.3)
is an example that reads in a sequence of floating-point numbers from standard
input and prints their average. It illustrates a key property of using an input stream:
the length of the stream is not known to the program. We type all the numbers that
we have, and then the program averages them. Before reading each number, the
program uses the method StdIn.isEmpty() to check whether there are any more
numbers in the input stream. How do we signal that we have no more data to type?
By convention, we type a special sequence of characters known as the end-of-file
sequence. Unfortunately, the terminal applications that we typically encounter on
modern operating systems use different conventions for this critically important
sequence. In this book, we use <Ctrl-D> (many systems require <Ctrl-D> to be
on a line by itself); the other widely used convention is <Ctrl-Z> on a line by itself.
Average is a simple program, but it represents a profound new capability in programming: with standard input, we can write programs that process an unlimited
amount of data. As you will see, writing such programs is an effective approach for
numerous data-processing applications.
Standard input is a substantial step up from the command-line-arguments model
that we have been using, for two reasons, as illustrated by TwentyQuestions and
Average. First, we can interact with our program—with command-line arguments,
we can provide data to the program only before it begins execution. Second, we can
read in large amounts of data—with command-line arguments, we can enter only
values that fit on the command line. Indeed, as illustrated by Average, the amount
of data can be potentially unlimited, and many programs are made simpler by that
assumption. A third raison d’être for standard input is that your operating system
makes it possible to change the source of standard input, so that you do not have
to type all the input. Next, we consider the mechanisms that enable this possibility.
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Program 1.5.3
Averaging a stream of numbers
public class Average
n
count of numbers read
{
sum
cumulated sum
public static void main(String[] args)
{ // Average the numbers on standard input.
double sum = 0.0;
int n = 0;
while (!StdIn.isEmpty())
{ // Read a number from standard input and add to sum.
double value = StdIn.readDouble();
sum += value;
n++;
}
double average = sum / n;
StdOut.println("Average is " + average);
}
}
This program reads in a sequence of floating-point numbers from standard input and prints
their average on standard output (provided that the sum does not overflow). From its point of
view, there is no limit on the size of the input stream. The commands on the right below use redirection and piping (discussed in the next subsection) to provide 100,000 numbers to average.
% java Average
10.0 5.0 6.0
3.0
7.0 32.0
<Ctrl-D>
Average is 10.5
% java RandomSeq 100000 > data.txt
% java Average < data.txt
Average is 0.5010473676174824
% java RandomSeq 100000 | java Average
Average is 0.5000499417963857
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139
Redirection and piping
For many applications, typing input data as a standard input stream from the terminal window is untenable because our program’s
processing power is then limited by the amount of data that we can type (and
our typing speed). Similarly, we often want to save the information printed on the
standard output stream for later use. To address such limitations, we next focus on
the idea that standard input is an abstraction—the program expects to read data
from an input stream but it has no dependence on the source of that input stream.
Standard output is a similar abstraction. The power of these abstractions derives
from our ability (through the operating system) to specify various other sources
for standard input and standard output, such as a file, the network, or another program. All modern operating systems implement these mechanisms.
Redirecting standard output to a file. By adding a simple directive to the command that invokes a program, we can redirect its standard output stream to a file,
either for permanent storage or for input to another program at a later time. For
example,
% java RandomSeq 1000 > data.txt
specifies that the standard output stream is not to be printed in the terminal
window, but instead is to be written to a text file named data.txt. Each call to
System.out.print() or System.out.println() appends text at the end of that
file. In this example, the end result is a file that contains 1,000 random values. No
output appears in the terminal window: it goes directly into the file named after the > symbol. Thus, we can save away information
% java RandomSeq 1000 > data.txt
for later retrieval. Note that we do not have to change
RandomSeq (Program 1.5.1) in any way for this mechRandomSeq
anism to work—it uses the standard output abstracdata.txt
tion and is unaffected by our use of a different implestandard output
mentation of that abstraction. You can use redirection
to save output from any program that you write. Once
Redirecting standard output to a file
you have expended a significant amount of effort to
obtain a result, you often want to save the result for
later reference. In a modern system, you can save some information by using cutand-paste or some similar mechanism that is provided by the operating system, but
cut-and-paste is inconvenient for large amounts of data. By contrast, redirection is
specifically designed to make it easy to handle large amounts of data.
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Program 1.5.4
A simple filter
public class RangeFilter
{
public static void main(String[] args)
{ // Filter out numbers not between lo and hi.
int lo = Integer.parseInt(args[0]);
int hi = Integer.parseInt(args[1]);
while (!StdIn.isEmpty())
{ // Process one number.
int value = StdIn.readInt();
if (value >= lo && value <= hi)
StdOut.print(value + " ");
}
StdOut.println();
}
}
lo
lower bound of range
upper bound of range
value current number
hi
This filter copies to the output stream the numbers from the input stream that fall inside the
range given by the command-line arguments. There is no limit on the length of the streams.
% java RangeFilter 100 400
358 1330 55 165 689 1014 3066 387 575 843 203 48 292 877 65 998
358 165 387 203 292
<Ctrl-D>
Redirecting from a file to standard input. Similarly, we can redirect the standard
input stream so that StdIn reads data from a file instead of the terminal window:
% java Average < data.txt
This command reads a sequence of numbers from the file data.txt and computes
their average value. Specifically, the < symbol is a directive that tells the operating
system to implement the standard input stream
% java Average < data.txt
by reading from the text file data.txt instead
data.txt
of waiting for the user to type something into
standard input
Average
Redirecting from a file to standard input
1.5 Input and Output
141
the terminal window. When the program calls StdIn.readDouble(), the operating system reads the value from the file. The file data.txt could have been created
by any application, not just a Java program—many applications on your computer
can create text files. This facility to redirect from a file to standard input enables us
to create data-driven code where you can change the data processed by a program
without having to change the program at all. Instead, you can keep data in files and
write programs that read from the standard input stream.
Connecting two programs. The most flexible way to implement the standard input and standard output abstractions is to specify that they are implemented by
our own programs! This mechanism is called piping. For example, the command
% java RandomSeq 1000 | java Average
specifies that the standard output stream for RandomSeq and the standard input
stream for Average are the same stream. The effect is as if RandomSeq were typing
the numbers it generates into the terminal window while Average is running. This
example also has the same effect as the following sequence of commands:
% java RandomSeq 1000 > data.txt
% java Average < data.txt
In this case, the file data.txt is not created. This difference is profound, because it
removes another limitation on the size of the input and output streams that we can
process. For example, you could replace 1000 in the example with 1000000000, even
though you might not have the space
to save a billion numbers on our com- % java RandomSeq 1000 | java Average
puter (you, however, do need the time
RandomSeq
to process them). When RandomSeq
calls System.out.println(), a string
standard input
standard output
is added to the end of the stream; when
Average
Average calls StdIn.readInt(), a
string is removed from the beginning
of the stream. The timing of precisely Piping the output of one program to the input of another
what happens is up to the operating system: it might run RandomSeq until it produces some output, and then run
Average to consume that output, or it might run Average until it needs some
input, and then run RandomSeq until it produces the needed input. The end result
is the same, but your programs are freed from worrying about such details because
they work solely with the standard input and standard output abstractions.
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Filters. Piping, a core feature of the original Unix system of the early 1970s, still
survives in modern systems because it is a simple abstraction for communicating
among disparate programs. Testimony to the power of this abstraction is that many
Unix programs are still being used today to process files that are thousands or millions of times larger than imagined by the programs’ authors. We can communicate
with other Java programs via method calls, but standard input and standard output
allow us to communicate with programs that were written at another time and,
perhaps, in another language. With standard input and standard output, we are
agreeing on a simple interface to the outside world.
For many common tasks, it is convenient to think of each program as a filter
that converts a standard input stream to a standard output stream in some way,
with piping as the command mechanism to connect programs together. For example, RangeFilter (Program 1.5.4) takes two command-line arguments and
prints on standard output those numbers from standard input that fall within the
specified range. You might imagine standard input to be measurement data from
some instrument, with the filter being used to throw away data outside the range
of interest for the experiment at hand.
Several standard filters that were designed for Unix still survive (sometimes
with different names) as commands in modern operating systems. For example,
the sort filter puts the lines on standard input in sorted order:
% java RandomSeq 6 | sort
0.035813305516568916
0.14306638757584322
0.348292877655532103
0.5761644592016527
0.7234592733392126
0.9795908813988247
We discuss sorting in Section 4.2. A second useful filter is grep, which prints the
lines from standard input that match a given pattern. For example, if you type
% grep lo < RangeFilter.java
you get the result
// Filter out numbers not between lo and hi.
int lo = Integer.parseInt(args[0]);
if (value >= lo && value <= hi)
1.5 Input and Output
Programmers often use tools such as grep to get a quick reminder of variable
names or language usage details. A third useful filter is more, which reads data from
standard input and displays it in your terminal window one screenful at a time. For
example, if you type
% java RandomSeq 1000 | more
you will see as many numbers as fit in your terminal window, but more will wait
for you to hit the space bar before displaying each succeeding screenful. The term
filter is perhaps misleading: it was meant to describe programs like RangeFilter
that write some subsequence of standard input to standard output, but it is now
often used to describe any program that reads from standard input and writes to
standard output.
Multiple streams. For many common tasks, we want to write programs that take
input from multiple sources and/or produce output intended for multiple destinations. In Section 3.1 we discuss our Out and In libraries, which generalize StdOut
and StdIn to allow for multiple input and output streams. These libraries include
provisions for redirecting these streams not only to and from files, but also from
web pages.
Processing large amounts of information plays an essential role in many applications of computing. A scientist may need to analyze data collected from a series of
experiments, a stock trader may wish to analyze information about recent financial
transactions, or a student may wish to maintain collections of music and movies. In these and countless other applications, data-driven programs are the norm.
Standard output, standard input, redirection, and piping provide us with the capability to address such applications with our Java programs. We can collect data
into files on our computer through the web or any of the standard devices and use
redirection and piping to connect data to our programs. Many (if not most) of the
programming examples that we consider throughout this book have this ability.
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Standard drawing Up to this point, our input/output abstractions have focused exclusively on text strings. Now we introduce an abstraction for producing
drawings as output. This library is easy to use and allows us to take advantage of a
visual medium to work with far more information than is possible with mere text.
As with StdIn and StdOut, our standard drawing abstraction is implemented
in a library StdDraw that you will need to make available to Java (see the first Q&A
at the end of this section). Standard drawing is very simple. We imagine an abstract
drawing device capable of drawing lines and points on a two-dimensional canvas.
The device is capable of responding to the commands that our programs issue in
the form of calls to methods in StdDraw such as the following:
public class StdDraw (basic drawing commands)
void line(double x0, double y0, double x1, double y1)
void point(double x, double y)
(1, 1)
Like the methods for standard input and standard
output, these methods are nearly self-document(x0, y0)
ing: StdDraw.line() draws a straight line segment connecting the point (x0 , y0) with the point
(x1 , y1) whose coordinates are given as arguments.
StdDraw.point() draws a spot centered on the
(x1, y1)
point (x, y) whose coordinates are given as arguments. The default scale is the unit square (all
x- and y-coordinates between 0 and 1). StdDraw
(0, 0)
displays the canvas in a window on your computStdDraw.line(x0, y0, x1, y1);
er’s screen, with black lines and points on a white
background. The window includes a menu option
to save your drawing to a file, in a format suitable for publishing on paper or on
the web.
Your first drawing. The HelloWorld equivalent for graphics programming with
StdDraw is to draw an equilateral triangle with a point inside. To form the triangle,
we draw three line segments: one from the point (0, 0) at the lower-left corner to
the point (1, 0), one from that point to the third point at (1/2, 3/2), and one from
that point back to (0, 0). As a final flourish, we draw a spot in the middle of the
triangle. Once you have successfully compiled and run Triangle, you are off and
1.5 Input and Output
running to write your own programs that
draw figures composed of line segments and
points. This ability literally adds a new dimension to the output that you can produce.
When you use a computer to create
drawings, you get immediate feedback (the
drawing) so that you can refine and improve
your program quickly. With a computer
program, you can create drawings that you
could not contemplate making by hand. In
particular, instead of viewing our data as
merely numbers, we can use pictures, which
are far more expressive. We will consider other graphics examples after we discuss a few
other drawing commands.
145
public class Triangle
{
public static void main(String[] args)
{
double t = Math.sqrt(3.0)/2.0;
StdDraw.line(0.0, 0.0, 1.0, 0.0);
StdDraw.line(1.0, 0.0, 0.5,
t);
StdDraw.line(0.5,
t, 0.0, 0.0);
StdDraw.point(0.5, t/3.0);
}
}
Control commands. The default canvas size
is 512-by-512 pixels; if you want to change
it, call setCanvasSize() before any drawing
commands. The default coordinate system
Your first drawing
for standard drawing is the unit square, but
we often want to draw plots at different scales.
For example, a typical situation is to use coordinates in some range for the x-coordinate, or the y-coordinate, or both. Also, we often want to draw line segments of
different thickness and points of different size from the standard. To accommodate
these needs, StdDraw has the following methods:
public class StdDraw (basic control commands)
void setCanvasSize(int w, int h)
create canvas in screen window of
width w and height h (in pixels)
void setXscale(double x0, double x1)
reset x-scale to (x0 , x1)
void setYscale(double y0, double y1)
reset y-scale to (y0 , y1)
void setPenRadius(double radius)
set pen radius to radius
Note: Methods with the same names but no arguments reset to default values the unit square for
the x- and y-scales, 0.002 for the pen radius.
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146
For example, the two-call sequence
int n = 50;
StdDraw.setXscale(0, n);
StdDraw.setYscale(0, n);
for (int i = 0; i <= n; i++)
StdDraw.line(0, n-i, i, 0);
(n, n)
(0, 0)
Scaling to integer coordinates
StdDraw.setXscale(x0, x1);
StdDraw.setYscale(y0, y1);
sets the drawing coordinates to be within a bounding box
whose lower-left corner is at (x0, y0) and whose upperright corner is at (x1, y1). Scaling is the simplest of the
transformations commonly used in graphics. In the applications that we consider in this chapter, we use it in a
straightforward way to match our drawings to our data.
The pen is circular, so that lines have rounded
ends, and when you set the pen radius to r and draw a
point, you get a circle of radius r. The default pen radius
is 0.002 and is not affected by coordinate scaling. This
default is about 1/500 the width of the default window,
so that if you draw 200 points equally spaced along a
horizontal or vertical line, you will be able to see individual circles, but if you draw 250 such points, the result will look like a line. When you issue the command
StdDraw.setPenRadius(0.01), you are saying that
you want the thickness of the line segments and the size
of the points to be five times the 0.002 standard.
Filtering data to standard drawing. One of the simplest applications of standard drawing is to plot data, by filtering it from standard input to standard drawing. PlotFilter (Program 1.5.5) is such a filter: it reads from standard input a
sequence of points defined by (x, y) coordinates and draws a spot at each point.
It adopts the convention that the first four numbers on standard input specify
the bounding box, so that it can scale the plot without having to make an extra
pass through all the points to determine the scale. The graphical representation of
points plotted in this way is far more expressive (and far more compact) than the
numbers themselves. The image that is produced by Program 1.5.5 makes it far
easier for us to infer properties of the points (such as, for example, clustering of
population centers when plotting points that represent city locations) than does a
list of the coordinates. Whenever we are processing data that represents the physical world, a visual image is likely to be one of the most meaningful ways that we
can use to display output. PlotFilter illustrates how easily you can create such
an image.
1.5 Input and Output
Program 1.5.5
147
Standard input-to-drawing filter
public class PlotFilter
{
public static void main(String[] args)
{
// Scale as per first four values.
double x0 = StdIn.readDouble();
double y0 = StdIn.readDouble();
double x1 = StdIn.readDouble();
double y1 = StdIn.readDouble();
StdDraw.setXscale(x0, x1);
StdDraw.setYscale(y0, y1);
x0
left bound
bottom bound
x1
right bound
y1
top bound
x, y current point
y0
// Read the points and plot to standard drawing.
while (!StdIn.isEmpty())
{
double x = StdIn.readDouble();
double y = StdIn.readDouble();
StdDraw.point(x, y);
}
}
}
This program reads a sequence of points from standard input and plots them to standard drawing. (By convention, the first four numbers are the minimum and maximum x- and y-coordinates.) The file USA.txt contains the coordinates of 13,509 cities in the United States
% java PlotFilter < USA.txt
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Plotting a function graph. Another important use of standard drawing is to plot
experimental data or the values of a mathematical function. For example, suppose
that we want to plot values of the function y = sin(4x) sin(20x) in the interval
[0, ]. Accomplishing this task is a prototypical example of sampling: there are an
infinite number of points in the interval but we have to make do with evaluating
the function at a finite number of such points. We sample the function by choosing a set of x-values, then computing y-values by evaluating the function at each
of these x-value. Plotting the function by connecting successive points with lines
produces what is known as a
piecewise linear approximation.
double[] x = new double[n+1];
The simplest way to proceed
double[] y = new double[n+1];
is to evenly space the x-values.
for (int i = 0; i <= n; i++)
First, we decide ahead of time
x[i] = Math.PI * i / n;
on a sample size, then we space
for (int i = 0; i <= n; i++)
y[i] = Math.sin(4*x[i]) + Math.sin(20*x[i]);
the x-values by the interval size
StdDraw.setXscale(0,
Math.PI);
divided by the sample size. To
StdDraw.setYscale(-2.0, 2.0);
make sure that the values we
for (int i = 1; i <= n; i++)
plot fall in the visible canvas,
StdDraw.line(x[i-1], y[i-1], x[i], y[i]);
we scale the x-axis correspondn = 20
n = 200
ing to the interval and the yaxis corresponding to the maximum and minimum values
of the function within the interval. The smoothness of the
curve depends on properties
Plotting a function graph
of the function and the size of
the sample. If the sample size is
too small, the rendition of the
function may not be at all accurate (it might not be very smooth, and it might miss
major fluctuations); if the sample is too large, producing the plot may be timeconsuming, since some functions are time-consuming to compute. (In Section 2.4,
we will look at a method for plotting a smooth curve without using an excessive
number of points.) You can use this same technique to plot the function graph of
any function you choose. That is, you can decide on an x-interval where you want
to plot the function, compute function values evenly spaced within that interval,
determine and set the y-scale, and draw the line segments.
1.5 Input and Output
Outline and filled shapes.
149
StdDraw also includes methods to draw circles, squares,
rectangles, and arbitrary polygons. Each shape defines an outline. When the method name is the name of a shape, that outline is traced by the drawing pen. When
the name begins with filled, the named shape is filled solid, not traced. As usual,
we summarize the available methods in an API:
public class StdDraw (shapes)
void circle(double x, double y, double radius)
void filledCircle(double x, double y, double radius)
void square(double x, double y, double r)
void filledSquare(double x, double y, double r)
void rectangle(double x, double y, double r1, double r2)
void filledRectangle(double x, double y, double r1, double r2)
void polygon(double[] x, double[] y)
void filledPolygon(double[] x, double[] y)
The arguments for circle() and filledCircle() define a circle of radius r centered at (x, y); the arguments for square() and filledSquare() define a square of
side length 2r centered at (x, y); the arguments for rectangle() and filledRectangle() define a rectangle of width 2r1 and height 2r2, centered at (x, y); and the
arguments for polygon() and filledPolygon() define a sequence of points that
are connected by line segments, including one from the last point to the first point.
(x0, y0)
r
(x1, y1)
r
r
(x, y)
(x, y)
StdDraw.circle(x, y, r);
StdDraw.square(x, y, r);
(x3, y3)
(x2, y2)
double[] x = {x0, x1, x2, x3};
double[] y = {y0, y1, y2, y3};
StdDraw.polygon(x, y);
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Text and color. Occasionally, you may wish to annotate or highlight various elements in your drawings. StdDraw has a method for drawing text, another for setting parameters associated with text, and another for changing the color of the ink
in the pen. We make scant use of these features in this book, but they can be very
useful, particularly for drawings on your computer screen. You will find many examples of their use on the booksite.
public class StdDraw (text and color commands)
void text(double x, double y, String s)
void setFont(Font font)
void setPenColor(Color color)
In this code, Font and Color are nonprimitive types that you will learn about in
Section 3.1. Until then, we leave the details to StdDraw. The available pen colors are
BLACK, BLUE, CYAN, DARK_GRAY, GRAY, GREEN, LIGHT_GRAY, MAGENTA, ORANGE, PINK,
RED, WHITE, YELLOW, and BOOK_BLUE, all of which are defined as constants within
StdDraw. For example, the call StdDraw.setPenColor(StdDraw.GRAY) changes
the pen to use gray ink. The default ink color is
StdDraw.square(.2, .8, .1);
BLACK. The default font in StdDraw suffices for most
StdDraw.filledSquare(.8, .8, .2);
of the drawings that you need (you can find inforStdDraw.circle(.8, .2, .2);
mation
on using other fonts on the booksite). For
double[] xd = { .1, .2, .3, .2 };
example,
you might wish to use these methods to
double[] yd = { .2, .3, .2, .1 };
annotate function graphs to highlight relevant valStdDraw.filledPolygon(xd, yd);
StdDraw.text(.2, .5, "black text");
ues, and you might find it useful to develop similar
StdDraw.setPenColor(StdDraw.WHITE);
methods to annotate other parts of your drawings.
StdDraw.text(.8, .8, "white text");
Shapes, color, and text are basic tools that you
can use to produce a dizzying variety of images, but
you should use them sparingly. Use of such artifacts usually presents a design challenge, and our
StdDraw commands are crude by the standards of
modern graphics libraries, so that you are likely to
need an extensive number of calls to them to produce the beautiful images that you may imagine. By
comparison, using color or labels to help focus on
important information in drawings is often worthwhile, as is using color to represent data values.
white text
black text
Shape and text examples
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151
Double buffering and computer animations.
StdDraw supports a powerful com-
puter graphics feature known as double buffering. When double buffering is enabled
by calling enableDoubleBuffering(), all drawing takes place on the offscreen canvas. The offscreen canvas is not displayed; it exists only in computer memory. Only
when you call show() does your drawing get copied from the offscreen canvas to
the onscreen canvas, where it is displayed in the standard drawing window. You
can think of double buffering as collecting all of the lines, points, shapes, and text
that you tell it to draw, and then drawing them all simultaneously, upon request.
Double buffering enables you to precisely control when the drawing takes place.
One reason to use double buffering is for efficiency when performing a
large number of drawing commands. Incrementally displaying a complex drawing while it is being created can be intolerably inefficient on many computer systems. For example, you can dramatically speed up Program 1.5.5 by adding a call
to enableDoubleBuffering() before the while loop and a call to show() after the
while loop. Now, the points appear all at once (instead of one at a time).
Our most important use of double buffering is to produce computer animations, where we create the illusion of motion by rapidly displaying static drawings.
Such effects can provide compelling and dynamic visualizations of scientific phenomenon. We can produce animations by repeating the following four steps:
• Clear the offscreen canvas.
• Draw objects on the offscreen canvas.
• Copy the offscreen canvas to the onscreen canvas.
• Wait for a short while.
In support of the first and last of these steps, StdDraw provides three additional
methods. The clear() methods clear the canvas, either to white or to a specified
color. To control the apparent speed of an animation, the pause() method takes
an argument dt and tells StdDraw to wait for dt milliseconds before processing
additional commands.
public class StdDraw (advanced control commands)
void enableDoubleBuffering()
enable double buffering
void disableDoubleBuffering()
disable double buffering
void show()
copy the offscreen canvas to the onscreen canvas
void clear()
clear the canvas to white (default)
void clear(Color color)
clear the canvas to color color
void pause(double dt)
pause dt milliseconds
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Elements of Programming
Bouncing ball. The “Hello, World” program for animation is to produce a
black ball that appears to move around on the canvas, bouncing off the boundary according to the laws of elastic collision. Suppose that the ball is at position
(rx , ry) and we want to create the impression of moving it to a nearby position, say,
(rx 0.01, ry 0.02). We do so in four steps:
• Clear the offscreen canvas to white.
• Draw a black ball at the new position on the offscreen canvas.
• Copy the offscreen canvas to the onscreen canvas.
• Wait for a short while.
To create the illusion of movement, we iterate these steps for a whole sequence
of positions of the ball (one that will form a straight line, in this case). Without
double buffering, the image of the ball will rapidly flicker between black and white
instead of creating a smooth animation.
BouncingBall (Program 1.5.6) implements these steps to create the illusion
of a ball moving in the 2-by-2 box centered at the origin. The current position of
the ball is (rx , ry), and we compute the new position at each step by adding vx to
rx and vy to ry. Since (vx , vy) is the fixed distance that the ball moves in each time
unit, it represents the velocity. To keep the ball in the standard drawing window,
we simulate the effect of the ball bouncing off the walls according to the laws of
elastic collision. This effect is easy to implement: when the ball hits a vertical wall,
we change the velocity in the x-direction from vx to –vx , and when the ball hits a
horizontal wall, we change the velocity in the y-direction from vy to –vy . Of course,
you have to download the code from the booksite and run it on your computer to
see motion. To make the image clearer on the printed page, we modified BouncingBall to use a gray background that also shows the track of the ball as it moves
(see Exercise 1.5.34).
Standard drawing completes our programming model by adding a “picture is worth
a thousand words” component. It is a natural abstraction that you can use to better
open up your programs to the outside world. With it, you can easily produce the
function graphs and visual representations of data that are commonly used in science and engineering. We will put it to such uses frequently throughout this book.
Any time that you spend now working with the sample programs on the last few
pages will be well worth the investment. You can find many useful examples on
the booksite and in the exercises, and you are certain to find some outlet for your
creativity by using StdDraw to meet various challenges. Can you draw an n-pointed
star? Can you make our bouncing ball actually bounce (by adding gravity)? You
may be surprised at how easily you can accomplish these and other tasks.
1.5 Input and Output
Program 1.5.6
153
Bouncing ball
public class BouncingBall
rx, ry position
{
vx, vy velocity
public static void main(String[] args)
dt
wait time
{ // Simulate the motion of a bouncing ball.
radius
ball radius
StdDraw.setXscale(-1.0, 1.0);
StdDraw.setYscale(-1.0, 1.0);
StdDraw.enableDoubleBuffering();
double rx = 0.480, ry = 0.860;
double vx = 0.015, vy = 0.023;
double radius = 0.05;
while(true)
{ // Update ball position and draw it.
if (Math.abs(rx + vx) + radius > 1.0) vx = -vx;
if (Math.abs(ry + vy) + radius > 1.0) vy = -vy;
rx += vx;
ry += vy;
StdDraw.clear();
StdDraw.filledCircle(rx, ry, radius);
StdDraw.show();
StdDraw.pause(20);
}
}
}
This program simulates the motion of a bouncing ball in the box with coordinates between
1 and +1. The ball bounces off the boundary according to the laws of inelastic collision. The
20-millisecond wait for StdDraw.pause() keeps the black image of the ball persistent on the
screen, even though most of the ball’s pixels alternate between black and white. The images
below, which show the track of the ball, are produced by a modified version of this code (see
Exercise 1.5.34).
100 steps
200 steps
500 steps
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This API table summarizes the StdDraw methods that we have considered:
public class StdDraw
drawing commands
void line(double x0, double y0, double x1, double y1)
void point(double x, double y)
void circle(double x, double y, double radius)
void filledCircle(double x, double y, double radius)
void square(double x, double y, double radius)
void filledSquare(double x, double y, double radius)
void rectangle(double x, double y, double r1, double r2)
void filledRectangle(double x, double y, double r1, double r2)
void polygon(double[] x, double[] y)
void filledPolygon(double[] x, double[] y)
void text(double x, double y, String s)
control commands
void setXscale(double x0, double x1)
reset x-scale to (x0 , x1)
void setYscale(double y0, double y1)
reset y-scale to (y0 , y1)
void setPenRadius(double radius)
set pen radius to radius
void setPenColor(Color color)
set pen color to color
void setFont(Font font)
set text font to font
void setCanvasSize(int w, int h)
set canvas size to w-by-h
void enableDoubleBuffering()
enable double buffering
void disableDoubleBuffering()
disable double buffering
void show()
copy the offscreen canvas to
the onscreen canvas
void clear(Color color)
clear the canvas to color color
void pause(int dt)
pause dt milliseconds
void save(String filename)
save to a .jpg or .png file
Note: Methods with the same names but no arguments reset to default values.
API for our library of static methods for standard drawing
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155
Standard audio
As a final example of a basic abstraction for output, we consider StdAudio, a library that you can use to play, manipulate, and synthesize sound.
You probably have used your computer to process music. Now you can write programs to do so. At the same time, you will learn some concepts behind a venerable
and important area of computer science and scientific computing: digital signal
processing. We will merely scratch the surface of this fascinating subject, but you
may be surprised at the simplicity of the underlying concepts.
Concert A. Sound is the perception of the vibration of molecules—in particular,
the vibration of our eardrums. Therefore, oscillation is the key to understanding
sound. Perhaps the simplest place to start is to consider the musical note A above
middle C, which is known as concert A. This note is nothing more than a sine wave,
scaled to oscillate at a frequency of 440 times per second. The function sin(t) repeats itself once every 2 units, so if we measure t in seconds and plot the function
sin(2t 440), we get a curve that oscillates 440 times per second. When you play
an A by plucking a guitar string, pushing air through a trumpet, or causing a small
cone to vibrate in a speaker, this sine wave is the prominent part of the sound that
you hear and recognize as concert A. We measure frequency in hertz (cycles per second). When you double or halve the frequency, you move up or down one octave
on the scale. For example, 880 hertz is one octave above concert A and 110 hertz is
two octaves below concert A. For reference, the frequency range of human hearing
is about 20 to 20,000 hertz. The amplitude (y-value) of a sound corresponds to the
volume. We plot our curves between 1 and 1 and assume that any devices that
record and play sound will scale as appropriate, with further scaling controlled by
you when you turn the volume knob.
1
0 2
4
3
6
5
9 11
7
8 10 12
♩♩♩♩♩♩♩♩
note
i
frequency
A
0
1
2
3
4
5
6
7
8
9
10
11
12
440.00
466.16
493.88
523.25
554.37
587.33
622.25
659.26
698.46
739.99
783.99
830.61
880.00
A♯ or B♭
B
C
C♯ or D♭
D
D♯ or E♭
E
F
F♯ or G♭
G
G♯ or A♭
A
440 2i/12
Notes, numbers, and waves
156
Elements of Programming
Other notes. A simple mathematical formula characterizes the other notes on the
chromatic scale. There are 12 notes on the chromatic scale, evenly spaced on a
logarithmic (base 2) scale. We get the i th note above a given note by multiplying its
frequency by the (i /12)th power of 2. In other words, the frequency of each note
in the chromatic scale is precisely the frequency of the previous note in the scale
multiplied by the twelfth root of 2 (about 1.06). This information suffices to create
music! For example, to play the tune Frère Jacques, play each of the notes A B C# A
by producing sine waves of the appropriate frequency for about half a second each,
and then repeat the pattern. The primary method in the StdAudio library, StdAudio.play(), allows you to do exactly this.
Sampling. For digital sound, we represent a curve by sampling it at regular intervals, in precisely the same manner as when we plot function graphs. We sample
sufficiently often that we have an accurate representation of the curve—a widely
used sampling rate for digital sound is 44,100 samples per second. For concert A,
that rate corresponds to plotting each cycle of the sine wave by sampling it at about
100 points. Since we sample at regular intervals, we only need to compute the ycoordinates of the sample points. It is that simple: we represent sound as an array of
real numbers (between 1 and 1). The method StdAudio.play() takes an array
as its argument and plays the sound represented by that array on your computer.
For example, suppose that you want to play concert A for 10 seconds. At
44,100 samples per second, you need a double array of length 441,001. To fill in
the array, use a for loop that samples the function sin(2t 440) at t = 0/44,100,
1/44,100, 2/44,100, 3/44,100, …, 441,000/44,100. Once we fill the array with these
values, we are ready for StdAudio.play(), as in the following code:
int SAMPLING_RATE = 44100;
// samples per second
int hz = 440;
// concert A
double duration = 10.0;
// ten seconds
int n = (int) (SAMPLING_RATE * duration);
double[] a = new double[n+1];
for (int i = 0; i <= n; i++)
a[i] = Math.sin(2 * Math.PI * i * hz / SAMPLING_RATE);
StdAudio.play(a);
This code is the “Hello, World” of digital audio. Once you use it to get your computer to play this note, you can write code to play other notes and make music!
The difference between creating sound and plotting an oscillating curve is nothing
1.5 Input and Output
157
more than the output device. Indeed, it is instructive and entertaining to send the
same numbers to both standard drawing and standard audio (see Exercise 1.5.27).
Saving to a file. Music can take up a lot of space on your
computer. At 44,100 samples per second, a four-minute
song corresponds to 4 60 44100 = 10,584,000 numbers. Therefore, it is common to represent the numbers
corresponding to a song in a binary format that uses less
space than the string-of-digits representation that we use
for standard input and output. Many such formats have
been developed in recent years—StdAudio uses the .wav
format. You can find some information about the .wav
format on the booksite, but you do not need to know the
details, because StdAudio takes care of the conversions
for you. Our standard library for audio allows you to read
.wav files, write .wav files, and convert .wav files to arrays
of double values for processing.
1/40 second (various sample rates)
5,512 samples/second, 137 samples
11,025 samples/second, 275 samples
22,050 samples/second, 551 samples
44,100 samples/second, 1,102 samples
(Program 1.5.7) is an example that
shows how you can use StdAudio to turn your computer
into a musical instrument. It takes notes from standard in- 44,100 samples/second (various times)
put, indexed on the chromatic scale from concert A, and
1/40 second, 1,102 samples
plays them on standard audio. You can imagine all sorts
of extensions on this basic scheme, some of which are addressed in the exercises.
PlayThatTune
1/1000 second
We include standard audio in our basic arsenal of programming tools because sound processing is one important application of scientific computing that is certainly familiar
to you. Not only has the commercial application of digital
signal processing had a phenomenal impact on modern
society, but the science and engineering behind it combine physics and computer science in interesting ways. We
will study more components of digital signal processing in
some detail later in the book. (For example, you will learn
in Section 2.1 how to create sounds that are more musical
than the pure sounds produced by PlayThatTune.)
1/200 second, 220 samples
1/1000 second
1/1,000 second, 44 samples
Sampling a sine wave
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Program 1.5.7
Digital signal processing
public class PlayThatTune
pitch
distance from A
{
duration
note play time
public static void main(String[] args)
hz
frequency
{ // Read a tune from StdIn and play it.
n
number of samples
int SAMPLING_RATE = 44100;
while (!StdIn.isEmpty())
a[]
sampled sine wave
{ // Read and play one note.
int pitch = StdIn.readInt();
double duration = StdIn.readDouble();
double hz = 440 * Math.pow(2, pitch / 12.0);
int n = (int) (SAMPLING_RATE * duration);
double[] a = new double[n+1];
for (int i = 0; i <= n; i++)
a[i] = Math.sin(2*Math.PI * i * hz / SAMPLING_RATE);
StdAudio.play(a);
}
}
}
This data-driven program turns your computer into a musical instrument. It reads notes
and durations from standard input and plays a pure tone corresponding to each note for the
specified duration on standard audio. Each note is specified as a pitch (distance from concert
A). After reading each note and duration, the program creates an array by sampling a sine
wave of the specified frequency and duration at 44,100 samples per second, and plays it using
StdAudio.play().
%
7
6
7
6
7
2
5
3
0
more elise.txt
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.50
% java PlayThatTune < elise.txt
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159
The API table below summarizes the methods in StdAudio:
public class StdAudio
void play(String filename)
play the given .wav file
void play(double[] a)
play the given sound wave
void play(double x)
play sample for 1/44,100 second
void save(String filename, double[] a) save to a .wav file
double[] read(String filename)
read from a .wav file
API for our library of static methods for standard audio
Summary
I/O is a compelling example of the power of abstraction because
standard input, standard output, standard drawing, and standard audio can be tied
to different physical devices at different times without making any changes to programs. Although devices may differ dramatically, we can write programs that can
do I/O without depending on the properties of specific devices. From this point
forward, we will use methods from StdOut, StdIn, StdDraw, and/or StdAudio in
nearly every program in this book. For economy, we collectively refer to these libraries as Std*. One important advantage of using such libraries is that you can
switch to new devices that are faster, are cheaper, or hold more data without changing your program at all. In such a situation, the details of the connection are a matter to be resolved between your operating system and the Std* implementations.
On modern systems, new devices are typically supplied with software that resolves
such details automatically both for the operating system and for Java.
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Q&A
Q. How can I make StdIn, StdOut, StdDraw, and StdAudio available to Java?
A. If you followed the step-by-step instructions on the booksite for installing Java,
these libraries should already be available to Java. Alternatively, you can copy the
files StdIn.java, StdOut.java, StdDraw.java, and StdAudio.java from the
booksite and put them in the same directory as the programs that use them.
Q. What does the error message Exception in thread "main" java.lang.NoClassDefFoundError: StdIn mean?
A. The library StdIn is not available to Java.
Q. Why are we not using the standard Java libraries for input, graphics, and sound?
A. We are using them, but we prefer to work with simpler abstract models. The Java
libraries behind StdIn, StdDraw, and StdAudio are built for production programming, and the libraries and their APIs are a bit unwieldy. To get an idea of what they
are like, look at the code in StdIn.java, StdDraw.java, and StdAudio.java.
Q. So, let me get this straight. If I use the format %2.4f for a double value, I get two
digits before the decimal point and four digits after, right?
A. No, that specifies 4 digits after the decimal point. The first value is the width of
the whole field. You want to use the format %7.2f to specify 7 characters in total,
4 before the decimal point, the decimal point itself, and 2 digits after the decimal
point.
Q. Which other conversion codes are there for printf()?
A. For integer values, there is
for octal and x for hexadecimal. There are also
numerous formats for dates and times. See the booksite for more information.
o
Q. Can my program reread data from standard input?
A. No. You get only one shot at it, in the same way that you cannot undo a
println() command.
1.5 Input and Output
Q. What happens if my program attempts to read data from standard input after it
is exhausted?
A. You will get an error. StdIn.isEmpty() allows you to avoid such an error by
checking whether there is more input available.
Q. Why does StdDraw.square(x, y, r) draw a square of width 2*r instead of r?
A. This makes it consistent with the function StdDraw.circle(x, y, r), in which
the third argument is the radius of the circle, not the diameter. In this context, r is
the radius of the biggest circle that can fit inside the square.
Q. My terminal window hangs at the end of a program using StdAudio. How can
I avoid having to use <Ctrl-C> to get a command prompt?
A. Add a call to
System.exit(0) as the last line in main(). Don’t ask why.
Q. Can I use negative integers to specify notes below concert A when making input
files for PlayThatTune?
A. Yes. Actually, our choice to put concert A at 0 is arbitrary. A popular standard,
known as the MIDI Tuning Standard, starts numbering at the C five octaves below
concert A. By that convention, concert A is 69 and you do not need to use negative
numbers.
Q. Why do I hear weird results on standard audio when I try to sonify a sine wave
with a frequency of 30,000 hertz (or more)?
A. The Nyquist frequency, defined as one-half the sampling frequency, represents
the highest frequency that can be reproduced. For standard audio, the sampling
frequency is 44,100 hertz, so the Nyquist frequency is 22,050 hertz.
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Exercises
1.5.1 Write a program that reads in integers (as many as the user enters) from
standard input and prints the maximum and minimum values.
1.5.2 Modify your program from the previous exercise to insist that the integers
must be positive (by prompting the user to enter positive integers whenever the
value entered is not positive).
1.5.3 Write a program that takes an integer command-line argument n, reads n
floating-point numbers from standard input, and prints their mean (average value)
and sample standard deviation (square root of the sum of the squares of their differences from the average, divided by n-1).
1.5.4 Extend your program from the previous exercise to create a filter that reads n
floating-point numbers from standard input, and prints those that are further than
1.5 standard deviations from the mean.
1.5.5 Write a program that reads in a sequence of integers and prints both the
integer that appears in a longest consecutive run and the length of that run. For
example, if the input is 1 2 2 1 5 1 1 7 7 7 7 1 1, then your program should
print Longest run: 4 consecutive 7s.
1.5.6 Write a filter that reads in a sequence of integers and prints the integers,
removing repeated values that appear consecutively. For example, if the input is
your program should print
1 2 2 1 5 1 1 7 7 7 7 1 1 1 1 1 1 1 1 1,
1 2 1 5 1 7 1.
1.5.7 Write a program that takes an integer command-line argument n, reads in
n-1 distinct integers between 1 and n, and determines the missing value.
1.5.8 Write a program that reads in positive floating-point numbers from standard input and prints their geometric and harmonic means. The geometric mean
of n positive numbers x1, x2, ..., xn is (x1 x2 ... xn)1/n. The harmonic mean is
n / (1/x1 + 1/x2 + ... + 1/xn). Hint : For the geometric mean, consider taking logarithms to avoid overflow.
1.5.9 Suppose that the file input.txt contains the two strings F and F. What does
the following command do (see Exercise 1.2.35)?
1.5 Input and Output
% java Dragon < input.txt | java Dragon | java Dragon
public class Dragon
{
public static void main(String[] args)
{
String dragon = StdIn.readString();
String nogard = StdIn.readString();
StdOut.print(dragon + "L" + nogard);
StdOut.print(" ");
StdOut.print(dragon + "R" + nogard);
StdOut.println();
}
}
1.5.10 Write a filter
TenPerLine that reads from standard input a sequence of
integers between 0 and 99 and prints them back, 10 integers per line, with columns
aligned. Then write a program RandomIntSeq that takes two integer commandline arguments m and n and prints n random integers between 0 and m-1. Test your
programs with the command java RandomIntSeq 200 100 | java TenPerLine.
1.5.11 Write a program that reads in text from standard input and prints the number of words in the text. For the purpose of this exercise, a word is a sequence of
non-whitespace characters that is surrounded by whitespace.
1.5.12 Write a program that reads in lines from standard input with each line
containing a name and two integers and then uses printf() to print a table with a
column of the names, the integers, and the result of dividing the first by the second,
accurate to three decimal places. You could use a program like this to tabulate batting averages for baseball players or grades for students.
1.5.13 Write a program that prints a table of the monthly payments, remaining
principal, and interest paid for a loan, taking three numbers as command-line
arguments: the number of years, the principal, and the interest rate (see Exercise 1.2.24).
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1.5.14 Which of the following require saving all the values from standard input (in
an array, say), and which could be implemented as a filter using only a fixed number
of variables? For each, the input comes from standard input and consists of n real
numbers between 0 and 1.
• Print the maximum and minimum numbers.
• Print the sum of the squares of the n numbers.
• Print the average of the n numbers.
• Print the median of the n numbers.
• Print the percentage of numbers greater than the average.
• Print the n numbers in increasing order.
• Print the n numbers in random order.
1.5.15 Write a program that takes three
command-line arguments x, y,
and z, reads from standard input a sequence of point coordinates (xi, yi, zi), and
prints the coordinates of the point closest to (x, y, z). Recall that the square of the
distance between (x , y , z) and (xi , yi , zi ) is (x xi )2 + (y yi )2 + (z zi )2. For efficiency, do not use Math.sqrt().
double
1.5.16 Given the positions and masses of a sequence of objects, write a program
to compute their center-of-mass, or centroid. The centroid is the average position of
the n objects, weighted by mass. If the positions and masses are given by (xi , yi, mi ),
then the centroid (x, y, m) is given by
m = m1 + m2 + ... + mn
x = (m1 x1 + ... + mn xn) / m
y = (m1 y1 + ... + mn yn ) / m
1.5.17 Write a program that reads in a sequence of real numbers between 1 and
1 and prints their average magnitude, average power, and the number of zero
crossings. The average magnitude is the average of the absolute values of the data
values. The average power is the average of the squares of the data values. The number of zero crossings is the number of times a data value transitions from a strictly
negative number to a strictly positive number, or vice versa. These three statistics
are widely used to analyze digital signals.
1.5 Input and Output
165
1.5.18 Write a program that takes an integer command-line argument n and plots
an n-by-n checkerboard with red and black squares. Color the lower-left square red.
1.5.19 Write a program that takes as command-line arguments an integer n and
a floating-point number p (between 0 and 1), plots n equally spaced points on the
circumference of a circle, and then, with probability p for each pair of points, draws
a gray line connecting them.
16 0.125
16 0.25
16 0.5
16 1.0
1.5.20 Write code to draw hearts, spades, clubs, and diamonds. To draw a heart,
draw a filled diamond, then attach two filled semicircles to the upper left and upper
right sides.
1.5.21 Write a program that takes an integer command-line argument n and plots
a rose with n petals (if n is odd) or 2n petals (if n is even), by plotting the polar
coordinates (r, ) of the function r = sin(n ) for ranging from 0 to 2 radians.
4
5
8
9
1.5.22 Write a program that takes a string command-line argument s and displays
it in banner style on the screen, moving from left to right and wrapping back to the
beginning of the string as the end is reached. Add a second command-line argument to control the speed.
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1.5.23 Modify
to take additional command-line arguments that
control the volume (multiply each sample value by the volume) and the tempo
(multiply each note’s duration by the tempo).
PlayThatTune
1.5.24 Write a program that takes the name of a
.wav file and a playback rate
r as command-line arguments and plays the file at the given rate. First, use
StdAudio.read() to read the file into an array a[]. If r = 1, play a[]; otherwise,
create a new array b[] of approximate size r times the length of a[]. If r < 1, populate b[] by sampling from the original; if r > 1, populate b[] by interpolating from
the original. Then play b[].
1.5.25 Write programs that uses StdDraw to create each of the following designs.
1.5.26 Write a program Circles that draws filled circles of random radii at random positions in the unit square, producing images like those below. Your program
should take four command-line arguments: the number of circles, the probability
that each circle is black, the minimum radius, and the maximum radius.
200 1 0.01 0.01
100 1 0.01 0.05
500 0.5 0.01 0.05
50 0.75 0.1 0.2
1.5 Input and Output
Creative Exercises
1.5.27 Visualizing audio. Modify PlayThatTune to send the values played to standard drawing, so that you can watch the sound waves as they are played. You will
have to experiment with plotting multiple curves in the drawing canvas to synchronize the sound and the picture.
1.5.28 Statistical polling. When collecting statistical data for certain political polls,
it is very important to obtain an unbiased sample of registered voters. Assume that
you have a file with n registered voters, one per line. Write a filter that prints a uniformly random sample of size m (see Program 1.4.1).
1.5.29 Terrain analysis. Suppose that a terrain is represented by a two-dimensional grid of elevation values (in meters). A peak is a grid point whose four neighboring cells (left, right, up, and down) have strictly lower elevation values. Write a
program Peaks that reads a terrain from standard input and then computes and
prints the number of peaks in the terrain.
1.5.30 Histogram. Suppose that the standard input stream is a sequence of double
values. Write a program that takes an integer n and two real numbers lo and hi as
command-line arguments and uses StdDraw to plot a histogram of the count of the
numbers in the standard input stream that fall in each of the n intervals defined by
dividing (lo , hi) into n equal-sized intervals.
