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!
Core: Implementing Markov Text Generation
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Из курса от партнера University of California San Diego
Data Structures and Performance
1400 оценки
University of California San Diego
1400 оценки
Курс 4 из 4 — Specialization Object Oriented Programming in Java
Из урока
Interfaces, Linked Lists vs. Arrays, and Correctness
This week we'll start talking about some of the basic concepts that one expects to find in a data structures course: the idea of data abstraction, and a data structure called a Linked List. Even though Linked Lists are not very efficient structures (for the most part), they do hit home the idea of "linking" pieces of data together in your computer's memory, rather than storing the data in one contiguous region. This linking idea will be central to many of the more advanced data structures, namely trees and graphs, that are coming up later in this course and in the next course in this specialization. In this module you'll also learn tools and procedures for unit testing your code, which is a way to make sure that what you've written is correct, and a staple practice of any sophisticated software developer.
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Christine AlvaradoAssociate Teaching Professor
Computer Science and Engineering
Mia MinnesAssistant Teaching Professor
Computer Science and Engineering
Leo PorterAssistant Teaching Professor
Computer Science and Engineering
[MUSIC]
So, in the previous video, we talked about this concept of using Markov
processes to generate random text after we train on some data set.
And so, now we're going to dive into the implementation of this idea.
So by the end of this video, you'll be able to describe the class design
of the Java classes that we're going to be using, both in the video and
in your project, to create a Markov text generator.
And then you'll also be able to implement some of the code that's needed for
the project.
So let's think back to what we said.
So at the beginning, we're going to build a model of the patterns that are implicit
in the data that we want to emulate by training on that data.
And the model that we're building will have these estates that
represent the current situation that we're in.
And then transitions between those states to say how to go forward and
how to generate more and more text.
And then once we've built our model, we're ready to go into that second stage
of the process and actually generate some text.
So let's focus the class design first.
So remember that when we talk about class design we have the notion of interfaces.
And with the interfaces we can specify what's the desired behavior of
our classes without going into the details of the implementation.
So when we have a MarkovTextGenerator
what we want it to be able to do is train on some input data or string.
And then we also wanted to be able to generate text,
perhaps a certain amount of text, and so we want to be able to give it
an integer which says the number of words we want to generate.
Now just to make our objects more useable and
more flexible, we're also going to add the functionality of retraining.
So we want to be able to train on some data and
then maybe generate some text based on that model, then throw it away and
then retrain on new data generate more text etc.
So that’s going to be the interface and
these are the methods that we’re specifying in this interface.
So, if we want to go ahead and implement that interface we need to define a class
that’s going to implement each one of these methods.
And let’s focus on the training piece first.
So, when we train on data, what we're training on is an input string.
And we're going to go back to the example from last time where we have that
Beatles song, Hello, Goodbye.
And so we want to think about our model capturing the quirks and
patterns in that body of text.
So that when we generate new text, we're going to emulate those quirks and
patterns as well.
So for each new word, what we need to do is keep track of the current
word that we're looking at and then what words might follow after that word.
Because our model is going to predict what next word to generate based on the current
state which we are going to think of as the current word.
And so what were looking for are likely consecutive pairs of words, and so
as we process the input string that we are looking at that's our training
set what we are trying to track is which words come after other words so
that we can mimic that later.
So, in order to put that into our class design, our concrete class that implements
the interface MarkovTextGenerator is going to have a list.
And we're going to use a generic to say, well, what kind of lists,
what objects are stored in this list.
Well, each object that stored in this list is a word node.
It's going to be something that captures what
the information that's contained in a word ought to be.
And so when we look at a specific word, what we care about is
what are the possible next moves after we see that word.
And so we're going to define another class called WordNode that keeps track of
the value of the word that we're thinking about, the current word.
And so that's our private String word.
And then it is going to keep a list of the possible next words that come after it.
Now from a WordNode we might want some methods.
We might want to get the value of the current word.
We might need to add a possible next word as we're training on the data.
If we come across this word again, and
we wanna add a new word to the collection of possible next moves.
And then when we actually generate text afterwards, we're going to need it to get
a random next word based on the current location we are in our state model.
And we'll come back to how we use that method in a little bit, but
basically what we're doing when we're training on data is we're going through
the array or list of words that we have, and
for each one of those words that we're looking at in our training set.
