Introduction

Loading...

From the course by Duke University

Java Programming: Principles of Software Design

601 оценки

Duke University

Java Programming: Principles of Software Design

601 оценки

Course 4 of 5 in the 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.

From the lesson

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

Meet the Instructors

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

We're going to use our Markov programs as the basis for

studying Java class design as part of how Java works with object oriented concepts.

We'll use the concepts developed in the programs

that generated text using Markov processes but we'll extend those concepts.

Instead of choosing letters based on other letters,

we'll choose words based on other words.

In an order zero program, we'd choose words at random, as though the keys on

our typewriter had words from a training text rather than letters.

But an Order-1 program would use one word to predict or choose the next word.

So that the word cat will be more likely to follow

the depending on the training text but less likely to follow dinosaur.

We'll use the Java interface developed in the last lesson but expand on the idea to

study not Java content by generating what we hope are amusing stories.

In particular, we'll look at how to design a class in Java so

that it can be used like string is used.

We'll look at a word equivalent of string, so

we'll support methods like dot length and dot substring.

But we'll see that we need to develop code, so that dot equals works,

and code so that an object can be a key in a HashMap.

These are standard Java concepts that apply to many classes,

not just the Word class, we'll design and implement.

Let's look at the output of a Word Markov program.

We'll compare the output of an order one example using letters and one using words.

We'll see that we can read the word example more easily, but

it won't make grammatical sense.

When we use an order two or

order three word program, the output will make more sense grammatically.

Is the training text for this order one letter Markov program recognizable?

It's unlikely and the words are hard to pronounce.

Hengabo owap.

Doesn't make much sense for example.

If we use an order one Markov program,

all the words are real words in that they occur in the training text.

But the order of words often doesn't make sense as you can see here with I beg your

tongue.

However, you might recognize some of the words and be able to determine that

the training text is Lewis Carroll's Alice in Wonderland.

You'll be able to train your program with any data, not just old children's books.

Is this order-1 text recognizable?

Again, the individual words are hard to pronounce.

The MarkovWordOne class we'll develop, can be run from the same MarkovRunner class

as the program that uses letters rather than words.

This is part of what Java interfaces enable.

Depending on your interest, you may have

an easier time recognizing the training text that generated these words.

If you don't recognize it, don't worry.

It don't matter.

This training text is the song Hello by Adele.

This might be recognizable today.

But will it be in a few years?

Hopefully you'll still be programming then to find out.

Ознакомьтесь с нашим каталогом

Присоединяйтесь бесплатно и получайте персонализированные рекомендации, обновления и предложения.

Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.

© Coursera Inc., 2018 Все права защищены.

Загрузить из App Store

Загрузить в Google Play