Supplemental Materials What is included with this book?. The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc. The Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included.
This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc. This book lays the foundation for programmers to build their skills. The focus is placed on how to implement effective programs using the JCL instead of producing mathematical proofs. The coverage is updated and streamlined to provide a more accessible approach to programming. They'll be able to develop a thorough understanding of basic data structures and algorithms through an objects-first approach. Data structures are discussed in the context of software engineering principles.
Updated case studies also show programmers how to apply essential design skills and concepts. Table of Contents.
2.2 Data Abstraction As we consider the wide set of things in the world that we would like to represent in our programs, we find that most of them have compound structure. For example, a geographic position has latitude and longitude coordinates. To represent positions, we would like our programming language to have the capacity to couple together a latitude and longitude to form a pair, a compound data value that our programs can manipulate as a single conceptual unit, but which also has two parts that can be considered individually.
The use of compound data enables us to increase the modularity of our programs. If we can manipulate geographic positions as whole values, then we can shield parts of our program that compute using positions from the details of how those positions are represented. The general technique of isolating the parts of a program that deal with how data are represented from the parts that deal with how data are manipulated is a powerful design methodology called data abstraction.
Data abstraction makes programs much easier to design, maintain, and modify. Data abstraction is similar in character to functional abstraction.
When we create a functional abstraction, the details of how a function is implemented can be suppressed, and the particular function itself can be replaced by any other function with the same overall behavior. In other words, we can make an abstraction that separates the way the function is used from the details of how the function is implemented. Analogously, data abstraction isolates how a compound data value is used from the details of how it is constructed.
The basic idea of data abstraction is to structure programs so that they operate on abstract data. That is, our programs should use data in such a way as to make as few assumptions about the data as possible. At the same time, a concrete data representation is defined as an independent part of the program. These two parts of a program, the part that operates on abstract data and the part that defines a concrete representation, are connected by a small set of functions that implement abstract data in terms of the concrete representation.
To illustrate this technique, we will consider how to design a set of functions for manipulating rational numbers. 1 / 3 0.333333 1 / 3 0.0 # Dividing integers yields an approximation True However, we can create an exact representation for rational numbers by combining together the numerator and denominator. We know from using functional abstractions that we can start programming productively before we have an implementation of some parts of our program. Let us begin by assuming that we already have a way of constructing a rational number from a numerator and a denominator. We also assume that, given a rational number, we have a way of selecting its numerator and its denominator component.
Let us further assume that the constructor and selectors are available as the following three functions:. rational(n, d) returns the rational number with numerator n and denominator d. numer(x) returns the numerator of the rational number x. denom(x) returns the denominator of the rational number x. We are using here a powerful strategy for designing programs: wishful thinking. We haven't yet said how a rational number is represented, or how the functions numer, denom, and rational should be implemented.
Even so, if we did define these three functions, we could then add, multiply, print, and test equality of rational numbers. half = rational ( 1, 2 ) printrational ( half ) 1 / 2 third = rational ( 1, 3 ) printrational ( mulrationals ( half, third )) 1 / 6 printrational ( addrationals ( third, third )) 6 / 9 As the example above shows, our rational number implementation does not reduce rational numbers to lowest terms. We can remedy this flaw by changing the implementation of rational. If we have a function for computing the greatest common denominator of two integers, we can use it to reduce the numerator and the denominator to lowest terms before constructing the pair.
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Problem Solving Definition
As with many useful tools, such a function already exists in the Python Library. 2.2.3 Abstraction Barriers Before continuing with more examples of compound data and data abstraction, let us consider some of the issues raised by the rational number example. We defined operations in terms of a constructor rational and selectors numer and denom. In general, the underlying idea of data abstraction is to identify a basic set of operations in terms of which all manipulations of values of some kind will be expressed, and then to use only those operations in manipulating the data. By restricting the use of operations in this way, it is much easier to change the representation of abstract data without changing the behavior of a program.
For rational numbers, different parts of the program manipulate rational numbers using different operations, as described in this table. Parts of the program that. Treat rationals as. Use rational numbers to perform computation whole data values addrational, mulrational, rationalsareequal, printrational Create rationals or implement rational operations numerators and denominators rational, numer, denom Implement selectors and constructor for rationals two-element lists list literals and element selection In each layer above, the functions in the final column enforce an abstraction barrier. These functions are called by a higher level and implemented using a lower level of abstraction.
An abstraction barrier violation occurs whenever a part of the program that can use a higher level function instead uses a function in a lower level. For example, a function that computes the square of a rational number is best implemented in terms of mulrational, which does not assume anything about the implementation of a rational number. def squarerationalviolatingtwice ( x ): return x 0. x 0 , x 1. x 1 Abstraction barriers make programs easier to maintain and to modify. The fewer functions that depend on a particular representation, the fewer changes are required when one wants to change that representation. All of these implementations of squarerational have the correct behavior, but only the first is robust to future changes.
The squarerational function would not require updating even if we altered the representation of rational numbers. By contrast, squarerationalviolatingonce would need to be changed whenever the selector or constructor signatures changed, and squarerationalviolatingtwice would require updating whenever the implementation of rational numbers changed. 2.2.4 The Properties of Data Abstraction barriers shape the way in which we think about data. A valid representation of a rational number is not restricted to any particular implementation (such as a two-element list); it is a value returned by rational that can be passed to numer, and denom. In addition, the appropriate relationship must hold among the constructor and selectors.