We have to decide wether we want to create a new WordNode object that is going to
basically be a state in our model.
So let's get a bit of a sense of how we do that.
And so when we start at the very beginning,
we've never seen the word you before, because this is the very beginning and so
you have to create a new WordNode object with the value for the word you.
And then we look at what word comes immediately after you at this point in
the input data set, and we say that say comes next.
And so the next words list for this particular instance of the object, for
this particular object at least contain say.
It's got to have that as a possible next word to you.
Now we come to the next word in our training set and it's a word that we
haven't seen before, so we have to create a new instance of the word node class.
That instance is going to have the instance variable word
set to the value say and it's possible next words is going to contain yes.
And so on and so forth as we go through this list.
And now the first interesting thing happens when we get to
the second time that we see the word say.
So in the song it goes, you say yes, I say no.
And so the second time we see the word say,
we don't want to create a new object that has the same value for the word in it.
Because what we're trying to do with this model is keep track of
the distinct possible words that we might have in our data set, and
for each one of those, what are all possible next words.
And so what we'd like to do is update the object that we already created whose value
for the variable word was say, and we want to add a new possible next word to it.
Now say is followed by the word no.
All right and so that's what we did with the object whose value for
the variable word was say.
All right.
So we keep on going and we keep on going, but
remember that our model actually had this notion of probabilities, right?
When we were thinking about the Markov model,
what we had was each word defined as state and then we had probabilities.
On the transitions for how likely was it
each of the possible next words was actually going to be given?
So, if we have the word hello at the end of a sentence, so
hello with a period, half the time, we a re going to have the word I afterwards,
but a sixth of the time, we are going to have the word why, or
a third of the time we are going to have the word you afterwards.
And it's not clear that we've captured that in our algorithm, so far,
of training through the data and just adding words to the list of next words.
But let's think about what we've done.
And in particular,
let's see what happens to the object who's word points to the word hello.
So, the first time we encounter hello in our song, and
you can go through the lyrics and see, it's followed by I.
And so the list nextWords gets the value, or gets I put into it.
And then the next time I comes again.
And notice that we just insert a second copy of the string I into this list.
Okay?
Next time we insert why, and next time we insert I again, and
then you, and then the next time will be a you as well.
And so as we're training on the data set, what we're doing is we're
concerned about whether the current word that we're looking at is new or not.
And every time we've got a new word, then we add a new object.
But then when we look up what the next word is, we don't care if we've seen it
before, we always just stick it into the list of nextWords.
And that's gonna be really important, because then when we go ahead and
generate text, what we're going to do, is when we generate the next random word for
a particular word, we're just going to pick randomly, uniformly,
between all of the words in our next word's list.
And that's gonna do exactly what we need in terms of the probabilities
because the probability of picking I from a list of six elements,
three of which are I is exactly three out of six.
If we pick each one of those elements with equal probability, and so what we've done
is we've stacked the list of next words so that the distribution of probabilities for
each distinct word matches exactly what we need.
And so that simple algorithm that we talked about where,
when we process each word we just stick the next word into the list of next words,
not caring if we've seen it before or not, does exactly the right thing for
generating the next words with the correct probabilities.
So now let's talk about how we would Implement this algorithm at a high level,
so we want to think about how we generate words
using the model that we've already built.
So we have at our disposal a list of word nodes.
And now what we'd like to do is use that list of word nodes to generate some text.
And so until we have enough words, because remember we've got
that integer argument that tells us how many words we're supposed to produce,
what we're going to do is look for the current word that we're currently at
through the list of word notes that we have.
We're going to look for which word note has that word as its value,
and in that node, we're going to generate a random number between 0 and
the number of words in that list of nextWords.
And we can generate random numbers in Java because we have this great package
called random.
And so
we just pick a random number between 0 and the size of the list, minus 1.
And just pick the next word whose index is that number, and so each of
the possible next words in that list will have equal probability of being picked.
And because we've stacked the list,
then we'll get the right distribution that we want.
And so we'll just print the word at that index.
We update what the currentWord is and we look for the currentWord node, and
we just keep on going and keep on going.
And now you're ready to begin your project and
implement this high level algorithm in Java.
So enjoy the class design, enjoy generating some really cool text.
And play around with what starter text you want to train on,
what training data sets you'll have.
And you'll notice that this can be a little bit absorbing.
Hours of fun can be had.
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