1.5.31 Spirographs. Write a program that takes three double command-line arguments R, r, and a and draws the resulting spirograph. A spirograph (technically,
an epicycloid) is a curve formed by rolling a circle of radius r around a larger fixed
circle of radius R. If the pen offset from the center of the rolling circle is (ra), then
the equation of the resulting curve at time t is given by
x(t ) = (R + r ) cos (t ) (r + a ) cos ((R + r )t /r)
y(t ) = (R + r ) sin (t ) (r + a ) sin ((R + r )t /r)
Such curves were popularized by a best-selling toy that contains discs with gear
teeth on the edges and small holes that you could put a pen in to trace spirographs.
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1.5.32 Clock. Write a program that displays an animation of the second, minute,
and hour hands of an analog clock. Use the method StdDraw.pause(1000) to
update the display roughly once per second.
1.5.33 Oscilloscope. Write a program that simulates the output of an oscilloscope
and produces Lissajous patterns. These patterns are named after the French physicist, Jules A. Lissajous, who studied the patterns that arise when two mutually perpendicular periodic disturbances occur simultaneously. Assume that the inputs are
sinusoidal, so that the following parametric equations describe the curve:
x(t ) = Ax sin (wx t + x)
y(t ) = A y sin (wy t + y)
Take the six arguments Ax ,, wx ,, x , Ay ,, wy , and y from the command line.
1.5.34 Bouncing ball with tracks. Modify BouncingBall to produce images like
the ones shown in the text, which show the track of the ball on a gray background.
1.5.35 Bouncing ball with gravity. Modify BouncingBall to incorporate gravity
in the vertical direction. Add calls to StdAudio.play() to add a sound effect when
the ball hits a wall and a different sound effect when it hits the floor.
1.5.36 Random tunes. Write a program that uses StdAudio to play random tunes.
Experiment with keeping in key, assigning high probabilities to whole steps, repetition, and other rules to produce reasonable melodies.
1.5.37 Tile patterns. Using your solution to Exercise 1.5.25, write a program
TilePattern that takes an integer command-line argument n and draws an n-by-n
pattern, using the tile of your choice. Add a second command-line argument that
adds a checkerboard option. Add a third command-line argument for color selection. Using the patterns on the facing page as a starting point, design a tile floor.
Be creative! Note: These are all designs from antiquity that you can find in many
ancient (and modern) buildings.
1.5 Input and Output
169
Elements of Programming
1.6 Case Study: Random Web Surfer
Communicating across the web has become an integral part of everyday life. This
communication is enabled in part by scientific studies of the structure of the web,
a subject of active research since its inception. We next consider a simple model of
the web that has proved to be a particularly successful approach to understanding
some of its properties. Variants of this
model are widely used and have been
1.6.1 Computing the transition matrix .173
a key factor in the explosive growth of
1.6.2 Simulating a random surfer. . . . . 175
search applications on the web.
1.6.3 Mixing a Markov chain . . . . . . . 182
The model is known as the random
Programs in this section
surfer model, and is simple to describe.
We consider the web to be a fixed set of
web pages, with each page containing a fixed set of hyperlinks, and each link a reference to some other page. (For brevity, we use the terms pages and links.) We study
what happens to a web surfer who randomly moves from page to page, either by
typing a page name into the address bar or by clicking a link on the current page.
The mathematical model that underlies the link structure of the web is known
as the graph, which we will consider in detail at the end of the book (in Section 4.5).
We defer discussion about processing graphs
aaa.edu
until then. Instead, we concentrate on calculations associated with a natural and wellwww.com
studied probabilistic model that accurately defff.org
www.com
fff.org
scribes the behavior of the random surfer.
aaa.edu
The first step in studying the random
mmm.net
surfer model is to formulate it more preciselinks
ttt.gov
ly. The crux of the matter is to specify what it
fff.org
means to randomly move from page to page.
mmm.net
The following intuitive 90–10 rule captures
both methods of moving to a new page: Assume
that 90% of the time the random surfer
mmm.net
ttt.gov
page
clicks
a
random link on the current page (each
aaa.edu
fff.org
link chosen with equal probability) and that
mmm.net
10% of the time the random surfer goes directly
to a random page (all pages on the web chosen
with equal probability).
Pages and links
1.6 Case Study: Random Web Surfer
171
You can immediately see that this model has flaws, because you know from
your own experience that the behavior of a real web surfer is not quite so simple:
• No one chooses links or pages with equal probability.
• There is no real potential to surf directly to each page on the web.
• The 90–10 (or any fixed) breakdown is just a guess.
• It does not take the back button or bookmarks into account.
Despite these flaws, the model is sufficiently rich that computer scientists have
learned a great deal about properties of the web by studying it. To appreciate the
model, consider the small example on the previous page. Which page do you think
the random surfer is most likely to visit?
Each person using the web behaves a bit like the random surfer, so understanding the fate of the random surfer is of intense interest to people building
web infrastructure and web applications. The model is a tool for understanding
the experience of each of the billions of web users. In this section, you will use the
basic programming tools from this chapter to study the model and its implications.
Input format
We want to be able to study the behavior of the random surfer
on various graphs, not just one example. Consequently,
% more tiny.txt
we want to write data-driven code, where we keep data
5
n
in files and write programs that read the data from stan- 0
3
0 1
dard input. The first step in this approach is to define an
1 2 1 2
1
input format that we can use to structure the informa1 3 1 3 1 4
tion in the input files. We are free to define any conve2 3
2
4
3 0
nient input format.
links
4 0 4 2
Later in the book, you will learn how to read web
pages in Java programs (Section 3.1) and to convert
Random surfer input format
from names to numbers (Section 4.4) as well as other
techniques for efficient graph processing. For now, we
assume that there are n web pages, numbered from 0 to n-1, and we represent links
with ordered pairs of such numbers, the first specifying the page containing the
link and the second specifying the page to which it refers. Given these conventions,
a straightforward input format for the random surfer problem is an input stream
consisting of an integer (the value of n) followed by a sequence of pairs of integers
(the representations of all the links). StdIn treats all sequences of whitespace characters as a single delimiter, so we are free to either put one link per line or arrange
them several to a line.
Elements of Programming
172
Transition matrix We use a two-dimensional matrix, which we refer to as the
transition matrix, to completely specify the behavior of the random surfer. With n
web pages, we define an n-by-n matrix such that the value in row i and column j
is the probability that the random surfer moves to page j when on page i. Our first
task is to write code that can create such a matrix for any given input. By the 90–10
rule, this computation is not difficult. We do so in three steps:
• Read n, and then create arrays counts[][] and outDegrees[].
• Read the links and accumulate counts so that counts[i][j] counts the
links from i to j and outDegrees[i] counts the links from i to anywhere.
• Use the 90–10 rule to compute the probabilities.
The first two steps are elementary, and the third is not much more difficult: multiply counts[i][j] by 0.90/outDegree[i] if there is a link from i to j (take a
random link with probability 0.9), and then add 0.10/n to each element (go to
a random page with probability 0.1). Transition (Program 1.6.1) performs this
calculation: it is a filter that reads a graph from standard input and prints the associated transition matrix to standard output.
The transition matrix is significant because each row represents a discrete probability distribution—the elements fully specify the behavior of the random surfer’s
next move, giving the probability of surfing to each page. Note in particular that
the elements sum to 1 (the surfer always goes somewhere).
The output of Transition defines another file format, one for matrices: the
numbers of rows and columns followed by the values of the matrix elements, in
row-major order. Now, we can write programs that read and process transition
matrices.
input graph
3
0
1
4
2
5
0
1
1
2
3
4
1
2
3
3
0
0
link counts
1 2
1 3
0
0
0
1
1
1 4
4 2
leap probabilities
.02 .02 .02 .02
.02 .02 .02 .02
.02 .02 .02 .02
.02 .02 .02 .02
.02 .02 .02 .02
.02
.02
.02
.02
.02
+
1
0
0
0
0
0
2
0
0
1
0
2
1
0
0
0
1
0
0
0
outdegrees
1
5
1
1
2
link probabilities
transition matrix
0 .90 0
0
0
.02 .92 .02 .02
0
0 .36 .36 .18
.02 .02 .38 .38
= .02 .02 .02 92
0
0
0 .90 0
.90 0
0
0
0
.92 .02 .02 .02
.45 0 .45 0
0
.47 .02 .47 .02
Transition matrix computation
.02
.20
.02
.02
.02
1.6 Case Study: Random Web Surfer
Program 1.6.1
173
Computing the transition matrix
public class Transition
{
public static void main(String[] args)
{
int n = StdIn.readInt();
int[][] counts = new int[n][n];
int[] outDegrees = new int[n];
while (!StdIn.isEmpty())
{ // Accumulate link counts.
int i = StdIn.readInt();
int j = StdIn.readInt();
outDegrees[i]++;
counts[i][j]++;
}
n
number of pages
counts[i][j]
count of links from
page i to page j
outDegrees[i]
count of links from
page i to anywhere
p
transition probability
StdOut.println(n + " " + n);
for (int i = 0; i < n; i++)
{ // Print probability distribution for row i.
for (int j = 0; j < n; j++)
{ // Print probability for row i and column j.
double p = 0.9*counts[i][j]/outDegrees[i] + 0.1/n;
StdOut.printf("%8.5f", p);
}
StdOut.println();
}
}
}
This program is a filter that reads links from standard input and produces the corresponding
transition matrix on standard output. First it processes the input to count the outlinks from
each page. Then it applies the 90–10 rule to compute the transition matrix (see text). It assumes
that there are no pages that have no outlinks in the input (see Exercise 1.6.3).
%
5
0
1
1
2
3
4
more tinyG.txt
1
2
3
3
0
0
1 2
1 3
4 2
1 4
% java Transition < tinyG.txt
5 5
0.02000 0.92000 0.02000 0.02000
0.02000 0.02000 0.38000 0.38000
0.02000 0.02000 0.02000 0.92000
0.92000 0.02000 0.02000 0.02000
0.47000 0.02000 0.47000 0.02000
0.02000
0.20000
0.02000
0.02000
0.02000
Elements of Programming
174
Simulation
Given the transition matrix, simulating the behavior of the random
surfer involves surprisingly little code, as you can see in RandomSurfer (Program
1.6.2). This program reads a transition matrix from standard input and surfs according to the rules, starting at page 0 and taking the number of moves as a command-line argument. It counts the number of times that the surfer visits each page.
Dividing that count by the number of moves yields an estimate of the probability
that a random surfer winds up on the page. This probability is known as the page’s
rank. In other words, RandomSurfer computes an estimate of all page ranks.
One random move. The key to the computation is the random move, which is
specified by the transition matrix. We maintain a variable page whose value is the
current location of the surfer. Row page of the matrix gives, for each j, the probability that the surfer next goes to j. In other words, when the surfer is at page, our
task is to generate a random integer between 0 and n-1 according to the distribution
given by row page in the transition maj
0
1
2
3
4
trix. How can we accomplish this task?
probabilities p[page][j] .47 .02 .47 .02 .02
We use a technique known as roulette.47 .49 .96 .98 1.0
cumulated sum values
wheel selection. We use Math.random()
generate .71, return 2
to generate a random number r between
0 and 1, but how does that help us get to
a random page? One way to answer this
question is to think of the probabilities
0.0
0.47 0.49
0.96 0.98 1.0
in row page as defining a set of n interGenerating a random integer from a discrete distribution vals in (0, 1), with each probability corresponding to an interval length. Then
our random variable r falls into one of the intervals, with probability precisely
specified by the interval length. This reasoning leads to the following code:
double sum = 0.0;
for (int j = 0; j < n; j++)
{ // Find interval containing r.
sum += p[page][j];
if (r < sum) { page = j; break; }
}
The variable sum tracks the endpoints of the intervals defined in row page, and
the for loop finds the interval containing the random value r. For example, suppose that the surfer is at page 4 in our example. The transition probabilities are
0.47, 0.02, 0.47, 0.02, and 0.02, and sum takes on the values 0.0, 0.47, 0.49, 0.96,
1.6 Case Study: Random Web Surfer
Program 1.6.2
175
Simulating a random surfer
public class RandomSurfer
{
public static void main(String[] args)
{ // Simulate random surfer.
int trials = Integer.parseInt(args[0]);
int n = StdIn.readInt();
StdIn.readInt();
// Read transition matrix.
double[][] p = new double[n][n];
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
p[i][j] = StdIn.readDouble();
trials
number of moves
n
number of pages
page
current page
p[i][j]
probability that the
surfer moves from
page i to page j
freq[i]
number of times the
surfer hits page i
int page = 0;
int[] freq = new int[n];
for (int t = 0; t < trials; t++)
{ // Make one random move to next page.
double r = Math.random();
double sum = 0.0;
for (int j = 0; j < n; j++)
{ // Find interval containing r.
sum += p[page][j];
if (r < sum) { page = j; break; }
}
freq[page]++;
}
for (int i = 0; i < n; i++)
// Print page ranks.
StdOut.printf("%8.5f", (double) freq[i] / trials);
StdOut.println();
}
}
This program uses a transition matrix to simulate the behavior of a random surfer. It takes
the number of moves as a command-line argument, reads the transition matrix, performs the
indicated number of moves as prescribed by the matrix, and prints the relative frequency of
hitting each page. The key to the computation is the random move to the next page (see text).
% java Transition < tinyG.txt | java RandomSurfer 100
0.24000 0.23000 0.16000 0.25000 0.12000
% java Transition < tinyG.txt | java RandomSurfer 1000000
0.27324 0.26568 0.14581 0.24737 0.06790
176
Elements of Programming
0.98, and 1.0. These values indicate that the probabilities define the five intervals
(0, 0.47), (0.47, 0.49), (0.49, 0.96), (0.96, 0.98), and (0.98, 1), one for each page.
Now, suppose that Math.random() returns the value 0.71 . We increment j from 0
to 1 to 2 and stop there, which indicates that 0.71 is in the interval (0.49, 0.96), so
we send the surfer to page 2. Then, we perform the same computation start at page
2, and the random surfer is off and surfing. For large n, we can use binary search
to substantially speed up this computation (see Exercise 4.2.38). Typically, we are
interested in speeding up the search in this situation because we are likely to need
a huge number of random moves, as you will see.
Markov chains. The random process that describes the surfer’s behavior is known
as a Markov chain, named after the Russian mathematician Andrey Markov, who
developed the concept in the early 20th century. Markov chains are widely applicable and well studied, and they have many remarkable and useful properties. For
example, you may have wondered why RandomSurfer starts the random surfer at
page 0—you might have expected a random choice. A basic limit theorem for Markov chains says that the surfer could start anywhere, because the probability that a
random surfer eventually winds up on any particular page is the same for all starting pages! No matter where the surfer starts, the process eventually stabilizes to a
point where further surfing provides no further information. This phenomenon is
known as mixing. Though this phenomenon is perhaps counterintuitive at first, it
explains coherent behavior in a situation that might seem chaotic. In the present
context, it captures the idea that the web looks pretty much the same to everyone
after surfing for a sufficiently long time. However, not all Markov chains have this
mixing property. For example, if we eliminate the random leap from our model,
certain configurations of web pages can present problems for the surfer. Indeed,
there exist on the web sets of pages known as spider traps, which are designed to
attract incoming links but have no outgoing links. Without the random leap, the
surfer could get stuck in a spider trap. The primary purpose of the 90–10 rule is to
guarantee mixing and eliminate such anomalies.
Page ranks. The RandomSurfer simulation is straightforward: it loops for the indicated number of moves, randomly surfing through the graph. Because of the
mixing phenomenon, increasing the number of iterations gives increasingly accurate estimates of the probability that the surfer lands on each page (the page ranks).
How do the results compare with your intuition when you first thought about the
question? You might have guessed that page 4 was the lowest-ranked page, but did
1.6 Case Study: Random Web Surfer
177
you think that pages 0 and 1 would rank higher than page 3? If we want to know
which page is the highest rank, we need more precision and more accuracy. RandomSurfer needs 10n moves to get answers precise to n decimal places and many
more moves for those answers to stabilize to an accurate value. For our example, it
takes tens of thousands of iterations to get answers accurate to two decimal places
and millions of iterations to get answers accurate to three places (see Exercise 1.6.5).
The end result is that page 0 beats page 1 by 27.3% to 26.6%. That such a tiny difference would appear in such a small problem is quite surprising: if you guessed that
page 0 is the most likely spot for the surfer to end up, you were lucky!
Accurate page rank estimates for the web are valuable in practice for many
reasons. First, using them to put in order the pages that match the search criteria
for web searches proved to be vastly more in line with people’s expectations than
previous methods. Next, this measure of confidence and reliability led to the investment of huge amounts of money in web advertising based on
page ranks. Even in our tiny example, page ranks might be used
3
0
to convince advertisers to pay up to four times as much to place
an ad on page 0 as on page 4. Computing page ranks is math1
ematically sound, an interesting computer science problem, and
2
4
big business, all rolled into one.
0 0.273
1
3
2
4
0.266
0.146
0.247
0.068
Visualizing the histogram. With
StdDraw, it is also easy to
create a visual representation that can give you a feeling for how
the random surfer visit frequencies converge to the page ranks.
If you enable double buffering; scale the x- and y-coordinates
appropriately; add this code
Page ranks with histogram
StdDraw.clear();
for (int i = 0; i < n; i++)
StdDraw.filledRectangle(i, freq[i]/2.0, 0.25, freq[i]/2.0);
StdDraw.show();
StdDraw.pause(10);
to the random move loop; and run RandomSurfer for a large number of trials,
then you will see a drawing of the frequency histogram that eventually stabilizes
to the page ranks. After you have used this tool once, you are likely to find yourself
using it every time you want to study a new model (perhaps with some minor adjustments to handle larger models).
Elements of Programming
178
Studying other models.
RandomSurfer and Transition are excellent examples of
data-driven programs. You can easily define a graph by creating a file like tiny.txt
that starts with an integer n and then specifies pairs of integers between 0 and n-1
that represent links connecting pages. You are encouraged to run it for various data
models as suggested in the exercises, or to make up some graphs of your own to
study. If you have ever wondered how web page ranking works, this calculation is
your chance to develop better intuition about what causes one page to be ranked
more highly than another. Which kind of page is likely to be rated highly? One that
has many links to other pages, or one that has just a few links to other pages? The
exercises in this section present many opportunities to study the behavior of the
random surfer. Since RandomSurfer uses standard input, you can also write simple
programs that generate large graphs, pipe their output through both Transition
and RandomSurfer, and in this way study the random surfer on large graphs. Such
flexibility is an important reason to use standard input and standard output.
Directly simulating the behavior of a random surfer to understand the structure
of the web is appealing, but it has limitations. Think about the following question:
could you use it to compute page ranks for a web graph with millions (or billions!)
of web pages and links? The quick answer to this question is no, because you cannot
even afford to store the transition matrix for such a large number of pages. A matrix for millions of pages would have trillions of elements. Do you have that much
space on your computer? Could you use RandomSurfer to find page ranks for a
smaller graph with, say, thousands of pages? To answer this question, you might
run multiple simulations, record the results for a large number of trials, and then
interpret those experimental results. We do use this approach for many scientific
problems (the gambler’s ruin problem is one example; Section 2.4 is devoted to
another), but it can be very time-consuming, as a huge number of trials may be
necessary to get the desired accuracy. Even for our tiny example, we saw that it takes
millions of iterations to get the page ranks accurate to three or four decimal places.
For larger graphs, the required number of iterations to obtain accurate estimates
becomes truly huge.
1.6 Case Study: Random Web Surfer
179
Mixing a Markov chain It is important to remember that the page ranks are a
property of the transition matrix, not any particular approach for computing them.
That is, RandomSurfer is just one way to compute page ranks. Fortunately, a simple
computational model based on a well-studied area of mathematics provides a far
more efficient approach than simulation to the problem of computing page ranks.
That model makes use of the basic arithmetic operations on two-dimensional matrices that we considered in Section 1.4.
Squaring a Markov chain. What is the probability that the random surfer will
move from page i to page j in two moves? The first move goes to an intermediate page k, so we calculate the probability of moving from i to k and then from
k to j for all possible k and add up the results. For our example, the probability
of moving from 1 to 2 in two moves is the probability of moving from 1 to 0 to 2
(0.02 0.02), plus the probability of moving from 1 to 1 to 2 (0.02 0.38), plus
the probability of moving from 1 to 2 to 2 (0.38 0.02), plus the probability of
moving from 1 to 3 to 2 (0.38 0.02), plus the probability of moving from 1 to
4 to 2 (0.20 0.47), which adds up to a grand total of 0.1172. The same process
works for each pair of pages. This calculation is
3
0
one that we have seen before, in the definition of
matrix multiplication: the element in row i and
1
column j in the result is the dot product of row i
probability of
and column j in the original. In other words, the
2
4
surfing from i to 2
result of multiplying p[][] by itself is a matrix
in one move
where the element in row i and column j is the p
.02 .92 .02 .02 .02
probability that the random surfer moves from
.02 .02 .38 .38 .20
page i to page j in two moves. Studying the ele.02 .02 .02 .92 .02
ments of the two-move transition matrix for our
probability of
surfing from 1 to i
.92 .02 .02 .02 .02
example is well worth your time and will help
in one move
you better understand the movement of the ran.47 .02 .47 .02 .02
dom surfer. For instance, the largest value in the p 2
square is the one in row 2 and column 0, reflect.05 .04 .36 .37 .19
probability of
ing the fact that a surfer starting on page 2 has
.45 .04 .12 .37 .02 surfing from 1 to 2
only one link out, to page 3, where there is also
in two moves
.86 .04 .04 .05 .02
only one link out, to page 0. Therefore, by far the
(dot product)
.05 .85 .04 .05 .02
most likely outcome for a surfer starting on page
.05 .44 .04 .45 .02
2 is to end up in page 0 after two moves. All of the
other two-move routes involve more choices and
Squaring a Markov chain
are less probable. It is important to note that this
Elements of Programming
180
is an exact computation (up to the limitations of Java’s floating-point precision);
in contrast, RandomSurfer produces an estimate and needs more iterations to get
a more accurate estimate.
The power method. We might then calculate the probabilities for three moves
by multiplying by p[][] again, and for four moves by multiplying by p[][] yet
again, and so forth. However, matrix–matrix multiplication is expensive, and we
are actually interested in a vector–matrix multiplication. For our example, we start
with the vector
[1.0 0.0 0.0 0.0 0.0 ]
which specifies that the random surfer starts on page 0. Multiplying this vector by
the transition matrix gives the vector
[.02 .92 .02 .02 .02 ]
which is the probabilities that the surfer winds up on each of the pages after one
step. Now, multiplying this vector by the transition matrix gives the vector
[.05 .04 .36 .37 .19 ]
which contains the probabilities that the surfer winds up on each of the pages after
two steps. For example, the probability of moving from 0 to 2 in two moves is the
probability of moving from 0 to 0 to 2 (0.02 0.02), plus the probability of moving from 0 to 1 to 2 (0.92 0.38), plus the probability of moving from 0 to 2 to 2
(0.02 0.02), plus the probability of moving from 0 to 3 to 2 (0.02 0.02), plus
the probability of moving from 0 to 4 to 2 (0.02 0.47), which adds up to a grand
total of 0.36. From these initial calculations, the pattern is clear: the vector giving the
probabilities that the random surfer is at each page after t steps is precisely the product
of the corresponding vector for t 1 steps and the transition matrix. By the basic limit
theorem for Markov chains, this process converges to the same vector no matter
where we start; in other words, after a sufficient number of moves, the probability that the surfer ends up on any given page is independent of the starting point.
Markov (Program 1.6.3) is an implementation that you can use to check convergence for our example. For instance, it gets the same results (the page ranks accurate to two decimal places) as RandomSurfer, but with just 20 matrix–vector multiplications instead of the tens of thousands of iterations needed by RandomSurfer.
Another 20 multiplications gives the results accurate to three decimal places, as
compared with millions of iterations for RandomSurfer, and just a few more give
the results to full precision (see Exercise 1.6.6).
1.6 Case Study: Random Web Surfer
ranks[]
first move
181
newRanks[]
p[][]
.02 .92 .02 .02 .02
.02 .02 .38 .38 .20
[ 1.0 0.0 0.0 0.0 0.0 ] *
.02 .02 .02 .92 .02
.92 .02 .02 .02 .02
.47 .02 .47 .02 .02
.02 .92 .02 .02 .02
.02 .02 .38 .38 .20
[ .02 .92 .02 .02 .02 ] *
probabilities of surfing
from 0 to i in one move
probabilities of surfing
from i to 2 in one move
second move
probabilities of surfing
from 0 to i in one move
= [ .02 .92 .02 .02 .02 ]
.02 .02 .02 .92 .02
.92 .02 .02 .02 .02
.47 .02 .47 .02 .02
probability of surfing from 0 to 2
in two moves (dot product)
= [ .05 .04 .36 .37 .19 ]
probabilities of surfing
from 0 to i in two moves
third move
probabilities of surfing
from 0 to i in two moves
.02 .92 .02 .02 .02
.02 .02 .38 .38 .20
[ .05 .04 .36 .37 .19 ] *
.02 .02 .02 .92 .02
.92 .02 .02 .02 .02
.47 .02 .47 .02 .02
20th move
probabilities of surfing
from 0 to i in 19 moves
= [ .44 .06 .12 .36 .03 ]
probabilities of surfing
from 0 to i in three moves
.
.
.
.02 .92 .02 .02 .02
.02 .02 .38 .38 .20
[ .27 .26 .15 .25 .07 ] *
.02 .02 .02 .92 .02
.92 .02 .02 .02 .02
.47 .02 .47 .02 .02
= [ .27 .26 .15 .25 .07 ]
probabilities of surfing
from 0 to i in 20 moves
(steady state)
The power method for computing page ranks (limit values of transition probabilities)
Elements of Programming
182
Program 1.6.3
Mixing a Markov chain
public class Markov
{ // Compute page ranks after trials moves.
public static void main(String[] args)
{
int trials = Integer.parseInt(args[0]);
int n = StdIn.readInt();
StdIn.readInt();
// Read transition matrix.
double[][] p = new double[n][n];
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
p[i][j] = StdIn.readDouble();
trials
number of moves
n
number of pages
p[][]
transition matrix
ranks[]
newRanks[]
page ranks
new page ranks
// Use the power method to compute page ranks.
double[] ranks = new double[n];
ranks[0] = 1.0;
for (int t = 0; t < trials; t++)
{ // Compute effect of next move on page ranks.
double[] newRanks = new double[n];
for (int j = 0; j < n; j++)
{ // New rank of page j is dot product
// of old ranks and column j of p[][].
for (int k = 0; k < n; k++)
newRanks[j] += ranks[k]*p[k][j];
}
for (int j = 0; j < n; j++)
ranks[j] = newRanks[j];
// Update ranks[].
}
for (int i = 0; i < n; i++)
// Print page ranks.
StdOut.printf("%8.5f", ranks[i]);
StdOut.println();
}
}
This program reads a transition matrix from standard input and computes the probabilities
that a random surfer lands on each page (page ranks) after the number of steps specified as
command-line argument.
% java Transition < tinyG.txt | java Markov 20
0.27245 0.26515 0.14669 0.24764 0.06806
% java Transition < tinyG.txt | java Markov 40
0.27303 0.26573 0.14618 0.24723 0.06783
1.6 Case Study: Random Web Surfer
183
25
34
0
7
10
19
29
41
2
40
33
15
49
8
44
45
28
1
14
48
22
39
18
21
6
42
13
23
47
31
11
32
12
30
26
27
5
37
9
16
43
4
24
38
3
17
35
36
46
20
6
22
Page ranks with histogram for a larger example
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
0.00226
0.01681
0.00909
0.00279
0.00572
0.01586
0.06644
0.02092
0.01718
0.03978
0.00200
0.02770
0.00638
0.04452
0.01793
0.02582
0.02309
0.00508
0.02308
0.02562
0.00352
0.03357
0.06288
0.04268
0.01072
0.00473
0.00559
0.00774
0.03738
0.00251
0.03705
0.02340
0.01772
0.01349
0.02363
0.01934
0.00330
0.03144
0.01162
0.02343
0.01677
0.02108
0.02120
0.01627
0.02270
0.00578
0.02343
0.02368
0.01948
0.01579
184
Elements of Programming
Markov chains are well studied, but their impact on the web was not truly felt
until 1998, when two graduate students—Sergey Brin and Lawrence Page—had
the audacity to build a Markov chain and compute the probabilities that a random
surfer hits each page for the whole web. Their work revolutionized web search and
is the basis for the page ranking method used by Google, the highly successful web
search company that they founded. Specifically, their idea was to present to the user
a list of web pages related to their search query in decreasing order of page rank. Page
ranks (and related techniques) now predominate because they provide users with
more relevant web pages for typical searches than earlier techniques (such as ordering pages by the number of incoming links). Computing page ranks is an enormously time-consuming task, due to the huge number of pages on the web, but
the result has turned out to be enormously profitable and well worth the expense.
Lessons Developing a full understanding of the random surfer model is beyond
the scope of this book. Instead, our purpose is to show you an application that
involves writing a bit more code than the short programs that we have been using
to teach specific concepts. Which specific lessons can we learn from this case study?
We already have a full computational model. Primitive types of data and strings,
conditionals and loops, arrays, and standard input/output/drawing/audio enable
you to address interesting problems of all sorts. Indeed, it is a basic precept of theoretical computer science that this model suffices to specify any computation that
can be performed on any reasonable computing device. In the next two chapters,
we discuss two critical ways in which the model has been extended to drastically
reduce the amount of time and effort required to develop large and complex programs.
Data-driven code is prevalent. The concept of using the standard input and output streams and saving data in files is a powerful one. We write filters to convert
from one kind of input to another, generators that can produce huge input files for
study, and programs that can handle a wide variety of models. We can save data for
archiving or later use. We can also process data derived from some other source and
then save it in a file, whether it is from a scientific instrument or a distant website.
The concept of data-driven code is an easy and flexible way to support this suite of
activities.
1.6 Case Study: Random Web Surfer
Accuracy can be elusive. It is a mistake to assume that a program produces accurate answers simply because it can print numbers to many decimal places of
precision. Often, the most difficult challenge that we face is ensuring that we have
accurate answers.
Uniform random numbers are only a start. When we speak informally about
random behavior, we often are thinking of something more complicated than the
“every value equally likely” model that Math.random() gives us. Many of the problems that we consider involve working with random numbers from other distributions, such as RandomSurfer.
Efficiency matters. It is also a mistake to assume that your computer is so fast
that it can do any computation. Some problems require much more computational
effort than others. For example, the method used in Markov is far more efficient
than directly simulating the behavior of a random surfer, but it is still too slow to
compute page ranks for the huge web graphs that arise in practice. Chapter 4 is devoted to a thorough discussion of evaluating the performance of the programs that
you write. We defer detailed consideration of such issues until then, but remember
that you always need to have some general idea of the performance requirements
of your programs.
Perhaps the most important lesson to learn from writing programs for complicated
problems like the example in this section is that debugging is difficult. The polished
programs in the book mask that lesson, but you can rest assured that each one is
the product of a long bout of testing, fixing bugs, and running the programs on
numerous inputs. Generally we avoid describing bugs and the process of fixing
them in the text because that makes for a boring account and overly focuses attention on bad code, but you can find some examples and descriptions in the exercises
and on the booksite.
185
Elements of Programming
186
Exercises
1.6.1 Modify
to take the leap probability as a command-line argument and use your modified version to examine the effect on page ranks of switching to an 80–20 rule or a 95–5 rule.
Transition
1.6.2 Modify Transition to ignore the effect of multiple links. That is, if there
are multiple links from one page to another, count them as one link. Create a small
example that shows how this modification can change the order of page ranks.
1.6.3 Modify Transition to handle pages with no outgoing links, by filling rows
corresponding to such pages with the value 1/n, where n is the number of columns.
1.6.4 The code fragment in RandomSurfer that generates the random move fails
if the probabilities in the row p[page] do not add up to 1. Explain what happens
in that case, and suggest a way to fix the problem.
1.6.5 Determine, to within a factor of 10, the number of iterations required by
RandomSurfer to compute page ranks accurate to 4 decimal places and to 5 decimal
places for tiny.txt.
1.6.6 Determine the number of iterations required by Markov to compute page
ranks accurate to 3 decimal places, to 4 decimal places, and to ten 10 places for
tiny.txt.
1.6.7 Download the file
from the booksite (which reflects the 50page example depicted in this section) and add to it links from page 23 to every
other page. Observe the effect on the page ranks, and discuss the result.
medium.txt
1.6.8 Add to medium.txt (see the previous exercise) links to page 23 from every
other page, observe the effect on the page ranks, and discuss the result.
1.6.9 Suppose that your page is page 23 in medium.txt. Is there a link that you
could add from your page to some other page that would raise the rank of your
page?
1.6.10 Suppose that your page is page 23 in medium.txt. Is there a link that you
could add from your page to some other page that would lower the rank of that
page?
1.6 Case Study: Random Web Surfer
1.6.11 Use Transition and RandomSurfer to determine the page ranks for the
eight-page graph shown below.
1.6.12 Use
Transition
page graph shown below.
3
0
5
1
4
7
2
Eight-page example
6
and Markov to determine the page ranks for the eight-
187
Elements of Programming
188
Creative Exercises
1.6.13 Matrix squaring. Write a program like Markov that computes page ranks
by repeatedly squaring the matrix, thus computing the sequence p, p 2, p 4, p 8, p 16,
and so forth. Verify that all of the rows in the matrix converge to the same values.
1.6.14 Random web. Write a generator for Transition that takes as commandline arguments a page count n and a link count m and prints to standard output n
followed by m random pairs of integers from 0 to n-1. (See Section 4.5 for a discussion of more realistic web models.)
1.6.15 Hubs and authorities. Add to your generator from the previous exercise a
fixed number of hubs, which have links pointing to them from 10% of the pages,
chosen at random, and authorities, which have links pointing from them to 10% of
the pages. Compute page ranks. Which rank higher, hubs or authorities?
1.6.16 Page ranks. Design a graph in which the highest-ranking page has fewer
links pointing to it than some other page.
1.6.17 Hitting time. The hitting time for a page is the expected number of moves
between times the random surfer visits the page. Run experiments to estimate the
hitting times for tiny.txt, compare hitting times with page ranks, formulate a
hypothesis about the relationship, and test your hypothesis on medium.txt.
1.6.18 Cover time. Write a program that estimates the time required for the random surfer to visit every page at least once, starting from a random page.
1.6.19 Graphical simulation. Create a graphical simulation where the size of the
dot representing each page is proportional to its page rank. To make your program
data driven, design a file format that includes coordinates specifying where each
page should be drawn. Test your program on medium.txt.
This page intentionally left blank
Chapter Two
Functions and Modules
2.1
2.2
2.3
2.4
Defining Functions . . . . . . . . . . .192
Libraries and Clients . . . . . . . . . .226
Recursion . . . . . . . . . . . . . . . . 262
Case Study: Percolation . . . . . . . . 300
T
his chapter centers on a construct that has as profound an impact on control
flow as do conditionals and loops: the function, which allows us to transfer control back and forth between different pieces of code. Functions (which are known
as static methods in Java) are important because they allow us to clearly separate
tasks within a program and because they provide a general mechanism that enables
us to reuse code.
We group functions together in modules, which we can compile independently. We use modules to break a computational task into subtasks of a reasonable size.
You will learn in this chapter how to build modules of your own and how to use
them, in a style of programming known as modular programming.
Some modules are developed with the primary intent of providing code that
can be reused later by many other programs. We refer to such modules as libraries.
In particular, we consider in this chapter libraries for generating random numbers,
analyzing data, and providing input/output for arrays. Libraries vastly extend the
set of operations that we use in our programs.
We pay special attention to functions that transfer control to themselves—a
process known as recursion. At first, recursion may seem counterintuitive, but it
allows us to develop simple programs that can address complex tasks that would
otherwise be much more difficult to carry out.
Whenever you can clearly separate tasks within programs, you should do so. We
repeat this mantra throughout this chapter, and end the chapter with a case study
showing how a complex programming task can be handled by breaking it into
smaller subtasks, then independently developing modules that interact with one
another to address the subtasks.
191
Functions and Modules
2.1 Defining Functions
The Java construct for implementing a function is known as the static method. The
modifier static distinguishes this kind of method from the kind discussed in
Chapter 3—we will apply it consistently for now and discuss the difference then.
You have actually been using static methods since the beginning of this book,
2.1.1 Harmonic numbers (revisited). . . 194
from mathematical functions such as
2.1.2 Gaussian functions . . . . . . . . . 203
Math.abs() and Math.sqrt() to all of
2.1.3 Coupon collector (revisited) . . . . 206
the methods in StdIn, StdOut, StdDraw,
2.1.4 Play that tune (revisited). . . . . . 213
and StdAudio. Indeed, every Java proPrograms in this section
gram that you have written has a static
method named main(). In this section,
you will learn how to define your own static methods.
In mathematics, a function maps an input value of one type (the domain) to
an output value of another type (the range). For example, the function f (x) = x 2
maps 2 to 4, 3 to 9, 4 to 16, and so forth. At first, we work with static methods that
implement mathematical functions, because they are so familiar. Many standard
mathematical functions are implemented in Java’s Math library, but scientists and
engineers work with a broad variety of mathematical functions, which cannot all
be included in the library. At the beginning of this section, you will learn how to
implement such functions on your own.
Later, you will learn that we can do more with static methods than implement
mathematical functions: static methods can have strings and other types as their
range or domain, and they can produce side effects such as printing output. We
also consider in this section how to use static methods to organize programs and
thus to simplify complicated programming tasks.
Static methods support a key concept that will pervade your approach to programming from this point forward: whenever you can clearly separate tasks within
programs, you should do so. We will be overemphasizing this point throughout this
section and reinforcing it throughout this book. When you write an essay, you break
it up into paragraphs; when you write a program, you will break it up into methods.
Separating a larger task into smaller ones is much more important in programming than in writing, because it greatly facilitates debugging, maintenance, and reuse, which are all critical in developing good software.
2.1 Defining Functions
193
Static methods As you know from using Java’s Math library, the use of static
methods is easy to understand. For example, when you write Math.abs(a-b) in a
program, the effect is as if you were to replace that code with the return value that
is produced by Java’s Math.abs() method when passed the expression a-b as an
argument. This usage is so intuitive that we have hardly needed to comment on
it. If you think about what the system has to do to create this effect, you will see
that it involves changing a program’s control flow. The implications of being able
to change the control flow in this way are as profound as doing so for conditionals
and loops.
You can define static methods other than main() in a .java file by specifying a method signature, followed by a sequence of statements that constitute the
method. We will consider the details shortly, but we begin with a simple example—
Harmonic (Program 2.1.1)—that illustrates how methods affect control flow. It
features a static method named harmonic() that takes an integer argument n and
returns the nth harmonic number (see Program 1.3.5).
Program 2.1.1 is superior to our original implementation for computing harmonic numbers (Program 1.3.5) because it clearly separates the two primary tasks
performed by the program: calculating harmonic numbers and interacting with
the user. (For purposes of illustration, Program 2.1.1 takes several command-line
arguments instead of just one.) Whenever you
can clearly separate tasks within programs, you
public class Harmonic
{
should do so.
Control flow. While
appeals to our
familiarity with mathematical functions, we will
examine it in detail so that you can think carefully about what a static method is and how it
operates. Harmonic comprises two static methods: harmonic() and main(). Even though
harmonic() appears first in the code, the first
statement that Java executes is, as usual, the
first statement in main(). The next few statements operate as usual, except that the code
harmonic(arg), which is known as a call on the
static method harmonic(), causes a transfer of
control to the first line of code in harmonic(),
each time that it is encountered. Moreover, Java
public static double harmonic(int n)
{
double sum = 0.0;
for (int i = 1; i <= n; i++)
sum += 1.0/i;
return sum;
}
Harmonic
public static void main(String[] args)
{
for (int i = 0; i < args.length; i++)
{
int arg = Integer.parseInt(args[i]);
double value = harmonic(arg);
StdOut.println(value);
}
}
}
Flow of control for a call on a static method
Functions and Modules
194
Program 2.1.1
Harmonic numbers (revisited)
public class Harmonic
{
public static double harmonic(int n)
{
double sum = 0.0;
for (int i = 1; i <= n; i++)
sum += 1.0/i;
return sum;
}
public static void main(String[] args)
{
for (int i = 0; i < args.length; i++)
{
int arg = Integer.parseInt(args[i]);
double value = harmonic(arg);
StdOut.println(value);
}
}
sum
cumulated sum
arg
value
argument
return value
}
This program defines two static methods, one named harmonic() that has integer argument n
and computes the nth harmonic numbers (see Program 1.3.5) and one named main(), which
tests harmonic() with integer arguments specified on the command line.
% java Harmonic 1 2 4
1.0
1.5
2.083333333333333
% java Harmonic 10 100 1000 10000
2.9289682539682538
5.187377517639621
7.485470860550343
9.787606036044348
initializes the parameter variable n in harmonic() to the value of arg in main()
at the time of the call. Then, Java executes the statements in harmonic() as usual, until it reaches a return statement, which transfers control back to the statement in main() containing the call on harmonic(). Moreover, the method call
harmonic(arg) produces a value—the value specified by the return statement,
which is the value of the variable sum in harmonic() at the time that the return
2.1 Defining Functions
195
statement is executed. Java then assigns this return value to the variable value. The
end result exactly matches our intuition: The first value assigned to value and
printed is 1.0—the value computed by code in harmonic() when the parameter
variable n is initialized to 1. The next value assigned to value and printed is 1.5—
the value computed by harmonic() when n is initialized to 2. The same process is
repeated for each command-line argument, transferring control back and forth
between harmonic() and main().
Function-call trace. One simple approach to following the
control flow through function calls is to imagine that each
function prints its name and argument value(s) when it is
called and its return value just before returning, with indentation added on calls and subtracted on returns. The result
enhances the process of tracing a program by printing the
values of its variables, which we have been using since Section 1.2. The added indentation exposes the flow of the control, and helps us check that each function has the effect that
we expect. Generally, adding calls on StdOut.println() to
trace any program’s control flow in this way is a fine way to
begin to understand what it is doing. If the return values
match our expectations, we need not trace the function code
in detail, saving us a substantial amount of work.
For the rest of this chapter, your programming will center
on creating and using static methods, so it is worthwhile to
consider in more detail their basic properties. Following that,
we will study several examples of function implementations
and applications.
i = 0
arg = 1
harmonic(1)
sum = 0.0
sum = 1.0
return 1.0
value = 1.0
i = 1
arg = 2
harmonic(2)
sum = 0.0
sum = 1.0
sum = 1.5
return 1.5
value = 1.5
i = 2
arg = 4
harmonic(4)
sum = 0.0
sum = 1.0
sum = 1.5
sum = 1.8333333333333333
sum = 2.083333333333333
return 2.083333333333333
value = 2.083333333333333
Function-call trace for
java Harmonic 1 2 4
Terminology. It is useful to draw a distinction between abstract concepts and Java mechanisms to implement them (the Java if statement
implements the conditional, the while statement implements the loop, and so
forth). Several concepts are rolled up in the idea of a mathematical function, and
there are Java constructs corresponding to each, as summarized in the table at the
top of the next page. While these formalisms have served mathematicians well for
centuries (and have served programmers well for decades), we will refrain from
considering in detail all of the implications of this correspondence and focus on
those that will help you learn to program.
Functions and Modules
196
concept
Java construct
description
function
static method
mapping
input value
argument
input to function
output value
return value
output from function
formula
method body
function definition
independent variable
parameter variable
symbolic placeholder for input value
When we use a symbolic name in a formula that defines a mathematical function
(such as f (x) = 1 + x + x2), the symbol x is a placeholder for some input value that
will be substituted into the formula to determine the output value. In Java, we use
a parameter variable as a symbolic placeholder and we refer to a particular input
value where the function is to be evaluated as an argument.
Static method definition. The first line of a static method definition, known as the
signature, gives a name to the method and to each parameter variable. It also specifies the type of each parameter variable and the return type of the method. The
signature consists of the keyword public; the keyword static; the return type; the
method name; and a sequence of zero or more parameter variable types and names,
separated by commas and enclosed in parentheses. We will discuss the meaning of
the public keyword in the next section and the meaning of the static keyword
in Chapter 3. (Technically, the signature in Java includes only the method name
and parameter types, but we leave that distinction for experts.) Following the signature is the body of the method,
argument parameter
method
return
enclosed in curly braces. The body signature
type
name
type
variable
consists of the kinds of statements
we discussed in Chapter 1. It also
public static double harmonic ( int n )
can contain a return statement,
{
which transfers control back to
double sum = 0.0;
local
the point where the static method variable
for (int i = 1; i <= n; i++)
was called and returns the result of
method
sum += 1.0/i;
body
return sum;
the computation or return value.
}
The body may declare local varireturn statement
ables, which are variables that are
Anatomy of a static method
available only inside the method
in which they are declared.
2.1 Defining Functions
Function calls. As you have already seen, a
197
for (int i = 0; i < args.length; i++)
static method call in Java is nothing more
{
than the method name followed by its arguarg = Integer.parseInt(args[i]);
ments, separated by commas and enclosed
double value = harmonic( arg );
in parentheses, in precisely the same form as
StdOut.prinln(value);
is customary for mathematical functions. As
argument
function call
}
noted in Section 1.2, a method call is an expression, so you can use it to build up more
Anatomy of a function call
complicated expressions. Similarly, an argument is an expression—Java evaluates the expression and passes the resulting value to the method. So, you can write code like
Math.exp(-x*x/2) / Math.sqrt(2*Math.PI) and Java knows what you mean.
Multiple arguments. Like a mathematical function, a Java static method can take
on more than one argument, and therefore can have more than one parameter
variable. For example, the following static method computes the length of the hypotenuse of a right triangle with sides of length a and b:
public static double hypotenuse(double a, double b)
{ return Math.sqrt(a*a + b*b); }
Although the parameter variables are of the same type in this case, in general they
can be of different types. The type and the name of each parameter variable are
declared in the function signature, with the declarations for each variable separated
by commas.
Multiple methods. You can define as many static methods as you want in a .java
file. Each method has a body that consists of a sequence of statements enclosed in
curly braces. These methods are independent and can appear in any order in the
file. A static method can call any other static method in the same file or any static
method in a Java library such as Math, as illustrated with this pair of methods:
public static double square(double a)
{ return a*a; }
public static double hypotenuse(double a, double b)
{ return Math.sqrt(square(a) + square(b)); }
Also, as we see in the next section, a static method can call static methods in other
files (provided they are accessible to Java). In Section 2.3, we consider the
ramifications of the idea that a static method can even call itself.
.java
Functions and Modules
198
Overloading. Static methods with different signatures are different static methods. For example, we often want to define the same operation for values of different
numeric types, as in the following static methods for computing absolute values:
public static int abs(int x)
{
if (x < 0) return -x;
else
return x;
}
public static double abs(double x)
{
if (x < 0.0) return -x;
else
return x;
}
These are two different methods, but are sufficiently similar so as to justify using the
same name (abs). Using the same name for two static methods whose signatures
differ is known as overloading, and is a common practice in Java programming. For
example, the Java Math library uses this approach to provide implementations of
Math.abs(), Math.min(), and Math.max() for all primitive numeric types. Another common use of overloading is to define two different versions of a method:
one that takes an argument and another that uses a default value for that argument.
