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Equals and HashCode Methods

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Java Programming: Principles of Software Design

Университет Дьюка

4.6 (оценок: 774) | Зарегистрировано учащихся: 51K

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

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.

Просмотреть программу курса

Получаемые навыки

Programming Principles, Interfaces, Software Design, Java Programming

Рецензии

4.6 (оценок: 774)

5 stars
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SB

Aug 13, 2016

This course was another of many well-taught courses of Duke University. The GPS library use to find locations of earthquakes particularly of very interesting. Thank you Duke University

LY

Sep 28, 2016

devoted professors, appropriate topics which can lead to the correct understanding of each Java object, so we can realize OO programming ideas, not only how to type codes.

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

Преподаватели

Robert Duvall
Lecturer
Owen Astrachan
Professor of the Practice
Andrew D. Hilton
Associate Professor of the Practice
Susan H. Rodger
Professor of the Practice

Текст видео

Hi!

We're going to develop a few of the methods that make WordGram functional

in our Markov programs.

We'll use the program WordGramTester.java to demonstrate techniques, ideas, and

test methods you might implement in any class, but in particular, for WordGram.

We'll show how to test .toString and

the WordGram constructor, though these are already written.

We'll see why .equals is needed,

and although we've discussed .equals in a previous course,

this will be a brief discussion on implementing .equals, not just calling it.

The .equals method will be needed for MarkovWord.getFollows to work correctly,

that will be enough for the mark off runner class work with Markov Word and

generating random text, we'll touch briefly on an advanced method.

We'll see how to implement .hashcode() so

that WordGram objects can be added to a hash map.

With knowledge we've .tostring, .equals, and

.hashcode, you'll be ready to tackle lots of class and program design.

Let's look at .equals() first.

In previous programs you've seen why you need to call .equals() with Strings.

Using == doesn't work, because that tests whether two objects are the same object.

That is, the same memory location, not whether they contain the same information.

In other programs you've called .equals.

Now we'll look at what's needed to write it.

We must adhere to Java's requirements for writing .equals.

The first requirement is that the parameter has type object.

That's the base or parent class for every class in Java.

The reasons for requiring this type will be explored if you take

a more advanced course in object oriented programming.

We won't be calling .equals with any types other than WordGram.

So, the first thing we do is cast the parameter o so

that the compiler will treat it as a WordGram object.

Casting, by putting the type in parenthesis, makes the compiler treat

the object reference by object reference by parameter of as having type WordGram,

which we do using the variable we've named other.

But any name can be used.

Then we compare our links with the link of the WordGram object,

reference by other and return fault as the links differ.

That's the first step in implementing .equals.

With .equals and that toString we're ready to code the mark off word classes.

But we'll take a look ahead at what you'll study in later courses.

And as an enhancement to the mark off classes we've been using.

We can store all the keys used in generating text is a HashMap.

We'd map the keys to a list of all characters that follow the key.

This technique works with letter or word Markov models.

We'll show this with this training text, and a two layered Markov model.

The training text starts with, the herd then, and

continues with more letters not shown.

The ideas to avoid scanning the text many, many times for keys like he.

In our model, we find he and a follow character,

then the next occurrence of he, then the next occurrence of he.

If we ever see the key he again, we need to rescan the text looking for

follow characters, repeating work we've all ready done before.

This could happen every time we generate he as a key and

need to find the follow characters.

Instead of scanning we could look up the key in a HashMap and

retrieve the following characters stored in a list.

This can be efficient as we'll see.

For this to work we need to implement the .hashCode method.

We'll provide a high level overview of HashMaps,

enough to get some understanding, but not lots of detail.

The idea is to convert an object into an integer hash code.

This hash code works as an index into hash map.

For strings, the object hello might be converted to have the hash code 3.217.

It's some number that tells us where in the HashMap to find hello.

The simplest idea for a hash code is to make every object have the same index.

A number like 17.

If we did this our code would work correctly if .equals was correct.

But the performance would be really bad.

Because every object would be stored in the same bucket, the word used for

a place in a HashMap where objects are stored.

Ideally, each object has a different number, so finding an object in a bucket

is easy, because it's the only object in the bucket.

You'll see details of how this works if you take another Java course,

like the UCSD specialization that follows this one.

A simple idea with better performance is possible too,

simply add the hash codes of each string in the WordGram.

Have fun with hashing objects, hopefully into lots of different buckets so

your hash is fast.

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