Support: More with Regular Expressions

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From the course by University of California San Diego

Data Structures and Performance

1524 ratings

University of California San Diego

Data Structures and Performance

1524 ratings

Course 4 of 4 in the Specialization Object Oriented Programming in Java

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science, and in particular, we recommend that you have taken the first course in this specialization (which also requires some previous experience with Java). In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables. You will explain how these data structures make programs more efficient and flexible. You will apply asymptotic Big-O analysis to describe the performance of algorithms and evaluate which strategy to use for efficient data retrieval, addition of new data, deletion of elements, and/or memory usage. The program you will build throughout this course allows its user to manage, manipulate and reason about large sets of textual data. This is an intermediate Java course, and we will build on your prior knowledge. This course is designed around the same video series as in our first course in this specialization, including explanations of core content, learner videos, student and engineer testimonials, and support videos -- to better allow you to choose your own path through the course!

From the lesson

Working with Strings

This week we're going to dive into the course programming project. In the first lesson you'll learn about Strings and Regular Expressions, and in the programming assignment this week you'll apply that knowledge to adding functionality to your text editor so that it can measure the "readability" of text by calculating something called the "Flesch Readability Score". This course is focused on building code that not only does interesting things, but also finishes them quickly. So, let's get started building some code!

Core: Introduction to the Flesch Readability Score5:44

Core: Basics of Strings in Java6:21

Core: Working with Strings in Java8:44

Support: For-Each Loops, part 1 of 51:50

Support: For-Each Loops, part 2 of 54:37

Support: For-Each Loops, part 3 of 54:31

Support: For-Each Loops, part 4 of 59:52

Support: For-Each Loops, part 5 of 52:01

Core: Introduction to Regular Expressions10:29

When I struggled: Regular expressions1:44

Concept Challenge: Regular Expressions9:10

Support: More with Regular Expressions8:50

Meet the Instructors

Christine Alvarado
Associate Teaching Professor
Computer Science and Engineering
Mia Minnes
Assistant Teaching Professor
Computer Science and Engineering
Leo Porter
Assistant Teaching Professor
Computer Science and Engineering

So in this support video, our goal is to walk through some of the regular

expressions that you'll be using in the project.

So, by the end of this video, you will be able to

write the regular expressions that you will need in the programming assignment.

So, big caution, spoiler alert,

by the end of this video, you will really see the solution.

So if, like me, you like to try to figure these things out on your own

before someone spoils the end and tells you the answer, stop now.

Try to work through it yourself, and then if you get stuck, do come back and

we'll work through it together.

But if you watch this video, you will see the solution.

So let's get to it.

But if you need to go away and then come back, please do that.

All right, so just remember the context what we're doing in this project

is we're processing this text document.

And what we'd like to do is break it apart into constituent pieces, and

then we'll be able to compute that flush index by looking at the numbers of words

and the number of syllables and the number of sentences.

And so we have the helper method that's given to us in the abstract class

Document, and this helper method is called, getTokens.

And what we do is we feed it in a string, and it outputs a list of strings.

So what we give the getTokens document, sorry, getTokens method

is a regular expression that defines the tokens that we want out.

So that regular expression is going to match to what we want to see

out of the big text that we give it, the big string that we give it.

Okay, and then what's returned to us is a list

of all those pieces of the big text that match the regular expression.

So then, our task is to fill in what

regular expression we want depending on the context.

So, for example, one of the first methods that you'll be writing in this project

is the getNumWords method.

And so what we'd like to do is figure out what the words are that are part of this

big, big string that represents the document that we're processing.

And so we'd like to separate out each word, and

then we'll have a big list of those words.

And then all we'll have to do afterwards is look at the size of that list, and

that size of that list of tokens that are returned is exactly the number of words

in the document.

So, the big conceptual challenge and nugget in this getNumWords method

is really focusing in on what the regular expression it is that we

should be using to separate out the words in the big document.

Okay, so we want to fill in those red question marks.

And what we'll be filling in those red question marks with

is the regular expression that will match any word.

Now, in order to understand what it means to match any word,

we need to figure out what constitutes a word.

So we're going to not try to delve too deeply into grammar and

dictionaries and language theory.

