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Из курса от партнера Duke University

Java Programming: Principles of Software Design

704 оценки

Duke University

Java Programming: Principles of Software Design

704 оценки

Курс 4 из 5 — Specialization Java Programming and Software Engineering Fundamentals

Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.

Из урока

N-Grams: Predictive Text

In this module, you will explore some of the underlying concepts of predictive text. The first lesson will introduce random character generation and then how to train the character selection based on an input text. The second lesson will extend this concept to complete words. By the end of this module, you will be able to: (1) base random text generation on the frequency of characters in a training text, (2) collect a set of characters that occur in a text after randomly chosen initial character(s) to create a semi-random text, (3) extend the predictive text generation to use whole words, and (4) implement your own .equals method to compare complex data types.

Introduction3:25

Order-One Concepts6:11

Order-One Helper Functions8:42

WordGram Class4:20

WordGram Class Implementation4:54

Equals and HashCode Methods5:09

Equals Method Implementation10:41

Познакомьтесь с преподавателями

Robert Duvall
Lecturer
Computer Science
Owen Astrachan
Professor of the Practice
Computer Science
Andrew D. Hilton
Associate Professor of the Practice
Electrical and Computer Engineering
Susan H. Rodger
Professor of the Practice
Computer Science

Hi, we've just completed the design and implementation of the class WordGram,

to enable random text generation by word rather than by letter.

This extends the predictive and occasionally amusing text generation,

that's the basis for

spam detection, predictive text in search engines and more.

We used word at a time, rather than letter at a time, to illustrate new concepts.

But a good initial design with the IMarkovModel interface allowed our new

classes to work with existing and previously tested client programs.

We developed MarkovWordOne first, it used one word to predict the next.

This was an easy transition from the latter Markov programs to using word gram.

It's a good idea in general to take small steps in developing

new programs with new ideas and changing design.

After the small step works, and

has been tested, take another step in extending designs.

As you take steps,

be sure to use the seven step process for algorithms as that's warranted.

We looked at a new class, WordGram, and it's internal representation and behavior.

Our design and implementation of MarkovWordOne used strings,

rather than characters, and was successful.

But we had to design and implement the WordGram class to extend our order one

MarkovWord program to order two, three, and more.

However, we could develop the WordGram class with familiar designs,

testings, programs, and interfaces.

We tested WordGram outside of the context of Markov text generation, but

we developed used cases with Markov to help guide the design of WordGram.

We tested .toString() and constructors first,

because it's tough to test and debug when you can't create and print an object.

When it was time to implement .wordAt() and .length() methods,

the similarity to similar methods in the string class helped.

We also had to understand how to implement .equals() and .hashCode(),

but only .equals() was needed in our code.

.hashCode() would be needed in more advanced uses of WordGram.

We encountered a few new ideas in implementing WordGram.

The .wordAt() method threw an exception when an index was out of bounds.

You can't index an array or

a string with values like -1, or the length of the array.

We made WordGram behave like String does,

throwing an exception that might help the programmer when bad indexes are made.

Typically, bad indexes don't occur in relief software, but

they do in the development of new software as it's being tested.

But understanding exceptions is part of being a software engineer or programmer.

We wrote functions that we didn't call.

Other methods might call them as .toString is called when an object is printed.

As we saw sometimes,

.toString is called explicitly, as when appending to a string builder.

When inserting it into a HashMap the .hashCode method gives an index for

what we call a bucket, where objects are stored.

The .equals() method helps distinguish objects that might end up in

the same bucket.

Happy programming.

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