That is, if we construct a rational number x from integers n and d, then it should be the case that numer(x)/denom(x) is equal to n/d. In general, we can express abstract data using a collection of selectors and constructors, together with some behavior conditions. As long as the behavior conditions are met (such as the division property above), the selectors and constructors constitute a valid representation of a kind of data. The implementation details below an abstraction barrier may change, but if the behavior does not, then the data abstraction remains valid, and any program written using this data abstraction will remain correct. This point of view can be applied broadly, including to the pair values that we used to implement rational numbers. We never actually said much about what a pair was, only that the language supplied the means to create and manipulate lists with two elements. Hindi typing training book pdf.
The behavior we require to implement a pair is that it glues two values together. Stated as a behavior condition,. If a pair p was constructed from values x and y, then select(p, 0) returns x, and select(p, 1) returns y. We don't actually need the list type to create pairs.
Instead, we can implement two functions pair and select that fulfill this description just as well as a two-element list. p = pair ( 20, 14 ) select ( p, 0 ) 20 select ( p, 1 ) 14 This use of higher-order functions corresponds to nothing like our intuitive notion of what data should be.
Nevertheless, these functions suffice to represent pairs in our programs. Functions are sufficient to represent compound data. The point of exhibiting the functional representation of a pair is not that Python actually works this way (lists are implemented more directly, for efficiency reasons) but that it could work this way. The functional representation, although obscure, is a perfectly adequate way to represent pairs, since it fulfills the only conditions that pairs need to fulfill. The practice of data abstraction allows us to switch among representations easily.
Description The Third Edition of Data Abstraction and Problem Solving with Java: Walls and Mirrors employs the analogies of Walls (data abstraction) and Mirrors (recursion) to teach Java programming design solutions, in a way that beginning students find accessible. The book has a student-friendly pedagogical approach that carefully accounts for the strengths and weaknesses of the Java language. With this book, students will gain a solid foundation in data abstraction, object-oriented programming, and other problem-solving techniques. Uses Java 6: This edition has been thoroughly revised to be compatible with the latest release of Java, known as Java 6.
All code has been completely revised to be Java 6 compliant. Generics are also an important part of Java 6, and this material is discussed in depth in Chapter 9, and then used throughout the remainder of the collections in the text. Enhanced Early Review of Java: We have increased the amount of coverage of the Java language in the first chapter of the book to help students make the transition from their introduction to Java course to this course. Chapter 1 provides a concise review of important Java material, including brief discussions on constructors, object equality, inheritance, and the Array class. A discussion of the Console class from Java 6 was also added to Chapter 1.
Chapter 9 focuses on advanced Java techniques, and includes an enhanced discussion of how to create an iterator class. Linked List: The node class for linked lists has been simplified. The implementation now assumes the node class is package access only, and the other classes in the same package have direct access to the data within a node. Students are asked to explore the implications of making the data private in a node as an exercise.
Updates the Use of the Java Collections Framework: The Java Collections Framework is discussed throughout the text, with a section added to show the JFC classes that parallel those presented in the text. The Deque class, added in Java 6, is presented in Chapter 8. Other enhancements: Additional changes aimed at improving the overall usability of the text include new exercises and a new cleaner design that enhances the book’s readability. About the Author(s) Dr. Janet Prichard is an assistant professor at Bryant College where she teaches web design and development, object-oriented computing, operating systems, and data structures courses.
She has a B.A. In mathematics from Providence College, and earned her M.S. In computer science from the University of Rhode Island. Her academic interests include real-time databases, database query languages, object-oriented analysis and design methodologies, database standards, client-server models, and Internet security. Prichard is the lead author of the Third Edition of Data Abstraction and Problem Solving with Java.
Carrano is Professor Emeritus of Computer Science at the University of Rhode Island. He received his Ph.D. Degree in Computer Science from Syracuse University in 1969.
His interests include data structures, computer science education, social issues in computing, and numerical computation. Professor Carrano is particularly interested in the design and delivery of undergraduate courses in computer science. Ctf themes for psp 6.60. He has authored several well-known computer science textbooks for undergraduates. Frank’s Making it Real blog extends his textbooks and lectures to a lively discussion with instructors and students about teaching and learning computer science. Follow Frank on Twitter: Find him on Facebook.
Supplemental Materials What is included with this book?. The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc. The Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.
Problem Solving With Java
This book lays the foundation for programmers to build their skills. The focus is placed on how to implement effective programs using the JCL instead of producing mathematical proofs. The coverage is updated and streamlined to provide a more accessible approach to programming.
Problem Solving Techniques
They'll be able to develop a thorough understanding of basic data structures and algorithms through an objects-first approach. Data structures are discussed in the context of software engineering principles. Updated case studies also show programmers how to apply essential design skills and concepts. Table of Contents.
Janet Prichard: Data Abstraction and Problem Solving with Java: Walls and Mirrors (3rd Edition) PDF Download, MOBi EPUB Kindle Description The Third Edition of Data Abstraction and Problem Solving with Java: Walls and Mirrors employs the analogies of Walls (data abstraction) and Mirrors (recursion) to teach Java programming design solutions, in a way that beginning students find accessible. The book has a student-friendly pedagogical approach that carefully accounts for the strengths and weaknesses of the Java language.