data编程代写、代写Python/C++程序

日期：2023-03-04 01:26

CA Assignment 1

Data Classification

Implementing Perceptron algorithm

Assessment Information

Assignment Number 1 (of 2)

Weighting 15%

Assignment Circulated 10 Feb 2023

Submission Mode Electronic via Canvas

Purpose of assessment The purpose of this assignment is to

demonstrate: (1) the understanding of the

Perceptron algorithm; (2) the ability to

implement the Perceptron algorithm for binary

classification; (3) the ability to evaluate a

classification algorithm; (4) the ability to turn

a binary classification algorithm to a

multi-class classification algorithm using the

1-vs-rest approach; (4) the ability to

incorporate regularisation into a classification

algorithm.

Learning outcome assessed (1) A critical awareness of current problems

and research issues in data mining. (3) The

ability to consistently apply knowledge

concerning current data mining research issues

in an original manner and produce work which

is at the forefront of current developments in

the sub-discipline of data mining.

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Objectives

This assignment requires you to implement the Perceptron algorithm using the Python programming language.

Assignment description

Download the CA1data.zip file. Inside, you will find two files: train.data and test.data, corresponding respectively to the train and test data to be used in this assignment. Each line in the

file represents a different train/test instance. The first four values (separated by commas) are

feature values for four features. The last element is the class label (class-1, class-2 or class-3).

Questions/Tasks

1. (15 marks) Explain the Perceptron algorithm (both the training and the test procedures)

for the binary classification case. Provide the pseudo code of the algorithm. It should be

the most basic version of the Perceptron algorithm, i.e. the one that was discussed in the

lectures.

2. (30 marks) Implement a binary perceptron. The implementation should be consistent

with the pseudo code in the answer to Question 1.

3. (15 marks) Use the binary perceptron to train classifiers to discriminate between

• class 1 and class 2,

• class 2 and class 3, and

• class 1 and class 3.

Report the train and test classification accuracies for each of the three classifiers after

training for 20 iterations. Which pair of classes is most difficult to separate?

4. (30 marks) Explain in your own words what the 1-vs-rest approach consist of. Extend the

binary perceptron that you implemented in part 3 above to perform multi-class classification

using the 1-vs-rest approach. Report the train and test classification accuracies for the

multi-class classifier after training for 20 iterations.

5. (10 marks) Add an `2 regularisation term to your multi-class classifier implemented in

part 4. Set the regularisation coefficient to 0.01, 0.1, 1.0, 10.0, 100.0 and compare the train

and test classification accuracies. What can you conclude from the results?

Submission Instructions

Submit via Canvas the following two files (please do NOT zip files into an archive)

1. the source code for all your programs (do not provide ipython/jupyter/colab notebooks, instead submit standalone code in a single .py file), and

2. a PDF file (report) of no more than 3 pages providing the answers to the questions.

It is extremely important that you provide the two files described above and not just the source

code!

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Important notes

(read carefully and double check compliance before submission)

1. No credit will be given for implementing any other type of classification algorithm or using

an existing library for classification instead of implementing it by yourself. However, you

are allowed to use

• numpy library for accessing data structures such as numpy.array;

• random module; and

• pandas.read_csv, csv.reader, or similar modules only for reading data from the files.

However, it is not a requirement of the assignment to use any of those modules.

2. Your program

• should run and produce all results for Questions 3, 4, and 5 in one click without

requiring any changes to the code;

• should output only the required data in a clearly structured way; it should NOT

output any intermediate steps;

• should assume that the input files are named ‘test.data’ and ‘train.data’, and are

located in the same folder as the program; in particular, it should NOT use absolute

paths.

3. Programs that do not run will result in a mark of zero!

4. Your code should be as clear as possible and should contain only the functionality needed

to answer the questions. Provide as much comments as needed to make sure that the logic

of the code is clear enough to a marker. Marks may be deducted if the code is obscure,

implements unnecessary functionality, or is overly complicated.

5. You are allowed to shuffle the data. If you use module random to shuffle the data, use

a fixed seed value so that your program always produces the same output. This output

should be exactly the one that you provide in the PDF report.

6. Your answers in the PDF report should be succinct, but complete and clear. The clarity

and presentation of the report will be assessed.

7. Your submission should be your own work. Do not copy or share! Make sure that you

clearly understand the severity of penalties for academic misconduct (https://www.liverp

ool.ac.uk/media/livacuk/tqsd/code-of-practice-on-assessment/appendix_L_cop_assess

.pdf).

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