代写CS 61B、代做java编程设计

 Project 2C: Ngordnet Enhancements | CS 61B Spring 2024
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CS 61B
Projects / Project 2C: Ngordnet Enhancements
Each assignment will have an FAQ linked at the top. You can also access it by adding “/faq” to
the end of the URL. The FAQ for Project 2C is located here.
In this project, you?ll complete your implementation of the NGordnet for k!=0 and
commonAncestors case.
As this is a quite new project, there may be occasional bugs or confusion with the spec. If you
notice anything of this sort, please post on Ed.
DANGER
Please read through the 2B spec before starting 2C.
DANGER
THE SETUP FOR THIS PROJECT IS DIFFERENT THAN THE OTHER LABS / PROJECTS.
PLEASE DO NOT SKIP THIS STEP!
Project 2C: Ngordnet Enhancements
FAQ
Checkpoint & Design Doc Due 03/15/2024
Coding Due 04/01/2024
Project Setup
Skeleton Setup
Similar to other assignments in this class, run git pull skeleton main to get the skeleton
code for this project.
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NOTE: You?ll notice that this skeleton is (almost) the exact same as the Project 2B
skeleton. This is intentional.
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Download the data files for this project using this link and move them into your proj2c
folder on the same level as src .
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Once you are done, your proj2c directory should look like this:
WARNING
While you can (and should!) certainly design for 2C in advance, we suggest only starting to
code after you get a full score on Project 2B just in case your implementation has any
subtle bugs in it.
WARNING
IMPORTANT NOTE: You should really complete Project 2B/C: Checkpoint first before
starting coding, or even designing your project. It will be helpful for your understanding of
the project. We will also require you to submit a design document to Gradescope. More
details about the design document can be found in Deliverables and Scoring.
This part of the project is designed for you to come up with an efficient and correct design for
your implementation. The design you come up with will be very important to handle these
cases. Please read the 2B & 2C spec carefully before starting your design document.
Copy your implementation from 2A for ngrams , including TimeSeries and NGramMap , into
the proj2c folder.
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Copy your implementation from 2B into the proj2c folder, since k!=0 &
commonAncestors will depend on your implementation from 2A and 2B.
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proj2c
├── data
│ ├── ngrams
│ └── wordnet
├── src
│ ├── <2B helper files>
│ ├── browser
│ ├── main
│ ├── ngrams
│ │ ├── <Your NGramMap implementation from 2A>
│ │ └── <Your TimeSeries implementation from 2A>
│ └── plotting
├── static
└── tests
Copy
Getting Started
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We?ve created two wonderful tools that you can (and should!) use to explore the dataset, see
how the staff solution behaves for specific inputs, and get expected outputs for your unit tests
(see Testing Your Code). We?ll link them here, as well as in other relevant parts of the spec.
Wordnet Visualizer: Useful for visually understanding how synsets and hyponyms work and
testing different words/lists of words for potential test case inputs. Click on the “?” bubbles
to learn how to use the various features of this tool!
Staff Solution Webpage: Useful for generating expected outputs for different test case
inputs. Use this to write your unit tests!
TASK
Read through the entire 2B/C spec and complete Project 2B/C: Checkpoint
After finishing the checkpoint, complete Design Document
In Project 2B, we handled the situation where k == 0 , which is the default value when the
user does not enter a k value.
Your required task is to handle the case where the user enters k . k represents the maximum
number of hyponyms that we want in our output. For example, if someone enters the word
“dog”, and then enters k = 5 , your code would return at most 5 words.
To choose the 5 hyponyms, you should return the k words which occurred the most times in
the time range requested. For example, if someone entered words = ["food", "cake"] ,
startYear = 1950 , endYear = 1990 , and k = 5 , then you would find the 5 most popular
words in that time period that are hyponyms of both food and cake. Here, the popularity is
defined as the total number of times the word appears over the entire time period requested.
The words should then be returned in alphabetical order. In this case, the answer is [cake,
cookie, kiss, snap, wafer] if we?re using top_14377_words.csv , total_counts.csv ,
synsets.txt , and hyponyms.txt .
DANGER
Be sure you are getting the words that appear with the highest counts, not the highest
weights. Otherwise, you will run into issues that are very difficult to debug!