Multiple return statements. You can put return statements in a method wherever you need them: control goes back to the calling program as soon as the first
statement is reached. This primality-testing function is an example of a
function that is natural to define using multiple return statements:
return
public static boolean isPrime(int n)
{
if (n < 2) return false;
for (int i = 2; i <= n/i; i++)
if (n % i == 0) return false;
return true;
}
Even though there may be multiple return statements, any static method returns a
single value each time it is invoked: the value following the first return statement
encountered. Some programmers insist on having only one return per method,
but we are not so strict in this book.
2.1 Defining Functions
absolute value of an
int value
public static int abs(int x)
{
if (x < 0) return -x;
else
return x;
}
absolute value of a
double value
public static double abs(double x)
{
if (x < 0.0) return -x;
else
return x;
}
public static boolean isPrime(int n)
{
if (n < 2) return false;
for (int i = 2; i <= n/i; i++)
if (n % i == 0) return false;
return true;
}
primality test
public static double hypotenuse(double a, double b)
{ return Math.sqrt(a*a + b*b); }
hypotenuse of
a right triangle
harmonic number
public static double harmonic(int n)
{
double sum = 0.0;
for (int i = 1; i <= n; i++)
sum += 1.0 / i;
return sum;
}
uniform random
integer in [0, n )
public static int uniform(int n)
{ return (int) (Math.random() * n);
draw a triangle
199
}
public static void drawTriangle(double x0, double y0,
double x1, double y1,
double x2, double y2 )
{
StdDraw.line(x0, y0, x1, y1);
StdDraw.line(x1, y1, x2, y2);
StdDraw.line(x2, y2, x0, y0);
}
Typical code for implementing functions (static methods)
Functions and Modules
200
Single return value. A Java method provides only one return value to the caller,
of the type declared in the method signature. This policy is not as restrictive as it
might seem because Java data types can contain more information than the value
of a single primitive type. For example, you will see later in this section that you can
use arrays as return values.
Scope. The scope of a variable is the part of the program that can refer to that variable by name. The general rule in Java is that the scope of the variables declared in
a block of statements is limited to the statements in that block. In particular, the
scope of a variable declared in a static method is limited to that method’s body.
Therefore, you cannot refer to a variable in one static method that is declared in
another. If the method includes smaller blocks—for example, the body of an if or
a for statement—the scope of any variables declared in one of those blocks is limited to just the statements within that block. Indeed, it is common practice to use
the same variable names in independent blocks of code. When we do so, we are declaring different independent variables. For example, we have been following this
practice when we use an index i in two different for loops in the same program. A
guiding principle when designing software is that each variable should be declared
so that its scope is as small as possible. One of the important reasons that we use
static methods is that they ease debugging by limiting variable scope.
scope of
n and sum
public class Harmonic
{
public static double harmonic(int n)
{
double sum = 0.0;
for (int i = 1; i <= n; i++)
sum += 1.0/i;
return sum;
}
this code cannot refer to
args[], arg, or value
scope of i
two different
variables named i
public static void main(String[] args)
{
for (int i = 0; i < args.legnth; i++)
{
int arg = Integer.parseInt(args[i]);
double value = harmonic(arg);
StdOut.println(value);
}
}
scope of
arg
}
Scope of local and parameter variables
scope of i
and args
this code cannot refer
to n or sum
2.1 Defining Functions
Side effects. In mathematics, a function maps one or more input values to some
output value. In computer programming, many functions fit that same model: they
accept one or more arguments, and their only purpose is to return a value. A pure
function is a function that, given the same arguments, always returns the same value,
without producing any observable side effects, such as consuming input, producing
output, or otherwise changing the state of the system. The functions harmonic(),
abs(), isPrime(), and hypotenuse() are examples of pure functions.
However, in computer programming it is also useful to define functions that
do produce side effects. In fact, we often define functions whose only purpose is to
produce side effects. In Java, a static method may use the keyword void as its return
type, to indicate that it has no return value. An explicit return is not necessary in
a void static method: control returns to the caller after Java executes the method’s
last statement.
For example, the static method StdOut.println() has the side effect of
printing the given argument to standard output (and has no return value). Similarly, the following static method has the side effect of drawing a triangle to standard drawing (and has no specified return value):
public static void drawTriangle(double x0, double y0,
double x1, double y1,
double x2, double y2)
{
StdDraw.line(x0, y0, x1, y1);
StdDraw.line(x1, y1, x2, y2);
StdDraw.line(x2, y2, x0, y0);
}
It is generally poor style to write a static method that both produces side effects
and returns a value. One notable exception arises in functions that read input. For
ex-ample, StdIn.readInt() both returns a value (an integer) and produces a side
effect (consuming one integer from standard input). In this book, we use void
static methods for two primary purposes:
• For I/O, using StdIn, StdOut, StdDraw, and StdAudio
• To manipulate the contents of arrays
You have been using void static methods for output since main() in HelloWorld,
and we will discuss their use with arrays later in this section. It is possible in Java to
write methods that have other side effects, but we will avoid doing so until Chapter
3, where we do so in a specific manner supported by Java.
201
202
Functions and Modules
Implementing mathematical functions Why not just use the methods that
are defined within Java, such as Math.sqrt()? The answer to this question is that
we do use such implementations when they are present. Unfortunately, there are an
unlimited number of mathematical functions that we may wish to use and only a
small set of functions in the library. When you encounter a mathematical function
that is not in the library, you need to implement a corresponding static method.
As an example, we consider the kind of code required for a familiar and important application that is of interest to many high school and college students in
the United States. In a recent year, more than 1 million students took a standard
college entrance examination. Scores range from 400 (lowest) to 1600 (highest) on
the multiple-choice parts of the test. These scores play a role in making important
decisions: for example, student athletes are required to have a score of at least 820,
and the minimum eligibility requirement for certain academic scholarships is 1500.
What percentage of test takers are ineligible for athletics? What percentage are eligible for the scholarships?
Two functions from statistics enable us to compute
probability density function
accurate answers to these questions. The Gaussian (nor1
mal) probability density function is characterized by the
familiar bell-shaped curve and defined by the formula
(x) ex 2 2 2 . The Gaussian cumulative distribution
function F(z) is defined to be the area under the curve de (x)
fined by f(x) above the x-axis and to the left of the vertical
area is (z0)
line x = z. These functions play an important role in science,
engineering, and finance because they arise as accurate
x
0
z0
models throughout the natural world and because they are
essential in understanding experimental error.
cumulative distribution function
In particular, these functions are known to accurately
1
describe the distribution of test scores in our example, as a
function of the mean (average value of the scores) and the
standard deviation (square root of the average of the sum
of the squares of the differences between each score and the
mean), which are published each year. Given the mean
(z0)
and the standard deviation of the test scores, the percentage of students with scores less than a given value z is closely
z
0
approximated by the function ((z )/). Static methz0
ods to calculate and are not available in Java’s Math
Gaussian probability functions
library, so we need to develop our own implementations.
2.1 Defining Functions
Program 2.1.2
203
Gaussian functions
public class Gaussian
{ // Implement Gaussian (normal) distribution functions.
public static double pdf(double x)
{
return Math.exp(-x*x/2) / Math.sqrt(2*Math.PI);
}
public static double cdf(double z)
{
if (z < -8.0) return 0.0;
if (z > 8.0) return 1.0;
double sum = 0.0;
double term = z;
for (int i = 3; sum != sum + term; i += 2)
{
sum = sum + term;
term = term * z * z / i;
}
return 0.5 + pdf(z) * sum;
}
sum
term
cumulated sum
current term
public static void main(String[] args)
{
double z
= Double.parseDouble(args[0]);
double mu
= Double.parseDouble(args[1]);
double sigma = Double.parseDouble(args[2]);
StdOut.printf("%.3f\n", cdf((z - mu) / sigma));
}
}
This code implements the Gaussian probability density function (pdf) and Gaussian cumulative distribution function (cdf), which are not implemented in Java’s Math library. The pdf()
implementation follows directly from its definition, and the cdf() implementation uses a Taylor series and also calls pdf() (see accompanying text and Exercise 1.3.38).
% java Gaussian 820 1019 209
0.171
% java Gaussian 1500 1019 209
0.989
% java Gaussian 1500 1025 231
0.980
204
Functions and Modules
Closed form. In the simplest situation, we have a closed-form mathematical formula defining our function in terms of functions that are implemented in the library. This situation is the case for —the Java Math library includes methods to
compute the exponential and the square root functions (and a constant value for
), so a static method pdf() corresponding to the mathematical definition is easy
to implement (see Program 2.1.2).
No closed form. Otherwise, we may need a more complicated algorithm to compute function values. This situation is the case for —no closed-form expression
exists for this function. Such algorithms sometimes follow immediately from Taylor series approximations, but developing reliably accurate implementations of
mathematical functions is an art that needs to be addressed carefully, taking advantage of the knowledge built up in mathematics over the past several centuries. Many
different approaches have been studied for evaluating . For example, a Taylor
series approximation to the ratio of and turns out to be an effective basis for
evaluating the function:
F(z) 12 f(z) ( z z 3 3 z 5 (35) z 7 (357) . . . )
This formula readily translates to the Java code for the static method cdf() in
Program 2.1.2. For small (respectively large) z, the value is extremely close to 0
(respectively 1), so the code directly returns 0 (respectively 1); otherwise, it uses the
Taylor series to add terms until the sum converges.
Running Gaussian with the appropriate arguments on the command line
tells us that about 17% of the test takers were ineligible for athletics and that only
about 1% qualified for the scholarship. In a year when the mean was 1025 and the
standard deviation 231, about 2% qualified for the scholarship.
Computing with mathematical functions of all kinds has always played a central
role in science and engineering. In a great many applications, the functions that
you need are expressed in terms of the functions in Java’s Math library, as we have
just seen with pdf(), or in terms of Taylor series approximations that are easy to
compute, as we have just seen with cdf(). Indeed, support for such computations
has played a central role throughout the evolution of computing systems and programming languages. You will find many examples on the booksite and throughout
this book.
2.1 Defining Functions
Using static methods to organize code
Beyond evaluating mathematical
functions, the process of calculating an output value on the basis of an input value
is important as a general technique for organizing control flow in any computation.
Doing so is a simple example of an extremely important principle that is a prime
guiding force for any good programmer: whenever you can clearly separate tasks
within programs, you should do so.
Functions are natural and universal for expressing computational tasks. Indeed, the “bird’s-eye view” of a Java program that we began with in Section 1.1 was
equivalent to a function: we began by thinking of a Java program as a function that
transforms command-line arguments into an output string. This view expresses
itself at many different levels of computation. In particular, it is generally the case
that a long program is more naturally expressed in terms of functions instead of
as a sequence of Java assignment, conditional, and loop statements. With the ability to define functions, we can better organize our programs by defining functions
within them when appropriate.
For example, Coupon (Program 2.1.3) is a version of CouponCollector
(Program 1.4.2) that better separates the individual components of the computation. If you study Program 1.4.2, you will identify three separate tasks:
• Given n, compute a random coupon value.
• Given n, do the coupon collection experiment.
• Get n from the command line, and then compute and print the result.
Coupon rearranges the code in CouponCollector to reflect the reality that these
three functions underlie the computation. With this organization, we could change
getCoupon() (for example, we might want to draw the random numbers from a
different distribution) or main() (for example, we might want to take multiple
inputs or run multiple experiments) without worrying about the effect of any
changes in collectCoupons().
Using static methods isolates the implementation of each component of the
collection experiment from others, or encapsulates them. Typically, programs have
many independent components, which magnifies the benefits of separating them
into different static methods. We will discuss these benefits in further detail after
we have seen several other examples, but you certainly can appreciate that it is better to express a computation in a program by breaking it up into functions, just as it
is better to express an idea in an essay by breaking it up into paragraphs. Whenever
you can clearly separate tasks within programs, you should do so.
205
Functions and Modules
206
Program 2.1.3
Coupon collector (revisited)
public class Coupon
{
public static int getCoupon(int n)
{ // Return a random integer between 0 and n-1.
return (int) (Math.random() * n);
}
public static int collectCoupons(int n)
{ // Collect coupons until getting one of each value
// and return the number of coupons collected.
boolean[] isCollected = new boolean[n];
int count = 0, distinct = 0;
while (distinct < n)
{
n
# coupon values (0 to n-1)
int r = getCoupon(n);
isCollected[i] has coupon i been collected?
count++;
count
# coupons collected
if (!isCollected[r])
distinct
# distinct coupons collected
distinct++;
r
random coupon
isCollected[r] = true;
}
return count;
}
public static void main(String[] args)
{ // Collect n different coupons.
int n = Integer.parseInt(args[0]);
int count = collectCoupons(n);
StdOut.println(count);
}
}
This version of Program 1.4.2 illustrates the style of encapsulating computations in static methods. This code has the same effect as CouponCollector, but better separates the code into its
three constituent pieces: generating a random integer between 0 and n-1, running a coupon
collection experiment, and managing the I/O.
% java Coupon 1000
6522
% java Coupon 10000
105798
% java Coupon 1000
6481
% java Coupon 1000000
12783771
2.1 Defining Functions
Passing arguments and returning values Next, we examine the specifics of
Java’s mechanisms for passing arguments to and returning values from functions.
These mechanisms are conceptually very simple, but it is worthwhile to take the
time to understand them fully, as the effects are actually profound. Understanding argument-passing and return-value mechanisms is key to learning any new
programming language.
Pass by value. You can use parameter variables anywhere in the code in the body
of the function in the same way you use local variables. The only difference between a parameter variable and a local variable is that Java evaluates the argument
provided by the calling code and initializes the parameter variable with the resulting value. This approach is known as pass by value. The method works with the
value of its arguments, not the arguments themselves. One consequence of this
approach is that changing the value of a parameter variable within a static method
has no effect on the calling code. (For clarity, we do not change parameter variables in the code in this book.) An alternative approach known as pass by reference,
where the method works directly with the calling code’s arguments, is favored in
some programming environments.
A static method can take an array as an argument or return an array to the caller.
This capability is a special case of Java’s object orientation, which is the subject of
Chapter 3. We consider it in the present context because the basic mechanisms
are easy to understand and to use, leading us to compact solutions to a number of
problems that naturally arise when we use arrays to help us process large amounts
of data.
Arrays as arguments. When a static method takes an array as an argument, it
implements a function that operates on an arbitrary number of values of the same
type. For example, the following static method computes the mean (average) of an
array of double values:
public static double mean(double[] a)
{
double sum = 0.0;
for (int i = 0; i < a.length; i++)
sum += a[i];
return sum / a.length;
}
207
Functions and Modules
208
We have been using arrays as arguments since our first program. The code
public static void main(String[] args)
defines main() as a static method that takes an array of strings as an argument and
returns nothing. By convention, the Java system collects the strings that you type
after the program name in the java command into an array and calls main() with
that array as argument. (Most programmers use the name args for the parameter
variable, even though any name at all would do.) Within main(), we can manipulate that array just like any other array.
Side effects with arrays. It is often the case that the purpose of a static method
that takes an array as argument is to produce a side effect (change values of array
elements). A prototypical example of such a method is one that exchanges the values at two given indices in a given array. We can adapt the code that we examined
at the beginning of Section 1.4:
public static void exchange(String[] a, int i, int j)
{
String temp = a[i];
a[i] = a[j];
a[j] = temp;
}
This implementation stems naturally from the Java array representation. The parameter variable in exchange() is a reference to the array, not a copy of the array
values: when you pass an array as an argument to a method, the method has an
opportunity to reassign values to the elements in that array. A second prototypical
example of a static method that takes an array argument and produces side effects is one that randomly shuffles the values in the array, using this version of the
algorithm that we examined in Section 1.4 (and the exchange() and uniform()
methods considered earlier in this section):
public static void shuffle(String[] a)
{
int n = a.length;
for (int i = 0; i < n; i++)
exchange(a, i, i + uniform(n-i));
}
2.1 Defining Functions
find the maximum
of the array values
dot product
exchange the values of
two elements
in an array
public static double max(double[] a)
{
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < a.length; i++)
if (a[i] > max) max = a[i];
return max;
}
public static double dot(double[] a, double[] b)
{
double sum = 0.0;
for (int i = 0; i < a.length; i++)
sum += a[i] * b[i];
return sum;
}
public static void exchange(String[] a, int i, int j)
{
String temp = a[i];
a[i] = a[j];
a[j] = temp;
}
print a onedimensional array
(and its length)
public static void print(double[] a)
{
StdOut.println(a.length);
for (int i = 0; i < a.length; i++)
StdOut.println(a[i]);
}
read a 2D array
of double values
(with dimensions)
in row-major order
public static double[][] readDouble2D()
{
int m = StdIn.readInt();
int n = StdIn.readInt();
double[][] a = new double[m][n];
for (int i = 0; i < m; i++)
for (int j = 0; j < n; j++)
a[i][j] = StdIn.readDouble();
return a;
}
Typical code for implementing functions with array arguments or return values
209
210
Functions and Modules
Similarly, we will consider in Section 4.2 methods that sort an array (rearrange its
values so that they are in order). All of these examples highlight the basic fact that
the mechanism for passing arrays in Java is call by value with respect to the array
reference but call by reference with respect to the array elements. Unlike primitivetype arguments, the changes that a method makes to the elements of an array are
reflected in the client program. A method that takes an array as its argument cannot change the array itself—the memory location, length, and type of the array are
the same as they were when the array was created—but a method can assign different values to the elements in the array.
Arrays as return values. A method that sorts, shuffles, or otherwise modifies an
array taken as an argument does not have to return a reference to that array, because it is changing the elements of a client array, not a copy. But there are many
situations where it is useful for a static method to provide an array as a return value.
Chief among these are static methods that create arrays for the purpose of returning multiple values of the same type to a client. For example, the following static
method creates and returns an array of the kind used by StdAudio (see Program
1.5.7): it contains values sampled from a sine wave of a given frequency (in hertz)
and duration (in seconds), sampled at the standard 44,100 samples per second.
public static double[] tone(double hz, double t)
{
int SAMPLING_RATE = 44100;
int n = (int) (SAMPLING_RATE * t);
double[] a = new double[n+1];
for (int i = 0; i <= n; i++)
a[i] = Math.sin(2 * Math.PI * i * hz / SAMPLING_RATE);
return a;
}
In this code, the length of the array returned depends on the duration: if the given
duration is t, the length of the array is about 44100*t. With static methods like this
one, we can write code that treats a sound wave as a single entity (an array containing sampled values), as we will see next in Program 2.1.4.
2.1 Defining Functions
Example: superposition of sound waves As discussed in Section 1.5, the
simple audio model that we studied there needs to be embellished to create sound
that resembles the sound produced by a musical instrument. Many different embellishments are possible; with static methods we can systematically apply them to
produce sound waves that are far more complicated than the simple sine waves that
we produced in Section 1.5. As an illustration of the effective use of static methods
to solve an interesting computational problem, we consider a program that has essentially the same functionality as PlayThatTune (Program 1.5.7), but adds harmonic tones one octave above and one octave below each note to produce a more
realistic sound.
Chords and harmonics. Notes like concert A have a pure sound that is not very
musical, because the sounds that you are accustomed to hearing have many other
components. The sound from the guitar string echoes off the wooden part of the
instrument, the walls of the room that
A
440.00
you are in, and so forth. You may think of
C♯
554.37
E
such effects as modifying the basic sine
659.26
wave.
For example, most musical instruA major chord
ments produce harmonics (the same note
in different octaves and not as loud), or
A
you might play chords (multiple notes
440.00
A
220.00
at the same time). To combine multiple
A
880.00
sounds, we use superposition: simply
concert A with harmonics
add the waves together and rescale to
make sure that all values stay between
1 and 1. As it turns out, when we suSuperposing waves to make composite sounds
perpose sine waves of different frequencies in this way, we can get arbitrarily
complicated waves. Indeed, one of the triumphs of 19th-century mathematics was
the development of the idea that any smooth periodic function can be expressed as
a sum of sine and cosine waves, known as a Fourier series. This mathematical idea
corresponds to the notion that we can create a large range of sounds with musical instruments or our vocal cords and that all sound consists of a composition of
various oscillating curves. Any sound corresponds to a curve and any curve corresponds to a sound, and we can create arbitrarily complex curves with superposition.
211
Functions and Modules
212
Weighted superposition. Since we represent sound waves by arrays of numbers
that represent their values at the same sample points, superposition is simple to
implement: we add together the values at each sample point to produce the combined result and then rescale. For greater control, we specify a relative weight for
each of the two waves to be added, with the property that the weights are positive
and sum to 1. For example, if we want the first sound to have three times the effect
of the second, we would assign the first a weight of 0.75 and the second a weight of
0.25. Now, if one wave is in an array a[] with relative weight awt and the other is
in an array b[] with relative weight bwt, we compute their weighted sum with the
following code:
double[] c = new double[a.length];
for (int i = 0; i < a.length; i++)
c[i] = a[i]*awt + b[i]*bwt;
The conditions that the weights are positive and sum to 1 ensure that this operation preserves our convention of keeping the values of all of our waves between 1
and 1.
0.982
lo = tone(220, 1.0/220.0)
lo[44] = 0.982
hi = tone(880, 1.0/220.0)
hi[44] = -0.693
-0.693
harmonics = superpose(lo, hi, 0.5, 0.5)
harmonics[44]
= 0.5*lo[44] + 0.5*hi[44]
= 0.5*0.982 + 0.5*0.693
= 0.144
0.144
concertA = tone(440, 1.0/220.0)
0.374
concertA[44] = 0.374
superpose(harmonics, concertA, 0.5, 0.5)
0.259
0.5*harmonics[44] + 0.5*concertA[44])
= 0.5*.144 + 0.5*0.374
= 0.259
44
Adding harmonics to concert A (1/220 second at 44,100 samples/second)
2.1 Defining Functions
Program 2.1.4
213
Play that tune (revisited)
public class PlayThatTuneDeluxe
{
public static double[] superpose(double[] a, double[] b,
double awt, double bwt)
{ // Weighted superposition of a and b.
hz
frequency
double[] c = new double[a.length];
a[] pure tone
for (int i = 0; i < a.length; i++)
c[i] = a[i]*awt + b[i]*bwt;
hi[] upper harmonic
return c;
lo[] lower harmonic
}
h[] tone with harmonics
public static double[] tone(double hz, double t)
{ /* see text */ }
public static double[] note(int pitch, double t)
{ // Play note of given pitch, with harmonics.
double hz = 440.0 * Math.pow(2, pitch / 12.0);
double[] a = tone(hz, t);
double[] hi = tone(2*hz, t);
double[] lo = tone(hz/2, t);
double[] h = superpose(hi, lo, 0.5, 0.5);
return superpose(a, h, 0.5, 0.5);
}
public static void main(String[] args)
{ // Read and play a tune, with harmonics.
while (!StdIn.isEmpty())
{ // Read and play a note, with harmonics.
int pitch = StdIn.readInt();
double duration = StdIn.readDouble();
double[] a = note(pitch, duration);
StdAudio.play(a);
}
}
}
This code embellishes the sounds produced by Program 1.5.7 by using static methods to create
harmonics, which results in a more realistic sound than the pure tone.
% more elise.txt
7 0.25
6 0.25
7 0.25
6 0.25
...
% java PlayThatTuneDeluxe < elise.txt
214
Functions and Modules
Program 2.1.4 is an implementation that applies these concepts to produce a more
realistic sound than that produced by Program 1.5.7. To do so, it makes use of functions to divide the computation into four parts:
• Given a frequency and duration, create a pure tone.
• Given two sound waves and relative weights, superpose them.
• Given a pitch and duration, create a note with harmonics.
• Read and play a sequence of pitch/duration pairs from standard input.
These tasks are each amenable to
implementation as a function, with
public class PlayThatTuneDeluxe
all of the functions then depend{
public static double[] superpose
ing on one another. Each function
(double[] a, double[] b,
double awt, double bwt)
{
is well defined and straightforward
double[] c = new double[a.length];
for (int i = 0; i < a.length; i++)
to implement. All of them (and
c[i] = a[i]*awt + b[i]*bwt;
return c;
StdAudio) represent sound as a se}
quence of floating-point numbers
kept in an array, corresponding to
public static double[] tone(double hz, double t)
{
int RATE = 44100;
sampling a sound wave at 44,100
int n = (int) (RATE * t);
double[] a = new double[n+1];
samples per second.
for (int i = 0; i <= n; i++)
a[i] = Math.sin(2 * Math.PI * i * hz / RATE);
Up to this point, the use
return a;
}
of functions has been somewhat
of a notational convenience. For
public static double[] note(int pitch, double t)
{
example, the control flow in
double hz = 440.0 * Math.pow(2, pitch / 12.0);
double[] a = tone(hz, t);
Program 2.1.1–2.1.3 is simple—
double[] hi = tone(2*hz, t);
each function is called in just one
double[] lo = tone(hz/2, t);
place in the code. By contrast,
double[] h = superpose(hi, lo, .5, .5);
PlayThatTuneDeluxe
(Program
return superpose(a, h, .5, .5);
2.1.4) is a convincing example of
}
the effectiveness of defining funcpublic static void main(String[] args)
tions to organize a computation
{
while (!StdIn.isEmpty())
because the functions are each
{
int pitch = StdIn.readInt();
called multiple times. For examdouble duration = StdIn.readDouble();
double[] a = note(pitch, duration);
ple, the function note() calls the
StdAudio.play(a);
}
function tone() three times and
}
the function sum() twice. With}
out functions methods, we would
need multiple copies of the code in
Flow of control among several static methods
2.1 Defining Functions
and sum(); with functions, we can deal directly with concepts close to the
application. Like loops, functions have a simple but profound effect: one sequence
of statements (those in the method definition) is executed multiple times during
the execution of our program—once for each time the function is called in the
control flow in main().
tone()
Functions (static methods) are important because they give us the ability to extend
the Java language within a program. Having implemented and debugged functions such as harmonic(), pdf(), cdf(), mean(), abs(), exchange(), shuffle(),
isPrime(), uniform(), superpose(), note(), and tone(), we can use them almost as if they were built into Java. The flexibility to do so opens up a whole new
world of programming. Before, you were safe in thinking about a Java program
as a sequence of statements. Now you need to think of a Java program as a set of
static methods that can call one another. The statement-to-statement control flow
to which you have been accustomed is still present within static methods, but programs have a higher-level control flow defined by static method calls and returns.
This ability enables you to think in terms of operations called for by the application,
not just the simple arithmetic operations on primitive types that are built into Java.
Whenever you can clearly separate tasks within programs, you should do so. The
examples in this section (and the programs throughout the rest of the book) clearly
illustrate the benefits of adhering to this maxim. With static methods, we can
• Divide a long sequence of statements into independent parts.
• Reuse code without having to copy it.
• Work with higher-level concepts (such as sound waves).
This produces code that is easier to understand, maintain, and debug than a long
program composed solely of Java assignment, conditional, and loop statements. In
the next section, we discuss the idea of using static methods defined in other programs, which again takes us to another level of programming.
215
Functions and Modules
216
Q&A
Q. What happens if I leave out the keyword static when defining a static method?
A. As usual, the best way to answer a question like this is to try it yourself and
see what happens. Here is the result of omitting the static modifier from
harmonic() in Harmonic:
Harmonic.java:15: error: non-static method harmonic(int)
cannot be referenced from a static context
double value = harmonic(arg);
^
1 error
Non-static methods are different from static methods. You will learn about the
former in Chapter 3.
Q. What happens if I write code after a return statement?
A. Once a return statement is reached, control immediately returns to the caller,
so any code after a return statement is useless. Java identifies this situation as a
compile-time error, reporting unreachable code.
Q. What happens if I do not include a return statement?
A. There is no problem, if the return type is
void. In this case, control will return to the caller after the last statement. When the return type is not void, Java
will report a missing return statement compile-time error if there is any path
through the code that does not end in a return statement.
Q. Why do I need to use the return type void? Why not just omit the return type?
A. Java requires it; we have to include it. Second-guessing a decision made by a
programming-language designer is the first step on the road to becoming one.
Q. Can I return from a void function by using return? If so, which return value
should I use?
A. Yes. Use the statement return; with no return value.
2.1 Defining Functions
Q. This issue with side effects and arrays passed as arguments is confusing. Is it
really all that important?
A. Yes. Properly controlling side effects is one of a programmer’s most important
tasks in large systems. Taking the time to be sure that you understand the difference
between passing a value (when arguments are of a primitive type) and passing a
reference (when arguments are arrays) will certainly be worthwhile. The very same
mechanism is used for all other types of data, as you will learn in Chapter 3.
Q. So why not just eliminate the possibility of side effects by making all arguments
pass by value, including arrays?
A. Think of a huge array with, say, millions of elements. Does it make sense to copy
all of those values for a static method that is going to exchange just two of them?
For this reason, most programming languages support passing an array to a function without creating a copy of the array elements—Matlab is a notable exception.
Q. In which order does Java evaluate method calls?
A. Regardless of operator precedence or associativity, Java evaluates subexpressions (including method calls) and argument lists from left to right. For example,
when evaluating the expression
f1() + f2() * f3(f4(), f5())
Java calls the methods in the order f1(), f2(), f4(), f5(), and f3(). This is most
relevant for methods that produce side effects. As a matter of style, we avoid writing code that depends on the order of evaluation.
217
Functions and Modules
218
Exercises
2.1.1 Write a static method max3() that takes three int arguments and returns
the value of the largest one. Add an overloaded function that does the same thing
with three double values.
2.1.2 Write a static method odd() that takes three boolean arguments and returns
true if
an odd number of the argument values are true, and false otherwise.
2.1.3 Write a static method majority() that takes three boolean arguments and
returns true if at least two of the argument values are true, and false otherwise.
Do not use an if statement.
2.1.4 Write a static method eq() that takes two int arrays as arguments and returns true if the arrays have the same length and all corresponding pairs of of elements are equal, and false otherwise.
2.1.5 Write a static method areTriangular() that takes three double arguments
and returns true if they could be the sides of a triangle (none of them is greater
than or equal to the sum of the other two). See Exercise 1.2.15.
2.1.6 Write a static method
sigmoid() that takes a double argument x and returns the double value obtained from the formula 1 (1 + ex).
2.1.7 Write a static method sqrt() that takes a double argument and returns the
square root of that number. Use Newton’s method (see Program 1.3.6) to compute
the result.
2.1.8 Give the function-call trace for java
Harmonic 3 5
2.1.9 Write a static method lg() that takes a double argument n and returns the
base-2 logarithm of n. You may use Java’s Math library.
2.1.10 Write a static method lg() that takes an int argument n and returns the
largest integer not larger than the base-2 logarithm of n. Do not use the Math library.
2.1.11 Write a static method signum() that takes an int argument n and returns
-1 if n is less than 0, 0 if n is equal to 0, and +1 if n is greater than 0.
2.1 Defining Functions
2.1.12 Consider the static method duplicate() below.
public static String duplicate(String s)
{
String t = s + s;
return t;
}
What does the following code fragment do?
String s = "Hello";
s = duplicate(s);
String t = "Bye";
t = duplicate(duplicate(duplicate(t)));
StdOut.println(s + t);
2.1.13 Consider the static method cube() below.
public static void cube(int i)
{
i = i * i * i;
}
How many times is the following for loop iterated?
for (int i = 0; i < 1000; i++)
cube(i);
Answer : Just 1,000 times. A call to cube() has no effect on the client code. It changes the value of its local parameter variable i, but that change has no effect on the i
in the for loop, which is a different variable. If you replace the call to cube(i) with
the statement i = i * i * i; (maybe that was what you were thinking), then
the loop is iterated five times, with i taking on the values 0, 1, 2, 9, and 730 at the
beginning of the five iterations.
219
Functions and Modules
220
2.1.14 The following checksum formula is widely used by banks and credit card
companies to validate legal account numbers:
d0 f (d1) d2 f (d3) d4 f (d5) … = 0 (mod 10)
The di are the decimal digits of the account number and f (d) is the sum of the
decimal digits of 2d (for example, f (7) = 5 because 2 7 = 14 and 1 4 = 5). For
example, 17,327 is valid because 1 + 5 + 3 + 4 + 7 = 20, which is a multiple of
10. Implement the function f and write a program to take a 10-digit integer as a
command-line argument and print a valid 11-digit number with the given integer
as its first 10 digits and the checksum as the last digit.
2.1.15 Given two stars with angles of declination and right ascension (d1, a1) and
(d2, a2), the angle they subtend is given by the formula
2 arcsin((sin2(d/2) + cos (d1)cos(d2)sin2(a/2))1/2)
where a1 and a2 are angles between 180 and 180 degrees, d1 and d2 are angles
between 90 and 90 degrees, a = a2 a1, and d = d2 d1. Write a program to take
the declination and right ascension of two stars as command-line arguments and
print the angle they subtend. Hint : Be careful about converting from degrees to
radians.
2.1.16 Write a static method scale() that takes a double array as its argument
and has the side effect of scaling the array so that each element is between 0 and
1 (by subtracting the minimum value from each element and then dividing each
element by the difference between the minimum and maximum values). Use the
max() method defined in the table in the text, and write and use a matching min()
method.
2.1.17 Write a static method reverse() that takes an array of strings as its argument and returns a new array with the strings in reverse order. (Do not change the
order of the strings in the argument array.) Write a static method reverseInplace()
that takes an array of strings as its argument and produces the side effect of reversing the order of the strings in the argument array.
2.1 Defining Functions
221
2.1.18 Write a static method
readBoolean2D() that reads a two-dimensional
boolean matrix (with dimensions) from standard input and returns the resulting
two-dimensional array.
2.1.19 Write a static method
histogram() that takes an int array a[] and an
integer m as arguments and returns an array of length m whose ith element is the
number of times the integer i appeared in a[]. Assuming the values in a[] are
all between 0 and m-1, the sum of the values in the returned array should equal
a.length.
2.1.20 Assemble code fragments in this section and in Section 1.4 to develop a
program that takes an integer command-line argument n and prints n five-card
hands, separated by blank lines, drawn from a randomly shuffled card deck, one
card per line using card names like Ace of Clubs.
2.1.21 Write a static method multiply() that takes two square matrices of the
same dimension as arguments and produces their product (another square matrix
of that same dimension). Extra credit : Make your program work whenever the
number of columns in the first matrix is equal to the number of rows in the second
matrix.
2.1.22 Write a static method
that takes a boolean array as its argument
and returns true if any of the elements in the array is true, and false otherwise.
Write a static method all() that takes an array of boolean values as its argument
and returns true if all of the elements in the array are true, and false otherwise.
any()
2.1.23 Develop a version of getCoupon() that better models the situation when
one of the coupons is rare: choose one of the n values at random, return that value
with probability 1 /(1,000n), and return all other values with equal probability. Extra credit : How does this change affect the expected number of coupons that need
to be collected in the coupon collector problem?
2.1.24 Modify PlayThatTune to add harmonics two octaves away from each note,
with half the weight of the one-octave harmonics.
Functions and Modules
222
Creative Exercises
2.1.25 Birthday problem. Develop a class with appropriate static methods for
studying the birthday problem (see Exercise 1.4.38).
2.1.26 Euler’s totient function. Euler’s totient function is an important function
in number theory: (n) is defined as the number of positive integers less than or
equal to n that are relatively prime with n (no factors in common with n other than
1). Write a class with a static method that takes an integer argument n and returns
(n), and a main() that takes an integer command-line argument, calls the method
with that argument, and prints the resulting value.
2.1.27 Harmonic numbers. Write a program Harmonic that contains three static
methods harmoinc(), harmoincSmall(), and harmonicLarge() for computing the harmonic numbers. The harmonicSmall() method should just compute
the sum (as in Program 1.3.5), the harmonicLarge() method should use the approximation Hn = loge(n ) 1/(2n ) 1/(12n 2) 1/(120n 4) (the number
= 0.577215664901532... is known as Euler’s constant), and the harmonic() method should call harmonicSmall() for n < 100 and harmonicLarge() otherwise.
2.1.28 Black–Scholes option valuation. The Black–Scholes formula supplies
the theoretical value of a European call option on a stock that pays no dividends, given the current stock price s, the exercise price x, the continuously compounded risk-free interest rate r, the volatility , and the time (in years) to maturity t. The Black–Scholes value is given by the formula s F(a) x e r t F(b),
where F(z) is the Gaussian cumulative distribution function, a = (ln(s x)
(r 2 2) t) / (t), and b = a t. Write a program that takes s, r, , and t from
the command line and prints the Black–Scholes value.
2.1.29 Fourier spikes. Write a program that takes a command-line argument n
and plots the function
(cos(t) cos(2 t) cos(3 t) … + cos(n t)) / n
for 500 equally spaced samples of t from 10 to 10 (in radians). Run your program
for n 5 and n 500. Note : You will observe that the sum converges to a spike
(0 everywhere except a single value). This property is the basis for a proof that any
smooth function can be expressed as a sum of sinusoids.
2.1 Defining Functions
223
2.1.30 Calendar. Write a program
Calendar that takes two integer commandline arguments m and y and prints the monthly calendar for month m of year y, as
in this example:
% java Calendar 2
February 2009
S M Tu W Th F
1 2 3 4 5 6
8 9 10 11 12 13
15 16 17 18 19 20
22 23 24 25 26 27
2009
S
7
14
21
28
Hint: See LeapYear (Program 1.2.4) and Exercise 1.2.29.
2.1.31 Horner’s method. Write a class
Horner with a method evaluate() that
takes a floating-point number x and array p[] as arguments and returns the result
of evaluating the polynomial whose coefficients are the elements in p[] at x:
p(x) = p0 p1x1 p2 x2 … pn2 xn2 pn1 xn1
Use Horner’s method, an efficient way to perform the computations that is suggested by the following parenthesization:
p(x) = p0 x (p1 x (p2 … x (pn2 x pn1)) . . . )
Write a test client with a static method exp() that uses evaluate() to compute
an approximation to e x, using the first n terms of the Taylor series expansion
e x = 1 + x + x 2/2! + x 3/3! + .... Your client should take a command-line argument x
and compare your result against that computed by Math.exp(x).
2.1.32 Chords. Develop a version of
PlayThatTune that can handle songs with
chords (including harmonics). Develop an input format that allows you to specify
different durations for each chord and different amplitude weights for each note
within a chord. Create test files that exercise your program with various chords and
harmonics, and create a version of Für Elise that uses them.
Functions and Modules
224
2.1.33 Benford’s law. The American astronomer Simon Newcomb observed a
quirk in a book that compiled logarithm tables: the beginning pages were much
grubbier than the ending pages. He suspected that scientists performed more computations with numbers starting with 1 than with 8 or 9, and postulated that, under
general circumstances, the leading digit is much more likely to be 1 (roughly 30%)
than the digit 9 (less than 4%). This phenomenon is known as Benford’s law and is
now often used as a statistical test. For example, IRS forensic accountants rely on
it to discover tax fraud. Write a program that reads in a sequence of integers from
standard input and tabulates the number of times each of the digits 1–9 is the leading digit, breaking the computation into a set of appropriate static methods. Use
your program to test the law on some tables of information from your computer or
from the web. Then, write a program to foil the IRS by generating random amounts
from $1.00 to $1,000.00 with the same distribution that you observed.
2.1.34 Binomial distribution. Write a function
public static double binomial(int n, int k, double p)
to compute the probability of obtaining exactly k heads in n biased coin flips (heads
with probability p) using the formula
f (n, k, p) = pk(1p)nk n! (k!(nk)!)
Hint : To stave off overflow, compute x = ln f (n, k, p) and then return ex. In main(),
take n and p from the command line and check that the sum over all values of k
between 0 and n is (approximately) 1. Also, compare every value computed with
the normal approximation
f (n, k, p) (np, np(1p))
(see Exercise 2.2.1).
2.1.35 Coupon collecting from a binomial distribution. Develop a version of
getCoupon() that uses binomial() from the previous exercise to return coupon
values according to the binomial distribution with p = 1/2. Hint : Generate a uniformly random number x between 0 and 1, then return the smallest value of k for
which the sum of f (n, j, p) for all j < k exceeds x. Extra credit : Develop a hypothesis
for describing the behavior of the coupon collector function under this assumption.
2.1 Defining Functions
225
2.1.36 Postal bar codes. The barcode used by the U.S. Postal System to route mail
is defined as follows: Each decimal digit in the ZIP code is encoded using a sequence
of three half-height and two full-height bars. The barcode starts and ends with a
full-height bar (the guard rail) and includes a checksum digit (after the five-digit
ZIP code or ZIP+4), computed by summing up the original digits modulo 10. Implement the following functions
• Draw a half-height or full-height bar on StdDraw.
08540
• Given a digit, draw its sequence of bars.
0 8 5 4 0 7
guard
• Compute the checksum digit.
rail
checksum
Also implement a test client that reads in a five- (or nine-)
digit
digit ZIP code as the command-line argument and draws
the corresponding postal bar code.
guard
rail
Functions and Modules
2.2 Libraries and Clients
Each program that you have written so far consists of Java code that resides in a
single .java file. For large programs, keeping all the code in a single file in this way
is restrictive and unnecessary. Fortunately,
it is very easy in Java to refer to a method
2.2.1 Random number library . . . . . . 234
in one file that is defined in another. This
2.2.2 Array I/O library. . . . . . . . . . . 238
2.2.3 Iterated function systems. . . . . . 241
ability has two important consequences
2.2.4 Data analysis library. . . . . . . . . 245
on our style of programming.
2.2.5 Plotting data values in an array. . . 247
First, it enables code reuse. One pro2.2.6 Bernoulli trials. . . . . . . . . . . . 250
gram can make use of code that is already
Programs in this section
written and debugged, not by copying the
code, but just by referring to it. This ability to define code that can be reused is an essential part of modern programming. It
amounts to extending Java—you can define and use your own operations on data.
Second, it enables modular programming. You can not only divide a program
up into static methods, as just described in Section 2.1, but also keep those methods in different files, grouped together according to the needs of the application.
Modular programming is important because it allows us to independently develop,
compile, and debug parts of big programs one piece at a time, leaving each finished
piece in its own file for later use without having to worry about its details again. We
develop libraries of static methods for use by any other program, keeping each library in its own file and using its methods in any other program. Java’s Math library
and our Std* libraries for input/output are examples that you have already used.
More importantly, you will soon see that it is very easy to define libraries of your
own. The ability to define libraries and then to use them in multiple programs is a
critical aspect of our ability to build programs to address complex tasks.
Having just moved in Section 2.1 from thinking of a Java program as a sequence of statements to thinking of a Java program as a class comprising a set of
static methods (one of which is main()), you will be ready after this section to
think of a Java program as a set of classes, each of which is an independent module
consisting of a set of methods. Since each method can call a method in another
class, all of your code can interact as a network of methods that call one another, grouped together in classes. With this capability, you can start to think about
managing complexity when programming by breaking up programming tasks into
classes that can be implemented and tested independently.
2.2 Libraries and Clients
227
Using static methods in other programs
To refer to a static method in one
class that is defined in another, we use the same mechanism that we have been using to invoke methods such as Math.sqrt() and StdOut.println():
• Make both classes accessible to Java (for example, by putting them both in
the same directory in your computer).
• To call a method, prepend its class name and a period separator.
For example, we might wish to write a simple client SAT.java that takes an SAT
score z from the command line and prints the percentage of students scoring less
than z in a given year (in which the mean score was 1,019 and its standard deviation
was 209). To get the job done, SAT.java needs to compute F((z1,019) 209), a
% java SAT 1019 209
...
%
Gaussian.java
public class Gaussian
{
SAT.java
public class SAT
{
public static double cdf(double z)
{
if (z < -8.0) return 0.0;
if (z > 8.0) return 1.0;
double sum = 0.0;
double term = z;
for (int i = 3; sum != sum + term; i += 2)
{
sum = sum + term;
term = term * z * z / i;
}
return 0.5
+
pdf(z)
* sum;
}
public static void main(String[] args)
{
double z = Double.parseDouble(args[0]);
double v = Gaussian.cdf((z - 1019)/209);
StdOut.println(v);
}
}
Math.java
public class Math
{
public static double exp(double x)
{
...
}
public static double pdf(double x)
{
return Math.exp(-x*x/2) /
Math.sqrt(2*Math.PI);
}
public static void main(String[] args)
{
double z
= Double.parseDouble(args[0]);
double mu
= Double.parseDouble(args[1]);
double sigma = Double.parseDouble(args[2]);
StdOut.println(cdf((z - mu) / sigma));
}
public static double sqrt(double x)
{
...
}
}
}
Flow of control in a modular program
Functions and Modules
228
task perfectly suited for the cdf() method in Gaussian.java (Program 2.1.2). All
that we need to do is to keep Gaussian.java in the same directory as SAT.java
and prepend the class name when calling cdf(). Moreover, any other class in
that directory can make use of the static methods defined in Gaussian, by calling Gaussian.pdf() or Gaussian.cdf(). The Math library is always accessible
in Java, so any class can call Math.sqrt() and Math.exp(), as usual. The files
Gaussian.java, SAT.java, and Math.java implement Java classes that interact
with one another: SAT calls a method in Gaussian, which calls another method in
Gaussian, which then calls two methods in Math.
The potential effect of programming by defining multiple files, each an independent class with multiple methods, is another profound change in our programming style. Generally, we refer to this approach as modular programming. We independently develop and debug methods for an application and then utilize them at
any later time. In this section, we will consider numerous illustrative examples to
help you get used to the idea. However, there are several details about the process
that we need to discuss before considering more examples.
The
public keyword. We have been identifying every static method as public
since HelloWorld. This modifier identifies the method as available for use by any
other program with access to the file. You can also identify methods as private
(and there are a few other categories), but you have no reason to do so at this point.
We will discuss various options in Section 3.3.
Each module is a class. We use the term module to refer to all the code that we
keep in a single file. In Java, by convention, each module is a Java class that is kept
in a file with the same name of the class but has a .java extension. In this chapter,
each class is merely a set of static methods (one of which is main()). You will
learn much more about the general structure of the Java class in Chapter 3.
The
.class file. When you compile the program (by typing javac followed by
the class name), the Java compiler makes a file with the class name followed by
a .class extension that has the code of your program in a language more suited
to your computer. If you have a .class file, you can use the module’s methods in
another program even without having the source code in the corresponding .java
file (but you are on your own if you discover a bug!).
2.2 Libraries and Clients
Compile when necessary. When you compile a program, Java typically compiles
everything that needs to be compiled in order to run that program. If you call
in SAT, then, when you type javac SAT.java, the compiler will
also check whether you modified Gaussian.java since the last time it was compiled (by checking the time it was last changed against the time Gaussian.class
was created). If so, it will also compile Gaussian.java! If you think about this approach, you will agree that it is actually quite helpful. After all, if you find a bug in
Gaussian.java (and fix it), you want all the classes that call methods in Gaussian
to use the new version.
Gaussian.cdf()
Multiple
main()
methods. Another subtle point is to note that more than one
class might have a main() method. In our example, both SAT and Gaussian have
their own main() method. If you recall the rule for executing a program, you will
see that there is no confusion: when you type java followed by a class name, Java
transfers control to the machine code corresponding to the main() method defined
in that class. Typically, we include a main() method in every class, to test and debug
its methods. When we want to run SAT, we type java SAT; when we want to debug
Gaussian, we type java Gaussian (with appropriate command-line arguments).
