代写COMP 527、代做Perceptron algorithm、代做Python语言、代写Python编程设计

日期：2019-03-01 09:09

COMP 527 - 2019 - CA Assignment 1

Data Classification

Implementing Perceptron algorithm

Assessment Information

Assignment Number 1 (of 2)

Weighting 12%

Assignment Circulated 28th January 2019

Deadline 5th March 2018, 15:00 UK Time (UTC)

Submission Mode Electronic via Departmental submission system

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.

Purpose of assessment This assignment assesses the understanding of the Perceptron

algorithm by implementing a binary Perceptron for text

clustering.

Marking criteria Marks for each question are indicated under the corresponding

question.

Submission necessary in order No

to satisfy Module requirements?

Late Submission Penalty Standard UoL Policy.

1

1 Objectives

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

language.

Note that no credit will be given for implementing any other types of classification

algorithms 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. But it is not a requirement

of the assignment to use numpy. You must provide a README file

describing how to run your code to re-produce your results.

2 Text Classification using Binary Perceptron Algorithm

Download the CA1data.zip file from the COMP 527 web site and uncompress it. 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) Explain the Perceptron algorithm for the binary classification case, providing its pseudo code. (20 marks)

(2) Prove that for a linearly separable dataset, perceptron algorithm will converge. (10 marks)

(3) Implement a binary perceptron. (20 marks)

(4) Use the binary perceptron to train classifiers to discriminate between (a) class 1 and class 2,

(b) class 2 and class 3 and (c) class 1 and class 3. Report the train and test classification

accuracies for each of the three classifiers after 20 iterations. Which pair of classes is most

difficult to separate? (20 marks)

(5) For the classifier (a) implemented in part (3) above, which feature is the most discriminative?

(5 marks)

(6) Extend the binary perceptron that you implemented in part (2) above to perform multi-class

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

for each of the three classes after training for 20 iterations. (15 marks),

(7) Add an ?2 regularisation term to your multi-class classifier implemented in question (5). Set

the regularisation coefficient to 0.01, 0.1, 1.0, 10.0, 100.0 and compare the train and test

classification accuracy for each of the three classes. (10 marks)

3 Deadline and Submission Instructions

? Submit

2

(a) the source code for all your programs (do not provide ipython/jupyter/colab notebooks,

instead submit standalone code in a single .py file),

(b) a README file (plain text) describing how to compile/run your code to produce the

various results required by the assignment, and

(c) a PDF file providing the answers to the questions.

Compress all of the above files into a single zip file and specify the filename as studentid.tgz

(replace “studentid” by your departmental student id). It is extremely important that you

provide all the files described above and not just the source code! File types other than zip

will not be accepted by the submission system. Every year there is a significant number of

submissions without a student id or a name. Obviously, if you do not write name or student

id then it is not possible to assign marks to you!

Submission is via the departmental submission system accessible from

http://intranet.csc.liv.ac.uk/cgi-bin/submit.pl?module=COMP527