Note that if the frontend doesn?t supply a year, default values of startYear = 1900 and endYear
= 2020 are provided by NGordnetQueryHandler.readQueryMap .
It might be hard to figure out the hyponyms of the words with k != 0 so we are providing
data that is easier to visualize! Below, you?ll see a modified version for EECS class
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Handling k != 0
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requirements, inspired by HKN. We have also provided the data that represents the graph
below ( frequency-EECS.csv , hyponyms-EECS.txt , synsets-EECS.txt ). If someone entered
words = ["CS61A"] , startYear = 2010 , endYear = 2020 , and k = 4 , you should receive
"[CS170, CS61A, CS61B, CS61C]" . This frequency-EECS.csv is a bit different from the
previous one since it has values with the same frequencies. We highly recommend you to take
a look at frequency-EECS.csv . Also, while you are designing your implementation, bear this in
mind that we can give you words with the same frequencies.
Project 2C: Ngordnet Enhancements - EECS Course Guide Edited 1 month ago
If a word never occurs in the time frame specified, i.e. the count is zero, it should not be
returned. In other words, if k > 0 , we should not show any words that do not appear in the
ngrams dataset.
If there are no words that have non-zero counts, you should return an empty list, i.e. [] .
If there are fewer than k words with non-zero counts, return only those words. For example if
you enter the word "potato" and enter k = 15 , but only 7 hyponyms of "potato" have
non-zero counts, you?d return only 7 words.
This task will be a little trickier since you?ll need to figure out how to pass information around
so that the HyponymsHandler knows how to access a useful NGramMap .
TASK
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Modify your HyponymsHandler and the rest of your implementation to deal with the k !=
0 case.
WARNING
EECS-course guide is not available on the interactive web staff solution so it won?t return
anything if you give the input CS61A .
DANGER
DO NOT MAKE A STATIC NGRAMMAP FOR THIS TASK! It might be tempting to simply
make some sort of public static NGramMap that can be accessed from anywhere in your
code. This is called a "global variable".
We strongly discourage this way of thinking about programming, and instead suggest that
you should be passing an NGramMap to either constructors or methods. We?ll come back
to talking about this during the software engineering lectures.
Until you use the autograder, you?ll need to construct your own test cases. We provided
one in the previous section: words = ["food", "cake"] , startYear = 1950 , endYear =
1990 , k = 5 .
When constructing your own test cases, consider making your own input files. Using the
large input files we provide is extremely tedious.
Up until now, we have only been concerned with finding the common hyponyms of words. For
the last part of this project, your task is to find the common ancestors.
That is, given a set of words, what words contain the given set of words as hyponyms?
For example, consider synsets16.txt and hyponyms16.txt from 2B:
Tips
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Finding Common Ancestors
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If we find the ancestors of "adjustment" , we should get "[adjustment, alteration, event,
happening, modification, natural_event, occurrence, occurrent]" , as shown in the
graph below.
This also should apply to words in multiple contexts, as seen with "change" :
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The ancestors of "change" should be "[act, action, alteration, change, event,
happening, human_action, human_activity, modification, natural_event, occurrence,
occurrent]" .
We can also ask for the common ancestors of sets of words, which can reveal some neat
relationships!
Here, we find the common ancestors of the words = ["change", "adjustment"] . The result
should be "[alteration, event, happening, modification, natural_event, occurrence,
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occurrent]" , which are all the words in the graph that contain both "change" and
"adjustment" as hyponyms. Note that "alteration" and "modification" are also included
in the result, contrary to what you might expect, as explained below.
Note: Be sure to take a word intersection rather than a node intersection just as in 2B, so the
common ancestors of ["test_subject", "math"] in the following graph should return "
[subject]" , as "subject" contains both "test_subject" and "math" as hyponyms, even
though "test_subject" and "math" are not directly connected in the graph.
We may also ask for common ancestors of three or more words.
Note that the outputs are in alphabetical order, and keep in mind that k != 0 can also apply
to this task.
Your query handling needs to remain efficient for common ancestors (i.e., the timeouts applied
to 2B still apply here). This means that going through every single word and checking if it
contains all the words in the query as hyponyms will be too slow on the larger datasets!