If you think of each program that you write as something that you might want to
make use of later, you will soon find yourself with all sorts of useful tools. Modular
programming allows us to view every solution to a computational problem that we
may develop as adding value to our computational environment.
For example, suppose that you need to evaluate F for some future application.
Why not just cut and paste the code that implements cdf() from Gaussian? That
would work, but would leave you with two copies of the code, making it more difficult to maintain. If you later want to fix or improve this code, you would need to
do so in both copies. Instead, you can just call Gaussian.cdf(). Our implementations and uses of our methods are soon going to proliferate, so having just one copy
of each is a worthy goal.
From this point forward, you should write every program by identifying a
reasonable way to divide the computation into separate parts of a manageable size
and implementing each part as if someone will want to use it later. Most frequently,
that someone will be you, and you will have yourself to thank for saving the effort
of rewriting and re-debugging code.
229
Functions and Modules
230
Libraries We refer to a module whose methods are primarily intended for use
by many other programs as a library. One of the most important characteristics of
programming in Java is that thousands of libraries have been predefined for your
use. We reveal information about those that might be of interest to you throughout
the book, but we will postpone a detailed discussion of the scope of Java libraries,
because many of them are designed for use by
client
experienced programmers. Instead, we focus in
this chapter on the even more important idea
Gaussian.pdf(x)
that we can build user-defined libraries, which
Gaussian.cdf(z)
are nothing more than classes that contain a set
of related methods for use by other programs.
calls library methods
No Java library can contain all the methods that
API
we might need for a given computation, so this
public class Gaussian
ability to create our own library of methods is
double pdf(double x)
(x)
a crucial step in addressing complex programdouble cdf(double z)
(z)
ming applications.
Clients. We use the term client to refer to
a program that calls a given library method.
When a class contains a method that is a client
of a method in another class, we say that the
first class is a client of the second class. In our
example, SAT is a client of Gaussian. A given
class might have multiple clients. For example,
all of the programs that you have written that
call Math.sqrt() or Math.random() are clients of Math.
APIs. Programmers normally think in terms
defines signatures
and describes
library methods
implementation
public class Gaussian
{ ...
public static double pdf(double x)
{ ... }
public static double cdf(double z)
{ ... }
}
Java code that
of a contract between the client and the impleimplements
library methods
mentation that is a clear specification of what
the method is to do. When you are writing both
Library abstraction
clients and implementations, you are making
contracts with yourself, which by itself is helpful because it provides extra help in debugging. More important, this approach enables code reuse. You have been able to write programs that are clients of Std* and
Math and other built-in Java classes because of an informal contract (an English-
2.2 Libraries and Clients
231
language description of what they are supposed to do) along with a precise specification of the signatures of the methods that are available for use. Collectively, this
information is known as an application programming interface (API). This same
mechanism is effective for user-defined libraries. The API allows any client to use
the library without having to examine the code in the implementation, as you have
been doing for Math and Std*. The guiding principle in API design is to provide to
clients the methods they need and no others. An API with a huge number of methods
may be a burden to implement; an API that is lacking important methods may be
unnecessarily inconvenient for clients.
Implementations. We use the term implementation to describe the Java code that
implements the methods in an API, kept by convention in a file with the library
name and a .java extension. Every Java program is an implementation of some
API, and no API is of any use without some implementation. Our goal when developing an implementation is to honor the terms of the contract. Often, there are
many ways to do so, and separating client code from implementation code gives us
the freedom to substitute new and improved implementations.
For example, consider the Gaussian distribution functions. These do not appear in
Java’s Math library but are important in applications, so it is worthwhile for us to
put them in a library where they can be accessed by future client programs and to
articulate this API:
public class Gaussian
double pdf(double x)
f(x)
double pdf(double x, double mu, double sigma)
f(x, , )
double cdf(double z)
(z)
double cdf(double z, double mu, double sigma)
(z, , )
API for our library of static methods for Gaussian distribution functions
The API includes not only the one-argument Gaussian distribution functions that
we have previously considered (see Program 2.1.2) but also three-argument
versions (in which the client specifies the mean and standard deviation of the distribution) that arise in many statistical applications. Implementing the threeargument Gaussian distribution functions is straightforward (see Exercise 2.2.1).
Functions and Modules
232
How much information should an API contain? This is a gray area and a hotly
debated issue among programmers and computer-science educators. We might try
to put as much information as possible in the API, but (as with any contract!) there
are limits to the amount of information that we can productively include. In this
book, we stick to a principle that parallels our guiding design principle: provide
to client programmers the information they need and no more. Doing so gives us
vastly more flexibility than the alternative of providing detailed information about
implementations. Indeed, any extra information amounts to implicitly extending
the contract, which is undesirable. Many programmers fall into the bad habit of
checking implementation code to try to understand what it does. Doing so might
lead to client code that depends on behavior not specified in the API, which would
not work with a new implementation. Implementations change more often than
you might think. For example, each new release of Java contains many new implementations of library functions.
Often, the implementation comes first. You might have a working module
that you later decide would be useful for some task, and you can just start using
its methods in other programs. In such a situation, it is wise to carefully articulate
the API at some point. The methods may not have been designed for reuse, so it is
worthwhile to use an API to do such a design (as we did for Gaussian).
The remainder of this section is devoted to several examples of libraries and
clients. Our purpose in considering these libraries is twofold. First, they provide
a richer programming environment for your use as you develop increasingly sophisticated client programs of your own. Second, they serve as examples for you to
study as you begin to develop libraries for your own use.
Random numbers
We have written several programs that use Math.random(),
but our code often uses particular idioms that convert the random double values
between 0 and 1 that Math.random() provides to the type of random numbers that
we want to use (random boolean values or random int values in a specified range,
for example). To effectively reuse our code that implements these idioms, we will,
from now on, use the StdRandom library in Program 2.2.1. StdRandom uses overloading to generate random numbers from various distributions. You can use any
of them in the same way that you use our standard I/O libraries (see the first Q&A
at the end of Section 2.1). As usual, we summarize the methods in our StdRandom
library with an API:
2.2 Libraries and Clients
233
public class StdRandom
void setSeed(long seed)
int uniform(int n)
double uniform(double lo, double hi)
boolean bernoulli(double p)
set the seed for reproducible results
integer between 0 and n-1
floating-point number between lo and hi
true with probability p, false otherwise
double gaussian()
Gaussian, mean 0, standard deviation 1
double gaussian(double mu, double sigma)
Gaussian, mean mu, standard deviation sigma
int discrete(double[] p)
void shuffle(double[] a)
i with probability p[i]
randomly shuffle the array a[]
API for our library of static methods for random numbers
These methods are sufficiently familiar that the short descriptions in the API suffice
to specify what they do. By collecting all of these methods that use Math.random()
to generate random numbers of various types in one file (StdRandom.java), we
concentrate our attention on generating random numbers to this one file (and
reuse the code in that file) instead of spreading them through every program that
uses these methods. Moreover, each program that uses one of these methods is
clearer than code that calls Math.random() directly, because its purpose for using
Math.random() is clearly articulated by the choice of method from StdRandom.
API design. We make certain assumptions about the values passed to each method
in StdRandom. For example, we assume that clients will call uniform(n) only for
positive integers n, bernoulli(p) only for p between 0 and 1, and discrete()
only for an array whose elements are between 0 and 1 and sum to 1. All of these
assumptions are part of the contract between the client and the implementation.
We strive to design libraries such that the contract is clear and unambiguous and
to avoid getting bogged down with details. As with many tasks in programming, a
good API design is often the result of several iterations of trying and living with
various possibilities. We always take special care in designing APIs, because when
we change an API we might have to change all clients and all implementations. Our
goal is to articulate what clients can expect separate from the code in the API. This
practice frees us to change the code, and perhaps to use an implementation that
achieves the desired effect more efficiently or with more accuracy.
Functions and Modules
234
Program 2.2.1
Random number library
public class StdRandom
{
public static int uniform(int n)
{ return (int) (Math.random() * n);
}
public static double uniform(double lo, double hi)
{ return lo + Math.random() * (hi - lo); }
public static boolean bernoulli(double p)
{ return Math.random() < p; }
public static double gaussian()
{ /* See Exercise 2.2.17. */ }
public static double gaussian(double mu, double sigma)
{ return mu + sigma * gaussian(); }
public static int discrete(double[] probabilities)
{ /* See Program 1.6.2. */ }
public static void shuffle(double[] a)
{ /* See Exercise 2.2.4. */ }
public static void main(String[] args)
{ /* See text. */ }
}
The methods in this library compute various types of random numbers: random nonnegative
integer less than a given value, uniformly distributed in a given range, random bit (Bernoulli),
standard Gaussian, Gaussian with given mean and standard deviation, and distributed according to a given discrete distribution.
% java StdRandom 5
90 26.36076 false 8.79269
13 18.02210 false 9.03992
58 56.41176 true 8.80501
29 16.68454 false 8.90827
85 86.24712 true 8.95228
0
1
0
0
0
2.2 Libraries and Clients
Unit testing. Even though we implement
235
without reference to any
particular client, it is good programming practice to include a test client main()
that, although not used when a client class uses the library, is helpful when debugging and testing the methods in the library. Whenever you create a library, you
should include a main() method for unit testing and debugging. Proper unit testing
can be a significant programming challenge in itself (for example, the best way of
testing whether the methods in StdRandom produce numbers that have the same
characteristics as truly random numbers is still debated by experts). At a minimum,
you should always include a main() method that
• Exercises all the code
• Provides some assurance that the code is working
• Takes an argument from the command line to allow more testing
Then, you should refine that main() method to do more exhaustive testing as you
use the library more extensively. For example, we might start with the following
code for StdRandom (leaving the testing of shuffle() for an exercise):
StdRandom
public static void main(String[] args)
{
int n = Integer.parseInt(args[0]);
double[] probabilities = { 0.5, 0.3, 0.1, 0.1 };
for (int i = 0; i < n; i++)
{
StdOut.printf(" %2d " , uniform(100));
StdOut.printf("%8.5f ", uniform(10.0, 99.0));
StdOut.printf("%5b " , bernoulli(0.5));
StdOut.printf("%7.5f ", gaussian(9.0, 0.2));
StdOut.printf("%2d " , discrete(probabilities));
StdOut.println();
}
}
When we include this code in StdRandom.java and invoke this method as illustrated in Program 2.2.1, the output includes no surprises: the integers in the first
column might be equally likely to be any value from 0 to 99; the numbers in the
second column might be uniformly spread between 10.0 and 99.0; about half of
the values in the third column are true; the numbers in the fourth column seem to
average about 9.0, and seem unlikely to be too far from 9.0; and the last column
seems to be not far from 50% 0s, 30% 1s, 10% 2s, and 10% 3s. If something seems
Functions and Modules
236
amiss in one of the columns, we can type java StdRandom 10 or 100 to see many
more results. In this particular case, we can (and should) do far more extensive
testing in a separate client to check that the numbers have many of the same properties as truly random numbers drawn from the cited distributions (see
Exercise 2.2.3). One effective approach is to write test clients that use StdDraw, as
data visualization can be a quick indication that a program is behaving as intended.
For example, a plot of a large number of points whose x- and y-coordinates are
both drawn from various distribupublic class RandomPoints
tions
often produces a pattern that
{
gives direct insight into the imporpublic static void main(String[] args)
{
tant properties of the distribution.
int n = Integer.parseInt(args[0]);
More important, a bug in the random
for (int i = 0; i < n; i++)
number generation code is likely to
{
double x = StdRandom.gaussian(.5, .2);
show up immediately in such a plot.
double y = StdRandom.gaussian(.5, .2);
StdDraw.point(x, y);
}
}
}
Stress testing. An extensively used li-
brary such as StdRandom should also
be subjected to stress testing, where
we make sure that it does not crash
when the client does not follow the
contract or makes some assumption
that is not explicitly covered. Java libraries have already been subjected
to such stress testing, which requires
carefully examining each line of code
and questioning whether some condition might cause a problem. What
should discrete() do if the array elements
do not sum to exactly 1? What
A StdRandom test client
if the argument is an array of length
0? What should the two-argument
uniform() do if one or both of its arguments is NaN? Infinity? Any question that
you can think of is fair game. Such cases are sometimes referred to as corner cases.
You are certain to encounter a teacher or a supervisor who is a stickler about corner
cases. With experience, most programmers learn to address them early, to avoid an
unpleasant bout of debugging later. Again, a reasonable approach is to implement
a stress test as a separate client.
2.2 Libraries and Clients
237
Input and output for arrays We have seen—and will continue to see—many
examples where we wish to keep data in arrays for processing. Accordingly, it is
useful to build a library that complements StdIn and StdOut by providing static
methods for reading arrays of primitive types from standard input and printing
them to standard output. The following API provides these methods:
public class StdArrayIO
double[] readDouble1D()
read a one-dimensional array of double values
double[][] readDouble2D()
read a two-dimensional array of double values
void print(double[] a)
print a one-dimensional array of double values
void print(double[][] a) print a two-dimensional array of double values
Note 1. 1D format is an integer n followed by n values.
Note 2. 2D format is two integers m and n followed by m × n values in row-major order.
Note 3. Methods for int and boolean are also included.
API for our library of static methods for array input and output
The first two notes at the bottom of the table reflect the idea that we need to settle
on a file format. For simplicity and harmony, we adopt the convention that all values appearing in standard input include the dimension(s) and appear in the order
indicated. The read*() methods expect input in this format; the print() methods produce output in this format. The third note at the bottom of the table indicates that StdArrayIO actually contains 12 methods—four each for int, double,
and boolean. The print() methods are overloaded (they all have the same name
print() but different types of arguments), but the read*() methods need different names, formed by adding the type name (capitalized, as in StdIn) followed by
1D or 2D.
Implementing these methods is straightforward from the array-processing code that we have considered in Section 1.4 and in Section 2.1, as shown in
StdArrayIO (Program 2.2.2). Packaging up all of these static methods into one
file—StdArrayIO.java—allows us to easily reuse the code and saves us from having to worry about the details of reading and printing arrays when writing client
programs later on.
Functions and Modules
238
Program 2.2.2
Array I/O library
public class StdArrayIO
{
public static double[] readDouble1D()
{ /* See Exercise 2.2.11. */ }
public static double[][] readDouble2D()
{
int m = StdIn.readInt();
int n = StdIn.readInt();
double[][] a = new double[m][n];
for (int i = 0; i < m; i++)
for (int j = 0; j < n; j++)
a[i][j] = StdIn.readDouble();
return a;
}
public static void print(double[] a)
{ /* See Exercise 2.2.11. */ }
% more tiny2D.txt
4 3
0.000 0.270 0.000
0.246 0.224 -0.036
0.222 0.176 0.0893
-0.032 0.739 0.270
% java StdArrayIO < tiny2D.txt
public static void print(double[][] a)
4 3
{
0.00000
int m = a.length;
0.24600
int n = a[0].length;
0.22200
System.out.println(m + " " + n);
-0.03200
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
StdOut.prinf("%9.5f ", a[i][j]);
StdOut.println();
}
StdOut.println();
}
0.27000
0.22400
0.17600
0.73900
0.00000
-0.03600
0.08930
0.27000
// Methods for other types are similar (see booksite).
public static void main(String[] args)
{ print(readDouble2D()); }
}
This library of static methods facilitates reading one-dimensional and two-dimensional
arrays from standard input and printing them to standard output. The file format includes
the dimensions (see accompanying text). Numbers in the output in the example are truncated.
2.2 Libraries and Clients
239
Iterated function systems
Scientists have discovered that complex visual images can arise unexpectedly from simple computational processes. With StdRandom,
StdDraw, and StdArrayIO, we can study the behavior of such systems.
Sierpinski triangle. As a first example, consider the following simple process:
Start by plotting a point at one of the vertices of a given equilateral triangle. Then
pick one of the three vertices at random and plot a new point halfway between the
point just plotted and that vertex. Continue performing this same operation. Each
time, we are pick a random vertex from the triangle to establish the line whose
midpoint will be the next point plotted. Since we make random choices, the set
of points should have some of the characteristics of random points, and that does
seem to be the case after the first few iterations:
(1/2, 3/2)
last point
midpoint
(0, 0)
(1, 0)
random vertex
A random process
We can study the process for a large number of iterations by writing a program to
plot trials points according to the rules:
double[] cx = { 0.000, 1.000, 0.500 };
double[] cy = { 0.000, 0.000, 0.866 };
double x = 0.0, y = 0.0;
for (int t = 0; t < trials; t++)
{
int r = StdRandom.uniform(3);
x = (x + cx[r]) / 2.0;
y = (y + cy[r]) / 2.0;
StdDraw.point(x, y);
}
We keep the x- and y-coordinates of the triangle vertices in the arrays cx[] and
cy[], respectively. We use StdRandom.uniform() to choose a random index r into
Functions and Modules
240
these arrays—the coordinates of the chosen vertex are (cx[r], cy[r]). The x-coordinate of the midpoint of the line from (x, y) to that vertex is given by the expression (x + cx[r])/2.0, and a similar calculation gives the y-coordinate. Adding a
call to StdDraw.point() and putting this code in a loop completes the implementation. Remarkably, despite the randomness, the same figure always emerges after
a large number of iterations! This figure is known as the Sierpinski triangle (see
Exercise 2.3.27). Understanding why such a regular figure should arise from such a
random process is a fascinating question.
A random process?
Barnsley fern. To add to the mystery, we can produce pictures of remarkable
diversity by playing the same game with different rules. One striking example is
known as the Barnsley fern. To generate it, we use the same process, but this time
driven by the following table of formulas. At each step, we choose the formulas to
use to update x and y with the indicated probability (1% of the time we use the first
pair of formulas, 85% of the time we use the second pair of formulas, and so forth).
probability
1%
85%
7%
7%
x-update
x=
0.500
x = 0.85x 0.04y 0.075
x = 0.20x 0.26y 0.400
x = 0.15x 0.28y 0.575
y-update
y=
0.16y
y = 0.04x 0.85y 0.180
y = 0.23x 0.22y 0.045
y = 0.26x 0.24y 0.086
2.2 Libraries and Clients
Program 2.2.3
241
Iterated function systems
public class IFS
{
public static void main(String[] args)
{ // Plot trials iterations of IFS on StdIn.
trials
int trials = Integer.parseInt(args[0]);
dist[]
double[] dist
= StdArrayIO.readDouble1D();
cx[][]
double[][] cx
= StdArrayIO.readDouble2D();
cy[][]
double[][] cy
= StdArrayIO.readDouble2D();
x, y
double x = 0.0, y = 0.0;
for (int t = 0; t < trials; t++)
{ // Plot 1 iteration.
int r = StdRandom.discrete(dist);
double x0 = cx[r][0]*x + cx[r][1]*y + cx[r][2];
double y0 = cy[r][0]*x + cy[r][1]*y + cy[r][2];
x = x0;
y = y0;
StdDraw.point(x, y);
}
}
}
iterations
probabilities
x coefficients
y coefficients
current point
This data-driven client of StdArrayIO, StdRandom, and StdDraw iterates the function system
defined by a 1-by-m vector (probabilities) and two m-by-3 matrices (coefficients for updating x and y, respectively) on standard input, plotting the result as a set of points on standard
drawing. Curiously, this code does not need to know the value of m, as it uses separate methods to create and process the matrices.
% more sierpinski.txt
3
.33 .33 .34
3 3
.50 .00 .00
.50 .00 .50
.50 .00 .25
3 3
.00 .50 .00
.00 .50 .00
.00 .50 .433
% java IFS 10000 < sierpinski.txt
Functions and Modules
242
% more barnsley.txt
4
.01 .85 .07 .07
4 3
.00 .00 .500
.85 .04 .075
.20 -.26 .400
-.15 .28 .575
4 3
.00 .16 .000
-.04 .85 .180
.23 .22 .045
.26 .24 -.086
% more tree.txt
6
.1 .1 .2 .2 .2 .2
6 3
.00 .00 .550
-.05 .00 .525
.46 -.15 .270
.47 -.15 .265
.43 .26 .290
.42 .26 .290
6 3
.00 .60 .000
-.50 .00 .750
.39 .38 .105
.17 .42 .465
-.25 .45 .625
-.35 .31 .525
% java IFS 20000 < barnsley.txt
% java IFS 20000 < tree.txt
% java IFS 20000 < coral.txt
% more coral.txt
3
.40 .15 .45
3 3
.3077 -.5315 .8863
.3077 -.0769 .2166
.0000 .5455 .0106
3 3
-.4615 -.2937 1.0962
.1538 -.4476 .3384
.6923 -.1958 .3808
Examples of iterated function systems
2.2 Libraries and Clients
243
We could write code just like the code we just wrote for the Sierpinski triangle
to iterate these rules, but matrix processing provides a uniform way to generalize
that code to handle any set of rules. We have m different transformations, chosen from a 1-by-m vector with StdRandom.discrete(). For each transformation,
we have an equation for updating x and an equation for updating y, so we use
two m-by-3 matrices for the equation coefficients, one for x and one for y. IFS
(Program 2.2.3) implements this data-driven version of the computation. This
program enables limitless exploration: it performs the iteration for any input containing a vector that defines the probability distribution and the two matrices that
define the coefficients, one for updating x and the other for updating y. For the coefficients just given, again, even though we choose a random equation at each step,
the same figure emerges every time that we do this computation: an image that
looks remarkably similar to a fern that you might see in the woods, not something
generated by a random process on a computer.
Generating a Barnsley fern
That the same short program that takes a few numbers from standard input
and plots points on standard drawing can (given different data) produce both the
Sierpinski triangle and the Barnsley fern (and many, many other images) is truly
remarkable. Because of its simplicity and the appeal of the results, this sort of calculation is useful in making synthetic images that have a realistic appearance in
computer-generated movies and games.
Perhaps more significantly, the ability to produce such realistic diagrams so
easily suggests intriguing scientific questions: What does computation tell us about
nature? What does nature tell us about computation?
Functions and Modules
244
Statistics Next, we consider a library for a set of mathematical calculations and
basic visualization tools that arise in all sorts of applications in science and engineering and are not all implemented in standard Java libraries. These calculations
relate to the task of understanding the statistical properties of a set of numbers.
Such a library is useful, for example, when we perform a series of scientific experiments that yield measurements of a quantity. One of the most important challenges facing modern scientists is proper analysis of such data, and computation is
playing an increasingly important role in such analysis. These basic data analysis
methods that we will consider are summarized in the following API:
public class StdStats
double
max(double[] a)
largest value
double
min(double[] a)
smallest value
double
mean(double[] a)
average
double var(double[] a)
sample variance
double stddev(double[] a)
sample standard deviation
double
median
median(double[] a)
void plotPoints(double[] a)
plot points at (i, a[i])
void plotLines(double[] a)
plot lines connecting points at (i, a[i])
void plotBars(double[] a)
plot bars to points at (i, a[i])
Note: Overloaded implementations are included for other numeric types.
API for our library of static methods for data analysis
Basic statistics. Suppose that we have n measurements x0, x1, …, xn1. The average
value of those measurements, otherwise known as the mean, is given by the formula ( x0 x1 … xn1 ) n and is an estimate of the value of the quantity.
The minimum and maximum values are also of interest, as is the median (the value
that is smaller than and larger than half the values). Also of interest is the sample
variance, which is given by the formula
2 ( ( x0 )2 ( x1 )2 … ( xn1 )2 ) ( n1 )
2.2 Libraries and Clients
Program 2.2.4
245
Data analysis library
public class StdStats
{
public static double max(double[] a)
{ // Compute maximum value in a[].
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < a.length; i++)
if (a[i] > max) max = a[i];
return max;
}
public static double mean(double[] a)
{ // Compute the average of the values in a[].
double sum = 0.0;
for (int i = 0; i < a.length; i++)
sum = sum + a[i];
return sum / a.length;
}
public static double var(double[] a)
{ // Compute the sample variance of the values in a[].
double avg = mean(a);
double sum = 0.0;
for (int i = 0; i < a.length; i++)
sum += (a[i] - avg) * (a[i] - avg);
return sum / (a.length - 1);
}
public static double stddev(double[] a)
{ return Math.sqrt(var(a)); }
// See Program 2.2.5 for plotting methods.
public static void main(String[] args)
{ /* See text. */ }
}
This code implements methods to compute the maximum, mean, variance, and standard
deviation of numbers in a client array. The method for computing the minimum is omitted;
plotting methods are in Program 2.2.5; see Exercise 4.2.20 for median().
% more tiny1D.txt
5
3.0 1.0 2.0 5.0 4.0
% java StdStats < tiny1D.txt
min
1.000
mean
3.000
max
5.000
std dev
1.581
246
Functions and Modules
and the sample standard deviation, the square root of the sample variance. StdStats
(Program 2.2.4) shows implementations of static methods for computing these
basic statistics (the median is more difficult to compute than the others—we will
consider the implementation of median() in Section 4.2). The main() test client
for StdStats reads numbers from standard input into an array and calls each of
the methods to print the minimum, mean, maximum, and standard deviation, as
follows:
public static void main(String[] args)
{
double[] a = StdArrayIO.readDouble1D();
StdOut.printf("
min %7.3f\n", min(a));
StdOut.printf("
mean %7.3f\n", mean(a));
StdOut.printf("
max %7.3f\n", max(a));
StdOut.printf("
std dev %7.3f\n", stddev(a));
}
As with StdRandom, a more extensive test of the calculations is called for (see
Exercise 2.2.3). Typically, as we debug or test new methods in the library, we adjust
the unit testing code accordingly, testing the methods one at a time. A mature and
widely used library like StdStats also deserves a stress-testing client for extensively
testing everything after any change. If you are interested in seeing what such a
client might look like, you can find one for StdStats on the booksite. Most experienced programmers will advise you that any time spent doing unit testing and
stress testing will more than pay for itself later.
Plotting. One important use of StdDraw is to help us visualize data rather than relying on tables of numbers. In a typical situation, we perform experiments, save the
experimental data in an array, and then compare the results against a model, perhaps a mathematical function that describes the data. To expedite this process for
the typical case where values of one variable are equally spaced, our StdStats library contains static methods that you can use for plotting data in an array. Program
2.2.5 is an implementation of the plotPoints(), plotLines(), and plotBars()
methods for StdStats. These methods display the values in the argument array at
evenly spaced intervals in the drawing window, either connected together by line
segments (lines), filled circles at each value (points), or bars from the x-axis to
the value (bars). They all plot the points with x-coordinate i and y-coordinate
a[i] using filled circles, lines through the points, and bars, respectively. In addition,
2.2 Libraries and Clients
Program 2.2.5
247
Plotting data values in an array
public static void plotPoints(double[] a)
{ // Plot points at (i, a[i]).
int n = a.length;
StdDraw.setXscale(-1, n);
StdDraw.setPenRadius(1/(3.0*n));
for (int i = 0; i < n; i++)
StdDraw.point(i, a[i]);
}
public static void plotLines(double[] a)
{ // Plot lines through points at (i, a[i]).
int n = a.length;
StdDraw.setXscale(-1, n);
StdDraw.setPenRadius();
for (int i = 1; i < n; i++)
StdDraw.line(i-1, a[i-1], i, a[i]);
}
public static void plotBars(double[] a)
{ // Plot bars from (0, a[i]) to (i, a[i]).
int n = a.length;
StdDraw.setXscale(-1, n);
for (int i = 0; i < n; i++)
StdDraw.filledRectangle(i, a[i]/2, 0.25, a[i]/2);
}
This code implements three methods in StdStats (Program 2.2.4) for plotting data. They
plot the points (i, a[i]) with filled circles, connecting line segments, and bars, respectively.
plotPoints(a);
int n = 20;
double[] a = new double[n];
for (int i = 0; i < n; i++)
a[i] = 1.0/(i+1);
(0.0, 1.0)
(9.0, 0.1)
plotLines(a);
plotBars(a);
Functions and Modules
248
they all rescale x to fill the drawing window (so that the points are evenly spaced
along the x-coordinate) and leave to the client scaling of the y-coordinates.
These methods are not intended to be a general-purpose plotting package,
but you can certainly think of all sorts of things that you might want to add: different types of spots, labeled axes, color, and many other artifacts are commonly
found in modern systems that can plot data. Some situations might call for more
complicated methods than these.
Our intent with StdStats is to introduce you to data analysis while showing
you how easy it is to define a library to take care of useful tasks. Indeed, this library
has already proved useful—we use these plotting methods to produce the figures in
this book that depict function graphs, sound waves, and experimental results. Next,
we consider several examples of their use.
Plotting function graphs. You can use
int n = 50;
the StdStats.plot*() methods to draw double[] a = new double[n+1];
a plot of the function graph for any func- for (int i = 0; i <= n; i++)
a[i] = Gaussian.pdf(-4.0 + 8.0*i/n);
tion at all: choose an x-interval where StdStats.plotPoints(a);
you want to plot the function, compute StdStats.plotLines(a);
function values evenly spaced through
that interval and store them in an array,
determine and set the y-scale, and then
call StdStats.plotLines() or another
plot*() method. For example, to plot a
sine function, rescale the y-axis to cover
Plotting a function graph
values between 1 and 1. Scaling the xaxis is automatically handled by the StdStats methods. If you do not know the range, you can handle the situation by
calling:
StdDraw.setYscale(StdStats.min(a), StdStats.max(a));
The smoothness of the curve is determined by properties of the function and by
the number of points plotted. As we discussed when first considering StdDraw, you
have to be careful to sample enough points to catch fluctuations in the function.
We will consider another approach to plotting functions based on sampling values
that are not equally spaced in Section 2.4.
2.2 Libraries and Clients
Plotting sound waves. Both the
249
library StdDraw.setYscale(-1.0, 1.0);
and the StdStats plot methods work with arrays that double[] hi;
contain sampled values at regular intervals. The dia- hi = PlayThatTune.tone(880, 0.01);
StdStats.plotPoints(hi);
grams of sound waves in Section 1.5 and at the beginning of this section were all produced by first scaling
the y-axis with StdDraw.setYscale(-1, 1), then
plotting the points with StdStats.plotPoints().
As you have seen, such plots give direct insight into
Plotting a sound wave
processing audio. You can also produce interesting effects by plotting sound waves as you play them with
StdAudio, although this task is a bit challenging because of the huge amount of
data involved (see Exercise 1.5.23).
StdAudio
Plotting experimental results. You can put multiple plots on the same drawing.
One typical reason to do so is to compare experimental results with a theoretical model. For example, Bernoulli (Program 2.2.6) counts the number of heads
found when a fair coin is flipped n times and compares the result with the predicted
Gaussian probability density function. A famous result from probability theory is
that the distribution of this quantity is the binomial distribution, which is extremely
well approximated by the Gaussian distribution with mean n/2 and standard deviation n/2. The more trials we perform, the more accurate the approximation.
The drawing produced by Bernoulli is a succinct summary of the results of the
experiment and a convincing validation of the theory. This example is prototypical
of a scientific approach to applications programming that we use often throughout
this book and that you should use whenever you run an experiment. If a theoretical
model that can explain your results is available, a visual plot comparing the experiment to the theory can validate both.
These few examples are intended to suggest what is possible with a well-designed library of static methods for data analysis. Several extensions and other ideas are explored in the exercises. You will find StdStats to be useful for basic plots, and you
are encouraged to experiment with these implementations and to modify them or
to add methods to make your own library that can draw plots of your own design.
As you continue to address an ever-widening circle of programming tasks, you will
naturally be drawn to the idea of developing tools like these for your own use.
Functions and Modules
250
Program 2.2.6
Bernoulli trials
public class Bernoulli
{
public static int binomial(int n)
{ // Simulate flipping a coin n times; return # heads.
int heads = 0;
for (int i = 0; i < n; i++)
if (StdRandom.bernoulli(0.5)) heads++;
return heads;
}
public static void main(String[] args)
{ // Perform Bernoulli trials, plot results and model.
int n = Integer.parseInt(args[0]);
int trials = Integer.parseInt(args[1]);
n
number of flips per trial
int[] freq = new int[n+1];
trials number of trials
for (int t = 0; t < trials; t++)
freq[] experimental results
freq[binomial(n)]++;
double[] norm = new double[n+1];
for (int i = 0; i <= n; i++)
norm[i] = (double) freq[i] / trials;
StdStats.plotBars(norm);
norm[]
phi[]
normalized results
Gaussian model
double mean
= n / 2.0;
double stddev = Math.sqrt(n) / 2.0;
double[] phi = new double[n+1];
for (int i = 0; i <= n; i++)
phi[i] = Gaussian.pdf(i, mean, stddev);
StdStats.plotLines(phi);
}
}
This StdStats, StdRandom, and Gaussian client provides visual evidence that the number of
heads observed when a fair coin is flipped n times obeys a Gaussian distribution.
% java Bernoulli 20 100000
2.2 Libraries and Clients
251
Modular programming The library implementations that we have developed
illustrate a programming style known as modular programming. Instead of writing
a new program that is self-contained within its own file to address a new problem,
we break up each task into smaller, more manageable subtasks, then implement
and independently debug code that addresses each subtask. Good libraries facilitate modular programming by allowing us to define and provide solutions for important subtasks for future clients. Whenever you can clearly separate tasks within a
program, you should do so. Java supports such separation by allowing us to independently debug and later use classes in separate files. Traditionally, programmers use
the term module to refer to code that can be compiled and run independently; in
Java, each class is a module.
IFS (Program 2.2.3) exemplifies modular programming. This relatively sophisticated computation is implemented with several relatively small modules, developed independently. It uses StdRandom and StdArrayIO, as well as the methods
from Integer and StdDraw that we
are accustomed to using. If we were
API
description
to put all of the code required for
Gaussian
Gaussian distribution functions
IFS in a single file, we would have a
StdRandom
random numbers
large amount of code on our hands
StdArrayIO
input and output for arrays
to maintain and debug; with modular
programming, we can study iterated
IFS
client for iterated function systems
function systems with some confiStdStats
functions for data analysis
dence that the arrays are read properly
Bernoulli
client for Bernoulli trials
and that the random number generator will produce properly distributed
Summary of classes in this section
values, because we already implemented and tested the code for these
tasks in separate modules.
Similarly, Bernoulli (Program 2.2.6) exemplifies modular programming. It
is a client of Gaussian, Integer, Math, StdRandom, and StdStats. Again, we can
have some confidence that the methods in these modules produce the expected
results because they are system libraries or libraries that we have tested, debugged,
and used before.
Functions and Modules
252
To describe the relationships among modules in a modular program, we often
draw a dependency graph, where we connect two class names with an arrow labeled
with the name of a method if the first class contains a method call and the second
class contains the definition of the method. Such diagrams play an important role
because understanding the relationships among modules is necessary for proper
development and maintenance.
IFS
parseInt()
parseInt()
Integer
readDouble1D()
readDouble2D()
discrete()
plotBars()
plotLines()
sqrt()
StdArrayIO
readDouble()
readInt()
Bernoulli
pdf()
bernoulli()
point()
StdStats
setPenRadius()
setXscale()
line()
StdIn
StdDraw
Gaussian
StdRandom
exp()
sqrt()
PI
random()
Math
Dependency graph (partial) for the modules in this section
We emphasize modular programming throughout this book because it has
many important advantages that have come to be accepted as essential in modern
programming, including the following:
• We can have programs of a reasonable size, even in large systems.
• Debugging is restricted to small pieces of code.
• We can reuse code without having to re-implement it.
• Maintaining (and improving) code is much simpler.
The importance of these advantages is difficult to overstate, so we will expand upon
each of them.
Programs of a reasonable size. No large task is so complex that it cannot be divided into smaller subtasks. If you find yourself with a program that stretches to more
than a few pages of code, you must ask yourself the following questions: Are there
subtasks that could be implemented separately? Could some of these subtasks be
2.2 Libraries and Clients
logically grouped together in a separate library? Could other clients use this code
in the future? At the other end of the range, if you find yourself with a huge number of tiny modules, you must ask yourself questions such as these: Is there some
group of subtasks that logically belong in the same module? Is each module likely
to be used by multiple clients? There is no hard-and-fast rule on module size: one
implementation of a critically important abstraction might properly be a few lines
of code, whereas another library with a large number of overloaded methods might
properly stretch to hundreds of lines of code.
Debugging. Tracing a program rapidly becomes more difficult as the number of
statements and interacting variables increases. Tracing a program with hundreds
of variables requires keeping track of hundreds of values, as any statement might
affect or be affected by any variable. To do so for hundreds or thousands of statements or more is untenable. With modular programming and our guiding principle of keeping the scope of variables local to the extent possible, we severely restrict the number of possibilities that we have to consider when debugging. Equally
important is the idea of a contract between client and implementation. Once we
are satisfied that an implementation is meeting its end of the bargain, we can debug
all its clients under that assumption.
Code reuse. Once we have implemented libraries such as StdStats and StdRandom,
we do not have to worry about writing code to compute averages or standard deviations or to generate random numbers again—we can simply reuse the code that
we have written. Moreover, we do not need to make copies of the code: any module
can just refer to any public method in any other module.
Maintenance. Like a good piece of writing, a good program can always be improved, and modular programming facilitates the process of continually improving your Java programs because improving a module improves all of its clients.
For example, it is normally the case that there are several different approaches to
solving a particular problem. With modular programming, you can implement
more than one and try them independently. More importantly, suppose that while
developing a new client, you find a bug in some module. With modular programming, fixing that bug essentially fixes bugs in all of the module’s clients.
253
254
Functions and Modules
If you encounter an old program (or a new program written by an old programmer!), you are likely to find one huge module—a long sequence of statements,
stretching to several pages or more, where any statement can refer to any variable
in the program. Old programs of this kind are found in critical parts of our computational infrastructure (for example, some nuclear power plants and some banks)
precisely because the programmers charged with maintaining them cannot even
understand them well enough to rewrite them in a modern language! With support
for modular programming, modern languages like Java help us avoid such situations by separately developing libraries of methods in independent classes.
The ability to share static methods among different files fundamentally extends our programming model in two different ways. First, it allows us to reuse
code without having to maintain multiple copies of it. Second, by allowing us to
organize a program into files of manageable size that can be independently debugged and compiled, it strongly supports our basic message: whenever you can
clearly separate tasks within a program, you should do so.
In this section, we have supplemented the Std* libraries of Section 1.5 with
several other libraries that you can use: Gaussian, StdArrayIO, StdRandom, and
StdStats. Furthermore, we have illustrated their use with several client programs.
These tools are centered on basic mathematical concepts that arise in any scientific
project or engineering task. Our intent is not just to provide tools, but also to illustrate that it is easy to create your own tools. The first question that most modern programmers ask when addressing a complex task is “Which tools do I need?”
When the needed tools are not conveniently available, the second question is “How
difficult would it be to implement them?” To be a good programmer, you need to
have the confidence to build a software tool when you need it and the wisdom to
know when it might be better to seek a solution in a library.
After libraries and modular programming, you have one more step to learn
a complete modern programming model: object-oriented programming, the topic
of Chapter 3. With object-oriented programming, you can build libraries of functions that use side effects (in a tightly controlled manner) to vastly extend the Java
programming model. Before moving to object-oriented programming, we consider in this chapter the profound ramifications of the idea that any method can call
itself (in Section 2.3) and a more extensive case study (in Section 2.4) of modular
programming than the small clients in this section.
2.2 Libraries and Clients
Q&A
Q. I tried to use StdRandom, but got the error message Exception in thread
"main" java.lang.NoClassDefFoundError: StdRandom. What’s wrong?
A. You need to make StdRandom accessible to Java. See the first Q&A at the end of
Section 1.5.
Q. Is there a keyword that identifies a class as a library?
A. No, any set of public methods will do. There is a bit of a conceptual leap in this
viewpoint because it is one thing to sit down to create a .java file that you will
compile and run, quite another thing to create a .java file that you will rely on
much later in the future, and still another thing to create a .java file for someone
else to use in the future. You need to develop some libraries for your own use before engaging in this sort of activity, which is the province of experienced systems
programmers.
Q. How do I develop a new version of a library that I have been using for a while?
A. With care. Any change to the API might break any client program, so it is best
to work in a separate directory. When you use this approach, you are working with
a copy of the code. If you are changing a library that has a lot of clients, you can
appreciate the problems faced by companies putting out new versions of their software. If you just want to add a few methods to a library, go ahead: that is usually
not too dangerous, though you should realize that you might find yourself in a
situation where you have to support that library for years!
Q. How do I know that an implementation behaves properly? Why not automatically check that it satisfies the API?
A. We use informal specifications because writing a detailed specification is not
much different from writing a program. Moreover, a fundamental tenet of theoretical computer science says that doing so does not even solve the basic problem,
because generally there is no way to check that two different programs perform the
same computation.
255
Functions and Modules
256
Exercises
2.2.1 Add to Gaussian (Program 2.1.2) an implementation of the three-argument
static method pdf(x, mu, sigma) specified in the API that computes the Gaussian
probability density function with a given mean and standard deviation , based
on the formula (x, , ) = f(( x ) / )/. Also add an implementation of the
associated cumulative distribution function cdf(z, mu, sigma), based on the formula (z, , ) = ((z ) / ).
2.2.2 Write a library of static methods that implements the hyperbolic functions
based on the definitions sinh(x) = (e x ex) / 2 and cosh(x) = (e x ex) / 2, with
tanh(x), coth(x), sech(x), and csch(x) defined in a manner analogous to standard
trigonometric functions.
2.2.3 Write a test client for both StdStats and StdRandom that checks that the
methods in both libraries operate as expected. Take a command-line argument n,
generate n random numbers using each of the methods in StdRandom, and print
their statistics. Extra credit : Defend the results that you get by comparing them to
those that are to be expected from analysis.
2.2.4 Add to StdRandom a method shuffle() that takes an array of double values
as argument and rearranges them in random order. Implement a test client that
checks that each permutation of the array is produced about the same number of
times. Add overloaded methods that take arrays of integers and strings.
2.2.5 Develop a client that does stress testing for StdRandom. Pay particular attention to discrete(). For example, do the probabilities sum to 1?
2.2.6 Write a static method that takes double values ymin and ymax (with ymin
strictly less than ymax), and a double array a[] as arguments and uses the StdStats
library to linearly scale the values in a[] so that they are all between ymin and ymax.
2.2.7 Write a Gaussian and StdStats client that explores the effects of changing
the mean and standard deviation for the Gaussian probability density function.
Create one plot with the Gaussian distributions having a fixed mean and various
standard deviations and another with Gaussian distributions having a fixed standard deviation and various means.
2.2 Libraries and Clients
257
2.2.8 Add a method exp() to StdRandom that takes an argument and returns a
random number drawn from the exponential distribution with rate . Hint: If x is a
random number uniformly distributed between 0 and 1, then ln x / is a random
number from the exponential distribution with rate .
2.2.9 Add to StdRandom a static method maxwellBoltzmann() that returns a random value drawn from a Maxwell–Boltzmann distribution with parameter . To
produce such a value, return the square root of the sum of the squares of three
random numbers drawn from the Gaussian distribution with mean 0 and standard
deviation . The speeds of molecules in an ideal gas obey a Maxwell–Boltzmann
distribution.
2.2.10 Modify Bernoulli (Program 2.2.6) to animate the bar graph, replotting it
after each experiment, so that you can watch it converge to the Gaussian distribution. Then add a command-line argument and an overloaded binomial() implementation to allow you to specify the probability p that a biased coin comes up
heads, and run experiments to get a feeling for the distribution corresponding to a
biased coin. Be sure to try values of p that are close to 0 and close to 1.
2.2.11 Develop a full implementation of StdArrayIO (implement all 12 methods
indicated in the API).
2.2.12 Write a library Matrix that implements the following API:
public class Matrix
double dot(double[] a, double[] b)
vector dot product
double[][] multiply(double[][] a, double[][] b)
matrix–matrix product
double[][] transpose(double[][] a)
transpose
double[] multiply(double[][] a, double[] x)
matrix–vector product
double[] multiply(double[] x, double[][] a)
vector–matrix product
(See Section 1.4.) As a test client, use the following code, which performs the same
calculation as Markov (Program 1.6.3):
Functions and Modules
258
public static void main(String[] args)
{
int trials = Integer.parseInt(args[0]);
double[][] p = StdArrayIO.readDouble2D();
double[] ranks = new double[p.length];
rank[0] = 1.0;
for (int t = 0; t < trials; t++)
ranks = Matrix.multiply(ranks, p);
StdArrayIO.print(ranks);
}
Mathematicians and scientists use mature libraries or special-purpose matrix-processing languages for such tasks. See the booksite for details on using such libraries.
2.2.13 Write a
Matrix client that implements the version of Markov described
in Section 1.6 but is based on squaring the matrix, instead of iterating the vector–
matrix multiplication.
2.2.14 Rewrite
RandomSurfer
(Program 1.6.2) using the StdArrayIO and
StdRandom libraries.
Partial solution.
...
double[][] p = StdArrayIO.readDouble2D();
int page = 0; // Start at page 0.
int[] freq = new int[n];
for (int t = 0; t < trials; t++)
{
page = StdRandom.discrete(p[page]);
freq[page]++;
}
...
2.2 Libraries and Clients
Creative Exercises
2.2.15 Sicherman dice. Suppose that you have two six-sided dice, one with faces
labeled 1, 3, 4, 5, 6, and 8 and the other with faces labeled 1, 2, 2, 3, 3, and 4. Compare the probabilities of occurrence of each of the values of the sum of the dice with
those for a standard pair of dice. Use StdRandom and StdStats.
2.2.16 Craps. The following are the rules for a pass bet in the game of craps. Roll
two six-sided dice, and let x be their sum.
• If x is 7 or 11, you win.
• If x is 2, 3, or 12, you lose.
Otherwise, repeatedly roll the two dice until their sum is either x or 7.
• If their sum is x, you win.
• If their sum is 7, you lose.
Write a modular program to estimate the probability of winning a pass bet. Modify
your program to handle loaded dice, where the probability of a die landing on 1
is taken from the command line, the probability of landing on 6 is 1/6 minus that
probability, and 2–5 are assumed equally likely. Hint : Use StdRandom.discrete().
2.2.17 Gaussian random values. Implement the no-argument gaussian() function in StdRandom (Program 2.2.1) using the Box–Muller formula (see Exercise
1.2.27). Next, consider an alternative approach, known as Marsaglia’s method, which
is based on generating a random point in the unit circle and using a form of the
Box–Muller formula (see the discussion of do-while at the end of Section 1.3).
public static double gaussian()
{
double r, x, y;
do
{
x = uniform(-1.0, 1.0);
y = uniform(-1.0, 1.0);
r = x*x + y*y;
} while (r >= 1 || r == 0);
return x * Math.sqrt(-2 * Math.log(r) / r);
}
For each approach, generate 10 million random values from the Gaussian distribution, and measure which is faster.
259
Functions and Modules
260
2.2.18 Dynamic histogram. Suppose that the standard input stream is a sequence
of double values. Write a program that takes an integer n and two double values
and hi from the command line and uses StdStats to plot a histogram of the
count of the numbers in the standard input stream that fall in each of the n intervals defined by dividing (lo , hi) into n equal-sized intervals. Use your program to
add code to your solution to Exercise 2.2.3 to plot a histogram of the distribution
of the numbers produced by each method, taking n from the command line.
lo
2.2.19 Stress test. Develop a client that does stress testing for
StdStats.
