~ DECRIPTION ~

AIM: Implement the perceptron algorithm

Coding language to use: Python programming language

There are 2 files named, train.data and test.data (I've pasted it below), they correspond to the train and test data respectively. Each line in the file represents a different train/test insurance that is used. The first four values that are separated by commas are feature values for four features. The last element is the class label (class-1, class-2 or class-3).

1 - Explain the Perceptron algorithm for the binary classification cause, providing its pseudo code

2 - Implement a binary perceptron

3 - Use the binary perceptron to train classifiers to discriminate between:

* class 1 and class 2

* class 3 and class 3

* 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 - Extend the binary perceptron that you implemented in part 3 above to classification accuracies after training for 20 iterations

5 - 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

ADDITIONAL NOTES:

- Cannot use existing libraries

- Provide a file that describes how to run the code, so it can be re-produced with Python code

DATA FILES: test.data and train.data are seen below

test.data

5.0,3.5,1.3,0.3,class-1 4.5,2.3,1.3,0.3,class-1 4.4,3.2,1.3,0.2,class-1 5.0,3.5,1.6,0.6,class-1 5.1,3.8,1.9,0.4,class-1 4.8,3.0,1.4,0.3,class-1 5.1,3.8,1.6,0.2,class-1 4.6,3.2,1.4,0.2,class-1 5.3,3.7,1.5,0.2,class-1 5.0,3.3,1.4,0.2,class-1 5.5,2.6,4.4,1.2,class-2 6.1,3.0,4.6,1.4,class-2 5.8,2.6,4.0,1.2,class-2 5.0,2.3,3.3,1.0,class-2 5.6,2.7,4.2,1.3,class-2 5.7,3.0,4.2,1.2,class-2 5.7,2.9,4.2,1.3,class-2 6.2,2.9,4.3,1.3,class-2 5.1,2.5,3.0,1.1,class-2 5.7,2.8,4.1,1.3,class-2 6.7,3.1,5.6,2.4,class-3 6.9,3.1,5.1,2.3,class-3 5.8,2.7,5.1,1.9,class-3 6.8,3.2,5.9,2.3,class-3 6.7,3.3,5.7,2.5,class-3 6.7,3.0,5.2,2.3,class-3 6.3,2.5,5.0,1.9,class-3 6.5,3.0,5.2,2.0,class-3 6.2,3.4,5.4,2.3,class-3 5.9,3.0,5.1,1.8,class-3