And we're going to use the definition of a word that we suggest in the project

description, and we're going to say that a word is a contiguous sequence of

alphabetic characters.

So that means that we're going to ignore things like apostrophes.

We're going to ignore numbers, numerals, and we're just going to

focus in on consecutive sequences of alphabetical characters.

Okay, and so let's take a minute or second and

try to think about how we would fill in a regular expression that represents whether

it matches any contiguous sequence of alphabetic characters.

So pause if you'd like and think about it.

And now let's at least try one approach.

So perhaps we might fill it with saying, well,

alphabetic characters are anything that's either lowercase or uppercase.

And I would like my regular expression to match any character in the set

a through z lowercase, or A through Z, uppercase.

And that seems like a reasonable approach.

So let's think about what's now circled in the regular expression, and

think about whether it works.

Well, what do you think?

There's a bit of a bug here, and

the bug comes with wanting words that have more than one character.

So, if you remember the square brackets notation,

what it means is I wanna match a single character that is within the collection of

symbols that I'm putting in the square brackets.

But for our words what we'd like is a sequence of characters.

And so what we're going to say one or more of the following set of characters.

And so what we need to do is to add that plus symbol after the square brackets,

and that will let us match to any contiguous non-empty sequence

of alphabetic characters.

All right, so this is the correct regular expression now for matching two words.

And so now the getNumWords method will process our big string that

represents our document and separate out each of the consecutive sequences

of alphabetic characters, and spit them out into one big list of strings.

And then we'll be able to return the size of that list

as the number of words in our document.

Okay, so we've got this method sorted out.

And now let's move on to the next method that you're implementing in the project.

And this would be a good point as well to pause if you feel pretty confident about

how, about this solution, and you want to try yourself for the next one.

The next method that we'll tackle is the getNumSentences.

So take this opportunity to work on it yourself, if you like.

And then come back to confirm your answer, if that's what you would like to do.

Okay, now that we're here, let's remember what constitutes a sentence.

So a sentence, well again we're not going to be to worried about grammatical,

and this isn't going to be a perfect solution.

But a pretty good approximation of what a sentence looks like is a sequence of

characters that at their end have an end of sentence punctuation.

So when we think about a sentence, we know that it's usually ended by either

a period or an exclamation mark or a question mark.

And so what we're going to do is that think about any sequence of characters

that has that at the end, we'd like to consider a sentence.

Now, we don't wanted to include that last symbol as part of our sentence.

And so we'd like just the beginning part to be considered the sentence.

And so now what we have to do is figure out what regular expression

will exactly match to the sentence part, without the punctuation at the end.

All right, so how do we fill in those question marks?

And this is a little bit different from what we were doing with the words before.

With the words, we were talking about what each character in the word would look

like, and we knew that they were alphabetical characters.

Whereas here, we're talking about some perhaps ill-defined

collection of characters, but all we care about it the very end.

And so that seems a little bit harder to do.

In order to rephrase this definition into something that maybe is more amenable to

writing as a regular expression, we wanna think about a sentence as, well,

it's a collection of things that don't in the middle of them include the end

of sentence punctuation.

So a way of working with this definition in a way that lets us

write it as a regular expression is to actually tweak it a bit, and

try to find an equivalent formulation that maybe matches the operations that we

have a little bit better.

And so, one way of thinking about this definition is that it says, we want

a sequence of characters that doesn't include the end of sentence punctuation.

So we want all of the characters in the string that we matched to

not to be end of sentence punctuation.

And we have a way of doing that in regular expressions.

So remember the caret symbol.

So this caret symbol says, I don't want things

from the following collection of characters in the string that I match to.

And so, which characters don't we want?

We don't want exclamation marks.

We don't want question marks, and we don't want periods.

But all the other characters are okay.

And we want any number of them.

So that + at the end of the regular expression

says that we would like to match a non-empty sequence of characters.

And all the characters that are not in that non-empty sequence have to

be something other than the end-of-sentence punctuation.

And so here we have the regular expression that will correctly match to

each of the sentences.

And so once we call getTokens with this pattern as its input,

then the result is going to be a list of strings.

Each string is going to be a sentence, and we can count the size of that list

to give us the number of sentences in our document.

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