Work
with a classmate, with one person writing code and the other testing it.
2.2.20 Gambler’s ruin. Develop a StdRandom client to study the gambler’s ruin
problem (see Program 1.3.8 and Exercise 1.3.24–25). Note : Defining a static method for the experiment is more difficult than for Bernoulli because you cannot
return two values.
2.2.21 IFS. Experiment with various inputs to IFS to create patterns of your own
design like the Sierpinski triangle, the Barnsley fern, or the other examples in the
table in the text. You might begin by experimenting with minor modifications to
the given inputs.
2.2.22 IFS matrix implementation. Write a version of
IFS that uses the static
method multiply() from Matrix (see Exercise 2.2.12) instead of the equations
that compute the new values of x0 and y0.
2.2.23 Library for properties of integers. Develop a library based on the functions
that we have considered in this book for computing properties of integers. Include
functions for determining whether a given integer is prime; determining whether
two integers are relatively prime; computing all the factors of a given integer; computing the greatest common divisor and least common multiple of two integers;
Euler’s totient function (Exercise 2.1.26); and any other functions that you think
might be useful. Include overloaded implementations for long values. Create an
API, a client that performs stress testing, and clients that solve several of the exercises earlier in this book.
2.2 Libraries and Clients
2.2.24 Music library. Develop a library based on the functions in PlayThatTune
(Program 2.1.4) that you can use to write client programs to create and manipulate
songs.
2.2.25 Voting machines. Develop a
StdRandom client (with appropriate static
methods of its own) to study the following problem: Suppose that in a population of 100 million voters, 51% vote for candidate A and 49% vote for candidate
B. However, the voting machines are prone to make mistakes, and 5% of the time
they produce the wrong answer. Assuming the errors are made independently and
at random, is a 5% error rate enough to invalidate the results of a close election?
What error rate can be tolerated?
2.2.26 Poker analysis. Write a StdRandom and StdStats client (with appropriate
static methods of its own) to estimate the probabilities of getting one pair, two pair,
three of a kind, a full house, and a flush in a five-card poker hand via simulation.
Divide your program into appropriate static methods and defend your design decisions. Extra credit : Add straight and straight flush to the list of possibilities.
2.2.27 Animated plots. Write a program that takes a command-line argument m
and produces a bar graph of the m most recent double values on standard input.
Use the same animation technique that we used for BouncingBall (Program 1.5.6):
erase, redraw, show, and wait briefly. Each time your program reads a new number,
it should redraw the whole bar graph. Since most of the picture does not change as
it is redrawn slightly to the left, your program will produce the effect of a fixed-size
window dynamically sliding over the input values. Use your program to plot a huge
time-variant data file, such as stock prices.
2.2.28 Array plot library. Develop your own plot methods that improve upon
those in StdStats. Be creative! Try to make a plotting library that you think will be
useful for some application in the future.
261
Functions and Modules
2.3 Recursion
The idea of calling one function from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Java and most modern
programming languages supports this possibility, which is known as recursion. In
this section, we will study examples of
elegant and efficient recursive solutions
2.3.1 Euclid’s algorithm. . . . . . . . . . 267
to a variety of problems. Recursion is a
2.3.2 Towers of Hanoi. . . . . . . . . . . 270
powerful programming technique that
2.3.3 Gray code. . . . . . . . . . . . . . . 275
we use often in this book. Recursive pro2.3.4 Recursive graphics. . . . . . . . . . 277
grams are often more compact and easier
2.3.5 Brownian bridge. . . . . . . . . . . 279
2.3.6 Longest common subsequence. . . 287
to understand than their nonrecursive
counterparts. Few programmers become
Programs in this section
sufficiently comfortable with recursion
to use it in everyday code, but solving a
problem with an elegantly crafted recursive program is a satisfying experience that
is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
recursive tree (to the left) resembles a real tree, and has a natural recursive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science. It
provides a simple computational model that embraces everything that can
be computed with any computer; it helps us to organize and to analyze
programs; and it is the key to numerous critically important computational applications, ranging from combinatorial search to tree data strucA recursive model
of the natural world tures that support information processing to the fast Fourier transform
for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and make the effort to convince yourself that recursive
programs have the intended effect.
2.3 Recursion
263
Functions and Modules
Functions and Modules
2.3 Recursion
Functions and Modules
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
2.3 Recursion
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
2.3 Recursion
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
seludoM dna snoitcnuF
noisruceR 3.2
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
ecnO .smelborp fo yteirav a ot snoitul
taht ees lliw uoy ,aedi eht ot desu teg uoy
esoprup-lareneg lufrewop a si noisrucer
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
Functions and Modules
2.3 Recursion
seludoM dna snoitcnuF
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
noisruceR 3.2
seludoM dna snoitcnuF
noisruceR 3.2
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
ecnO .smelborp fo yteirav a ot snoitul
taht ees lliw uoy ,aedi eht ot desu teg uoy
esoprup-lareneg lufrewop a si noisrucer
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
ecnO .smelborp fo yteirav a ot snoitul
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
taht ees lliw uoy ,aedi eht ot desu teg uoy
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
esoprup-lareneg lufrewop a si noisrucer
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
Functions and Modules
Functions and Modules
2.3 Recursion
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
2.3 Recursion
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
seludoM dna snoitcnuF
noisruceR 3.2
seludoM dna snoitcnuF
noisruceR 3.2
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
ecnO .smelborp fo yteirav a ot snoitul
taht ees lliw uoy ,aedi eht ot desu teg uoy
esoprup-lareneg lufrewop a si noisrucer
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
Functions and Modules
2.3 Recursion
seludoM dna snoitcnuF
THE IDEA OF CALLING ONE FUNCTION from another immediately suggests the possibility
of a function calling itself. The function-call mechanism in Python and in most
modern programming languages supports this possibility, which is known as recursion. In this section, we will study examples of elegant and efficient recursive so2.3.1 Euclid’s algorithm . . . . . . . . . . 295
lutions to a variety of problems. Once
2.3.2 Towers of Hanoi . . . . . . . . . . . 298
you get used to the idea, you will see that
2.3.3 Gray code . . . . . . . . . . . . . . 303
recursion is a powerful general-purpose
2.3.4 Recursive graphics . . . . . . . . . 305
2.3.5 Brownian bridge . . . . . . . . . . 307
programming technique with many attractive properties. It is a fundamental
Programs in this section
tool that we use often in this
book. Recursive programs are
often more compact and easier to understand than their nonrecursive
counterparts. Few programmers become sufficiently comfortable with
recursion to use it in everyday code, but solving a problem with an elegantly crafted recursive program is a satisfying experience that is certainly accessible to every programmer (even you!).
Recursion is much more than a programming technique. In many
settings, it is a useful way to describe the natural world. For example, the
A recursive model
recursive tree (to the left) resembles a real tree, and has a natural recurof the natural world
sive description. Many, many phenomena are well explained by recursive
models. In particular, recursion plays a central role in computer science.
It provides a simple computational model that embraces everything that can be
computed with any computer; it helps us to organize and to analyze programs; and
it is the key to numerous critically important computational applications, ranging
from combinatorial search to tree data structures that support information processing to the fast Fourier transform for signal processing.
One important reason to embrace recursion is that it provides a straightforward way to build simple mathematical models that we can use to prove important
facts about our programs. The proof technique that we use to do so is known as
mathematical induction. Generally, we avoid going into the details of mathematical
proofs in this book, but you will see in this section that it is worthwhile to understand that point of view and to make the effort to convince yourself that recursive
programs have the intended effect.
noisruceR 3.2
seludoM dna snoitcnuF
noisruceR 3.2
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
ecnO .smelborp fo yteirav a ot snoitul
taht ees lliw uoy ,aedi eht ot desu teg uoy
esoprup-lareneg lufrewop a si noisrucer
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
ecnO .smelborp fo yteirav a ot snoitul
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
taht ees lliw uoy ,aedi eht ot desu teg uoy
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
esoprup-lareneg lufrewop a si noisrucer
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
ytilibissop eht stseggus yletaidemmi rehtona morf NOITCNUF ENO GNILLAC FO AEDI EHT
tsom ni dna nohtyP ni msinahcem llac-noitcnuf ehT .flesti gnillac noitcnuf a fo
-rucer sa nwonk si hcihw ,ytilibissop siht stroppus segaugnal gnimmargorp nredom
-maxe yduts lliw ew ,noitces siht nI .nois
-os evisrucer tneicfife dna tnagele fo selp
592 . . . . . . . . . . mhtirogla s’dilcuE 1.3.2
ecnO .smelborp fo yteirav a ot snoitul
892 . . . . . . . . . . . ionaH fo srewoT 2.3.2
taht ees lliw uoy ,aedi eht ot desu teg uoy
303 . . . . . . . . . . . . . . edoc yarG 3.3.2
esoprup-lareneg lufrewop a si noisrucer
503 . . . . . . . . . scihparg evisruceR 4.3.2
703 . . . . . . . . . . egdirb nainworB 5.3.2
-ta ynam htiw euqinhcet gnimmargorp
latnemadnuf a si tI .seitreporp evitcart
noitces siht ni smargorP
siht ni netfo esu ew taht loot
era smargorp evisruceR .koob
evisrucernon rieht naht dnatsrednu ot reisae dna tcapmoc erom netfo
htiw elbatrofmoc yltneicfifus emoceb sremmargorp weF .strapretnuoc
-ele na htiw melborp a gnivlos tub ,edoc yadyreve ni ti esu ot noisrucer
-niatrec si taht ecneirepxe gniyfsitas a si margorp evisrucer detfarc yltnag
.)!uoy neve( remmargorp yreve ot elbissecca yl
ynam nI .euqinhcet gnimmargorp a naht erom hcum si noisruceR
eht ,elpmaxe roF .dlrow larutan eht ebircsed ot yaw lufesu a si ti ,sgnittes
ledom evisrucer A
-rucer larutan a sah dna ,eert laer a selbmeser )tfel eht ot( eert evisrucer
dlrow larutan eht fo
evisrucer yb denialpxe llew era anemonehp ynam ,ynaM .noitpircsed evis
.ecneics retupmoc ni elor lartnec a syalp noisrucer ,ralucitrap nI .sledom
eb nac taht gnihtyreve secarbme taht ledom lanoitatupmoc elpmis a sedivorp tI
dna ;smargorp ezylana ot dna ezinagro ot su spleh ti ;retupmoc yna htiw detupmoc
gnignar ,snoitacilppa lanoitatupmoc tnatropmi yllacitirc suoremun ot yek eht si ti
-orp noitamrofni troppus taht serutcurts atad eert ot hcraes lairotanibmoc morf
.gnissecorp langis rof mrofsnart reiruoF tsaf eht ot gnissec
-rofthgiarts a sedivorp ti taht si noisrucer ecarbme ot nosaer tnatropmi enO
tnatropmi evorp ot esu nac ew taht sledom lacitamehtam elpmis dliub ot yaw draw
sa nwonk si os od ot esu ew taht euqinhcet foorp ehT .smargorp ruo tuoba stcaf
lacitamehtam fo sliated eht otni gniog diova ew ,yllareneG .noitcudni lacitamehtam
-rednu ot elihwhtrow si ti taht noitces siht ni ees lliw uoy tub ,koob siht ni sfoorp
evisrucer taht flesruoy ecnivnoc ot troffe eht ekam ot dna weiv fo tniop taht dnats
.tceffe dednetni eht evah smargorp
A recursive image
Functions and Modules
264
Your first recursive program The “Hello, World” for recursion is the factorial
function, defined for positive integers n by the equation
n ! = n (n1) (n2) … 2 1
In other words, n! is the product of the positive integers less than or equal to n. Now,
n! is easy to compute with a for loop, but an even easier method is to use the following recursive function:
public static long factorial(int n)
{
if (n == 1) return 1;
return n * factorial(n-1);
}
This function calls itself. The implementation clearly produces the desired effect.
You can persuade yourself that it does so by noting that factorial() returns 1 =
1! when n is 1 and that if it properly computes the value
(n1) ! = (n1) (n2) … 2 1
then it properly computes the value
n ! = n (n1)!
= n (n1) (n2) … 2 1
factorial(5)
To compute factorial(5), the recursive funcfactorial(4)
factorial(3)
tion multiplies 5 by factorial(4); to compute
factorial(2)
factorial(4), it multiplies 4 by factorial(3);
factorial(1)
and so forth. This process is repeated until calling
return 1
return 2*1 = 2
factorial(1), which directly returns the value 1.
return 3*2 = 6
We can trace this computation in precisely the same
return 4*6 = 24
return 5*24 = 120
way that we trace any sequence of function calls.
Since we treat all of the calls as being independent
copies of the code, the fact that they are recursive is Function-call trace for factorial(5)
immaterial.
Our factorial() implementation exhibits the two main components
that are required for every recursive function. First, the base case returns a value without making any subsequent recursive calls. It does this for one or more
special input values for which the function can be evaluated without recursion.
For factorial(), the base case is n = 1. Second, the reduction step is the central
2.3 Recursion
265
part of a recursive function. It relates the value of the function
1 1
2 2
at one (or more) arguments to the value of function at one (or
3 6
more) other arguments. For factorial(), the reduction step is
4 24
5 120
n * factorial(n-1). All recursive functions must have these
6 720
two components. Furthermore, the sequence of argument values
7 5040
must converge to the base case. For factorial(), the value of n
8 40320
9 362880
decreases by 1 for each call, so the sequence of argument values
10 3628800
converges to the base case n = 1.
11 39916800
Tiny programs such as factorial() perhaps become 12 479001600
slightly clearer if we put the reduction step in an else clause. 13 6227020800
14 87178291200
However, adopting this convention for every recursive program 15 1307674368000
would unnecessarily complicate larger programs because it 16 20922789888000
355687428096000
would involve putting most of the code (for the reduction step) 17
18 6402373705728000
within curly braces after the else. Instead, we adopt the conven- 19 121645100408832000
tion of always putting the base case as the first statement, end- 20 2432902008176640000
ing with a return, and then devoting the rest of the code to the
Values of n! in long
reduction step.
The factorial() implementation itself is not particularly
useful in practice because n! grows so quickly that the multiplication will overflow
a long and produce incorrect answers for n > 20. But the same technique is effective for computing all sorts of functions. For example, the recursive function
public static double harmonic(int n)
{
if (n == 1) return 1.0;
return harmonic(n-1) + 1.0/n;
}
computes the nth harmonic numbers (see Program 1.3.5) when n is small, based
on the following equations:
Hn = 1 + 1/2 + … + 1/n
= (1 + 1/2 + … + 1/(n1)) + 1/n
= Hn1 + 1/n
Indeed, this same approach is effective for computing, with only a few lines of code,
the value of any finite sum (or product) for which you have a compact formula.
Recursive functions like these are just loops in disguise, but recursion can help us
better understand the underlying computation.
266
Functions and Modules
Mathematical induction Recursive programming is directly related to mathematical induction, a technique that is widely used for proving facts about the natural numbers.
Proving that a statement involving an integer n is true for infinitely many
values of n by mathematical induction involves the following two steps:
• The base case: prove the statement true for some specific value or values of
n (usually 0 or 1).
• The induction step (the central part of the proof): assume the statement to
be true for all positive integers less than n, then use that fact to prove it true
for n.
Such a proof suffices to show that the statement is true for infinitely many values of
n: we can start at the base case, and use our proof to establish that the statement is
true for each larger value of n, one by one.
Everyone’s first induction proof is to demonstrate that the sum of the positive
integers less than or equal to n is given by the formula n (n + 1) / 2. That is, we wish
to prove that the following equation is valid for all n 1:
1 + 2 + 3 … + (n1) + n = n (n + 1) / 2
The equation is certainly true for n = 1 (base case) because 1 = 1(1 + 1) / 2. If we
assume it to be true for all positive integers less than n, then, in particular, it is true
for n1, so
1 + 2 + 3 … + (n1) = (n1) n / 2
and we can add n to both sides of this equation and simplify to get the desired
equation (induction step).
Every time we write a recursive program, we need mathematical induction to
be convinced that the program has the desired effect. The correspondence between
induction and recursion is self-evident. The difference in nomenclature indicates
a difference in outlook: in a recursive program, our outlook is to get a computation done by reducing to a smaller problem, so we use the term reduction step; in
an induction proof, our outlook is to establish the truth of the statement for larger
problems, so we use the term induction step.
When we write recursive programs we usually do not write down a full formal
proof that they produce the desired result, but we are always dependent upon the
existence of such a proof. We often appeal to an informal induction proof to convince ourselves that a recursive program operates as expected. For example, we just
discussed an informal proof to become convinced that factorial() computes the
product of the positive integers less than or equal to n.
2.3 Recursion
Program 2.3.1
267
Euclid’s algorithm
public class Euclid
{
public static int gcd(int p, int q)
{
if (q == 0) return p;
return gcd(q, p % q);
}
public static void main(String[] args)
{
int p = Integer.parseInt(args[0]);
int q = Integer.parseInt(args[1]);
int divisor = gcd(p, q);
StdOut.println(divisor);
}
}
p, q
divisor
arguments
greatest common divisor
% java Euclid 1440 408
24
% java Euclid 314159 271828
1
This program prints the greatest common divisor of its two command-line arguments, using a
recursive implementation of Euclid’s algorithm.
Euclid’s algorithm
The greatest common divisor (gcd) of two positive integers
is the largest integer that divides evenly into both of them. For example, the greatest
common divisor of 102 and 68 is 34 since both 102 and 68 are multiples of 34, but
no integer larger than 34 divides evenly into 102 and 68. You may recall learning
about the greatest common divisor when you learned to reduce fractions. For example, we can simplify 68/102 to 2/3 by dividing both numerator and denominator
by 34, their gcd. Finding the gcd of huge numbers is an important problem that
arises in many commercial applications, including the famous RSA cryptosystem.
We can efficiently compute the gcd using the following property, which holds
for positive integers p and q:
If p > q, the gcd of p and q is the same as the gcd of q and p % q.
268
Functions and Modules
To convince yourself of this fact, first note that the gcd of p and q is the same as the
gcd of q and pq, because a number divides both p and q if and only if it divides
both q and pq. By the same argument, q and p2q, q and p3q, and so forth have
the same gcd, and one way to compute p % q is to subtract q from p until getting a
number less than q.
The static method gcd() in Euclid (Program 2.3.1) is a compact recursive
function whose reduction step is based on this property. The base case is when q
is 0, with gcd(p, 0) = p. To see that the reduction step converges to the base case,
observe that the second argument value strictly decreases
in each recursive call since p % q < q. If p < q, the gcd(1440, 408)
gcd(408, 216)
first recursive call effectively switches the order of the two
gcd(216, 192)
gcd(192, 24)
arguments. In fact, the second argument value decreases
gcd(24, 0)
by at least a factor of 2 for every second recursive call, so
return 24
the sequence of argument values quickly converges to the
return 24
return 24
base case (see Exercise 2.3.11). This recursive solution to
return 24
the problem of computing the greatest common divisor is
return 24
known as Euclid’s algorithm and is one of the oldest known Function-call trace for gcd()
algorithms—it is more than 2,000 years old.
Towers of Hanoi No discussion of recursion would be complete without the
ancient towers of Hanoi problem. In this problem, we have three poles and n discs
that fit onto the poles. The discs differ in size and are initially stacked on one of
the poles, in order from largest (disc n) at the bottom to smallest (disc 1) at the top.
The task is to move all n discs to another pole, while obeying the following rules:
• Move only one disc at a time.
• Never place a larger disc on a smaller one.
One legend says that the world will end when a certain group of monks accomplishes this task in a temple with 64 golden discs on three diamond needles. But
how can the monks accomplish the task at all, playing by the rules?
To solve the problem, our goal is to issue a sequence of instructions for moving the discs. We assume that the poles are arranged in a row, and that each instruction to move a disc specifies its number and whether to move it left or right.
If a disc is on the left pole, an instruction to move left means to wrap to the right
pole; if a disc is on the right pole, an instruction to move right means to wrap
to the left pole. When the discs are all on one pole, there are two possible moves
(move the smallest disc left or right); otherwise, there are three possible moves
2.3 Recursion
269
(move the smallest disc left or right, or make the one legal
move involving the other two poles). Choosing among these
possibilities on each move to achieve the goal is a challenge
that requires a plan. Recursion provides just the plan that
we need, based on the following idea: first we move the top
n1 discs to an empty pole, then we move the largest disc
to the other empty pole (where it does not interfere with the
smaller ones), and then we complete the job by moving the
n1 discs onto the largest disc.
TowersOfHanoi (Program 2.3.2) is a direct implementation of this recursive strategy. It takes a command-line
argument n and prints the solution to the towers of Hanoi
problem on n discs. The recursive function moves() prints
the sequence of moves to move the stack of discs to the
left (if the argument left is true) or to the right (if left
is false). It does so exactly according to the plan just described.
Function-call trees To better understand the behav-
1
start position
move n–1 discs to the right (recursively)
move largest disc left (wrap to rightmost)
move n–1 discs to the right (recursively)
Recursive plan for towers of Hanoi
ior of modular programs that have multiple recursive calls
(such as TowersOfHanoi), we use a visual representation known as a function-call
tree. Specifically, we represent each method call as a tree node, depicted as a circle
labeled with the values of the arguments for that call. Below each tree node, we
draw the tree nodes corresponding to each call in that use of the method (in order
from left to right) and lines connecting to them. This diagram contains all the information we need to understand the behavior of the program. It contains a tree
node for each function call.
We can use function-call trees to understand the behavior of any modular
program, but they are particularly useful in exposing the behavior of recursive
programs. For example, the tree
corresponding to a call to move()
4
in TowersOfHanoi is easy to con3
3
struct. Start by drawing a tree
node labeled with the values of
2
2
2
2
the command-line arguments.
1
1
1
1
1
1
1
The first argument is the number
Function-call tree for moves(4,
true) in TowersOfHanoi
Functions and Modules
270
Program 2.3.2
Towers of Hanoi
public class TowersOfHanoi
{
public static void moves(int n, boolean left)
{
if (n == 0) return;
moves(n-1, !left);
if (left) StdOut.println(n + " left");
else
StdOut.println(n + " right");
moves(n-1, !left);
}
public static void main(String[] args)
{ // Read n, print moves to move n discs left.
int n = Integer.parseInt(args[0]);
moves(n, true);
}
}
The recursive method moves() prints the
true) or to the right (if left is false).
% java TowersOfHanoi 1
1 left
%
1
2
1
java TowersOfHanoi 2
right
left
right
%
1
2
1
3
1
2
1
java TowersOfHanoi 3
left
right
left
left
left
right
left
n
left
number of discs
direction to move pile
moves needed to move n discs to the left (if left is
%
1
2
1
3
1
2
1
4
1
2
1
3
1
2
1
java TowersOfHanoi 4
right
left
right
right
right
left
right
left
right
left
right
right
right
left
right
2.3 Recursion
271
of discs in the pile to be moved (and the label of the disc to actually be moved);
the second is the direction to move the disc. For clarity, we depict the direction (a
boolean value) as an arrow that points left or right, since that is our interpretation
of the value—the direction to move the piece. Then draw two tree nodes below
with the number of discs decremented by 1 and the direction switched, and continue doing so until only nodes with labels corresponding
moves(4, true)
to a first argument value 1 have no nodes below them.
moves(3, false)
moves(2, true)
These nodes correspond to calls on moves() that do
moves(1, false)
1 right
not lead to further recursive calls.
Take a moment to study the function-call tree
2 left
depicted earlier in this section and to compare it with
moves(1, false)
1 right
the corresponding function-call trace depicted at right.
When you do so, you will see that the recursion tree is
3 right
moves(2, true)
just a compact representation of the trace. In particumoves(1, false)
lar, reading the node labels from left to right gives the
1 right
moves needed to solve the problem.
2 left
Moreover, when you study the tree, you probably
moves(1, false)
notice several patterns, including the following two:
1 right
• Alternate moves involve the smallest disc.
3 discs moved right
• That disc always moves in the same direction.
4 left
These observations are relevant because they give a
disc 4 moved left
moves(3, false)
solution to the problem that does not require recurmoves(2, true)
sion (or even a computer): every other move involves
moves(1, false)
1 right
the smallest disc (including the first and last), and each
intervening move is the only legal move at the time
2 left
not involving the smallest disc. We can prove that this
moves(1, false)
1 right
approach produces the same outcome as the recursive
program, using induction. Having started centuries
3 right
moves(2, true)
ago without the benefit of a computer, perhaps our
moves(1, false)
1 right
monks are using this approach.
Trees are relevant and important in understand2 left
ing recursion because the tree is a quintessential recurmoves(1, false)
sive object. As an abstract mathematical model, trees
1 right
play an essential role in many applications, and in
3 discs moved right
Chapter 4, we will consider the use of trees as a compuFunction-call trace for moves(4, true)
tational model to structure data for efficient processing.
272
Functions and Modules
Exponential time One advantage of using recursion is that often we can develop mathematical models that allow us to prove important facts about the behavior of recursive programs. For the towers of Hanoi problem, we can estimate the
amount of time until the end of the world (assuming that the legend is true). This
exercise is important not just because it tells us that the end of the world is quite far
off (even if the legend is true), but also because it provides insight that can help us
avoid writing programs that will not finish until then.
The mathematical model for the towers of Hanoi problem is simple: if we
define the function T(n) to be the number of discs moved by TowersOfHanoi to
solve an n-disc problem, then the recursive code implies that T(n) must satisfy the
following equation:
T(n) = 2 T(n1) 1 for n > 1, with T(1) = 1
Such an equation is known in discrete mathematics as a recurrence relation. Recurrence relations naturally arise in the study of recursive programs. We can often use
them to derive a closed-form expression for the quantity of interest. For T(n), you
may have already guessed from the initial values T(1) = 1, T(2) = 3, T(3), = 7, and
T(4) = 15 that T(n) = 2 n 1. The recurrence relation provides a way to prove this
to be true, by mathematical induction:
• Base case : T(1) = 2n 1 = 1
• Induction step: if T(n1)= 2n1 1, T(n) = 2 (2n1 1) 1 = 2n 1
Therefore, by induction, T(n) = 2n 1 for all n > 0. The minimum possible
number of moves also satisfies the same recurrence (see Exercise 2.3.11).
(30, 230)
Knowing the value of T(n), we can estimate the amount of time required to perform all the moves. If the monks move discs at the rate of one
per second, it would take more than one week for them to finish a 20-disc
problem, more than 34 years to finish a 30-disc problem, and more than
348 centuries for them to finish a 40-disc problem (assuming that they do
not make a mistake). The 64-disc problem would take more than 5.8 billion centuries. The end of the world is likely to be even further off than
that because those monks presumably never have had the benefit of using
Program 2.3.2, and might not be able to move the discs so rapidly or to
figure out so quickly which disc to move next.
Even computers are no match for exponential growth. A computer
that can do a billion operations per second will still take centuries to do 264
(20, 220)
1,000
operations, and no computer will ever do 2
operations, say. The lesson
is profound: with recursion, you can easily write simple short programs
Exponential
growth
2.3 Recursion
273
that take exponential time, but they simply will not run to completion when you
try to run them for large n. Novices are often skeptical of this basic fact, so it is
worth your while to pause now to think about it. To convince yourself that it is true,
take the print statements out of TowersOfHanoi and run it for increasing values of
n starting at 20. You can easily verify that each time you increase the value of n by 1,
the running time doubles, and you will quickly lose patience waiting for it to finish.
If you wait for an hour for some value of n, you will wait more than a day for n + 5,
more than a month for n + 10, and more than a century for n + 20 (no one has that
much patience). Your computer is just not fast enough to run every short Java program that you write, no matter how simple the program might seem! Beware of
programs that might require exponential time.
We are often interested in predicting the running time of our programs. In
Section 4.1, we will discuss the use of the same process that we just used to help
estimate the running time of other programs.
Gray codes
The towers of Hanoi problem is no toy. It is intimately related to
basic algorithms for manipulating numbers and discrete objects. As an example,
we consider Gray codes, a mathematical abstraction with numerous applications.
The playwright Samuel Beckett, perhaps best known for Waiting for Godot,
wrote a play called Quad that had the following property: starting with an empty
stage, characters enter and exit one at a time so that each subset of characters on
the stage appears exactly once. How did Beckett generate the stage directions for
this play?
One way to represent a subset of n discrete objects is to
code
subset
empty
use a string of n bits. For Beckett’s problem, we use a 4-bit 0 0 0 0
0
0
0
1
1
string, with bits numbered from right to left and a bit value of 1 0 0 1 1
2 1
indicating the character onstage. For example, the string 0 1 0 0 0 1 0
2
3 2
1 corresponds to the scene with characters 3 and 1 onstage. This 0 1 1 0
0 1 1 1
3 2 1
representation gives a quick proof of a basic fact: the number 0 1 0 1
3 1
3
different subsets of n objects is exactly 2 n. Quad has four charac- 0 1 0 0
1 1 0 0
4 3
4
ters, so there are 2 = 16 different scenes. Our task is to generate 1 1 0 1 4 3 1
1 1 1 1 4 3 2 1
the stage directions.
1 1 1 0
4 3 2
n
An n-bit Gray code is a list of the 2 different n-bit binary 1 0 1 0
4 2
numbers such that each element in the list differs in precisely 1 0 1 1 4 2 1
4 1
one bit from its predecessor. Gray codes directly apply to Beck- 11 00 00 10
4
ett’s problem because changing the value of a bit from 0 to 1
move
enter
enter
exit
enter
enter
exit
exit
enter
enter
enter
exit
exit
enter
exit
exit
Gray code representations
1
2
1
3
1
2
1
4
1
2
1
3
1
2
1
274
Functions and Modules
corresponds to a character entering the subset onstage; changing a bit from 1 to 0
corresponds to a character exiting the subset.
How do we generate a Gray code? A recursive plan that is very similar to the
one that we used for the towers of Hanoi problem is effective. The n-bit binaryreflected Gray code is defined recursively as follows:
• The (n1) bit code, with 0 prepended to each word, followed by
• The (n1) bit code in reverse order, with 1 prepended to each word
The 0-bit code is defined to be empty, so the 1-bit code is 0 followed by 1. From this
recursive definition, we can verify by induction that the n-bit binary reflected Gray
code has the required property: adjacent codewords differ in one bit position. It is
true by the inductive hypothesis, except possibly for the last codeword in the first
half and the first codeword in the second half: this pair differs only in their first bit.
The recursive definition leads, after some
1-bit code
3-bit code
careful thought, to the implementation in Beckett
(Program 2.3.3) for printing Beckett’s stage direc- 2-bit 0 0
4-bit 0 0 0 0
0 1
tions. This program is remarkably similar to Tow0 0 0 1
1 1
0 0 1 1
ersOfHanoi. Indeed, except for nomenclature, the
1 0
0 0 1 0
only difference is in the values of the second argu1-bit code
0 1 1 0
(reversed)
0 1 1 1
ments in the recursive calls!
0 1 0 1
2-bit code
As with the directions in TowersOfHanoi, the
0 1 0 0
1 1 0 0
enter and exit directions are redundant in Beckett, 3-bit 0 0 0
1 1 0 1
0 0 1
since exit is issued only when an actor is onstage,
1 1 1 1
0 1 1
1 1 1 0
0 1 0
and enter is issued only when an actor is not on1 0 1 0
1 1 0
stage. Indeed, both Beckett and TowersOfHanoi
1 0 1 1
1 1 1
1 0 0 1
1 0 1
directly involve the ruler function that we consid1 0 0 0
1 0 0
ered in one of our first programs (Program 1.2.1).
3-bit code
Without the printing instructions, they both imple2-bit code
(reversed)
(reversed)
ment a simple recursive function that could allow
2-, 3-, and 4-bit Gray codes
Ruler to print the values of the ruler function for
any value given as a command-line argument.
Gray codes have many applications, ranging
from analog-to-digital converters to experimental design. They have been used in
pulse code communication, the minimization of logic circuits, and hypercube architectures, and were even proposed to organize books on library shelves.
2.3 Recursion
Program 2.3.3
275
Gray code
public class Beckett
{
public static void moves(int n, boolean enter)
{
if (n == 0) return;
moves(n-1, true);
if (enter) StdOut.println("enter " + n);
else
StdOut.println("exit " + n);
moves(n-1, false);
}
n
enter
number of actors
stage direction
public static void main(String[] args)
{
int n = Integer.parseInt(args[0]);
moves(n, true);
}
}
This recursive program gives Beckett’s stage instructions (the bit positions that change in a
binary-reflected Gray code). The bit position that changes is precisely described by the ruler
function, and (of course) each actor alternately enters and exits.
% java Beckett 1
enter 1
% java Beckett 2
enter 1
enter 2
exit 1
% java Beckett 3
enter 1
enter 2
exit 1
enter 3
enter 1
exit 2
exit 1
% java Beckett 4
enter 1
enter 2
exit 1
enter 3
enter 1
exit 2
exit 1
enter 4
enter 1
enter 2
exit 1
exit 3
enter 1
exit 2
exit 1
276
Functions and Modules
Recursive graphics Simple recursive drawing schemes can lead to pictures
that are remarkably intricate. Recursive drawings not only relate to numerous applications, but also provide an appealing platform for developing a better understanding of properties of recursive functions, because we can watch the process of
a recursive figure taking shape.
As a first simple example, consider Htree (Program 2.3.4), which, given a
command-line argument n, draws an H-tree of order n, defined as follows: The base
case is to draw nothing for n = 0. The reduction step is to draw, within the unit
square
• three lines in the shape of the letter H
• four H-trees of order n1, one centered at each tip of the H
with the additional proviso that the H-trees of order n1 are halved in size.
Drawings like these have many practical applications. For example, consider a cable company that needs to run cable to all of the order 1
homes distributed throughout its region. A reasonable strategy is to
use an H-tree to get the signal to a suitable number of centers distributed throughout the region, then run cables connecting each home
to the nearest center. The same problem is faced by computer designorder 2
ers who want to distribute power or signal throughout an integrated
circuit chip.
Though every drawing is in a fixed-size window, H-trees certainly exhibit exponential growth. An H-tree of order n connects 4n
centers, so you would be trying to plot more than a million lines with
n = 10, and more than a billion with n = 15. The program will certainly order 3
not finish the drawing with n = 30.
If you take a moment to run Htree on your computer for a
drawing that takes a minute or so to complete, you will, just by watching the drawing progress, have the opportunity to gain substantial insight into the nature of recursive programs, because you can see the
H-trees
order in which the H figures appear and how they form into H-trees.
An even more instructive exercise, which derives from the fact that
the same drawing results no matter in which order the recursive draw() calls and
the StdDraw.line() calls appear, is to observe the effect of rearranging the order
of these calls on the order in which the lines appear in the emerging drawing (see
Exercise 2.3.14).
2.3 Recursion
Program 2.3.4
277
Recursive graphics
public class Htree
{
public static void draw(int n, double size, double x, double y)
{ // Draw an H-tree centered at x, y
n
depth
// of depth n and given size.
size line length
if (n == 0) return;
x, y center
double x0 = x - size/2, x1 = x + size/2;
double y0 = y - size/2, y1 = y + size/2;
StdDraw.line(x0, y, x1, y);
StdDraw.line(x0, y0, x0, y1);
StdDraw.line(x1, y0, x1, y1);
(x0, y1)
(x1, y1)
draw(n-1, size/2, x0, y0);
draw(n-1, size/2, x0, y1);
(x, y)
draw(n-1, size/2, x1, y0);
size
draw(n-1, size/2, x1, y1);
}
public static void main(String[] args)
{
int n = Integer.parseInt(args[0]);
draw(n, 0.5, 0.5, 0.5);
}
(x0, y0)
}
The function draw() draws three lines, each of length size, in the shape of the letter H, centered at (x, y). Then, it calls itself recursively for each of the four tips, halving the size argument in each call and using an integer argument n to control the depth of the recursion.
% java Htree 3
% java Htree 4
% java Htree 5
(x1, y0)
Functions and Modules
278
Brownian bridge
An H-tree is a simple example of a fractal: a geometric shape
that can be divided into parts, each of which is (approximately) a reduced-size copy
of the original. Fractals are easy to produce with recursive programs, although scientists, mathematicians, and programmers study them from many different points
of view. We have already encountered fractals several times in this book—for example, IFS (Program 2.2.3).
The study of fractals plays an important and lasting role in artistic expression,
economic analysis, and scientific discovery. Artists and scientists use fractals to
build compact models of complex shapes that arise in nature and resist description
using conventional geometry, such as clouds, plants, mountains, riverbeds, human
skin, and many others. Economists use fractals to model function graphs of economic indicators.
Fractional Brownian motion is a mathematical model for creating realistic
fractal models for many naturally rugged shapes. It is used in computational finance and in the study of many natural phenomena, including ocean flows and
nerve membranes. Computing the exact fractals specified by the model can be a
difficult challenge, but it is not difficult to compute approximations with recursive
programs.
Brownian (Program 2.3.5) produces a function graph that approximates a
simple example of fractional Brownian motion known as a Brownian bridge and
closely related functions. You can think of this graph as
a random walk that connects the two points (x0, y0) and
(x m , y m + )
(x1, y1), controlled by a few parameters. The implemen(x 1 , y 1 )
tation is based on the midpoint displacement method,
random
which is a recursive plan for drawing the plot within displacement
(x m , y m )
the x-interval [x0, x1]. The base case (when the length
of the interval is smaller than a given tolerance) is to
(x 0 , y 0 )
draw a straight line connecting the two endpoints. The
reduction case is to divide the interval into two halves,
Brownian bridge calculation
proceeding as follows:
• Compute the midpoint (xm, ym) of the interval.
• Add to the y-coordinate ym of the midpoint a random value , drawn from
the Gaussian distribution with mean 0 and a given variance.
• Recur on the subintervals, dividing the variance by a given scaling factor s.
The shape of the curve is controlled by two parameters: the volatility (initial value
of the variance) controls the distance the function graph strays from the straight
2.3 Recursion
Program 2.3.5
279
Brownian bridge
public class Brownian
{
public static void curve(double x0, double y0,
x0, y0
double x1, double y1,
x1, y1
double var, double s)
xm, ym
{
if (x1 - x0 < 0.01)
delta
{
var
StdDraw.line(x0, y0, x1, y1);
return;
hurst
}
double xm = (x0 + x1) / 2;
double ym = (y0 + y1) / 2;
double delta = StdRandom.gaussian(0, Math.sqrt(var));
curve(x0, y0, xm, ym + delta, var/s, s);
curve(xm, ym+delta, x1, y1, var/s, s);
}
public static void main(String[] args)
{
double hurst = Double.parseDouble(args[0]);
double s = Math.pow(2, 2*hurst);
curve(0, 0.5, 1.0, 0.5, 0.01, s);
}
}
left endpoint
right endpoint
middle
displacement
variance
Hurst exponent
By adding a small, random Gaussian to a recursive program that would otherwise plot a
straight line, we get fractal curves. The command-line argument hurst, known as the Hurst
exponent, controls the smoothness of the curves.
% java Brownian 1
% java Brownian 0.5
% java Brownian 0.05
Functions and Modules
280
line connecting the points, and the Hurst exponent controls the smoothness of
the curve. We denote the Hurst exponent by H and divide the variance by 22H at
each recursive level. When H is 1/2 (halved at each level), the curve is a Brownian bridge—a continuous version of the gambler’s ruin problem (see Program
1.3.8). When 0 < H < 1/2, the displacements tend to increase, resulting in a rougher
curve. Finally, when 2 > H > 1/2, the displacements tend to decrease, resulting in
a smoother curve. The value 2 H is known as the fractal dimension of the curve.
The volatility and initial endpoints of the interval have to do with scale and
positioning. The main() test client in Brownian allows you to experiment with
the Hurst exponent. With values larger than 1/2, you get plots that look something
like the horizon in a mountainous landscape; with values smaller than 1/2, you get
plots similar to those you might see for the value of a stock index.
Extending the midpoint displacement method to two dimensions yields fractals known as plasma clouds. To draw a rectangular plasma cloud, we use a recursive
plan where the base case is to draw a rectangle of a given color and the reduction
step is to draw a plasma cloud in each of the four quadrants with colors that are
perturbed from the average with a random Gaussian. Using the same volatility
and smoothness controls as in Brownian, we can produce synthetic clouds that are
remarkably realistic. We can use the same code to produce synthetic terrain, by interpreting the color value as the altitude. Variants of this scheme are widely used in
the entertainment industry to generate background scenery for movies and games.
1
2
3
4
8
5
6
7
Plasma clouds
2.3 Recursion
Pitfalls of recursion
By now, you are perhaps persuaded that recursion can
help you to write compact and elegant programs. As you begin to craft your own
recursive programs, you need to be aware of several common pitfalls that can arise.
We have already discussed one of them in some detail (the running time of your
program might grow exponentially). Once identified, these problems are generally
not difficult to overcome, but you will learn to be very careful to avoid them when
writing recursive programs.
Missing base case. Consider the following recursive function, which is supposed
to compute harmonic numbers, but is missing a base case:
public static double harmonic(int n)
{
return harmonic(n-1) + 1.0/n;
}
If you run a client that calls this function, it will repeatedly call itself and never
return, so your program will never terminate. You probably already have encountered infinite loops, where you invoke your program and nothing happens (or perhaps you get an unending sequence of printed output). With infinite recursion,
however, the result is different because the system keeps track of each recursive call
(using a mechanism that we will discuss in Section 4.3, based on a data structure
known as a stack) and eventually runs out of memory trying to do so. Eventually,
Java reports a StackOverflowError at run time. When you write a recursive program, you should always try to convince yourself that it has the desired effect by an
informal argument based on mathematical induction. Doing so might uncover a
missing base case.
No guarantee of convergence. Another common problem is to include within a
recursive function a recursive call to solve a subproblem that is not smaller than the
original problem. For example, the following method goes into an infinite recursive loop for any value of its argument (except 1) because the sequence of argument
values does not converge to the base case:
public static double harmonic(int n)
{
if (n == 1) return 1.0;
return harmonic(n) + 1.0/n;
}
281
Functions and Modules
282
Bugs like this one are easy to spot, but subtle versions of the same problem can be
harder to identify. You may find several examples in the exercises at the end of this
section.
Excessive memory requirements. If a function calls itself recursively an excessive
number of times before returning, the memory required by Java to keep track of
the recursive calls may be prohibitive, resulting in a StackOverflowError. To get
an idea of how much memory is involved, run a small set of experiments using our
recursive function for computing the harmonic numbers for increasing values of n:
public static double harmonic(int n)
{
if (n == 1) return 1.0;
return harmonic(n-1) + 1.0/n;
}
The point at which you get StackOverflowError will give you some idea of how
much memory Java uses to implement recursion. By contrast, you can run Program
1.3.5 to compute Hn for huge n using only a tiny bit of memory.
Excessive recomputation. The temptation to write a simple recursive function to
solve a problem must always be tempered by the understanding that a function
might take exponential time (unnecessarily) due to excessive recomputation. This
effect is possible even in the simplest recursive functions, and you certainly need to
learn to avoid it. For example, the Fibonacci sequence
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, …
is defined by the recurrence Fn = Fn1 + Fn2 for n 2 with F0 = 0 and F1 = 1. The
Fibonacci sequence has many interesting properties and arise in numerous applications. A novice programmer might implement this recursive function to compute
numbers in the Fibonacci sequence:
// Warning: this function is spectacularly inefficient.
public static long fibonacci(int n)
{
if (n == 0) return 0;
if (n == 1) return 1;
return fibonacci(n-1) + fibonacci(n-2);
}
2.3 Recursion
fibonacci(8)
fibonacci(7)
fibonacci(6)
fibonacci(5)
fibonacci(4)
fibonacci(3)
fibonacci(2)
fibonacci(1)
return 1
fibonacci(0)
return 0
return 1
fibonacci(1)
return 1
return 2
fibonacci(2)
fibonacci(1)
return 1
fibonacci(0)
return 0
return 1
return 3
fibonacci(3)
fibonacci(2)
fibonacci(1)
return 1
fibonacci(0)
return 0
return 1
fibonacci(1)
return 1
return 2
return 5
fibonacci(4)
fibonacci(3)
fibonacci(2)
.
.
.
Wrong way to compute Fibonacci numbers
283
However, this function is spectacularly inefficient! Novice programmers often refuse to
believe this fact, and run code like this expecting that the computer is certainly fast enough
to crank out an answer. Go ahead; see if your
computer is fast enough to use this function to
compute fibonacci(50). To see why it is futile to do so, consider what the function does to
compute fibonacci(8) = 21. It first computes
fibonacci(7) = 13 and fibonacci(6) = 8. To
compute fibonacci(7), it recursively computes
fibonacci(6) = 8 again and fibonacci(5) = 5.
Things rapidly get worse because both times it
computes fibonacci(6), it ignores the fact
that it already computed fibonacci(5), and
so forth. In fact, the number of times this program computes fibonacci(1) when computing fibonacci(n) is precisely Fn (see Exercise
2.3.12). The mistake of recomputation is
compounded exponentially. As an example,
fibonacci(200) makes F200 > 1043 recursive
calls to fibonacci(1)! No imaginable computer will ever be able to do this many calculations.
Beware of programs that might require exponential time. Many calculations that arise and find
natural expression as recursive functions fall
into this category. Do not fall into the trap of
implementing and trying to run them.
Next, we consider a systematic technique known
as dynamic programming, an elegant technique
for avoiding such problems. The idea is to avoid
the excessive recomputation inherent in some
recursive functions by saving away the previously computed values for later reuse, instead of
constantly recomputing them.
Functions and Modules
284
Dynamic programming
A general approach to implementing recursive programs, known as dynamic programming, provides effective and elegant solutions to
a wide class of problems. The basic idea is to recursively divide a complex problem
into a number of simpler subproblems; store the answer to each of these subproblems; and, ultimately, use the stored answers to solve the original problem. By solving each subproblem only once (instead of over and over), this technique avoids a
potential exponential blow-up in the running time.
For example, if our original problem is to compute the nth Fibonacci number,
then it is natural to define n + 1 subproblems, where subproblem i is to compute
the ith Fibonacci number for each 0 i n. We can solve subproblem i easily if
we already know the solutions to smaller subproblems—specifically, subproblems
i1 and i2. Moreover, the solution to our original problem is simply the solution
to one of the subproblems—subproblem n.
Top-down dynamic programming. In top-down dynamic programming, we
store or cache the result of each subproblem that we solve, so that the next time we
need to solve the same subproblem, we can use the cached values instead of solving
the subproblem from scratch. For our Fibonacci example, we use an array f[] to
store the Fibonacci numbers that have already been computed. We accomplish this
in Java by using a static variable, also known as a class variable or global variable,
that is declared outside of any method. This allows us to save information from one
function call to the next.
public class TopDownFibonacci
cached values
{
private static long[] f = new long[92];
static variable
(declared outside
of any method)
}
public static long fibonacci(int n)
{
return cached value
(if previously computed)
if (n == 0) return 0;
if (n == 1) return 1;
if (f[n] > 0) return f[n];
f[n] = fibonacci(n-1) + fibonacci(n-2);
return f[n];
}
compute and cache value
Top-down dynamic programming approach for computing Fibonacci numbers
Top-down dynamic programming is also known as memoization because it avoids
duplicating work by remembering the results of function calls.