You will need to modify your HyponymsHandler class to account for the type of query, i.e.,
hyponyms or common ancestors. This should look similar to how you found startYear ,
endYear , or k , and this will be specified for you with NgordnetQueryType.HYPONYMS or
NgordnetQueryType.ANCESTORS , respectively.
TASK
Modify your HyponymsHandler and the rest of your implementation to handle common
ancestor queries in addition to hyponym queries.
As mentioned before, you should not need to copy-paste your code or do anything too drastic
to handle this task. Consider how you can use the same data structures and methods from
before to solve this problem, perhaps with a few tweaks.
NgordnetQueryType
Design Tips
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Helper methods are your friends! If you find yourself writing similar code more than once,
consider making a helper method that you can call from both places that does the common
work for you.
For Project 2C, the only required deliverable is the HyponymsHandler.java file, in addition to
any helper classes. However, we will not be directly grading these classes, since they can vary
from student to student.
Project 2B/C: Checkpoint: 5 points - Due March 15th
Project 2C Coding: 25 points - Due April 1st
HyponymsHandler popularity-hardcoded: 20%, k != 0
HyponymsHandler popularity-randomized: 30%, k != 0
HyponymsHandler common-ancestors: 50%
In addition to Project 2C, you will also have to turn in your design document. This will be worth
5 points and it is due March 15th. The design document?s main purpose is to serve as a
foundation for your project. It is important to think and ideate before coding. What we are
looking for in the design document:
Identify the data structures we have learned in the class that you will be using in your
implementation.
Pseudocode / general overview of your algorithm for your implementation.
Your design document should be around 1 - 2 pages long. Design document will be mainly
graded on effort, thought and completion.
Please make a copy of this template and submit to Gradescope.
Don?t worry if you decide to change your design document after. You are free to do so! We
want you to think about the implementation before coding therefore we require you to submit
your design as the part of the project.
The token limiting policy for this project will be as follows: You will start with 8 tokens, each of
which has a 24-hour refresh time.
We?ve provided you with two short unit test files for this project in the proj2c/tests
directory:
TestOneWordKNot0Hyponyms.java
Deliverables and Scoring

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TestCommonAncestors.java
These test files are not comprehensive; in fact, they each only contain one sanity check
test. You should fill each file with more unit tests, and also use them as a template to create
two new test files for the respective cases.
If you need help figuring out what the expected outputs of your tests should be, you should
use the two tools that we linked in the Getting Started section.
Use the small files while testing! This decreases the startup time to run Main.java and
makes it easier to reason about the code. If you?re running Main.java , these files are set in
the first few lines of the main method. For unit tests, the file names are passed into the
getHyponymsHandler method.
You can run Main.java with the debugger to debug different inputs quickly. After clicking
the “Hyponyms” button, your code will execute with the debugger - breakpoints will be
triggered, you can use the variables window, etc.
There are a lot of moving parts to this project. Don?t start by debugging line-by-line.
Instead, narrow down which function/region of your code is not working correctly then
search more closely in those lines.
Check the FAQ for common issues and questions.
Throughout this assignment, we?ve had you use your front end to test your code. Our grader is
not sophisticated enough to pretend to be a web browser and call your code. Instead, we?ll
need you to provide a method in the proj2c_testing.AutograderBuddy class that provides a
handler that can deal with hyponyms requests.
When you ran git pull skeleton main at the start of this spec, you should have received a
file called AutograderBuddy.java
Just like 2B, open AutograderBuddy.java and fill in the getHyponymsHandler method such
that it returns a HyponymsHandler that uses the four given files. Your code here will probably
be similar to your code in Main.java .
Now that you?ve created proj2c.testing.AutograderBuddy , you can submit to the
autograder. If you fail any tests, you should be able to replicate them locally as JUnit tests by
building on the test files above. If any additional datafiles are needed, they will be added to this
section as links.

Submitting Your Code
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If you?d like to go above and beyond in this project (and even explore some front-end
development), read through the Optional Features spec!
The WordNet part of this assignment is loosely adapted from Alina Ene and Kevin Wayne?s
Wordnet assignment at Princeton University.
Optional Extra Features
Acknowledgements

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