2.3 Recursion
Bottom-up dynamic programming. In bottom-up dynamic programming, we
compute solutions to all of the subproblems, starting with the “simplest” subproblems and gradually building up solutions to more and more complicated subproblems. To apply bottom-up dynamic programming, we must order the subproblems
so that each subsequent subproblem can be solved by combining solutions to subproblems earlier in the order (which have already been solved). For our Fibonacci
example, this is easy: solve the subproblems in the order 0, 1, and 2, and so forth.
By the time we need to solve subproblem i, we have already solved all smaller subproblems—in particular, subproblems i1 and i2.
public static long fibonacci(int n)
{
long[] f = new int[n+1];
f[0] = 0;
f[1] = 1;
for (int i = 2; i <= n; i++)
f[i] = f[i-1] + f[i-2];
return f[n];
}
When the ordering of the subproblems is clear, and space is available to store all the
solutions, bottom-up dynamic programming is a very effective approach.
Next, we consider a more sophisticated application of dynamic programming,
where the order of solving the subproblems is not so clear (until you see it). Unlike the problem of computing Fibonacci numbers, this problem would be much
more difficult to solve without thinking recursively and also applying a bottom-up
dynamic programming approach.
Longest common subsequence problem. We consider a fundamental string-processing problem that arises in computational biology and other domains. Given
two strings x and y, we wish to determine how similar they are. Some examples
include comparing two DNA sequences for homology, two English words for spelling, or two Java files for repeated code. One measure of similarity is the length of
the longest common subsequence (LCS). If we delete some characters from x and
some characters from y, and the resulting two strings are equal, we call the resulting
string a common subsequence. The LCS problem is to find a common subsequence
of two strings that is as long as possible. For example, the LCS of GGCACCACG and
ACGGCGGATACG is GGCAACG, a string of length 7.
285
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286
Algorithms to compute the LCS are used in data comparison programs like
the diff command in Unix, which has been used for decades by programmers
wanting to understand differences and similarities in their text files. Similar algorithms play important roles in scientific applications, such as the Smith–Waterman
algorithm in computational biology and the Viterbi algorithm in digital communications theory.
Longest common subsequence recurrence. Now we describe a recursive formulation that enables us to find the LCS of two given strings s and t. Let m and n be the
lengths of s and t, respectively. We use the notation s[i..m) to denote the suffix
of s starting at index i, and t[j..n) to denote the suffix of t starting at index j.
On the one hand, if s and t begin with the same character, then the LCS of x and
y contains that first character. Thus, our problem reduces to finding the LCS of the
suffixes s[1..m) and t[1..n). On the other hand, if s and t begin with different
characters, both characters cannot be part of a common subsequence, so we can
safely discard one or the other. In either case, the problem reduces to finding the
LCS of two strings—either s[0..m) and t[1..n) or s[1..m) and t[0..n)—one
of which is strictly shorter. In general, if we let opt[i][j] denote the length of the
LCS of the suffixes s[i..m) and t[j..m), then the following recurrence expresses
opt[i][j] in terms of the length of the LCS for shorter suffixes.
opt[i][j] =
0
opt[i+1, j+1] + 1
max(opt[i, j+1], opt[i+1, j])
Dynamic programming solution.
if i = m or j = n
if s[i] = t[j]
otherwise
LongestCommonSubsequence (Program 2.3.6)
begins with a bottom-up dynamic programming approach to solving this recurrence. We maintain a two-dimensional array opt[i][j] that stores the length of
the LCS of the suffixes s[i..m) and t[j..n). Initially, the bottom row (the values
for i = m) and the right column (the values for j = n) are 0. These are the initial
values. From the recurrence, the order of the rest of the computation is clear: we
start with opt[m][n]. Then, as long as we decrease either i or j or both, we know
that we will have computed what we need to compute opt[i][j], since the two
options involve an opt[][] entry with a larger value of i or j or both. The method
lcs() in Program 2.3.6 computes the elements in opt[][] by filling in values in
rows from bottom to top (i = m-1 to 0) and from right to left in each row (j = n-1
to 0). The alternative choice of filling in values in columns from right to left and
2.3 Recursion
Program 2.3.6
287
Longest common subsequence
public class LongestCommonSubsequence
{
public static String lcs(String s, String t)
{ // Compute length of LCS for all subproblems.
int m = s.length(), n = t.length();
int[][] opt = new int[m+1][n+1];
for (int i = m-1; i >= 0; i--)
for (int j = n-1; j >= 0; j--)
if (s.charAt(i) == t.charAt(j))
opt[i][j] = opt[i+1][j+1] + 1;
else
opt[i][j] = Math.max(opt[i+1][j], opt[i][j+1]);
// Recover LCS itself.
String lcs = "";
int i = 0, j = 0;
s, t
while(i < m && j < n)
m, n
{
if (s.charAt(i) == t.charAt(j))
opt[i][j]
{
lcs
lcs += s.charAt(i);
i++;
j++;
}
else if (opt[i+1][j] >= opt[i][j+1]) i++;
else
j++;
}
return lcs;
two strings
lengths of two strings
length of LCS of
x[i..m) and y[j..n)
longest common subsequence
}
public static void main(String[] args)
{ StdOut.println(lcs(args[0], args[1]));
}
}
The function lcs() computes and returns the LCS of two strings s and t using bottom-up
dynamic programming. The method call s.charAt(i) returns character i of string s.
% java LongestCommonSubsequence GGCACCACG ACGGCGGATACG
GGCAACG
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288
from bottom to top in each row would work as well. The diagram at the bottom
of this page has a blue arrow pointing to each entry that indicates which value was
used to compute it. (When there is a tie in computing the maximum, both options
are shown.)
The final challenge is to recover the longest common subsequence itself, not
just its length. The key idea is to retrace the steps of the dynamic programming
algorithm backward, rediscovering the path of choices (highlighted in gray in the
diagram) from opt[0][0] to opt[m][n]. To determine the choice that led to
opt[i][j], we consider the three possibilities:
• The character s[i] equals t[j]. In this case, we must have opt[i][j] =
opt[i+1][j+1] + 1, and the next character in the LCS is s[i] (or t[j]), so
we include the character s[i] (or t[j]) in the LCS and continue tracing
back from opt[i+1][j+1].
• The LCS does not contain s[i]. In this case, opt[i][j] = opt[i+1][j]
and we continue tracing back from opt[i+1][j].
• The LCS does not contain t[j]. In this case, opt[i][j] = opt[i][j+1]
and we continue tracing back from opt[i][j+1].
We begin tracing back at opt[0][0] and continue until we reach opt[m][n]. At
each step in the traceback either i increases or j increases (or both), so the process
terminates after at most m + n iterations of the while loop.
j
0
1
2
3
4
5
6
7
8
9 10 11 12
s[j] A
C
G
G
C
G
G
A
T
A
C
G
-
i t[i]
0
G
7
7
7
6
6
6
5
4
3
3
2
1
0
1
G
6
6
6
6
5
5
5
4
3
3
2
1
0
2
C
6
5
5
5
5
4
4
4
3
3
2
1
0
3
A
6
5
4
4
4
4
4
4
3
3
2
1
0
4
C
5
5
4
4
4
3
3
3
3
3
2
1
0
5
C
4
4
4
4
4
3
3
3
3
3
2
1
0
6
A
3
3
3
3
3
3
3
3
3
3
2
1
0
7
C
2
2
2
2
2
2
2
2
2
2
2
1
0
8
G
1
1
1
1
1
1
1
1
1
1
1
1
0
9
-
0
0
0
0
0
0
0
0
0
0
0
0
0
Longest common subsequence of G G C A C C A C G and A C G G C G G A T A C G
2.3 Recursion
Dynamic programming is a fundamental algorithm design paradigm, intimately
linked to recursion. If you take later courses in algorithms or operations research,
you are sure to learn more about it. The idea of recursion is fundamental in computation, and the idea of avoiding recomputation of values that have been computed before is certainly a natural one. Not all problems immediately lend themselves
to a recursive formulation, and not all recursive formulations admit an order of
computation that easily avoids recomputation—arranging for both can seem a bit
miraculous when one first encounters it, as you have just seen for the LCS problem.
Perspective Programmers who do not use recursion are missing two opportunities. First recursion leads to compact solutions to complex problems. Second,
recursive solutions embody an argument that the program operates as anticipated.
In the early days of computing, the overhead associated with recursive programs
was prohibitive in some systems, and many people avoided recursion. In modern
systems like Java, recursion is often the method of choice.
Recursive functions truly illustrate the power of a carefully articulated abstraction. While the concept of a function having the ability to call itself seems
absurd to many people at first, the many examples that we have considered are
certainly evidence that mastering recursion is essential to understanding and exploiting computation and in understanding the role of computational models in
studying natural phenomena.
Recursion has reinforced for us the idea of proving that a program operates
as intended. The natural connection between recursion and mathematical induction is essential. For everyday programming, our interest in correctness is to save
time and energy tracking down bugs. In modern applications, security and privacy
concerns make correctness an essential part of programming. If the programmer
cannot be convinced that an application works as intended, how can a user who
wants to keep personal data private and secure be so convinced?
Recursion is the last piece in a programming model that served to build much
of the computational infrastructure that was developed as computers emerged to
take a central role in daily life in the latter part of the 20th century. Programs built
from libraries of functions consisting of statements that operate on primitive types
of data, conditionals, loops, and function calls (including recursive ones) can solve
important problems of all sorts. In the next section, we emphasize this point and
review these concepts in the context of a large application. In Chapter 3 and in
Chapter 4, we will examine extensions to these basic ideas that embrace the more
expansive style of programming that now dominates the computing landscape.
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290
Q&A
Q. Are there situations when iteration is the only option available to address a
problem?
A. No, any loop can be replaced by a recursive function, though the recursive version might require excessive memory.
Q. Are there situations when recursion is the only option available to address a
problem?
A. No, any recursive function can be replaced by an iterative counterpart. In
Section 4.3, we will see how compilers produce code for function calls by using a
data structure called a stack.
Q. Which should I prefer, recursion or iteration?
A. Whichever leads to the simpler, more easily understood, or more efficient code.
Q. I get the concern about excessive space and excessive recomputation in recursive code. Anything else to be concerned about?
A. Be extremely wary of creating arrays in recursive code. The amount of space
used can pile up very quickly, as can the amount of time required for memory
management.
2.3 Recursion
Exercises
2.3.1 What happens if you call factorial() with a negative value of n? With a
large value of, say, 35?
2.3.2 Write a recursive function that takes an integer n as its argument and returns
ln (n !).
2.3.3 Give the sequence of integers printed by a call to ex233(6):
public static void ex233(int n)
{
if (n <= 0) return;
StdOut.println(n);
ex233(n-2);
ex233(n-3);
StdOut.println(n);
}
2.3.4 Give the value of ex234(6):
public static String ex234(int n)
{
if (n <= 0) return "";
return ex234(n-3) + n + ex234(n-2) + n;
}
2.3.5 Criticize the following recursive function:
public static String ex235(int n)
{
String s = ex235(n-3) + n + ex235(n-2) + n;
if (n <= 0) return "";
return s;
}
Answer : The base case will never be reached because the base case appears after
the reduction step. A call to ex235(3) will result in calls to ex235(0), ex235(-3),
ex235(-6), and so forth until a StackOverflowError.
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2.3.6 Given four positive integers a, b, c, and d, explain what value is computed by
gcd(gcd(a, b), gcd(c, d)).
2.3.7 Explain in terms of integers and divisors the effect of the following Euclidlike function:
public static boolean gcdlike(int p, int q)
{
if (q == 0) return (p == 1);
return gcdlike(q, p % q);
}
2.3.8 Consider the following recursive function:
public static int mystery(int a, int b)
{
if (b == 0) return 0;
if (b % 2 == 0) return mystery(a+a, b/2);
return mystery(a+a, b/2) + a;
}
What are the values of mystery(2, 25) and mystery(3, 11)? Given positive
integers a and b, describe what value mystery(a, b) computes. Then answer the
same question, but replace + with * and return 0 with return 1.
2.3.9 Write a recursive program Ruler to plot the subdivisions of a ruler using
StdDraw, as in Program 1.2.1.
2.3.10 Solve the following recurrence relations, all with T(1) = 1. Assume n is a
power of 2.
• T(n) = T(n/2) + 1
• T(n) = 2T(n/2) + 1
• T(n) = 2T(n/2) + n
• T(n) = 4T(n/2) + 3
2.3.11 Prove by induction that the minimum possible number of moves needed
to solve the towers of Hanoi satisfies the same recurrence as the number of moves
used by our recursive solution.
2.3 Recursion
293
2.3.12 Prove by induction that the recursive program given in the text makes exactly Fn recursive calls to fibonacci(1) when computing fibonacci(n).
2.3.13 Prove that the second argument to gcd() decreases by at least a factor of
2 for every second recursive call, and then prove that gcd(p, q) uses at most
2 log2 n + 1 recursive calls where n is the larger of p and q.
2.3.14 Modify
(Program 2.3.4) to animate the drawing of the H-tree.
Next, rearrange the order of the recursive calls (and the base case), view the resulting animation, and explain each outcome.
20%
Htree
40%
60%
80%
100%
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Creative Exercises
2.3.15 Binary representation. Write a program that takes a positive integer n (in
decimal) as a command-line argument and prints its binary representation. Recall,
in Program 1.3.7, that we used the method of subtracting out powers of 2. Now, use
the following simpler method: repeatedly divide 2 into n and read the remainders
backward. First, write a while loop to carry out this computation and print the bits
in the wrong order. Then, use recursion to print the bits in the correct order.
2.3.16 A4 paper. The width-to-height ratio of paper in the ISO format is the
square root of 2 to 1. Format A0 has an area of 1 square meter. Format A1 is A0 cut
with a vertical line into two equal halves, A2 is A1 cut with a horizontal line into two
halves, and so on. Write a program that takes an integer command-line argument
n and uses StdDraw to show how to cut a sheet of A0 paper into 2n pieces.
2.3.17 Permutations. Write a program Permutations that takes an integer command-line argument n and prints all n ! permutations of the n letters starting at a
(assume that n is no greater than 26). A permutation of n elements is one of the
n ! possible orderings of the elements. As an example, when n = 3, you should get
the following output (but do not worry about the order in which you enumerate
them):
bca cba cab acb bac abc
2.3.18 Permutations of size k. Modify Permutations from the previous exercise
so that it takes two command-line arguments n and k, and prints all P(n , k) =
n ! / (nk)! permutations that contain exactly k of the n elements. Below is the
desired output when k = 2 and n = 4 (again, do not worry about the order):
ab ac ad ba bc bd ca cb cd da db dc
2.3.19 Combinations. Write a program Combinations that takes an integer command-line argument n and prints all 2n combinations of any size. A combination is
a subset of the n elements, independent of order. As an example, when n = 3, you
should get the following output:
a ab abc ac b bc c
Note that your program needs to print the empty string (subset of size 0).
2.3 Recursion
2.3.20 Combinations of size k. Modify
Combinations from the previous exercise so that it takes two integer command-line arguments n and k, and prints all
C(n, k) = n ! / (k !(nk)!) combinations of size k. For example, when n = 5 and k = 3,
you should get the following output:
abc abd abe acd ace ade bcd bce bde cde
2.3.21 Hamming distance. The Hamming distance between two bit strings of
length n is equal to the number of bits in which the two strings differ. Write a program that reads in an integer k and a bit string s from the command line, and prints
all bit strings that have Hamming distance at most k from s. For example, if k is 2
and s is 0000, then your program should print
0011 0101 0110 1001 1010 1100
Hint : Choose k of the bits in s to flip.
2.3.22 Recursive squares. Write a program to produce each of the following recursive patterns. The ratio of the sizes of the squares is 2.2:1. To draw a shaded square,
draw a filled gray square, then an unfilled black square.
2.3.23 Pancake flipping. You have a stack of n pancakes of varying sizes on a griddle. Your goal is to rearrange the stack in order so that the largest pancake is on
the bottom and the smallest one is on top. You are only permitted to flip the top k
pancakes, thereby reversing their order. Devise a recursive scheme to arrange the
pancakes in the proper order that uses at most 2n 3 flips.
295
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296
2.3.24 Gray code. Modify Beckett (Program 2.3.3) to print the Gray code (not
just the sequence of bit positions that change).
2.3.25 Towers of Hanoi variant. Consider the following variant of the towers of
Hanoi problem. There are 2n discs of increasing size stored on three poles. Initially
all of the discs with odd size (1, 3, ..., 2n-1) are piled on the left pole from top to bottom in increasing order of size; all of the discs with even size (2, 4, ..., 2n)
are piled on the right pole. Write a program to provide instructions for order 1
moving the odd discs to the right pole and the even discs to the left pole,
obeying the same rules as for towers of Hanoi.
2.3.26 Animated towers of Hanoi. Use StdDraw to animate a solution to
the towers of Hanoi problem, moving the discs at a rate of approximately
1 per second.
order 2
2.3.27 Sierpinski triangles. Write a recursive program to draw Sierpinski triangles (see Program 2.2.3). As with Htree, use a command-line
argument to control the depth of the recursion.
order 3
2.3.28 Binomial distribution. Estimate the number of recursive calls
that would be used by the code
public static double binomial(int n, int k)
{
if ((n == 0) && (k == 0)) return 1.0;
if ((n < 0) || (k < 0))
return 0.0;
return (binomial(n-1, k) + binomial(n-1, k-1))/2.0;
}
Sierpinski
triangles
to compute binomial(100, 50). Develop a better implementation that is based
on dynamic programming. Hint : See Exercise 1.4.41.
2.3.29 Collatz function. Consider the following recursive function, which is related to a famous unsolved problem in number theory, known as the Collatz problem,
or the 3n+1 problem:
2.3 Recursion
297
public static void collatz(int n)
{
StdOut.print(n + " ");
if (n == 1) return;
if (n % 2 == 0) collatz(n / 2);
else
collatz(3*n + 1);
}
For example, a call to collatz(7) prints the sequence
7 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
as a consequence of 17 recursive calls. Write a program that takes a command-line
argument n and returns the value of i < n for which the number of recursive
calls for collatz(i) is maximized. The unsolved problem is that no one knows
whether the function terminates for all integers (mathematical induction is no help,
because one of the recursive calls is for a larger value of the argument).
2.3.30 Brownian island. B. Mandelbrot asked the famous question How long is
the coast of Britain? Modify Brownian to get a program BrownianIsland that plots
Brownian islands, whose coastlines resemble that of Great Britain. The modifications are simple: first, change curve() to add a random Gaussian to the x-coordinate as well as to the y-coordinate; second, change main() to draw a curve from the
point at the center of the canvas back to itself. Experiment with various values of
the parameters to get your program to produce islands with a realistic look.
Brownian islands with Hurst exponent of 0.76
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298
2.3.31 Plasma clouds. Write a recursive program to draw plasma clouds, using the
method suggested in the text.
2.3.32 A strange function. Consider McCarthy’s 91 function:
public static int mcCarthy(int n)
{
if (n > 100) return n - 10;
return mcCarthy(mcCarthy(n+11));
}
Determine the value of mcCarthy(50) without using a computer. Give the number
of recursive calls used by mcCarthy() to compute this result. Prove that the base
case is reached for all positive integers n or find a value of n for which this function
goes into an infinite recursive loop.
2.3.33 Recursive tree. Write a program Tree that takes a command-line argument
n and produces the following recursive patterns for n equal to 1, 2, 3, 4, and 8.
1
2
3
4
8
2.3.34 Longest palindromic subsequence. Write a program LongestPalindromicSubsequence that takes a string as a command-line argument and determines the
longest subsequence of the string that is a palindrome (the same when read forward
or backward). Hint : Compute the longest common subsequence of the string and
its reverse.
2.3 Recursion
2.3.35 Longest common subsequence of three strings. Given three strings, write a
program that computes the longest common subsequence of the three strings.
2.3.36 Longest strictly increasing subsequence. Given an integer array, find the
longest subsequence that is strictly increasing. Hint : Compute the longest common subsequence of the original array and a sorted version of the array, where any
duplicate values are removed.
2.3.37 Longest common strictly increasing subsequence. Given two integer arrays,
find the longest increasing subsequence that is common to both arrays.
2.3.38 Binomial coefficients. The binomial coefficient C(n, k) is the number of
ways of choosing a subset of k elements from a set of n elements. Pascal’s identity
expresses the binomial coefficient C(n, k) in terms of smaller binomial coefficients:
C(n, k) = C(n1, k1) + C(n1, k), with C(n, 0) = 1 for each integer n. Write a
recursive function (do not use dynamic programming) to computer C(n, k). How
long does it take to computer C(100, 15)? Repeat the question, first using top-down
dynamic programming, then using bottom-up dynamic programming.
2.3.39 Painting houses. Your job is to paint a row of n houses red, green, or blue
so as to minimize total cost, where cost(i, color) = cost to pain house i the specified color. You may not paint two adjacent houses the same color. Write a program
to determine an optimal solution to the problem. Hint : Use bottom-up dynamic
programming and solve the following subproblems for each i = 1, 2, …, n:
• red(i) = min cost to paint houses 1, 2, …, i so that the house i is red
• green(i) = min cost to paint houses 1, 2, …, i so that the house i is green
• blue(i) = min cost to paint houses 1, 2, …, i so that the house i is blue
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Functions and Modules
2.4 Case Study: Percolation
The programming tools that we have considered to this point allow us to attack all
manner of important problems. We conclude our study of functions and modules
by considering a case study of developing a program to solve an interesting scientific problem. Our purpose in doing so is to review the basic elements that we have
covered, in the context of the various
challenges that you might face in solv2.4.1 Percolation scaffolding . . . . . . . 304
2.4.2 Vertical percolation detection. . . . 306
ing a specific problem, and to illustrate
2.4.3 Visualization client . . . . . . . . . 309
a programming style that you can apply
2.4.4 Percolation probability estimate . . 311
broadly.
2.4.5 Percolation detection. . . . . . . . 313
Our example applies a widely appli2.4.6 Adaptive plot client. . . . . . . . . 316
cable computational technique known as
Programs in this section
Monte Carlo simulation to study a natural
model known as percolation. The term
“Monte Carlo simulation” is broadly used to encompass any computational technique that employs randomness to estimate an unknown quantity by performing
multiple trials (known as simulations). We have used it in several other contexts already—for example, in the gambler’s ruin and coupon collector problems. Rather
than develop a complete mathematical model or measure all possible outcomes of
an experiment, we rely on the laws of probability.
In this case study we will learn quite a bit about percolation, a model which
underlies many natural phenomena. Our focus, however, is on the process of developing modular programs to address computational tasks. We identify subtasks that
can be independently addressed, striving to identify the key underlying abstractions and asking ourselves questions such as the following: Is there some specific
subtask that would help solve this problem? What are the essential characteristics
of this specific subtask? Might a solution that addresses these essential characteristics be useful in solving other problems? Asking such questions pays significant
dividend, because they lead us to develop software that is easier to create, debug,
and reuse, so that we can more quickly address the main problem of interest.
2.4 Case Study: Percolation
Percolation
301
It is not unusual for local interactions in a system to imply global
properties. For example, an electrical engineer might be interested in composite systems consisting of randomly distributed insulating and metallic materials:
which fraction of the materials need to be metallic so that the composite system is
an electrical conductor? As another example, a geologist might be interested in a
porous landscape with water on the surface (or oil below). Under which conditions
will the water be able to drain through to the bottom (or the oil to gush through
to the surface)? Scientists have defined an abstract process known as percolation
to model such situations. It has been studied widely, and shown to be an accurate
model in a dizzying variety of applications, beyond insulating materials and porous substances to the spread of forest fires and disease epidemics to evolution to
the study of the Internet.
For simplicity, we begin by working in two dimensions
percolates
blocked
and model the system as an n-by-n grid of sites. Each site is
site
either blocked or open; open sites are initially empty. A full
site is an open site that can be connected to an open site in
full
the top row via a chain of neighboring (left, right, up, down)
open
site
open sites. If there is a full site in the bottom row, then we empty
open
say that the system percolates. In other words, a system per- site
open site connected to top
colates if we fill all open sites connected to the top row and
does not percolate
that process fills some open site on the bottom row. For the
insulating/metallic materials example, the open sites correspond to metallic materials, so that a system that percolates has a metallic path from top to bottom, with full sites
conducting. For the porous substance example, the open
sites correspond to empty space through which water might
flow, so that a system that percolates lets water fill open sites,
no open site connected to top
flowing from top to bottom.
In a famous scientific problem that has been heavily
Percolation examples
studied for decades, scientists are interested in the following question: if sites are independently set to be open with
site vacancy probability p (and therefore blocked with probability 1p), what is the
probability that the system percolates? No mathematical solution to this problem
has yet been derived. Our task is to write computer programs to help study the
problem.
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Functions and Modules
Basic scaffolding To address percolation with a Java program, we face numerous decisions and challenges, and we certainly will end up with much more code
than in the short programs that we have considered so far in this book. Our goal
is to illustrate an incremental style of programming where we independently develop modules that address parts of the problem, building confidence with a small
computational infrastructure of our own design and construction as we proceed.
The first step is to pick a representation of the data. This decision can have
substantial impact on the kind of code that we write later, so it is not to be taken
lightly. Indeed, it is often the case that we learn something while working with a
chosen representation that causes us to scrap it and start all over using a new one.
For percolation, the path to an effective representation is
percolation system
clear: use an n-by-n array. Which type of data should we use for
each element? One possibility is to use integers, with the convention that 0 indicates an empty site, 1 indicates a blocked site, and
2 indicates a full site. Alternatively, note that we typically describe
sites in terms of questions: Is the site open or blocked? Is the site
full or empty? This characteristic of the elements suggests that we
blocked sites
might use n-by-n arrays in which element is either true or false.
1 1 0 0 0 1 1 1
We refer to such two-dimensional arrays as boolean matrices. Us0 1 1 0 0 0 0 0
0 0 0 1 1 0 0 1
ing boolean matrices leads to code that is easier to understand
1 1 0 0 1 0 0 0
than the alternative.
1 0 0 0 1 0 0 1
1 0 1 1 1 1 0 0
Boolean matrices are fundamental mathematical objects
0 1 0 1 0 0 0 0
0 0 0 0 1 0 1 1
with many applications. Java does not provide direct support for
operations on boolean matrices, but we can use the methods in
open sites
0 0 1 1 1 0 0 0
StdArrayIO (see Program 2.2.2) to read and write them. This
1 0 0 1 1 1 1 1
choice illustrates a basic principle that often comes up in pro1 1 1 0 0 1 1 0
0 0 1 1 0 1 1 1
gramming: the effort required to build a more general tool usually
0 1 1 1 0 1 1 0
pays
dividends.
0 1 0 0 0 0 1 1
1 0 1 0 1 1 1 1
Eventually, we will want to work with random data, but we
1 1 1 1 0 1 0 0
also want to be able to read and write to files because debugging
full sites
programs with random inputs can be counterproductive. With
0 0 1 1 1 0 0 0
0 0 0 1 1 1 1 1
random data, you get different input each time that you run the
0 0 0 0 0 1 1 0
program; after fixing a bug, what you want to see is the same input
0 0 0 0 0 1 1 1
0 0 0 0 0 1 1 0
that you just used, to check that the fix was effective. Accordingly,
0 0 0 0 0 0 1 1
it is best to start with some specific cases that we understand, kept
0 0 0 0 1 1 1 1
0 0 0 0 0 1 0 0
in files formatted compatible with StdArrayIO (dimensions folPercolation representations
lowed by 0 and 1 values in row-major order).
2.4 Case Study: Percolation
When you start working on a new problem that involves several files, it is
usually worthwhile to create a new folder (directory) to isolate those files from
others that you may be working on. For example, we might create a folder named
percolation to store all of the files for this case study. To get started, we can implement and debug the basic code for reading and writing percolation systems, create
test files, check that the files are compatible with the code, and so forth, before
worrying about percolation at all. This type of code, sometimes called scaffolding,
is straightforward to implement, but making sure that it is solid at the outset will
save us from distraction when approaching the main problem.
Now we can turn to the code for testing whether a boolean matrix represents
a system that percolates. Referring to the helpful interpretation in which we can
think of the task as simulating what would happen if the top were flooded with water (does it flow to the bottom or not?), our first design decision is that we will want
to have a flow() method that takes as an argument a boolean matrix isOpen[][]
that specifies which sites are open and returns another boolean matrix isFull[][]
that specifies which sites are full. For the moment, we will not worry at all about
how to implement this method; we are just deciding how to organize the computation. It is also clear that we will want client code to be able to use a percolates()
method that checks whether the array returned by flow() has any full sites on the
bottom.
Percolation (Program 2.4.1) summarizes these decisions. It does not perform any interesting computation, but after running and debugging this code we
can start thinking about actually solving the problem. A method that performs no
computation, such as flow(), is sometimes called a stub. Having this stub allows us
to test and debug percolates() and main() in the context in which we will need
them. We refer to code like Program 2.4.1 as scaffolding. As with scaffolding that
construction workers use when erecting a building, this kind of code provides the
support that we need to develop a program. By fully implementing and debugging
this code (much, if not all, of which we need, anyway) at the outset, we provide a
sound basis for building code to solve the problem at hand. Often, we carry the
analogy one step further and remove the scaffolding (or replace it with something
better) after the implementation is complete.
303
Functions and Modules
304
Program 2.4.1
Percolation scaffolding
public class Percolation
{
public static boolean[][] flow(boolean[][] isOpen)
{
int n = isOpen.length;
boolean[][] isFull = new boolean[n][n];
// The isFull[][] matrix computation goes here.
return isFull;
}
public static boolean percolates(boolean[][] isOpen)
{
boolean[][] isFull = flow(isOpen);
int n = isOpen.length;
for (int j = 0; j < n; j++)
n
if (isFull[n-1][j]) return true;
isFull[][]
return false;
isOpen[][]
}
system size (n-by-n)
full sites
open sites
public static void main(String[] args)
{
boolean[][] isOpen = StdArrayIO.readBoolean2D();
StdArrayIO.print(flow(isOpen));
StdOut.println(percolates(isOpen));
}
}
To get started with percolation, we implement and debug this code, which handles all the
straightforward tasks surrounding the computation. The primary function flow() returns a
boolean matrix giving the full sites (none, in the placeholder code here). The helper function
percolates() checks the bottom row of the returned matrix to decide whether the system
percolates. The test client main() reads a boolean matrix from standard input and prints the
result of calling flow() and percolates() for that matrix.
%
5
0
0
1
1
0
more test5.txt
5
1 1 0 1
0 1 1 1
1 0 1 1
0 0 0 1
1 1 1 1
% java Percolation < test5.txt
5 5
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
false
2.4 Case Study: Percolation
305
Vertical percolation Given a boolean matrix that represents the open sites,
how do we figure out whether it represents a system that percolates? As we will see
later in this section, this computation turns out to be directly related to a fundamental question in computer science. For the moment, we will consider a much
simpler version of the problem that we call vertical percolation.
The simplification is to restrict attention to vertical convertically percolates
nection paths. If such a path connects top to bottom in a system, we say that the system vertically percolates along the path
(and that the system itself vertically percolates). This restriction is perhaps intuitive if we are talking about sand traveling
through cement, but not if we are talking about water traveling
site connected to top
through cement or about electrical conductivity. Simple as it is,
with a vertical path
vertical percolation is a problem that is interesting in its own
does not vertically percolate
right because it suggests various mathematical questions. Does
the restriction make a significant difference? How many vertical percolation paths do we expect?
Determining the sites that are filled by some path that
is connected vertically to the top is a simple calculation. We
initialize the top row of our result array from the top row of
no open site connected to
the percolation system, with full sites corresponding to open
top with a vertical path
ones. Then, moving from top to bottom, we fill in each row of
the array by checking the corresponding row of the percolation
Vertical percolation
system. Proceeding from top to bottom, we fill in the rows of
isFull[][] to mark as true all elements that correspond to
sites in isOpen[][] that are vertically connected to a full site on the previous row.
Program 2.4.2 is an implementation of flow() for Percolation that returns a
boolean matrix of full sites (true if connected to the top via a vertical path, false
otherwise).
Testing After we become convinced that our code is behaving as planned, we want to run it on a broader variety
of test cases and address some of our scientific questions.
At this point, our initial scaffolding becomes less useful,
as representing large boolean matrices with 0s and 1s on
standard input and standard output and maintaining large
numbers of test cases quickly becomes unwieldy. Instead,
connected to top via a
vertical path of filled sites
not connected to top
via such a path
connected to top
via such a path
Vertical percolation calculation
Functions and Modules
306
Program 2.4.2
Vertical percolation detection
public static boolean[][] flow(boolean[][] isOpen)
{ // Compute full sites for vertical percolation.
int n = isOpen.length;
boolean[][] isFull = new boolean[n][n];
n
for (int j = 0; j < n; j++)
isFull[][]
isFull[0][j] = isOpen[0][j];
isOpen[][]
for (int i = 1; i < n; i++)
for (int j = 0; j < n; j++)
isFull[i][j] = isOpen[i][j] && isFull[i-1][j];
return isFull;
}
system size (n-by-n)
full sites
open sites
Substituting this method for the stub in Program 2.4.1 gives a solution to the vertical-only
percolation problem that solves our test case as expected (see text).
%
5
0
0
1
1
0
more test5.txt
5
1 1 0 1
0 1 1 1
1 0 1 1
0 0 0 1
1 1 1 1
% java Percolation < test5.txt
5 5
0 1 1 0 1
0 0 1 0 1
0 0 0 0 1
0 0 0 0 1
0 0 0 0 1
true
we want to automatically generate test cases and observe the operation of our code
on them, to be sure that it is operating as we expect. Specifically, to gain confidence
in our code and to develop a better understanding of percolation, our next goals
are to:
• Test our code for large random boolean matrices.
• Estimate the probability that a system percolates for a given p.
To accomplish these goals, we need new clients that are slightly more sophisticated
than the scaffolding we used to get the program up and running. Our modular programming style is to develop such clients in independent classes without modifying
our percolation code at all.
2.4 Case Study: Percolation
Data visualization. We can work with much bigger problem instances if we use
StdDraw for output. The following static method for Percolation allows us to
visualize the contents of boolean matrices as a subdivision of the StdDraw canvas
into squares, one for each site:
public static void show(boolean[][] a, boolean which)
{
int n = a.length;
StdDraw.setXscale(-1, n);
StdDraw.setYscale(-1, n);
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
if (a[i][j] == which)
StdDraw.filledSquare(j, n-i-1, 0.5);
}
The second argument which specifies which squares we want to fill—those corresponding to true elements or those corresponding to false elements. This
method is a bit of a diversion from the calculation, but pays dividends in its ability
to help us visualize large problem instances. Using show() to draw our boolean
matrices representing blocked and full sites in different colors gives a compelling
visual representation of percolation.
Monte Carlo simulation. We want our code to work properly for any boolean
matrix. Moreover, the scientific question of interest involves random boolean matrices. To this end, we add another static method to Percolation:
public static boolean[][] random(int n, double p)
{
boolean[][] a = new boolean[n][n];
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
a[i][j] = StdRandom.bernoulli(p);
return a;
}
This method generates a random n-by-n boolean matrix of any given size n, each
element true with probability p.
Having debugged our code on a few specific test cases, we are ready to test
it on random systems. It is possible that such cases may uncover a few more bugs,
so some care is in order to check results. However, having debugged our code for
a small system, we can proceed with some confidence. It is easier to focus on new
bugs after eliminating the obvious bugs.
307
308
Functions and Modules
With these tools, a client for testing our percolation code on a much larger set of
trials is straightforward. PercolationVisualizer (Program 2.4.3) consists of just
a main() method that takes n and p from the command line and displays the result
of the percolation flow calculation.
This kind of client is typical. Our eventual goal is to compute an accurate
estimate of percolation probabilities, perhaps by running a large number of trials, but this simple tool gives us the opportunity to gain more familiarity with the
problem by studying some large cases (while at the same time gaining confidence
that our code is working properly). Before reading further, you are encouraged to
download and run this code from the booksite to study the percolation process.
When you run PercolationVisualizer for moderate-size n (50 to 100, say) and
various p, you will immediately be drawn into using this program to try to answer
some questions about percolation. Clearly, the system never percolates when p is
low and always percolates when p is very high. How does it behave for intermediate
values of p? How does the behavior change as n increases?
Estimating probabilities The next step in our program development process
is to write code to estimate the probability that a random system (of size n with
site vacancy probability p) percolates. We refer to this quantity as the percolation
probability. To estimate its value, we simply run a number of trials. The situation
is no different from our study of coin flipping (see Program 2.2.6), but instead of
flipping a coin, we generate a random system and check whether it percolates.
PercolationProbability (Program 2.4.4) encapsulates this computation
in a method estimate(), which takes three arguments n, p, and trials and returns an estimate of the probability that an n-by-n system with site vacancy probability p percolates, obtained by generating trials random systems and calculating the fraction of them that percolate.
How many trials do we need to obtain an accurate estimate? This question
is addressed by basic methods in probability and statistics, which are beyond the
scope of this book, but we can get a feeling for the problem with computational
experience. With just a few runs of PercolationProbability, you can learn that
if the site vacancy probability is close to either 0 or 1, then we do not need many
trials, but that there are values for which we need as many as 10,000 trials to be
able to estimate it within two decimal places. To study the situation in more detail,
we might modify PercolationProbability to produce output like Bernoulli
(Program 2.2.6), plotting a histogram of the data points so that we can see the distribution of values (see Exercise 2.4.9).
2.4 Case Study: Percolation
Program 2.4.3
309
Visualization client
public class PercolationVisualizer
n
{
p
public static void main(String[] args)
isOpen[][]
{
isFull[][]
int n = Integer.parseInt(args[0]);
double p = Double.parseDouble(args[1]);
StdDraw.enableDoubleBuffering();
system size (n-by-n)
site vacancy probability
open sites
full sites
// Draw blocked sites in black.
boolean[][] isOpen = Percolation.random(n, p);
StdDraw.setPenColor(StdDraw.BLACK);
Percolation.show(isOpen, false);
// Draw full sites in blue.
StdDraw.setPenColor(StdDraw.BOOK_BLUE);
boolean[][] isFull = Percolation.flow(isOpen);
Percolation.show(isFull, true);
StdDraw.show();
}
}
This client takes two command-line argument n and p, generates an n-by-n random system
with site vacancy probability p, determines which sites are full, and draws the result on standard drawing. The diagrams below show the results for vertical percolation.
% java PercolationVisualizer 20 0.9
% java PercolationVisualizer 20 0.95
310
Functions and Modules
Using PercolationProbability.estimate() represents a giant leap in the
amount of computation that we are doing. All of a sudden, it makes sense to run
thousands of trials. It would be unwise to try to do so without first having thoroughly debugged our percolation methods. Also, we need to begin to take the time
required to complete the computation into account. The basic methodology for
doing so is the topic of Section 4.1, but the structure of these programs is sufficiently simple that we can do a quick calculation, which we can verify by running
the program. If we perform T trials, each of which involves n 2 sites, then the total
running time of PercolationProbability.estimate() is proportional to n 2T. If
we increase T by a factor of 10 (to gain more precision), the running time increases
by about a factor of 10. If we increase n by a factor of 10 (to study percolation for
larger systems), the running time increases by about a factor of 100.
Can we run this program to determine percolation probabilities for a system
with billions of sites with several digits of precision? No computer is fast enough
to use PercolationProbability.estimate() for this purpose. Moreover, in a
scientific experiment on percolation, the value of n is likely to be much higher. We
can hope to formulate a hypothesis from our simulation that can be tested experimentally on a much larger system, but not to precisely simulate a system that corresponds atom-for-atom with the real world. Simplification of this sort is essential
in science.
You are encouraged to download PercolationProbability from the booksite to get a feel for both the percolation probabilities and the amount of time
required to compute them. When you do so, you are not just learning more about
percolation, but are also testing the hypothesis that the models we have just described apply to the running times of our simulations of the percolation process.
What is the probability that a system with site vacancy probability p vertically
percolates? Vertical percolation is sufficiently simple that elementary probabilistic
models can yield an exact formula for this quantity, which we can validate experimentally with PercolationProbability. Since our only reason for studying vertical percolation was an easy starting point around which we could develop supporting software for studying percolation methods, we leave further study of vertical
percolation for an exercise (see Exercise 2.4.11) and turn to the main problem.
2.4 Case Study: Percolation
Program 2.4.4
311
Percolation probability estimate
public class PercolationProbability
{
public static double estimate(int n, double p, int trials)
{ // Generate trials random n-by-n systems; return empirical
// percolation probability estimate.
int count = 0;
for (int t = 0; t < trials; t++)
{ // Generate one random n-by-n boolean matrix.
boolean[][] isOpen = Percolation.random(n, p);
if (Percolation.percolates(isOpen)) count++;
}
return (double) count / trials;
n
system size (n-by-n)
}
p
site vacancy probability
public static void main(String[] args)
trials
number of trials
{
int n = Integer.parseInt(args[0]);
double p = Double.parseDouble(args[1]);
int trials = Integer.parseInt(args[2]);
double q = estimate(n, p, trials);
StdOut.println(q);
isOpen[][]
q
open sites
percolation probability
}
}
The method estimate() generates trials random n-by-n systems with site vacancy probability p and computes the fraction of them that percolate. This is a Bernoulli process, like coin
flipping (see Program 2.2.6). Increasing the number of trials increases the accuracy of the
estimate. If p is close to 0 or to 1, not many trials are needed to achieve an accurate estimate.
The results below are for vertical percolation.
% java PercolationProbability 20 0.05 10
0.0
% java PercolationProbability 20 0.95 10
1.0
% java PercolationProbability 20 0.85 10
0.7
% java PercolationProbability 20 0.85 1000
0.564
% java PercolationProbability 40 0.85 100
0.1
312
Functions and Modules
Recursive solution for percolation How do we test whether a system percolates in the general case when any path starting at the top and ending at the bottom
(not just a vertical one) will do the job?
Remarkably, we can solve this problem with a compact program, based on
a classic recursive scheme known as depth-first search. Program 2.4.5 is an implementation of flow() that computes the matrix isFull[][], based on a recursive
four-argument version of flow() that takes as arguments the site vacancy matrix
isOpen[][], the current matrix isFull[][], and a site position specified by a row
index i and a column index j. The base case is a recursive call that just returns (we
refer to such a call as a null call), for one of the following reasons:
• Either i or j is outside the array bounds.
flow(...,0,0)
• The site is blocked
(isOpen[i][j] is false).
flow(...,1,0)
• We have already marked the site as full
(isFull[i][j] is true).
The reduction step is to mark the site as filled
flow(...,0,3)
and issue recursive calls for the site’s four
neighbors: isOpen[i+1][j], isOpen[i][j+1],
flow(...,0,4)
isOpen[i][j-1], and isOpen[i-1][j]. The
one-argument flow() calls the recursive method for every site on the top row. The recursion
flow(...,1,4)
always terminates because each recursive call
either is null or marks a new site as full. We can
flow(...,2,4)
show by an induction-based argument (as usual for recursive programs) that a site is marked
flow(...,3,4)
as full if and only if it is connected to one of the
sites on the top row.
Tracing the operation of flow() on a tiny
flow(...,3,3)
test case is an instructive exercise. You will see
that it calls flow() for every site that can be
flow(...,4,3)
reached via a path of open sites from the top
row. This example illustrates that simple recurflow(...,3,2)
sive programs can mask computations that otherwise are quite sophisticated. This method is a
special case of the depth-first search algorithm,
flow(...,2,2)
which has many important applications.
Recursive percolation (null calls omitted)
2.4 Case Study: Percolation
Program 2.4.5
313
Percolation detection
public static boolean[][] flow(boolean[][] isOpen)
{ // Fill every site reachable from the top row.
int n = isOpen.length;
n
system size (n-by-n)
boolean[][] isFull = new boolean[n][n];
isOpen[][]
open sites
for (int j = 0; j < n; j++)
isFull[][] full sites
flow(isOpen, isFull, 0, j);
i, j
current site row, column
return isFull;
}
public static void flow(boolean[][] isOpen,
boolean[][] isFull, int i, int j)
{ // Fill every site reachable from (i, j).
int n = isFull.length;
if (i < 0 || i >= n) return;
if (j < 0 || j >= n) return;
if (!isOpen[i][j]) return;
if (isFull[i][j]) return;
isFull[i][j] = true;
flow(isOpen, isFull, i+1, j); // Down.
flow(isOpen, isFull, i, j+1); // Right.
flow(isOpen, isFull, i, j-1); // Left.
flow(isOpen, isFull, i-1, j); // Up.
}
Substituting these methods for the stub in Program 2.4.1 gives a depth-first-search-based solution to the percolation problem. The recursive flow() sets to true the element in isFull[][]
corresponding to any site that can be reached from isOpen[i][j] via a chain of neighboring
open sites. The one-argument flow() calls the recursive method for every site on the top row.
%
8
0
1
1
0
0
0
1
1
more test8.txt
8
0 1 1 1 0 0 0
0 0 1 1 1 1 1
1 1 0 0 1 1 0
0 1 1 0 1 1 1
1 1 1 0 1 1 0
1 0 0 0 0 1 1
0 1 0 1 1 1 1
1 1 1 0 1 0 0
% java Percolation < test8.txt
8 8
0 0 1 1 1 0 0 0
0 0 0 1 1 1 1 1
0 0 0 0 0 1 1 0
0 0 0 0 0 1 1 1
0 0 0 0 0 1 1 0
0 0 0 0 0 0 1 1
0 0 0 0 1 1 1 1
0 0 0 0 0 1 0 0
true
Functions and Modules
314
To avoid conflict with our solution for vertical percolation (Program
2.4.2), we might rename that class PercolationVertical, making another copy
of Percolation (Program 2.4.1) and substituting the two flow() methods
in Program 2.4.5 for the placeholder flow(). Then, we can visualize and perform experiments with this algorithm with the PercolationVisualizer and
PercolationProbability tools that we have developed. If you do so, and try various values for n and p, you will quickly get a feeling for the situation: the systems
always percolate when the site vacancy probability p is high and never percolate
when p is low, and (particularly as n increases) there is a value of p above which the
systems (almost) always percolate and below which they (almost) never percolate.
p = 0.65
p = 0.60
p = 0.55
Percolation is less probable as the site vacancy probability p decreases
Having debugged PercolationVisualizer and PercolationProbability
on the simple vertical percolation process, we can use them with more confidence to study percolation, and turn quickly to study the scientific problem of
interest. Note that if we want to experiment with vertical percolation again, we
would need to edit PercolationVisualizer and PercolationProbability to
refer to PercolationVertical instead of Percolation, or write other clients of
both PercolationVertical and Percolation that run methods in both classes
to compare them.
Adaptive plot
To gain more insight into percolation, the next step in program
development is to write a program that plots the percolation probability as a function of the site vacancy probability p for a given value of n. Perhaps the best way
to produce such a plot is to first derive a mathematical equation for the function,
and then use that equation to make the plot. For percolation, however, no one has
been able to derive such an equation, so the next option is to use the Monte Carlo
method: run simulations and plot the results.
2.4 Case Study: Percolation
315
Immediately, we are faced with numerous decisions. For how many values of
p should we compute an estimate of the percolation probability? Which values of p
should we choose? How much precision should we aim for in these calculations?
These decisions constitute an experimental design problem. Much as we might like
to instantly produce an accurate rendition of the curve for any given n, the computation cost can be prohibitive. For example, the first thing that comes to mind is to
plot, say, 100 to 1,000 equally spaced points, using StdStats (Program 2.2.5). But,
as you learned from using PercolationProbability, computing a sufficiently
precise value of the percolation probability for each point might take several seconds or longer, so the whole plot might take minutes or hours or even longer.
Moreover, it is clear that a lot of this computation time is completely wasted, because we know that values for small p are 0 and values for large p are 1. We might
prefer to spend that time on more precise computations for intermediate p. How
should we proceed?
PercolationPlot (Program 2.4.6) implements a
(x m , f(x m ))
recursive approach with the same structure as Brownian
(Program 2.3.5) that is widely applicable to similar prob(x 1 , y 1 )
error
lems. The basic idea is simple: we choose the maximum distolerance
tance that we wish to allow between values of the x-coordi(x m , y m )
nate (which we refer to as the gap tolerance), the maximum
gap tolerance
known error that we wish to tolerate in the y-coordinate (x 0 , y 0 )
(which we refer to as the error tolerance), and the number
of trials T per point that we wish to perform. The recursive
Adaptive plot tolerances
method draws the plot within a given interval [x0, x1], from
(x0, y0) to (x1, y1). For our problem, the plot is from (0, 0) to
(1, 1). The base case (if the distance between x0 and x1 is less than the gap tolerance,
or the distance between the line connecting the two endpoints and the value of the
function at the midpoint is less than the error tolerance) is to simply draw a line
from (x0, y0) to (x1, y1). The reduction step is to (recursively) plot the two halves of
the curve, from (x0, y0) to (xm, f (xm)) and from (xm, f (xm)) to (x1, y1).
The code in PercolationPlot is relatively simple and produces a goodlooking curve at relatively low cost. We can use it to study the shape of the curve
for various values of n or choose smaller tolerances to be more confident that the
curve is close to the actual values. Precise mathematical statements about quality
of approximation can, in principle, be derived, but it is perhaps not appropriate
to go into too much detail while exploring and experimenting, since our goal is
simply to develop a hypothesis about percolation that can be tested by scientific
experimentation.
Functions and Modules
316
Program 2.4.6
Adaptive plot client
public class PercolationPlot
{
public static void curve(int n,
double x0, double y0,
double x1, double y1)
{ // Perform experiments and plot results.
double gap = 0.01;
double err = 0.0025;
int trials = 10000;
double xm = (x0 + x1)/2;
double ym = (y0 + y1)/2;
double fxm = PercolationProbability.estimate(n,
if (x1 - x0 < gap || Math.abs(ym - fxm) < err)
{
StdDraw.line(x0, y0, x1, y1);
return;
}
curve(n, x0, y0, xm, fxm);
StdDraw.filledCircle(xm, fxm, 0.005);
curve(n, xm, fxm, x1, y1);
}
n
system size
x0, y0
left endpoint
x1, y1
right endpoint
xm, trials);
xm, ym
midpoint
fxm
value at midpoint
gap
gap tolerance
err
error tolerance
trials
number of trials
public static void main(String[] args)
{ // Plot experimental curve for n-by-n percolation system.
int n = Integer.parseInt(args[0]);
curve(n, 0.0, 0.0, 1.0, 1.0);
}
}
This recursive program draws a plot of the percolation probability (experimental observations)
against the site vacancy probability p (control variable) for random n-by-n systems.
% java PercolationPlot 20
% java PercolationPlot 100
1
1
percolation
probability
percolation
probability
0
0
0.593
1
site vacancy probability p
0
0.593
1
site vacancy probability p
2.4 Case Study: Percolation
317
Indeed, the curves produced by PercolationPlot immediately confirm the
hypothesis that there is a threshold value (about 0.593): if p is greater than the
threshold, then the system almost certainly percolates; if p is less than the threshold,
then the system almost certainly does not percolate.
PercolationPlot.curve()
As n increases, the curve approaches a step function
PercolationProbability.estimate()
Percolation.random()
that changes value from 0 to 1 at the threshold. This
StdRandom.bernoulli()
.
phenomenon, known as a phase transition, is found in
. n times
.
many physical systems.
StdRandom.bernoulli()
return
The simple form of the output of Program 2.4.6
Percolation.percolates()
masks the huge amount of computation behind it. For
flow()
return
example, the curve drawn for n = 100 has 18 points,
return
.
each the result of 10,000 trials, with each trial involv. T times
.
ing n 2 sites. Generating and testing each site involves
Percolation.random()
StdRandom.bernoulli()
a few lines of code, so this plot comes at the cost of
.
. n times
executing billions of statements. There are two lessons
.
StdRandom.bernoulli()
to be learned from this observation. First, we need
return
Percolation.percolates()
to have confidence in any line of code that might be
flow()
executed billions of times, so our care in developing
return
return
and debugging code incrementally is justified. Second,
return
.
although we might be interested in systems that are
. once for each point
.
much larger, we need further study in computer sciPercolationProbability.estimate()
Percolation.random()
ence to be able to handle larger cases—that is, to deStdRandom.bernoulli()
.
velop faster algorithms and a framework for knowing
. n times
.
their performance characteristics.
StdRandom.bernoulli()
return
With this reuse of all of our software, we can
Percolation.percolates()
flow()
study all sorts of variants on the percolation problem,
return
just by implementing different flow() methods. For
return
.
.
example, if you leave out the last recursive call in the
. T times
Percolation.random()
recursive flow() method in Program 2.4.5, it tests
StdRandom.bernoulli()
.
for a type of percolation known as directed percola. n times
.
tion, where paths that go up are not considered. This
StdRandom.bernoulli()
return
model might be important for a situation like a liqPercolation.percolates()
uid percolating through porous rock, where gravity
flow()
return
might play a role, but not for a situation like electrical
return
connectivity. If you run PercolationPlot for both
return
return
methods, will you be able to discern the difference
(see Exercise 2.4.10)?
Function-call trace for PercolationPlot
2
2
2
2
Functions and Modules
318
To model physical situations such as water flowing through porous substances,
we need to use three-dimensional arrays. Is there a similar threshold in the threedimensional problem? If so, what is its value? Depth-first search is effective for
studying this question, though the addition of another dimension requires that
we pay even more attention to the computational cost of determining whether a
system percolates (see Exercise 2.4.18). Scientists also study more complex lattice
structures that are not well modeled by multidimensional arrays—we will see how
to model such structures in Section 4.5.
Percolation is interesting to study via in silico experimentation because no
one has been able to derive the threshold value mathematically for several natural
models. The only way that scientists know the value is by using simulations like
Percolation. A scientist needs to do experiments to see whether the percolation
model reflects what is observed in nature, perhaps through refining the model (for
example, using a different lattice structure). Percolation is an example of an increasing number of problems where computer science of the kind described here is
an essential part of the scientific process.
Lessons
We might have approached the problem of studying percolation by sitting down to design and implement a single program, which probably would run
to hundreds of lines, to produce the kind of plots that are drawn by Program 2.4.6.
In the early days of computing, programmers had little choice but to work with
such programs, and would spend enormous amounts of time isolating bugs and
correcting design decisions. With modern programming tools like Java, we can
do better, using the incremental modular style of programming presented in this
chapter and keeping in mind some of the lessons that we have learned.
Expect bugs. Every interesting piece of code that you write is going to have at least
one or two bugs, if not many more. By running small pieces of code on small test
cases that you understand, you can more easily isolate any bugs and then more
easily fix them when you find them. Once debugged, you can depend on using a
library as a building block for any client.
2.4 Case Study: Percolation
319
Keep modules small. You can focus attention on at most a few dozen lines of code
at a time, so you may as well break your code into small modules as you write it.
Some classes that contain libraries of related methods may eventually grow to contain hundreds of lines of code; otherwise, we work with small files.
Limit interactions. In a well-designed modular program, most modules should
depend on just a few others. In particular, a module that calls a large number of
other modules needs to be divided
Percolation
Percolation
into smaller pieces. Modules that are
StdDraw
Visualizer
Plot
called by a large number of other modules (you should have only a few) need
StdRandom
special attention, because if you do
need to make changes in a module’s
Percolation
API, you have to reflect those changes
in all its clients.
Develop code incrementally. You
StdOut
should run and debug each small
Percolation
module as you implement it. That way,
Probability
StdArrayIO
you are never working with more than
a few dozen lines of unreliable code
at any given time. If you put all your
StdIn
code in one big module, it is difficult
to be confident that any of it is free Case study dependency graph (not including system calls)
from bugs. Running code early also
forces you to think sooner rather than later about I/O formats, the nature of problem instances, and other issues. Experience gained when thinking about such issues
and debugging related code makes the code that you develop later in the process
more effective.
Solve an easier problem. Some working solution is better than no solution, so it is
typical to begin by putting together the simplest code that you can craft that solves
a given problem, as we did with vertical percolation. This implementation is the
first step in a process of continual refinements and improvements as we develop a
more complete understanding of the problem by examining a broader variety of
test cases and developing support software such as our PercolationVisualizer
and PercolationProbability classes.
Functions and Modules
320
Consider a recursive solution. Recursion is an indispensable tool in modern programming that you should learn to trust. If you are not already convinced of this
fact by the simplicity and elegance of Percolation and PercolationPlot, you
might wish to try to develop a nonrecursive program for testing whether a system
percolates and then reconsider the issue.
Build tools when appropriate. Our visualization method
show() and random
boolean matrix generation method random() are certainly useful for many other
applications, as is the adaptive plotting method of PercolationPlot. Incorporating these methods into appropriate libraries would be simple. It is no more difficult
(indeed, perhaps easier) to implement general-purpose methods like these than it
would be to implement special-purpose methods for percolation.
Reuse software when possible. Our
StdIn, StdRandom, and StdDraw libraries all simplified the process of developing the code in this section, and we were
also immediately able to reuse programs such as PercolationVisualizer,
PercolationProbability, and PercolationPlot for percolation after developing them for vertical percolation. After you have written a few programs of this
kind, you might find yourself developing versions of these programs that you can
reuse for other Monte Carlo simulations or other experimental data analysis problems.
The primary purpose of this case study is to convince you that modular programming will take you much further than you could get without it. Although no approach to programming is a panacea, the tools and approach that we have discussed in this section will allow you to attack complex programming tasks that
might otherwise be far beyond your reach.
The success of modular programming is only a start. Modern programming
systems have a vastly more flexible programming model than the class-as-a-libraryof-static-methods model that we have been considering. In the next two chapters,
we develop this model, along with many examples that illustrate its utility.
2.4 Case Study: Percolation
Q&A
Q. Editing PercolationVisualizer and PercolationProbability to rename
Percolation to PercolationVertical or whatever method we want to study
seems to be a bother. Is there a way to avoid doing so?
A. Yes, this is a key issue to be revisited in Chapter 3. In the meantime, you can
keep the implementations in separate subdirectories, but that can get confusing.
Advanced Java mechanisms (such as the classpath) are also helpful, but they also
have their own problems.
Q. That recursive flow() method makes me nervous. How can I better understand
what it’s doing?
A. Run it for small examples of your own making, instrumented with instructions
to print a function-call trace. After a few runs, you will gain confidence that it always marks as full the sites connected to the start site via a chain of neighboring
open sites.
Q. Is there a simple nonrecursive approach to identifying the full sites?
A. There are several methods that perform the same basic computation. We will
revisit the problem in Section 4.5, where we consider breadth-first search. In the
meantime, working on developing a nonrecursive implementation of flow() is
certain to be an instructive exercise, if you are interested.
Q. PercolationPlot (Program 2.4.6) seems to involve a huge amount of computation to produce a simple function graph. Is there some better way?
A. Well, the best would be a simple mathematical formula describing the function,
but that has eluded scientists for decades. Until scientists discover such a formula,
they must resort to computational experiments like the ones in this section.
321
Functions and Modules
322
Exercises
2.4.1 Write a program that takes a command-line argument n and creates an
n-by-n boolean matrix with the element in row i and column j set to true if i and
j are relatively prime, then shows the matrix on the standard drawing (see Exercise
1.4.16). Then, write a similar program to draw the Hadamard matrix of order n
(see Exercise 1.4.29). Finally, write a program to draw the boolean matrix such that
the element in row n and column j is set to true if the coefficient of x j in (1 + x)i
(binomial coefficient) is odd (see Exercise 1.4.41). You may be surprised at the pattern formed by the third example.
2.4.2 Implement a print() method for Percolation that prints 1 for blocked
sites, 0 for open sites, and * for full sites.
2.4.3 Give the recursive calls for flow() in Program 2.4.5 given the following input:
3
1
0
1
3
0 1
0 0
1 0
2.4.4 Write a client of
Percolation like PercolationVisualizer that does a
series of experiments for a value of n taken from the command line where the site
vacancy probability p increases from 0 to 1 by a given increment (also taken from
the command line).
2.4.5 Describe the order in which the sites are marked when Percolation is used
on a system with no blocked sites. Which is the last site marked? What is the depth
of the recursion?
2.4.6 Experiment with using
PercolationPlot to plot various mathematical
call PercolationProbability.estimate() with
functions (by replacing the
a different expression that evaluates a mathematical function). Try the function
f(x) = sin x + cos 10x to see how the plot adapts to an oscillating curve, and come
up with interesting plots for three or four functions of your own choosing.
2.4 Case Study: Percolation
323
2.4.7 Modify Percolation to animate the flow computation, showing the sites
filling one by one. Check your answer to the previous exercise.
2.4.8 Modify
Percolation to compute that maximum depth of the recursion
used in the flow calculation. Plot the expected value of that quantity as a function
of the site vacancy probability p. How does your answer change if the order of the
recursive calls is reversed?
2.4.9 Modify PercolationProbability to produce output like that produced by
Bernoulli (Program 2.2.6). Extra credit : Use your program to validate the hypothesis that the data obeys a Gaussian distribution.
2.4.10 Create a program
that tests for
directed percolation (by leaving off the last recursive call in the recursive flow() method in Program 2.4.5, as described in the text),
then use PercolationPlot to draw a plot of the directed percolation probability as a function of the site vacancy probability p.
PercolationDirected
2.4.11 Write a client of Percolation and PercolationDirected
that takes a site vacancy probability p from the command line and
prints an estimate of the probability that a system percolates but
does not percolate down. Use enough experiments to get an estimate that is accurate to three decimal places.
percolates (path never goes up)
does not percolate
Directed percolation
Functions and Modules
324
Creative Exercises
2.4.12 Vertical percolation. Show that a system with site vacancy probability p vertically percolates with probability 1 (1 p n)n, and use PercolationProbability
to validate your analysis for various values of n.
2.4.13 Rectangular percolation systems. Modify the code in this section to allow
you to study percolation in rectangular systems. Compare the percolation probability plots of systems whose ratio of width to height is 2 to 1 with those whose
ratio is 1 to 2.
2.4.14 Adaptive plotting. Modify PercolationPlot to take its control parameters
(gap tolerance, error tolerance, and number of trials) as command-line arguments.
Experiment with various values of the parameters to learn their effect on the quality
of the curve and the cost of computing it. Briefly describe your findings.
2.4.15 Nonrecursive directed percolation. Write a nonrecursive program that tests
for directed percolation by moving from top to bottom as in our vertical percolation code. Base your solution on the following
connected to top via a path of
computation: if any site in a contiguous subfilled sites that never goes up
row of open sites in the current row is connected to some full site on the previous row,
then all of the sites in the subrow become full.
2.4.16 Fast percolation test. Modify the re-
not connected to top
connected to top
cursive flow() method in Program 2.4.5 so
(by such a path)
that it returns as soon as it finds a site on the
Directed percolation calculation
bottom row (and fills no more sites). Hint: Use
an argument done that is true if the bottom
has been hit, false otherwise. Give a rough estimate of the performance improvement factor for this change when running PercolationPlot. Use values of n for
which the programs run at least a few seconds but not more than a few minutes.
Note that the improvement is ineffective unless the first recursive call in flow() is
for the site below the current site.
2.4 Case Study: Percolation
2.4.17 Bond percolation. Write a modular program for studying percolation under the assumption that the edges of the grid provide connectivity. That is, an edge
can be either empty or full, and a system percolates if there is a path consisting of
full edges that goes from top to bottom. Note : This problem has been solved analytically, so your simulations should validate the hypothesis that the bond percolation threshold approaches 1/2 as n gets large.
325
percolates
does not
2.4.18 Percolation in three dimensions. Implement a class Percolation3D and a
class BooleanMatrix3D (for I/O and random generation) to study percolation in
three-dimensional cubes, generalizing the two-dimensional case studied in this section. A percolation system is an n-by-n-by-n cube of sites that are unit cubes, each
open with probability p and blocked with probability 1p. Paths can connect an
open cube with any open cube that shares a common face (one of six neighbors,
except on the boundary). The system percolates if there exists a path connecting
any open site on the bottom plane to any open site on the top plane. Use a recursive version of flow() like Program 2.4.5, but with six recursive calls instead of
four. Plot the percolation probability versus site vacancy probability p for as large a
value of n as you can. Be sure to develop your solution incrementally, as emphasized
throughout this section.
2.4.19 Bond percolation on a triangular grid. Write a modular program for
percolates
studying bond percolation on a triangular grid, where the system is composed
of 2n 2 equilateral triangles packed together in an n-by-n grid of rhombus
shapes. Each interior point has six bonds; each point on the edge has four; and
each corner point has two.
does not
Functions and Modules
326
2.4.20 Game of Life. Implement a class GameOfLife that simulates Conway’s
Game of Life. Consider a boolean matrix corresponding to a system of cells that we
refer to as being either live or dead. The game consists of checking and perhaps updating the value of each cell, depending on the values of its neighbors (the adjacent
cells in every direction, including diagonals). Live cells remain live and dead cells
remain dead, with the following exceptions:
• A dead cell with exactly three live neighbors becomes live.
• A live cell with exactly one live neighbor becomes dead.
• A live cell with more than three live neighbors becomes dead.
Initialize with a random boolean matrix, or use one of the starting patterns on the
booksite. This game has been heavily studied, and relates to foundations of computer science (see the booksite for more information).
time t
time t + 1
time t + 2
time t + 3
Five generations of a glider
time t + 4
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Chapter Three
Object-Oriented Programming
3.1
3.2
3.3
3.4
Using Data Types . . . . . . . . . . . .330
Creating Data Types . . . . . . . . . .382
Designing Data Types . . . . . . . . . 428
Case Study: N-Body Simulation . . . 478
Y
our next step in programming effectively is conceptually simple. Now that you
know how to use primitive types of data, you will learn in this chapter how to
use, create, and design higher-level data types.
An abstraction is a simplified description of something that captures its essential elements while suppressing all other details. In science, engineering, and
programming, we are always striving to understand complex systems through abstraction. In Java programming, we do so with object-oriented programming, where
we break a large and potentially complex program into a set of interacting elements,
or objects. The idea originates from modeling (in software) real-world entities such
as electrons, people, buildings, or solar systems and readily extends to modeling
abstract entities such as bits, numbers, colors, images, or programs.
A data type is a set of values and a set of operations defined on those values.
The values and operations for primitive types such as int and double are predefined by Java. In object-oriented programming, we write Java code to define new
data types. An object is an entity that holds a data-type value; you can manipulate
this data-type value by applying one of the object’s data-type operations.
This ability to define new data types and to manipulate objects holding datatype values is also known as data abstraction, and leads us to a style of modular programming that naturally extends the procedural programming style for primitive
types that was the basis for Chapter 2. A data type allows us to isolate data as well
as functions. Our mantra for this chapter is this: whenever you can clearly separate
data and associated tasks within a computation, you should do so.
329
Object-Oriented Programming
3.1 Using Data Types
Organizing data for processing is an essential step in the development of a computer program. Programming in Java is largely based on doing so with data types
known as reference types that are designed to support object-oriented programming, a style of programming that facilitates
organizing and processing data.
3.1.1 Identifying a potential gene. . . . . 337
The eight primitive data types
3.1.2 Albers squares. . . . . . . . . . . . 342
3.1.3 Luminance library. . . . . . . . . . 345
(boolean, byte, char, double, float, int,
3.1.4 Converting color to grayscale. . . . 348
long, and short) that you have been using
3.1.5 Image scaling. . . . . . . . . . . . . 350
are supplemented in Java by extensive librar3.1.6 Fade effect . . . . . . . . . . . . . . 352
ies of reference types that are tailored for a
3.1.7 Concatenating files . . . . . . . . . 356
3.1.8 Screen scraping for stock quotes . . 359
large variety of applications. The String
3.1.9 Splitting a file. . . . . . . . . . . . . 360
data type is one such example that you have
Programs in this section
already used. You will learn more about the
String data type in this section, as well as
how to use several other reference types for
image processing and input/output. Some of them are built into Java (String and
Color), and some were developed for this book (In, Out, Draw, and Picture) and
are useful as general resources.
You certainly noticed in the first two chapters of this book that our programs
were largely confined to operations on numbers. Of course, the reason is that Java’s
primitive types represent numbers. The one exception has been strings, a reference
type that is built into Java. With reference types you can write programs that operate not just on strings, but on images, sounds, or any of hundreds of other abstractions that are available in Java’s libraries or on our booksite.
In this section, we focus on client programs that use existing data types, to
give you some concrete reference points for understanding these new concepts
and to illustrate their broad reach. We will consider programs that manipulate
strings, colors, images, files, and web pages—quite a leap from the primitive types
of Chapter 1.
In the next section, you will take another leap, by learning how to define your
own data types to implement any abstraction whatsoever, taking you to a whole
new level of programming. Writing programs that operate on your own types of
data is an extremely powerful and useful style of programming that has dominated
the landscape for many years.
3.1 Using Data Types
Basic definitions
A data type is a set of values and a set of operations defined on
those values. This statement is one of several mantras that we repeat often because
of its importance. In Chapter 1, we discussed in detail Java’s primitive data types.
For example, the values of the primitive data type int are integers between 231
and 231 1; the operations defined for the int data type include those for basic
arithmetic and comparisons, such as +, *, %, <, and >.
You also have been using a data type that is not primitive—the String data
type. You know that values of the String data type are sequences of characters and
that you can perform the operation of concatenating two String values to produce
a String result. You will learn in this section that there are dozens of other operations available for processing strings, such as finding a string’s length, extracting
individual characters from the string, and comparing two strings.
Every data type is defined by its set of values and the operations defined on
them, but when we use the data type, we focus on the operations, not the values.
When you write programs that use int or double values, you are not concerning
yourself with how they are represented (we never did spell out the details), and the
same holds true when you write programs that use reference types, such as String,
Color, or Picture. In other words, you do not need to know how a data type is
implemented to be able to use it (yet another mantra)
The String data type. As a running example, we will revisit Java’s String data
type in the context of object-oriented programming. We do so for two reasons.
First, you have been using the String data type since your first program, so it is a
familiar example. Second, string processing is critical to many computational applications. Strings lie at the heart of our ability to compile and run Java programs
and to perform many other core computations; they are the basis of the information-processing systems that are critical to most business systems; people use them
every day when typing into email, blog, or chat applications or preparing documents for publication; and they have proved to be critical ingredients in scientific
progress in several fields, particularly molecular biology.
We will write programs that declare, create, and manipulate values of type
String. We begin by describing the String API, which documents the available
operations. Then, we consider Java language mechanisms for declaring variables,
creating objects to hold data-type values, and invoking instance methods to apply
data-type operations. These mechanisms differ from the corresponding ones for
primitive types, though you will notice many similarities.
331
Object-Oriented Programming
332
API. The Java class provides a mechanism for defining data types. In a class, we
specify the data-type values and implement the data-type operations. To fulfill our
promise that you do not need to know how a data type is implemented to be able to
use it, we specify the behavior of classes for clients by listing their instance methods
in an API (application programming interface), in the same manner as we have been
doing for libraries of static methods. The purpose of an API is to provide the information that you need to write a client program that uses the data type.
The following table summarizes the instance methods from Java’s String API
that we use most often; the full API has more than 60 methods! Several of the
methods use integers to refer to a character’s index within a string; as with arrays,
these indices start at 0.
public class String
(Java string data type)
String(String s)
create a string with the same value as s
String(char[] a)
create a string that represents the same
sequence of characters as in a[]
int length()
char charAt(int i)
String substring(int i, int j)
number of characters
the character at index i
characters at indices i through (j-1)
boolean contains(String substring)
does this string contain substring ?
boolean startsWith(String pre)
does this string start with pre ?
boolean endsWith(String post)
does this string end with post ?
int indexOf(String pattern)
index of first occurrence of pattern
int indexOf(String pattern, int i)
index of first occurrence of pattern after i
String concat(String t)
int compareTo(String t)
this string with t appended
string comparison
String toLowerCase()
this string, with lowercase letters
String toUpperCase()
this string, with uppercase letters
String replaceAll(String a, String b)
this string, with as replaced by bs
String[] split(String delimiter)
boolean equals(Object t)
int hashCode()
strings between occurrences of delimiter
is this string’s value the same as t’s ?
an integer hash code
See the online documentation and booksite for many other available methods.
Excerpts from the API for Java’s String data type
3.1 Using Data Types
333
The first entry, with the same name as the class and no return type, defines a
special method known as a constructor. The other entries define instance methods
that can take arguments and return values in the same manner as the static methods that we have been using, but they are not static methods: they implement operations for the data type. For example, the instance method length() returns
the number of characters in the string and charAt() returns the character at a
specified index.
Declaring variables. You declare variables of a reference type in precisely the
same way that you declare variables of a primitive type, using a declaration statement consisting of the data type name followed by a variable name. For example,
the statement
String s;
declares a variable s of type String. This statement does not create anything; it just
says that we will use the variable name s to refer to a String object. By convention, reference types begin with uppercase letters and primitive types begin with
lowercase letters.
Creating objects. In Java, each data-type value is
declare a variable (object name)
stored in an object. When a client invokes a coninvoke a constructor to create an object
structor, the Java system creates (or instantiates)
String s;
an individual object (or instance). To invoke a constructor, use the keyword new; followed by the class s = new String("Hello, World") ;
name; followed by the constructor’s arguments, char c = s .charAt(4) ;
enclosed in parentheses and separated by commas,
object name
invoke an instance method
in the same manner as a static method call. For exthat operates on the object’s value
ample, new String("Hello, World") creates a
Using a reference data type
new String object corresponding to the sequence
of characters Hello, World. Typically, client code
invokes a constructor to create an object and assigns it to a variable in the same line
of code as the declaration:
String s = new String("Hello, World");
You can create any number of objects from the same class; each object has its own
identity and may or may not store the same value as another object of the same
type. For example, the code
Object-Oriented Programming
334
String s1 = new String("Cat");
String s2 = new String("Dog");
String s3 = new String("Cat");
creates three different String objects. In particular, s1 and s3 refer to different
objects, even though the two objects represent the same sequence of characters.
Invoking instance methods. The most important difference between a variable of
a reference type and a variable of a primitive type is that you can use reference-type
variables to invoke the methods that implement data-type operations (in contrast
to the built-in syntax involving
String a = new String("now is");
operators such as + and * that
String b = new String("the time");
String c = new String(" the");
we used with primitive types).
Such methods are known as
instance method call return type
return value
instance methods. Invoking (or
6
int
a.length()
calling) an instance method is
'i'
char
a.charAt(4)
similar to calling a static meth"w i"
String
a.substring(2, 5)
od in another class, except that b.startsWith("the") boolean
true
4
a.indexOf("is")
int
an instance method is associ"now is the"
a.concat(c)
String
ated not just with a class, but
b.replace("t", "T")
"The Time"
String
also with an individual object.
a.split(" ")
{ "now", "is"
String[]
Accordingly, we typically use
b.equals(c)
false
boolean
an object name (variable of the
given type) instead of the class
Examples of String data-type operations
name to identify the method.
For example, if s1 and s2 are variables of type String as defined earlier, then
s1.length() returns the integer 3, s1.charAt(1) returns the character 'a', and
s1.concat(s2) returns a new string CatDog.
String shortcuts. As you already know, Java provides special language support for
the String data type. You can create a String object using a string literal instead
of an explicit constructor call. Also, you can concatenate two strings using the
string concatenation operator (+) instead of making an explicit call to the concat() method. We introduced the longhand version here solely to demonstrate the
syntax you need for other data types; these two shortcuts are unique to the String
data type.
shorthand
longhand
String s = "abc";
String t = r + s;
String s = new String("abc");
String t = r.concat(s);
}
3.1 Using Data Types
335
The following code fragments illustrate the use of various string-processing
methods. This code clearly exhibits the idea of developing an abstract model and
separating the code that implements the abstraction from the code that uses it.
This ability characterizes object-oriented programming and is a turning point in
this book: we have not yet seen any code of this nature, but virtually all of the code
that we write from this point forward will be based on defining and invoking methods that implement data-type operations.
extract file name
and extension from a
command-line
argument
String s = args[0];
int dot = s.indexOf(".");
String base
= s.substring(0, dot);
String extension = s.substring(dot + 1, s.length());
print all lines on
standard input
that contain a string
specified as a
command-line
argument
String query = args[0];
while (StdIn.hasNextLine())
{
String line = StdIn.readLine();
if (line.contains(query))
StdOut.println(line);
}
is the string
a palindrome?
translate from
DNA to mRNA
(replace 'T' with 'U')
public static boolean isPalindrome(String s)
{
int n = s.length();
for (int i = 0; i < n/2; i++)
if (s.charAt(i) != s.charAt(n-1-i))
return false;
return true;
}
public static String translate(String dna)
{
dna = dna.toUpperCase();
String rna = dna.replaceAll("T", "U");
return rna;
}
Typical string-processing code
Object-Oriented Programming
336
String-processing application: genomics
To give you more experience
with string processing, we will give a very brief overview of the field of genomics
and consider a program that a bioinformatician might use to identify potential
genes. Biologists use a simple model to represent the building blocks of life, in
which the letters A, C, G, and T represent the four bases in the DNA of living organisms. In each living organism, these basic building blocks appear in a set of long
sequences (one for each chromosome) known as a genome. Understanding properties of genomes is a key to understanding the processes that manifest themselves
in living organisms. The genomic sequences for many living things are known, including the human genome, which is a sequence of about 3 billion bases. Since the
sequences have been identified, scientists have begun composing computer programs to study their structure. String processing is now one of the most important
methodologies—experimental or computational—in molecular biology.
Gene prediction. A gene is a substring of a genome that represents a functional
unit of critical importance in understanding life processes. A gene consists of a
sequence of codons, each of which is a sequence of three bases that represents one
amino acid. The start codon ATG marks the beginning of a gene, and any of the stop
codons TAG, TAA, or TGA marks the end of a gene (and no other occurrences of any
of these stop codons can appear within the gene). One of the first steps in analyzing a genome is to identify its potential genes, which is a string-processing problem
that Java’s String data type equips us to solve.
PotentialGene (Program 3.1.1) is a program that serves as a first step. The
isPotentialGene() function takes a DNA string as an argument and determines
whether it corresponds to a potential gene based on the following criteria: length
is a multiple of 3, starts with the start codon, ends with a stop codon, and has
no intervening stop codons. To make the determination, the program uses a variety of string instance methods: length(), charAt(), startsWith(), endsWith(),
substring(), and equals().
Although the rules that define genes are a bit more complicated than those
we have sketched here, PotentialGene exemplifies how a basic knowledge of programming can enable a scientist to study genomic sequences more effectively.
In the present context, our interest in the String data type is that it illustrates
what a data type can be—a well-developed encapsulation of an important abstraction that is useful to clients. Before proceeding to other examples, we consider a few
basic properties of reference types and objects in Java.
3.1 Using Data Types
Program 3.1.1
337
Identifying a potential gene
public class PotentialGene
{
public static boolean isPotentialGene(String dna)
{
// Length is a multiple of 3.
if (dna.length() % 3 != 0) return false;
// Starts with start codon.
if (!dna.startsWith("ATG")) return false;
// No intervening stop codons.
for (int i = 3; i < dna.length() - 3;
{
if (i % 3 == 0)
{
String codon = dna.substring(i,
if (codon.equals("TAA")) return
if (codon.equals("TAG")) return
if (codon.equals("TGA")) return
}
}
//
if
if
if
i++)
dna
codon
string to analyze
3 consecutive bases
i+3);
false;
false;
false;
Ends with a stop codon.
(dna.endsWith("TAA")) return true;
(dna.endsWith("TAG")) return true;
(dna.endsWith("TGA")) return true;
return false;
}
}
The isPotentialGene() function takes a DNA string as an argument and determines whether it corresponds to a potential gene: length is a multiple of 3, starts with the start codon (ATG),
ends with a stop codon (TAA or TAG or TGA), and has no intervening stop codons. See Exercise
3.1.19 for the test client.
% java PotentialGene ATGCGCCTGCGTCTGTACTAG
true
% java PotentialGene ATGCGCTGCGTCTGTACTAG
false
338
Object-Oriented Programming
Object references. A constructor creates an object and returns to the client a reference to that object, not the object itself (hence the name reference type). What is an
object reference? Nothing more than a mechanism for accessing an object. There
are several different ways for Java to implement references, but we do not need to
know the details to use them. Still, it is worthwhile to have a mental model of one
common implementation. One approach is for new to assign one object
memory space to hold the object’s current data-type value
and return a pointer (memory address) to that space. We rereference
fer to the memory address associated with the object as the
c1
459
object’s identity.
Why not just process the object itself? For small objects,
length
it might make sense to do so, but for large objects, cost be- 459
3
460
C
comes an issue: data-type values can consume large amounts
characters
461
A
of memory. It does not make sense to copy or move all of its
462
T
data every time that we pass an object as an argument to a
method. If this reasoning seems familiar to you, it is because
we have used precisely the same reasoning before, when talking about passing arrays as arguments to static methods in
Section 2.1. Indeed, arrays are objects, as we will see later in two objects
this section. By contrast, primitive types have values that are
natural to represent directly in memory, so that it does not
c1
459
make sense to use a reference to access each value.
c2
611
We will discuss properties of object references in more
identity
of c1
detail after you have seen several examples of client code that
use reference types.
459
3
Using objects. A variable declaration gives us a variable
name for an object that we can use in code in much the same
way as we use a variable name for an int or double:
• As an argument or return value for a method
• In an assignment statement
• In an array
We have been using String objects in this way ever since
HelloWorld: most of our programs call StdOut.println()
with a String argument, and all of our programs have a
main() method that takes an argument that is a String
460
461
462
C
A
T
identity
of c2
611
612
613
614
3
D
O
G
Object representation
3.1 Using Data Types
array. As we have already seen, there is one critically important addition to this list
for variables that refer to objects:
• To invoke an instance method defined on it
This usage is not available for variables of a primitive type, where operations are
built into the language and invoked only via operators such as +, -, *, and /.
Uninitialized variables. When you declare a variable of a reference type but do
not assign a value to it, the variable is uninitialized, which leads to the same behavior as for primitive types when you try to use the variable. For example, the code
String bad;
boolean value = bad.startsWith("Hello");
leads to the compile-time error variable bad might not have been initialized because it is trying to use an uninitialized variable.
Type conversion. If you want to convert an object from one type to another, you
have to write code to do it. Often, there is no issue, because values for different data
types are so different that no conversion is contemplated. For instance, what would
it mean to convert a String object to a Color object? But there is one important
case where conversion is very often worthwhile: all Java reference types have a special instance method toString() that returns a String object. The nature of the
conversion is completely up to the implementation, but usually the string encodes
the object’s value. Programmers typically call the toString() method to print
traces when debugging code. Java automatically calls the toString() method in
certain situations, including with string concatenation and StdOut.println().
For example, for any object reference x, Java automatically converts the expression
"x = " + x to "x = " + x.toString() and the expression StdOut.println(x) to
StdOut.println(x.toString()). We will examine the Java language mechanism
that enables this feature in Section 3.3.
Accessing a reference data type. As with libraries of static methods, the code that
implements each class resides in a file that has the same name as the class but carries a .java extension. To write a client program that uses a data type, you need to
make the class available to Java. The String data type is part of the Java language,
so it is always available. You can make a user-defined data type available either by
placing a copy of the .java file in the same directory as the client or by using Java’s
classpath mechanism (described on the booksite). With this understood, you will
next learn how to use a data type in your own client code.
339
Object-Oriented Programming
340
Distinction between instance methods and static methods. Finally, you are
ready to appreciate the meaning of the modifier static that we have been using
since Program 1.1.1—one of the last mysterious details in the Java programs that
you have been writing. The primary purpose of static methods is to implement
functions; the primary purpose of instance (non-static) methods is to implement
data-type operations. You can distinguish between the uses of the two types of
methods in our client code, because a static method call typically starts with a class
name (uppercase, by convention) and an instance method call typically starts with
an object name (lowercase, by convention). These differences are summarized in
the following table, but after you have written some client code yourself, you will
be able to quickly recognize the difference.
instance method
static method
sample call
s.startsWith("Hello")
Math.sqrt(2.0)
invoked with
object name (or object reference)
class name
parameters
reference to invoking object and argument(s)
argument(s)
primary purpose
manipulate object’s value
compute return value
Instance methods versus static methods
The basic concepts that we have just covered are the starting point for objectoriented programming, so it is worthwhile to briefly summarize them here. A data
type is a set of values and a set of operations defined on those values. We implement
data types in independent modules and write client programs that use them. An
object is an instance of a data type. Objects are characterized by three essential properties: state, behavior, and identity. The state of an object is a value from its data
type. The behavior of an object is defined by the data type’s operations. The identity
of an object is the location in memory where it is stored. In object-oriented programming, we invoke constructors to create objects and then modify their state by
invoking their instance methods. In Java, we manipulate objects via object references.
To demonstrate the power of object orientation, we next consider several
more examples. First, we consider the familiar world of image processing, where
we process Color and Picture objects. Then, we revisit our input/output libraries
in the context of object-oriented programming, enabling us to access information
from files and the web.
3.1 Using Data Types
341
Color. Color is a sensation in the eye from electromagnetic radiation. Since we
want to view and manipulate color images on our computers, color is a widely used
abstraction in computer graphics, and Java provides a Color data type. In professional publishing, in print, and on the web, working with color is a complex task.
For example, the appearance of a color image depends in a significant way on the
medium used to present it. The Color data type separates the creative designer’s
problem of specifying a desired color from the system’s problem of faithfully reproducing it.
Java has hundreds of data types in its libraries, so we need to explicitly list
which Java libraries we are using in our program to avoid naming conflicts. Specifically, we include the statement
import java.awt.Color;
at the beginning of any program that uses Color. (Until now, we have been using
standard Java libraries or our own, so there has been no need to import them.)
To represent color values, Color uses the RGB color model
where a color is defined by three integers (each between 0 and 255) red green blue
that represent the intensity of the red, green, and blue (respective- 255 0
0
red
ly) components of the color. Other color values are obtained by
255
0
green
0
mixing the red, green, and blue components. That is, the data-type
0
255
blue
0
values of Color are three 8-bit integers. We do not need to know
0
0
black
0
whether the implementation uses int, short, or char values to 100 100 100 dark gray
represent these integers. With this convention, Java is using 24 bits 255 255 255 white
yellow
to represent each color and can represent 2563 224 16.7 mil- 255 255 0
0
255 magenta
255
lion possible colors. Scientists estimate that the human eye can dis90
166 this color
9
tinguish only about 10 million distinct colors.
The Color data type has a constructor that takes three integer
Some color values
arguments. For example, you can write
Color red
= new Color(255,
Color bookBlue = new Color( 9,
0,
0);
90, 166);
to create objects whose values represent pure red and the blue used to print this
book, respectively. We have been using colors in StdDraw since Section 1.5, but
have been limited to a set of predefined colors, such as StdDraw.BLACK, StdDraw.
RED, and StdDraw.PINK. Now you have millions of colors available for your use.
AlbersSquares (Program 3.1.2) is a StdDraw client that allows you to experiment
with them.
Object-Oriented Programming
342
Program 3.1.2
Albers squares
import java.awt.Color;
public class AlbersSquares
{
public static void main(String[] args)
{
int r1 = Integer.parseInt(args[0]);
int g1 = Integer.parseInt(args[1]);
int b1 = Integer.parseInt(args[2]);
Color c1 = new Color(r1, g1, b1);
int r2 =
int g2 =
int b2 =
Color c2
Integer.parseInt(args[3]);
Integer.parseInt(args[4]);
Integer.parseInt(args[5]);
= new Color(r2, g2, b2);
StdDraw.setPenColor(c1);
StdDraw.filledSquare(.25,
StdDraw.setPenColor(c2);
StdDraw.filledSquare(.25,
StdDraw.setPenColor(c2);
StdDraw.filledSquare(.75,
StdDraw.setPenColor(c1);
StdDraw.filledSquare(.75,
r1, g1, b1
c1
RGB values
first color
r2, g2, b2
RGB values
c2
second color
0.5, 0.2);
0.5, 0.1);
0.5, 0.2);
0.5, 0.1);
}
}
This program displays the two colors entered in RGB representation on the command line in
the familiar format developed in the 1960s by the color theorist Josef Albers, which revolutionized the way that people think about color.
% java AlbersSquares 9 90 166
100 100 100
3.1 Using Data Types
343
As usual, when we address a new abstraction, we are introducing you to Color
by describing the essential elements of Java’s color model, not all of the details. The
API for Color contains several constructors and more than 20 methods; the ones
that we will use are briefly summarized next.
public class java.awt.Color
Color(int r, int g, int b)
int getRed()
red intensity
int getGreen()
green intensity
int getBlue()
blue intensity
Color brighter()
brighter version of this color
Color darker()
darker version of this color
String toString()
string representation of this color
String equals(Object c)
is this color’s value the same as c ?
See the online documentation and booksite for other available methods.
Excerpts from the API for Java’s Color data type
Our primary purpose is to use Color as an example to illustrate object-oriented programming, while at the same time developing a few useful tools that we
can use to write programs that process colors. Accordingly, we choose one color
property as an example to convince you that writing object-oriented code to process abstract concepts like color is a convenient and useful approach.
Luminance. The quality of the images on modern displays such as LCD monitors,
plasma TVs, and cellphone screens depends on an understanding of a color property known as monochrome luminance, or effective brightness. A standard formula
for luminance is derived from the eye’s sensitivity to red, green, and blue. It is a
linear combination of the three intensities: if a color’s red, green, and blue values
are r, g, and b, respectively, then its monochrome luminance Y is defined by this
equation:
Y = 0.299 r + 0.587g + 0.114b
Since the coefficients are positive and sum to 1, and the intensities are all integers
between 0 and 255, the luminance is a real number between 0 and 255.
Object-Oriented Programming
344
Grayscale. The RGB color model has the prop-
red
green
blue
erty that when all three color intensities are the
same, the resulting color is on a grayscale that
ranges from black (all 0s) to white (all 255s). To
print a color photograph in a black-and-white
newspaper (or a book), we need a function to
convert from color to grayscale. A simple way
to convert a color to grayscale is to replace the
color with a new one whose red, green, and blue
values equal its monochrome luminance.
9
90
166
this color
74
74
74
grayscale version
0
0
0
black
0.299 * 9 + 0.587 * 90 + 0.114 * 166 = 74.445
Grayscale example
Color compatibility. The monochrome luminance is also crucial in determining whether two colors are compatible, in the sense
that printing text in one of the colors on a background in the other color will be
readable. A widely used rule of thumb is that the difference between the luminance
of the foreground and background colors should be at least 128. For example, black
text on a white background has a luminance difference of 255, but black text on a
(book) blue background has a luminance difference of only 74. This rule is important in the design of advertising, road signs, websites, and many other applications.
Luminance (Program 3.1.3) is a library of static methods that we can use to convert
a color to grayscale and to test whether
two colors are compatible. The static luminance
difference
methods in Luminance illustrate the util0
232
compatible
ity of using data types to organize infor74
158
mation. Using Color objects as argucompatible
ments and return values substantially
232
74
not compatible
simplifies the implementation: the alternative of passing around three intensity
Compatibility example
values is cumbersome and returning
multiple values is not possible without
reference types.
Having an abstraction for color is important not just for direct use, but also in
building higher-level data types that have Color values. Next, we illustrate this
point by building on the color abstraction to develop a data type that allows us to
write programs to process digital images.
3.1 Using Data Types
Program 3.1.3
345
Luminance library
import java.awt.Color;
public class Luminance
{
public static double intensity(Color color)
{ // Monochrome luminance of color.
int r = color.getRed();
int g = color.getGreen();
int b = color.getBlue();
return 0.299*r + 0.587*g + 0.114*b;
}
public static Color toGray(Color color)
{ // Use luminance to convert to grayscale.
int y = (int) Math.round(intensity(color));
Color gray = new Color(y, y, y);
return gray;
}
r, g, b
y
RGB values
luminance of color
public static boolean areCompatible(Color a, Color b)
{ // True if colors are compatible, false otherwise.
return Math.abs(intensity(a) - intensity(b)) >= 128.0;
}
public static void main(String[] args)
{ // Are the two specified RGB colors compatible?
int[] a = new int[6];
for (int i = 0; i < 6; i++)
a[]
a[i] = Integer.parseInt(args[i]);
Color c1 = new Color(a[0], a[1], a[2]);
c1
Color c2 = new Color(a[3], a[4], a[5]);
c2
StdOut.println(areCompatible(c1, c2));
}
int values of args[]
first color
second color
}
This library comprises three important functions for manipulating color: monochrome luminance, conversion to grayscale, and background/foreground compatibility.
% java Luminance 232 232 232
true
% java Luminance
9 90 166
true
% java Luminance
9 90 166
false
0
0
0
232 232 232
0
0
0
Object-Oriented Programming
346
Digital image processing You are familiar with the concept of a photograph.
Technically, we might define a photograph as a two-dimensional image created
by collecting and focusing visible wavelengths of electromagnetic radiation that
constitutes a representation of a scene at a point in time. That technical definition
is beyond our scope, except to note that the history of photography is a history of
technological development. During the last century, photography was based on
chemical processes, but its future is now based in computation. Your camera and
your cellphone are computers with lenses and light-sensitive devices capable of
capturing images in digital form, and your computer has photo-editing software
that allows you to process those images. You can crop them, enlarge and reduce
them, adjust the contrast, brighten or darken them, remove redeye, or perform
scores of other operations. Many such operations are remarkably easy to implement, given a simple basic data type that captures the idea of a digital image, as you
will now see.
Digital images. Which set of values do we need to process digital images, and
pixel
(0, 0)
row
height
which operations do we need to perform on those values? The basic abstraction for
computer displays is the same one that is used for digital photographs and is very
simple: a digital image is a rectangular grid of pixels (picture elements), where the
color of each pixel is individually defined. Digital images are sometimes referred
to as raster or bitmapped images. In contrast, the types of images that we produce
with StdDraw (which involve geometric objects such as points, lines, circles, and
squares)are referred to as vector images.
Our class Picture is a data type for digital images whose
pixels are
references to
definition follows immediately from the digital image abstracColor objects
column
tion. The set of values is nothing more than a two-dimensional matrix of Color values, and the operations are what you
might expect: create a blank image with a given width and
height, load an image from a file, set the value of a pixel to a
given color, return the color of a given pixel, return the width
or the height, show the image in a window on your computer
screen, and save the image to a file. In this description, we intentionally use the word matrix instead of array to emphasize
that we are referring to an abstraction (a matrix of pixels), not
a specific implementation (a Java two-dimensional array of
Color objects). You do not need to know how a data type is
width
Anatomy of a digital image
3.1 Using Data Types
347
implemented to be able to use it. Indeed, typical images have so many pixels that
implementations are likely to use a more efficient representation than an array of
Color objects. In any case, to write client programs that manipulate images, you
just need to know this API:
public class Picture
Picture(String filename)
create a picture from a file
Picture(int w, int h)
create a blank w-by-h picture
int width()
return the width of the picture
int height()
return the height of the picture
Color get(int col, int row)
return the color of pixel (col, row)
void set(int col, int row, Color c)
set the color of pixel (col, row) to c
void show()
display the picture in a window
void save(String filename)
save the picture to a file
API for our data type for image processing
By convention, (0, 0) is the upper-leftmost pixel, so the image is laid as in the
customary order for two-dimensional arrays (by contrast, the convention for
StdDraw is to have the point (0,0) at the lower-left corner, so that drawings are
oriented as in the customary manner for Cartesian coordinates). Most imageprocessing programs are filters that scan through all of the pixels in a source image
and then perform some computation to determine the color of each pixel in a target image. The supported file formats for the first constructor and the save()
method are the widely used PNG and JPEG formats, so that you can write programs to process your own digital photos and add the results to an album or a
website. The show() window also has an interactive option for saving to a file.
These methods, together with Java’s Color data type, open the door to image processing.
Grayscale. You will find many examples of color images on the booksite, and all of
the methods that we describe are effective for full-color images, but all our example
images in this book will be grayscale. Accordingly, our first task is to write a program that converts images from color to grayscale. This task is a prototypical
image-processing task: for each pixel in the source, we set a pixel in the target to a
Object-Oriented Programming
348
Program 3.1.4
Converting color to grayscale
import java.awt.Color;
picture
public class Grayscale
col, row
{
color
public static void main(String[] args)
gray
{ // Show image in grayscale.
Picture picture = new Picture(args[0]);
for (int col = 0; col < picture.width(); col++)
{
for (int row = 0; row < picture.height(); row++)
{
Color color = picture.get(col, row);
Color gray = Luminance.toGray(color);
picture.set(col, row, gray);
}
}
picture.show();
}
}
image from file
pixel coordinates
pixel color
pixel grayscale
This program illustrates a simple image-processing client. First, it creates a Picture object initialized with an image file named by the command-line argument. Then it converts each pixel
in the picture to grayscale by creating a grayscale version of each pixel’s color and resetting the
pixel to that color. Finally, it shows the picture. You can perceive individual pixels in the picture
on the right, which was upscaled from a low-resolution picture (see “Scaling” on the next page).
% java Grayscale mandrill.jpg
% java Grayscale darwin.jpg
3.1 Using Data Types
349
different color. Grayscale (Program 3.1.4) is a filter that takes a file name from the
command line and produces a grayscale version of that image. It creates a new
Picture object initialized with the color image, then sets the color of each pixel to
a new Color having a grayscale value computed by applying the toGray() method
in Luminance (Program 3.1.3) to the color of the corresponding pixdownscaling
el in the source.
source
Scaling. One of the most common image-processing tasks is to
make an image smaller or larger. Examples of this basic operation,
known as scaling, include making small thumbnail photos for use in
a chat room or a cellphone, changing the size of a high-resolution
photo to make it fit into a specific space in a printed publication or
on a web page, and zooming in on a satellite photograph or an image produced by a microscope. In optical systems, we can just move
target
a lens to achieve a desired scale, but in digital imagery, we have to do
more work.
In some cases, the strategy is clear. For example, if the target image is to be half the size (in each dimension) of the source image, we
simply choose half the pixels, say, by deleting half the rows and half upscaling
the columns. This technique is known as sampling. If the target image
source
is to be double the size (in each dimension) of the source image, we
can replace each source pixel by four target pixels of the same color.
Note that we can lose information when we downscale, so halving
an image and then doubling it generally does not give back the same
target
image.
A single strategy is effective for both downscaling and upscaling. Our goal is to produce the target image, so we proceed through
the pixels in the target, one by one, scaling each pixel’s coordinates to
identify a pixel in the source whose color can be assigned to the target.
If the width and height of the source are ws and hs (respectively) and
the width and height of the target are wt and ht (respectively), then we
scale the column index by ws /wt and the row index by hs /ht. That is,
Scaling a digital image
we get the color of the pixel in column c and row r and of the target
from column cws /wt and row rhs /ht in the source. For example,
if we are halving the size of an image, the scale factors are 2, so the pixel in column
3 and row 2 of the target gets the color of the pixel in column 6 and row 4 of the
source; if we are doubling the size of the image, the scale factors are 1/2, so the pixel
Object-Oriented Programming
350
Program 3.1.5
Image scaling
public class Scale
{
public static void main(String[] args)
w, h
target dimensions
{
source
source image
int w = Integer.parseInt(args[1]);
int h = Integer.parseInt(args[2]);
target
target image
Picture source = new Picture(args[0]);
colT, rowT target pixel coords
Picture target = new Picture(w, h);
colS, rowS source pixel coords
for (int colT = 0; colT < w; colT++)
{
for (int rowT = 0; rowT < h; rowT++)
{
int colS = colT * source.width() / w;
int rowS = rowT * source.height() / h;
target.set(colT, rowT, source.get(colS, rowS));
}
}
source.show();
target.show();
}
}
This program takes the name of an image file and two integers (width w and height h) as
command-line arguments, scales the picture to w-by-h, and displays both images.
% java Scale mandrill.jpg 800 800
600 300
200 400
200 200
3.1 Using Data Types
351
in column 4 and row 6 of the target gets the color of the pixel in column 2 and
row 3 of the source. Scale (Program 3.1.5) is an implementation of this strategy.
More sophisticated strategies can be effective for low-resolution images of the sort
that you might find on old web pages or from old cameras. For example, we might
downscale to half size by averaging the values of four pixels in the source to make
one pixel in the target. For the high-resolution images that are common in most
applications today, the simple approach used in Scale is effective.
The same basic idea of computing the color value of each target pixel as a
function of the color values of specific source pixels is effective for all sorts of
image-processing tasks. Next, we consider one more example, and you will find
numerous other examples in the exercises and on
% java Fade mandrill.jpg darwin.jpg
the booksite.
Fade effect. Our final image-processing example
is an entertaining computation where we transform one image into another in a series of discrete steps. Such a transformation is sometimes
known as a fade effect. Fade (Program 3.1.6) is a
Picture and Color client that uses a linear interpolation strategy to implement this effect. It computes n1 intermediate pictures, with each pixel
in picture i being a weighted average of the corresponding pixels in the source and target. The
static method blend() implements the interpolation: the source color is weighted by a factor of
1 i / n and the target color by a factor of i / n
(when i is 0, we have the source color, and when i
is n, we have the target color). This simple computation can produce striking results. When you
run Fade on your computer, the change appears
to happen dynamically. Try running it on some
images from your photo library. Note that Fade
assumes that the images have the same width and
height; if you have images for which this is not
the case, you can use Scale to created a scaled
version of one or both of them for Fade.
9
Object-Oriented Programming
352
Program 3.1.6
Fade effect
import java.awt.Color;
public class Fade
{
public static Color blend(Color c1, Color c2, double alpha)
{ // Compute blend of colors c1 and c2, weighted by alpha.
double r = (1-alpha)*c1.getRed()
+ alpha*c2.getRed();
double g = (1-alpha)*c1.getGreen() + alpha*c2.getGreen();
double b = (1-alpha)*c1.getBlue() + alpha*c2.getBlue();
return new Color((int) r, (int) g, (int) b);
}
public static void main(String[] args)
{ // Show m-image fade sequence from source to target.
Picture source = new Picture(args[0]);
Picture target = new Picture(args[1]);
int n = Integer.parseInt(args[2]);
int width = source.width();
int height = source.height();
Picture picture = new Picture(width, height);
for (int i = 0; i <= n; i++)
n
number of pictures
{
for (int col = 0; col < width; col++)
picture current picture
{
i
picture counter
for (int row = 0; row < height; row++)
c1
source color
{
Color c1 = source.get(col, row);
c2
target color
Color c2 = target.get(col, row);
color
blended color
double alpha = (double) i / n;
Color color = blend(c1, c2, alpha);
picture.set(col, row, color);
}
}
picture.show();
}
}
}
To fade from one picture into another in n steps, we set each pixel in picture i to a weighted average of the corresponding pixel in the source and destination pictures, with the source getting
weight 1 i / n and the destination getting weight i / n. An example transformation is shown
on the facing page.
3.1 Using Data Types
Input and output revisited In Section 1.5 you learned how to read and write
numbers and text using StdIn and StdOut and to make drawings with StdDraw.
You have certainly come to appreciate the utility of these mechanism in getting
information into and out of your programs. One reason that they are convenient is
that the “standard” conventions make them accessible from anywhere within a program. One disadvantage of these conventions is that they leave us dependent upon
the operating system’s piping and redirection mechanism for access to files, and
they restrict us to working with just one input file, one output file, and one drawing
for any given program. With object-oriented programming, we can define mechanisms that are similar to those in StdIn, StdOut, and StdDraw but allow us to work
with multiple input streams, output streams, and drawings within one program.
Specifically, we define in
input streams
this section the data types In,
Out, and Draw for input streams,
output streams, and drawings,
respectively. As usual, you must
make these classes accessible to
command-line
standard input
Java
(see the Q&A at the end of
arguments
Section 1.5).
pictures
These data types give us the
flexibility that we need to address
many common data-processing
output streams
tasks within our Java programs.
Rather than being restricted to
just one input stream, one outdrawings
put stream, and one drawing,
we can easily define multiple
standard output
objects of each type, connecting
the streams to various sources
and destinations. We also get
the flexibility to assign such obA bird’s-eye view of a Java program (revisited again)
jects to variables, pass them as
arguments or return values from
methods, and create arrays of them, manipulating them just as we manipulate objects of any type. We will consider several examples of their use after we have presented the APIs.
353
Object-Oriented Programming
354
Input stream data type. Our In data type is a more general version of StdIn that
supports reading numbers and text from files and websites as well as the standard
input stream. It implements the input stream data type, with the API at the bottom
of this page. Instead of being restricted to one abstract input stream (standard
input), this data type gives you the ability to directly specify the source of an input
stream. Moreover, that source can be either a file or a website. When you call the
constructor with a string argument, the constructor first tries to find a file in the
current directory of your local computer with that name. If it cannot do so, it assumes the argument is a website name and tries to connect to that website. (If no
such website exists, it generates a run-time exception.) In either case, the specified
file or website becomes the source of the input for the input stream object thus created, and the read*() methods will read input from that stream.
public class In
In()
create an input stream from standard input
In(String name)
create an input stream from a file or website
instance methods that read individual tokens from the input stream
boolean isEmpty()
int readInt()
double readDouble()
is standard input empty (or only whitespace)?
read a token, convert it to an int, and return it
read a token, convert it to a double, and return it
...
instance methods that read characters from the input stream
boolean hasNextChar()
does standard input have any remaining characters?
char readChar()
read a character from standard input and return it
instance methods that read lines from the input stream
boolean hasNextLine()
String readLine()
does standard input have a next line?
read the rest of the line and return it as a String
instance methods that read the rest of the input stream
int[] readAllInts()
read all remaining tokens; return as array of integers
double[] readAllDoubles()
read all remaining tokens; return as array of doubles
...
Note: All operations supported by StdIn are also supported for In objects.
API for our data type for input streams
3.1 Using Data Types
355
This arrangement makes it possible to process multiple files within the same
program. Moreover, the ability to directly access the web opens up the whole web
as potential input for your programs. For example, it allows you to process data
that is provided and maintained by someone else. You can find such files all over
the web. Scientists now regularly post data files with measurements or results of experiments, ranging from genome and protein sequences to satellite photographs to
astronomical observations; financial services companies, such as stock exchanges,
regularly publish on the web detailed information about the performance of stock
and other financial instruments; governments publish election results; and so forth.
Now you can write Java programs that read these kinds of files directly. The In data
type gives you a great deal of flexibility to take advantage of the multitude of data
sources that are now available.
Output stream data type. Similarly, our Out data type is a more general version
of StdOut that supports printing text to a variety of output streams, including
standard output and files. Again, the API specifies the same methods as its StdOut
counterpart. You specify the file that you want to use for output by using the oneargument constructor with the file’s name as the argument. Out interprets this
string as the name of a new file on your local computer, and sends its output there.
If you use the no-argument constructor, then you obtain the standard output
stream.
public class Out
Out()
create an output stream to standard output
Out(String name)
create an output stream to a file
void print(String s)
print s to the output stream
void println(String s)
print s and a newline to the output stream
void println()
print a newline to the output stream
void printf(String format, ...)
print the arguments to the output stream,
as specified by the format string format
API for our data type for output streams
Object-Oriented Programming
356
Program 3.1.7
Concatenating files
public class Cat
{
public static void main(String[] args)
{
Out out = new Out(args[args.length-1]);
for (int i = 0; i < args.length - 1; i++)
{
In in = new In(args[i]);
String s = in.readAll();
out.println(s);
}
}
}
out
i
in
s
output stream
argument index
current input stream
contents of in
This program creates an output file whose name is given by the last command-line argument
and whose contents are the concatenation of the input files whose names are given as the other
command-line arguments.
% more in1.txt
This is
% more in2.txt
a tiny
test.
% java Cat in1.txt in2.txt out.txt
% more out.txt
This is
a tiny
test.
File concatenation and filtering. Program 3.1.7 is a sample client of In and Out
that uses multiple input streams to concatenate several input files into a single output file. Some operating systems have a command known as cat that implements
this function. However, a Java program that does the same thing is perhaps more
useful, because we can tailor it to filter the input files in various ways: we might
wish to ignore irrelevant information, change the format, or select only some of the
data, to name just a few examples. We now consider one example of such processing, and you will find several others in the exercises.
3.1 Using Data Types
357
Screen scraping. The combination of the In data type (which allows us to create an input stream from any page on the web) and the String data type (which
provides powerful tools for processing text strings) opens up the entire web to direct access by our Java programs, without any direct dependence on the operating
system or browser. One paradigm is known as screen scraping: the goal is to extract
some information from a web page with a program, rather than having to browse
to find it. To do so, we take advantage of the fact that many web pages are defined
with text files in a highly structured format (because they are created by computer
programs!). Your browser has a mechanism that allows you to examine the source
code that produces the web page that you are ...
viewing, and by examining that source you can (GOOG)</h2> <span class="rtq_
often figure out what to do.
exch"><span class="rtq_dash">-</span>
Suppose that we want to take a stock trad- NMS </span><span class="wl_sign">
ing symbol as a command-line argument and </span></div></div>
print that stock’s current trading price. Such in- <div class="yfi_rt_quote_summary_rt_top
formation is published on the web by financial sigfig_promo_1"><div>
service companies and Internet service provid- <span class="time_rtq_ticker">
<span id="yfs_l84goog">1,100.62</span>
ers. For example, you can find the stock price
</span> <span class="down_r time_rtq_
of a company whose symbol is goog by brows- content"><span id="yfs_c63_goog">
ing to http://finance.yahoo.com/q?s=goog. ...
Like many web pages, the name encodes an
HTML code from the web
argument (goog), and we could substitute any
other ticker symbol to get a web page with financial information for any other company.
Also, like many other files on the web, the referenced file is a text file, written in a
formatting language known as HTML. From the point of view of a Java program, it
is just a String value accessible through an In object. You can use your browser to
download the source of that file, or you could use
% java Cat "http://finance.yahoo.com/q?s=goog" goog.html
to put the source into a file goog.html on your local computer (though there is no
real need to do so). Now, suppose that goog is trading at $1,100.62 at the moment.
If you search for the string "1,100.62" in the source of that page, you will find the
stock price buried within some HTML code. Without having to know details of
HTML, you can figure out something about the context in which the price appears.
In this case, you can see that the stock price is enclosed between the substrings
<span id=”yfs_l84goog”> and </span>.
358
Object-Oriented Programming
With the String data type’s indexOf() and substring() methods, you easily can grab this information, as illustrated in StockQuote (Program 3.1.8). This
program depends on the web page format used by http://finance.yahoo.com;
if this format changes, StockQuote will not work. Indeed, by the time you read
this page, the format may have changed. Even so, making appropriate changes is
not likely to be difficult. You can entertain yourself by embellishing StockQuote
in all kinds of interesting ways. For example, you could grab the stock price on a
periodic basis and plot it, compute a moving average, or save the results to a file
for later analysis. Of course, the same technique works for sources of data found all
over the web, as you can see in examples in the exercises at the end of this section
and on the booksite.
Extracting data. The ability to maintain multiple input and output streams gives
us a great deal of flexibility in meeting the challenges of processing large amounts
of data coming from a variety of sources. We consider one more example: Suppose
that a scientist or a financial analyst has a large amount of data within a spreadsheet
program. Typically such spreadsheets are tables with a relatively large number of
rows and a relatively small number of columns. You are not likely to be interested
in all the data in the spreadsheet, but you may be interested in a few of the columns.
You can do some calculations within the spreadsheet program (this is its purpose,
after all), but you certainly do not have the flexibility that you have with Java programming. One way to address this situation is to have the spreadsheet export the
data to a text file, using some special character to delimit the columns, and then
write a Java program that reads that file from an input stream. One standard practice is to use commas as delimiters: print one line per row, with commas separating
column entries. Such files are known as comma-separated-value or .csv files. With
the split() method in Java’s String data type, we can read the file line-by-line
and isolate the data that we want. We will see several examples of this approach
later in the book. Split (Program 3.1.9) is an In and Out client that goes one step
further: it creates multiple output streams and makes one file for each column.
These examples are convincing illustrations of the utility of working with text files,
with multiple input and output streams, and with direct access to web pages. Web
pages are written in HTML precisely so that they are accessible to any program that
can read strings. People use text formats such as .csv files rather than data formats
that are beholden to particular applications precisely to allow as many people as
possible to access the data with simple programs like Split.
3.1 Using Data Types
Program 3.1.8
359
Screen scraping for stock quotes
public class StockQuote
{
private static String readHTML(String symbol)
{ // Return HTML corresponding to stock symbol.
In page = new In("http://finance.yahoo.com/q?s=" + symbol);
return page.readAll();
}
symbol stock symbol
public static double priceOf(String symbol)
page
{ // Return current stock price for symbol.
String html = readHTML(symbol);
int p
= html.indexOf("yfs_l84", 0);
int from = html.indexOf(">", p);
int to
= html.indexOf("</span>", from);
String price = html.substring(from + 1, to);
return Double.parseDouble(price.replaceAll(",", ""));
}
public static void main(String[] args)
{ // Print price of stock specified by symbol.
String symbol = args[0];
double price = priceOf(symbol);
StdOut.println(price);
}
input stream
html
contents of page
p
yfs_184 index
from
to
price
> index
</span> index
current price
}
This program accepts a stock ticker symbol as a command-line argument and prints to standard output the current stock price for that stock, as reported by the website http://finance.
yahoo.com. It uses the indexOf(), substring(), and replaceAll() methods from String.
% java StockQuote goog
1100.62
% java StockQuote adbe
70.51
Object-Oriented Programming
360
Program 3.1.9
Splitting a file
public class Split
{
public static void main(String[] args)
{ // Split file by column into n files.
String name = args[0];
int n = Integer.parseInt(args[1]);
String delimiter = ",";
name
n
delimiter
in
out[]
// Create output streams.
Out[] out = new Out[n];
for (int i = 0; i < n; i++)
out[i] = new Out(name + i + ".txt");
line
fields[]
base file name
number of fields
delimiter (comma)
input stream
output streams
current line
values in current line
In in = new In(name + ".csv");
while (in.hasNextLine())
{ // Read a line and write fields to output streams.
String line = in.readLine();
String[] fields = line.split(delimiter);
for (int i = 0; i < n; i++)
out[i].println(fields[i]);
}
}
}
This program uses multiple output streams to split a .csv file into separate files, one for each
comma-delimited field. The name of the output file corresponding to the ith field is formed by
concatenating i and then .csv to the end of the original file name.
% more DJIA.csv
...
31-Oct-29,264.97,7150000,273.51
30-Oct-29,230.98,10730000,258.47
29-Oct-29,252.38,16410000,230.07
28-Oct-29,295.18,9210000,260.64
25-Oct-29,299.47,5920000,301.22
24-Oct-29,305.85,12900000,299.47
23-Oct-29,326.51,6370000,305.85
22-Oct-29,322.03,4130000,326.51
21-Oct-29,323.87,6090000,320.91
...
% java Split DJIA 4
% more DJIA2.txt
...
7150000
10730000
16410000
9210000
5920000
12900000
6370000
4130000
6090000
...
3.1 Using Data Types
Drawing data type. When using the Picture data type that we considered earlier
in this section, we could write programs that manipulated multiple pictures, arrays of pictures, and so forth, precisely because the data type provides us with the
capability for computing with Picture objects. Naturally, we would like the same
capability for computing with the kinds of geometric objects that we create with
StdDraw. Accordingly, we have a Draw data type with the following API:
public class Draw
Draw()
drawing commands
void line(double x0, double y0, double x1, double y1)
void point(double x, double y)
void circle(double x, double y, double radius)
void filledCircle(double x, double y, double radius)
...
control commands
void setXscale(double x0, double x1)
void setYscale(double y0, double y1)
void setPenRadius(double radius)
...
Note: All operations supported by StdDraw are also supported for Draw objects.
As for any data type, you can create a new drawing by using new to create a
object, assign it to a variable, and use that variable name to call the methods
that create the graphics. For example, the code
Draw
Draw draw = new Draw();
draw.circle(0.5, 0.5, 0.2);
draws a circle in the center of a window on your screen. As with Picture, each
drawing has its own window, so that you can address applications that call for displaying multiple different drawings at the same time.
361
Object-Oriented Programming
362
Properties of reference types
Now that you have seen several examples of
reference types (Charge, Color, Picture, String, In, Out, and Draw) and client
programs that use them, we discuss in more detail some of their essential properties. To a large extent, Java protects novice programmers from having to know these
details. Experienced programmers, however, know that a firm understanding of
these properties is helpful in writing correct, effective, and efficient object-oriented
programs.
A reference captures the distinction between a thing and its name. This distinction is a familiar one, as illustrated in these examples:
type
typical object
typical name
website
our booksite
http://introcs.cs.princeton.edu
person
father of computer science
Alan Turing
planet
third rock from the sun
Earth
building
our office
35 Olden Street
ship
superliner that sank in 1912
RMS Titanic
number
circumference/diameter of a circle
Picture
new Picture("mandrill.jpg")
picture
A given object may have multiple names, but each object has its own identity. We
can create a new name for an object without changing the object’s value (via an
assignment statement), but when we change an object’s value (by invoking an instance method), all of the object’s names refer to the changed object.
The following analogy may help you keep this crucial distinction clear in your
mind. Suppose that you want to have your house painted, so you write the street
address of your house in pencil on a piece of paper and give it to a few house painters. Now, if you hire one of the painters to paint the house, it becomes a different
color. No changes have been made to any of the pieces of paper, but the house that
they all refer to has changed. One of the painters might erase what you’ve written
and write the address of another house, but changing what is written on one piece
of paper does not change what is written on another piece of paper. Java references
are like the pieces of paper: they hold names of objects. Changing a reference does
not change the object, but changing an object makes the change apparent to everyone having a reference to it.
3.1 Using Data Types
The famous Belgian artist René Magritte captured this
same concept in a painting where he created an image of a pipe
along with the caption ceci n’est pas une pipe (this is not a pipe)
below it. We might interpret the caption as saying that the image is not actually a pipe, just an image of a pipe. Or perhaps
Magritte meant that the caption is neither a pipe nor an image
of a pipe, just a caption! In the present context, this image reinforces the idea that a reference to an object is nothing more
than a reference; it is not the object itself.
363
© 2015 C. Herscovici / Artists Rights Society (ARS), New York
This is a picture of a pipe
Aliasing. An assignment statement with a reference type creates a second copy of
the reference. The assignment statement does not create a new object, just another
reference to an existing object. This situation is known as aliasing: both variables
refer to the same object. Aliasing also arises when passing an object reference to a
method: The parameter variable becomes another reference to the corresponding
object. The effect of aliasing is a bit unexpected, because it is different from that
for variables holding values of a primitive type. Be sure that you understand the difference. If x and y are variables of a primitive type, then the
a;
assignment statement x = y copies the value of y to x. For Color
a = new Color(160,
reference types, the reference is copied (not the value).
Color b = a;
Aliasing is a common source of bugs in Java programs,
as illustrated by the following example:
Picture a = new Picture("mandrill.jpg");
Picture b = a;
a.set(col, row, color1); // a updated
b.set(col, row, color2); // a updated again
After the second assignment statement, variables a and b
both refer to the same Picture object. Changing the state
of an object impacts all code involving aliased variables referencing that object. We are used to thinking of two different variables of primitive types as being independent, but
that intuition does not carry over to reference objects. For
example, if the preceding code assumes that a and b refer to
different Picture objects, then it will produce the wrong result. Such aliasing bugs are common in programs written by
people without much experience in using reference objects
(that’s you, so pay attention here!).
a
b
811
812
813
811
811
160
82
45
Aliasing
82, 45);
references to
same object
sienna
364
Object-Oriented Programming
Immutable types. For this very reason, it is common to define data types whose
values cannot change. An object from a data type is immutable if its data-type value
cannot change once created. An immutable data type is one in which all objects of
that type are immutable. For example, String is an immutable data type because
there are no operations available to clients that change a string’s characters. In contrast, a mutable data type is one in which objects of that type have values that are
designed to change. For example, Picture is mutable data type because we can
change pixel colors. We will consider immutability in more detail in Section 3.3.
Comparing objects. When applied to reference types, the
== operator checks
whether the two object references are equal (that is, whether they point to the same
object). That is not the same as checking whether the objects have the same value.
For example, consider the following code:
Color a = new Color(160, 82, 45);
Color b = new Color(160, 82, 45);
Color c = b;
Now (a == b) is false and (b == c) is true, but when you are thinking about
equality testing for Color, you probably are thinking that you want to test whether
their values are the same—you might want all three of these to test as equal. Java
does not have an automatic mechanism for testing the equality of object values,
which leaves programmers with the opportunity (and responsibility) to define it
for themselves by defining for any class a customized method named equals(), as
described in Section 3.3. For example, Color has such a method, and a.equals(c)
is true in our example. String also contains an implementation of equals() because we often want to test whether two String objects have the same value (the
same sequence of characters).
Pass by value. When you call a method with arguments, the effect in Java is as if
each argument were to appear on the right-hand side of an assignment statement
with the corresponding argument name on the left-hand side. That is, Java passes a
copy of the argument value from the caller to the method. If the argument value is
a primitive type, Java passes a copy of that value; if the argument value is an object
reference, Java passes a copy of the object reference. This arrangement is known as
pass by value.
One important consequence of this arrangement is that a method cannot directly change the value of a caller’s variable. For primitive types, this policy is what
3.1 Using Data Types
365
we expect (the two variables are independent), but each time that we use a reference type as a method argument, we create an alias, so we must be cautious. For
example, if we pass an object reference of type Picture to a method, the method
cannot change the caller’s object reference (for example, make it refer to a different
Picture), but it can change the value of the object, such as by invoking the set()
method to change a pixel’s color.
Arrays are objects. In Java, every value of any nonprimitive type is an object. In
particular, arrays are objects. As with strings, special language support is provided
for certain operations on arrays: declarations, initialization, and indexing. As with
any other object, when we pass an array to a method or use an array variable on the
right-hand side of an assignment statement, we are making a copy of the array reference, not a copy of the array. Arrays are mutable objects—a convention that is appropriate for the typical case where we expect the method to be able to modify the
array by rearranging the values of its elements, as in, for example, the exchange()
and shuffle() methods that we considered in Section 2.1.
Arrays of objects. Array elements can be of any type, as we have
already seen on several occasions, from args[] (an array of strings)
in our main() implementations, to the array of Out objects in
Program 3.1.9. When we create an array of objects, we do so in two
steps:
• Create the array by using new and the square bracket syntax
for array creation
• Create each object in the array, by using new to call a constructor
For example, we would use the following code to create an array of
two Color objects:
Color[] a = new Color[2];
a[0] = new Color(255, 255, 0);
a[1] = new Color(160, 82, 45);
Naturally, an array of objects in Java is an array of object references,
not the objects themselves. If the objects are large, then we gain efficiency by not having to move them around, just their references.
If they are small, we lose efficiency by having to follow a reference
each time we need to get to some information.
a
123
124
323
2
a.length
a[0]
323
324
459
611
459
460
461
255
255
0
yellow
611
612
613
160
82
45
sienna
a[1]
An array of objects
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Safe pointers. To provide the capability to manipulate memory addresses that refer to data, many programming languages include the pointer (which is like the Java
reference) as a primitive data type. Programming with pointers is notoriously error
prone, so operations provided for pointers need to be carefully designed to help
programmers avoid errors. Java takes this point of view to an extreme (one that is
favored by many modern programming-language designers). In Java, there is only
one way to create a reference (with new) and only one way to manipulate that reference (with an assignment statement). That is, the only things that a programmer
can do with references is to create them and copy them. In programming-language
jargon, Java references are known as safe pointers, because Java can guarantee that
each reference points to an object of the specified type (and not to an arbitrary
memory address). Programmers used to writing code that directly manipulates
pointers think of Java as having no pointers at all, but
people still debate whether it is desirable to have unsafe Color a, b;
a = new Color(160, 82, 45);
pointers. In short, when you program in Java, you will b = new Color(255, 255, 0);
not be directly manipulating memory addresses, but if b = a;
you find yourself doing so in some other language in
the future, be careful!
Orphaned objects. The ability to assign different objects to a reference variable creates the possibility that
a program may have created an object that it can no
longer reference. For example, consider the three assignment statements in the figure at right. After the
third assignment statement, not only do a and b refer
to the same Color object (the one whose RGB values
are 160, 82, and 45), but also there is no longer a reference to the Color object that was created and used to
initialize b. The only reference to that object was in the
variable b, and this reference was overwritten by the assignment, so there is no way to refer to the object again.
Such an object is said to be orphaned. Objects are also
orphaned when they go out of scope. Java programmers pay little attention to orphaned objects because
the system automatically reuses the memory that they
occupy, as we discuss next.
a
b
811
811
references to
same object
orphaned
object
655
656
657
255
255
0
yellow
811
812
813
160
82
45
sienna
An orphaned object
3.1 Using Data Types
Memory management. Programs tend to create huge numbers of objects but
have a need for only a small number of them at any given point in time. Accordingly, programming languages and systems need mechanisms to allocate memory
for data-type values during the time they are needed and to free the memory when
they are no longer needed (for an object, sometime after it is orphaned). Memory
management is easier for primitive types because all of the information needed
for memory allocation is known at compile time. Java (and most other systems)
reserves memory for variables when they are declared and frees that memory when
they go out of scope. Memory management for objects is more complicated: Java
knows to allocate memory for an object when it is created (with new), but cannot
know precisely when to free the memory associated with that object because the
dynamics of a program in execution determine when the object is orphaned.
Memory leaks. In many languages (such as C and C++), the programmer is
responsible for both allocating and freeing memory. Doing so is tedious and
notoriously error prone. For example, suppose that a program deallocates the
memory for an object, but then continues to refer to it (perhaps much later in the
program). In the meantime, the system may have reallocated the same memory
for another use, so all kinds of havoc can result. Another insidious problem occurs
when a programmer neglects to ensure that the memory for an orphaned object is
deallocated. This bug is known as a memory leak because it can result in a steadily
increasing amount of memory devoted to orphaned objects (and therefore not
available for use). The effect is that performance degrades, as if memory were
leaking out of your computer. Have you ever had to reboot your computer because
it was gradually getting less and less responsive? A common cause of such behavior
is a memory leak in one of your applications.
Garbage collection. One of Java’s most significant features is its ability to automatically manage memory. The idea is to free the programmer from the responsibility of managing memory by keeping track of orphaned objects and returning
the memory they use to a pool of free memory. Reclaiming memory in this way is
known as garbage collection, and Java’s safe pointer policy enables it to do this efficiently and automatically. Programmers still debate whether the overhead of automatic garbage collection justifies the convenience of not having to worry about
memory management. The same conclusion that we drew for pointers holds: when
you program in Java, you will not be writing code to allocate and free memory, but
if you find yourself doing so in some other language in the future, be careful!
367
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Object-Oriented Programming
For reference, we summarize the examples that we have considered in this section
in the table below. These examples are chosen to help you understand the essential
properties of data types and object-oriented programming.
A data type is a set of values and a set of operations defined on those values. With
primitive data types, we worked with a small and simple set of values. Strings, colors, pictures, and I/O streams are high-level data types that indicate the breadth of
applicability of data abstraction. You do not need to know how a data type is implemented to be able to use it. Each data type (there are hundreds in the Java libraries,
and you will soon learn to create your own) is characterized by an API (application
programming interface) that provides the inAPI
description
formation that you need to use it. A client program creates objects that hold data-type values
Color
colors
and invokes instance methods to manipulate
Picture
digital images
those values. We write client programs with
String
character strings
the basic statements and control constructs
In
input streams
that you learned in Chapters 1 and 2, but now
Out
output streams
have the capability to work with a vast variety of data types, not just the primitive ones
Draw
drawings
to which you have grown accustomed. With
Summary of data types in this section
greater experience, you will find that this ability opens up new horizons in programming.
When properly designed, data types lead to client programs that are clearer,
easier to develop, and easier to maintain than equivalent programs that do not take
advantage of data abstraction. The client programs in this section are testimony
to this claim. Moreover, as you will see in the next section, implementing a data
type is a straightforward application of the basic programming skills that you have
already learned. In particular, addressing a large and complex application becomes
a process of understanding its data and the operations to be performed on it, then
writing programs that directly reflect this understanding. Once you have learned to
do so, you might wonder how programmers ever developed large programs without using data abstraction.
3.1 Using Data Types
Q&A
Q. Why the distinction between primitive and reference types?
A. Performance. Java provides the wrapper reference types Integer, Double, and
so forth that correspond to primitive types and can be used by programmers who
prefer to ignore the distinction (for details, see Section 3.3). Primitive types are
closer to the types of data that are supported by computer hardware, so programs
that use them usually run faster and consume less memory than programs that use
the corresponding reference types.
Q. What happens if I forget to use new when creating an object?
A. To Java, it looks as though you want to call a static method with a return value
of the object type. Since you have not defined such a method, the error message is
the same as when you refer to an undefined symbol. If you compile the code
Color sienna = Color(160, 82, 45);
you get this error message:
cannot find symbol
symbol : method Color(int,int,int)
Constructors do not provide return values (their signature has no return type)—
they can only follow new. You get the same kind of error message if you provide the
wrong number of arguments to a constructor or method.
Q. Why can we print an object x with the function call StdOut.println(x), as
opposed to StdOut.println(x.toString())?
A. Good question. That latter code works fine, but Java saves us some typing by
automatically invoking the toString() method in such situations. In Section 3.3,
we will discuss Java’s mechanism for ensuring that this is the case.
Q. What is the difference between =, ==, and equals()?
A. The single equals sign (=) is the basis of the assignment statement—you certainly are familiar with that. The double equals sign (==) is a binary operator for
checking whether its two operands are identical. If the operands are of a primitive
type, the result is true if they have the same value, and false otherwise. If the op-
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370
erands are object references, the result is true if they refer to the same object, and
otherwise. That is, we use == to test object identity equality. The data-type
method equals() is included in every Java type so that the implementation can
provide the capability for clients to test whether two objects have the same value.
Note that (a == b) implies a.equals(b), but not the other way around.
false
Q. How can I arrange to pass an array as an argument to a function in such a way
that the function cannot change the values of the elements in the array?
A. There is no direct way to do so—arrays are mutable. In Section 3.3, you will
see how to achieve the same effect by building a wrapper data type and passing an
object reference of that type instead (see Vector, in Program 3.3.3).
Q. What happens if I forget to use new when creating an array of objects?
A. You need to use new for each object that you create, so when you create an array
of n objects, you need to use new n + 1 times: once for the array and once for each
of the n objects. If you forget to create the array:
Color[] colors;
colors[0] = new Color(255, 0, 0);
you get the same error message that you would get when trying to assign a value to
any uninitialized variable:
variable colors might not have been initialized
colors[0] = new Color(255, 0, 0);
^
In contrast, if you forget to use new when creating an object within the array and
then try to use it to invoke a method:
Color[] colors = new Color[2];
int red = colors[0].getRed();
you get a NullPointerException. As usual, the best way to answer such questions
is to write and compile such code yourself, then try to interpret Java’s error message.
Doing so might help you more quickly recognize mistakes later.
3.1 Using Data Types
371
Q. Where can I find more details on how Java implements references and garbage
collection?
A. One Java system might differ completely from another. For example, one natural scheme is to use a pointer (machine address); another is to use a handle (a
pointer to a pointer). The former gives faster access to data; the latter facilitates
garbage collection.
Q. Why red, green, and blue instead of red, yellow, and blue?
A. In theory, any three colors that contain some amount of each primary would
work, but two different color models have evolved: one (RGB) that has proven
to produce good colors on television screens, computer monitors, and digital
cameras, and the other (CMYK) that is typically used for the printed page (see
Exercise 1.2.32). CMYK does include yellow (cyan, magenta, yellow, and black).
Two different color models are appropriate because printed inks absorb color; thus,
where there are two different inks, there are more colors absorbed and fewer reflected. Conversely, video displays emit color, so where there are two different-colored
pixels, there are more colors emitted.
Q. What exactly is the purpose of an import statement?
A. Not much: it just saves some typing. For example, in Program 3.1.2, it enables
you to abbreviate java.awt.Color with Color everywhere in your code.
Q. Is there anything wrong with allocating and deallocating thousands of
Color
objects, as in Grayscale (Program 3.1.4)?
A. All programming-language constructs come at some cost. In this case the cost
is reasonable, since the time to allocate Color objects is tiny compared to the time
to draw the image.
Object-Oriented Programming
372
Q. Why does the String method call s.substring(i,
j) return the substring of
s starting at index i and ending at j-1 (and not j)?
A. Why do the indices of an array a[] go from 0 to a.length-1 instead of from
to length? Programming-language designers make choices; we live with them.
One nice consequence of this convention is that the length of the extracted substring is j-i.
1
Q. What is the difference between pass by value and pass by reference?
Q. With pass by value, when you call a method with arguments, each argument
is evaluated and a copy of the resulting value is passed to the method. This means
that if a method directly modifies an argument variable, that modification is not
visible to the caller. With pass by reference, the memory address of each argument is
passed to the method. This means that if a method modifies an argument variable,
that modification is visible to the caller. Technically, Java is a purely pass-by-value
language, in which the value is either a primitive-type value or an object reference.
As a result, when you pass a primitive-type value to a method, the method cannot
modify the corresponding value in the caller; when you pass an object reference to
a method, the method cannot modify the object reference (say, to refer to a different object), but it can change the underlying object (by using the object reference
to invoke one of the object’s methods). For this reason, some Java programmers
use the term pass by object reference to refer to Java’s argument-passing conventions
for reference types.
Q. I noticed that the argument to the equals() method in String and Color is of
type Object. Shouldn’t the argument be of type String and Color, respectively?
A. No. In Java, the
equals() method is a special and its argument type should
always be Object. This is an artifact of the inheritance mechanism that Java uses to
support the equals() method, which we consider on page 454. For now, you can
safely ignore the distinction.
Q. Why is the image-processing data type named Picture instead of Image?
A. There is already a built-in Java library named Image.
3.1 Using Data Types
Exercises
3.1.1 Write a static method reverse() that takes a string as an argument and returns a string that contains the same sequence of characters as the argument string
but in reverse order.
3.1.2 Write a program that takes from the command line three integers between
0 and 255 that represent red, green, and blue values of a color and then creates and
shows a 256-by-256 Picture in which each pixel has that color.
3.1.3 Modify AlbersSquares (Program 3.1.2) to take nine command-line arguments that specify three colors and then draws the six squares showing all the Albers
squares with the large square in each color and the small square in each different
color.
3.1.4 Write a program that takes the name of a grayscale image file as a
command-line argument and uses StdDraw to plot a histogram of the frequency of
occurrence of each of the 256 grayscale intensities.
3.1.5 Write a program that takes the name of an image file as a command-line
argument and flips the image horizontally.
3.1.6 Write a program that takes the name of an image file as a command-line
argument, and creates and shows three Picture objects, one that contains only the
red components, one for green, and one for blue.
3.1.7 Write a program that takes the name of an image file as a command-line
argument and prints the pixel coordinates of the lower-left corner and the upperright corner of the smallest bounding box (rectangle parallel to the x- and y-axes)
that contains all of the non-white pixels.
3.1.8 Write a program that takes as command-line arguments the name of an
image file and the pixel coordinates of a rectangle within the image; reads from
standard input a list of Color values (represented as triples of int values); and
serves as a filter, printing those color values for which all pixels in the rectangle are
background/foreground compatible. (Such a filter can be used to pick a color for
text to label an image.)
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Object-Oriented Programming
374
3.1.9 Write a static method isValidDNA() that takes a string as its argument and
returns true if and only if it is composed entirely of the characters A, T, C, and G.
3.1.10 Write a function complementWatsonCrick() that takes a DNA string as
its argument and returns its Watson–Crick complement: replace A with T, C with G,
and vice versa.
3.1.11 Write a function
isWatsonCrickPalindrome() that takes a DNA string
as its input and returns true if the string is a Watson–Crick complemented palindrome, and false otherwise. A Watson–Crick complemented palindrome is a DNA
string that is equal to the reverse of its Watson–Crick complement.
3.1.12 Write a program to check whether an ISBN number is valid (see Exercise
1.3.35), taking into account that an ISBN number can have hyphens inserted at
arbitrary places.
3.1.13 What does the following code fragment print?
String string1 = "hello";
String string2 = string1;
string1 = "world";
StdOut.println(string1);
StdOut.println(string2);
3.1.14 What does the following code fragment print?
String s = "Hello World";
s.toUpperCase();
s.substring(6, 11);
StdOut.println(s);
Answer: "Hello World". String objects are immutable—string methods each return a new String object with the appropriate value (but they do not change the
value of the object that was used to invoke them). This code ignores the objects
returned and just prints the original string. To print "WORLD", replace the second
and third statements with s = s.toUpperCase() and s = s.substring(6, 11).
3.1 Using Data Types
3.1.15 A string s is a circular shift of a string t if it matches when the characters of
one string are circularly shifted by some number of positions. For example, ACTGACG is a circular shift of TGACGAC, and vice versa. Detecting this condition is important in the study of genomic sequences. Write a function isCircularShift()
that checks whether two given strings s and t are circular shifts of one another.
Hint : The solution is a one-liner with indexOf() and string concatenation.
3.1.16 Given a string that represents a domain name, write a code fragment to
determine its top-level domain. For example, the top-level domain of the string
cs.princeton.edu is edu.
3.1.17 Write a static method that takes a domain name as its argument and returns the reverse domain name (reverse the order of the strings between periods).
For example, the reverse domain name of cs.princeton.edu is edu.princeton.
cs. This computation is useful for web log analysis. (See Exercise 4.2.36.)
3.1.18 What does the following recursive function return?
public static String mystery(String s)
{
int n = s.length();
if (n <= 1) return s;
String a = s.substring(0, n/2);
String b = s.substring(n/2, n);
return mystery(b) + mystery(a);
}
3.1.19 Write a test client for PotentialGene (Program 3.1.1) that takes a string as
a command-line argument and reports whether it is a potential gene.
3.1.20 Write a version of
PotentialGene (Program 3.1.1) that finds all potential genes contained as substrings within a long DNA string. Add a command-line
argument to allow the user to specify the minimum length of a potential gene.
3.1.21 Write a filter that reads text from an input stream and prints it to an output
stream, removing any lines that consist only of whitespace.
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Object-Oriented Programming
376
3.1.22 Write a program that takes a start string and a stop string as commandline arguments and prints all substrings of a given string that start with the first,
end with the second, and otherwise contain neither. Note: Be especially careful of
overlaps!
3.1.23 Modify StockQuote (Program 3.1.8) to take multiple symbols on the command line.
3.1.24 The example file DJIA.csv used for Split (Program 3.1.9) lists the date,
high price, volume, and low price of the Dow Jones stock market average for every
day since records have been kept. Download this file from the booksite and write a
program that creates two Draw objects, one for the prices and one for the volumes,
and plots them at a rate taken from the command line.
3.1.25 Write a program Merge that takes a delimiter string followed by an arbitrary number of file names as command-line arguments; concatenates the corresponding lines of each file, separated by the delimiter; and then prints the result to
standard output, thus performing the opposite operation of Split (Program 3.1.9).
3.1.26 Find a website that publishes the current temperature in your area, and
write a screen-scraper program Weather so that typing java Weather followed by
your ZIP code will give you a weather forecast.
3.1.27 Suppose that a[] and b[] are both integer arrays consisting of millions of
integers. What does the following code do, and how long does it take?
int[] temp = a; a = b; b = temp;
Solution. It swaps the arrays, but it does so by copying object references, so that it
is not necessary to copy millions of values.
3.1.28 Describe the effect of the following function.
public void swap(Color a, Color b)
{
Color temp = a;
a = b;
b = temp;
}
3.1 Using Data Types
Creative Exercises
3.1.29 Picture file format. Write a library of static methods RawPicture with
read() and write() methods for saving and reading pictures from a file. The
write() method takes a Picture and the name of a file as arguments and writes
the picture to the specified file, using the following format: if the picture is w-byh, write w, then h, then w × h triples of integers representing the pixel color values,
in row-major order. The read() method takes the name of a picture file as an
argument and returns a Picture, which it creates by reading a picture from the
specified file, in the format just described. Note: Be aware that this will use up much
more disk space than necessary—the standard formats compress this information
so that it will not take up so much space.
3.1.30 Sound visualization. Write a program that uses StdAudio and Picture to
create an interesting two-dimensional color visualization of a sound file while it is
playing. Be creative!
3.1.31 Kamasutra cipher. Write a filter KamasutraCipher that takes two strings
as command-line argument (the key strings), then reads strings (separated by
whitespace) from standard input, substitutes for each letter as specified by the key
strings, and prints the result to standard output. This operation is the basis for one
of the earliest known cryptographic systems. The condition on the key strings is
that they must be of equal length and that any letter in standard input must appear in exactly one of them. For example, if the two keys are THEQUICKBROWN and
FXJMPSVLAZYDG, then we make the table
T H E Q U I C K B R O W N
F X J M P S V L A Z Y D G
which tells us that we should substitute F for T, T for F, H for X, X for H, and so
forth when filtering standard input to standard output. The message is encoded
by replacing each letter with its pair. For example, the message MEET AT ELEVEN is
encoded as QJJF BF JKJCJG. The person receiving