train.data

5.1,3.5,1.4,0.2,class-1 4.9,3.0,1.4,0.2,class-1 4.7,3.2,1.3,0.2,class-1 4.6,3.1,1.5,0.2,class-1 5.0,3.6,1.4,0.2,class-1 5.4,3.9,1.7,0.4,class-1 4.6,3.4,1.4,0.3,class-1 5.0,3.4,1.5,0.2,class-1 4.4,2.9,1.4,0.2,class-1 4.9,3.1,1.5,0.1,class-1 5.4,3.7,1.5,0.2,class-1 4.8,3.4,1.6,0.2,class-1 4.8,3.0,1.4,0.1,class-1 4.3,3.0,1.1,0.1,class-1 5.8,4.0,1.2,0.2,class-1 5.7,4.4,1.5,0.4,class-1 5.4,3.9,1.3,0.4,class-1 5.1,3.5,1.4,0.3,class-1 5.7,3.8,1.7,0.3,class-1 5.1,3.8,1.5,0.3,class-1 5.4,3.4,1.7,0.2,class-1 5.1,3.7,1.5,0.4,class-1 4.6,3.6,1.0,0.2,class-1 5.1,3.3,1.7,0.5,class-1 4.8,3.4,1.9,0.2,class-1 5.0,3.0,1.6,0.2,class-1 5.0,3.4,1.6,0.4,class-1 5.2,3.5,1.5,0.2,class-1 5.2,3.4,1.4,0.2,class-1 4.7,3.2,1.6,0.2,class-1 4.8,3.1,1.6,0.2,class-1 5.4,3.4,1.5,0.4,class-1 5.2,4.1,1.5,0.1,class-1 5.5,4.2,1.4,0.2,class-1 4.9,3.1,1.5,0.1,class-1 5.0,3.2,1.2,0.2,class-1 5.5,3.5,1.3,0.2,class-1 4.9,3.1,1.5,0.1,class-1 4.4,3.0,1.3,0.2,class-1 5.1,3.4,1.5,0.2,class-1 7.0,3.2,4.7,1.4,class-2 6.4,3.2,4.5,1.5,class-2 6.9,3.1,4.9,1.5,class-2 5.5,2.3,4.0,1.3,class-2 6.5,2.8,4.6,1.5,class-2 5.7,2.8,4.5,1.3,class-2 6.3,3.3,4.7,1.6,class-2 4.9,2.4,3.3,1.0,class-2 6.6,2.9,4.6,1.3,class-2 5.2,2.7,3.9,1.4,class-2 5.0,2.0,3.5,1.0,class-2 5.9,3.0,4.2,1.5,class-2 6.0,2.2,4.0,1.0,class-2 6.1,2.9,4.7,1.4,class-2 5.6,2.9,3.6,1.3,class-2 6.7,3.1,4.4,1.4,class-2 5.6,3.0,4.5,1.5,class-2 5.8,2.7,4.1,1.0,class-2 6.2,2.2,4.5,1.5,class-2 5.6,2.5,3.9,1.1,class-2 5.9,3.2,4.8,1.8,class-2 6.1,2.8,4.0,1.3,class-2 6.3,2.5,4.9,1.5,class-2 6.1,2.8,4.7,1.2,class-2 6.4,2.9,4.3,1.3,class-2 6.6,3.0,4.4,1.4,class-2 6.8,2.8,4.8,1.4,class-2 6.7,3.0,5.0,1.7,class-2 6.0,2.9,4.5,1.5,class-2 5.7,2.6,3.5,1.0,class-2 5.5,2.4,3.8,1.1,class-2 5.5,2.4,3.7,1.0,class-2 5.8,2.7,3.9,1.2,class-2 6.0,2.7,5.1,1.6,class-2 5.4,3.0,4.5,1.5,class-2 6.0,3.4,4.5,1.6,class-2 6.7,3.1,4.7,1.5,class-2 6.3,2.3,4.4,1.3,class-2 5.6,3.0,4.1,1.3,class-2 5.5,2.5,4.0,1.3,class-2 6.3,3.3,6.0,2.5,class-3 5.8,2.7,5.1,1.9,class-3 7.1,3.0,5.9,2.1,class-3 6.3,2.9,5.6,1.8,class-3 6.5,3.0,5.8,2.2,class-3 7.6,3.0,6.6,2.1,class-3 4.9,2.5,4.5,1.7,class-3 7.3,2.9,6.3,1.8,class-3 6.7,2.5,5.8,1.8,class-3 7.2,3.6,6.1,2.5,class-3 6.5,3.2,5.1,2.0,class-3 6.4,2.7,5.3,1.9,class-3 6.8,3.0,5.5,2.1,class-3 5.7,2.5,5.0,2.0,class-3 5.8,2.8,5.1,2.4,class-3 6.4,3.2,5.3,2.3,class-3 6.5,3.0,5.5,1.8,class-3 7.7,3.8,6.7,2.2,class-3 7.7,2.6,6.9,2.3,class-3 6.0,2.2,5.0,1.5,class-3 6.9,3.2,5.7,2.3,class-3 5.6,2.8,4.9,2.0,class-3 7.7,2.8,6.7,2.0,class-3 6.3,2.7,4.9,1.8,class-3 6.7,3.3,5.7,2.1,class-3 7.2,3.2,6.0,1.8,class-3 6.2,2.8,4.8,1.8,class-3 6.1,3.0,4.9,1.8,class-3 6.4,2.8,5.6,2.1,class-3 7.2,3.0,5.8,1.6,class-3 7.4,2.8,6.1,1.9,class-3 7.9,3.8,6.4,2.0,class-3 6.4,2.8,5.6,2.2,class-3 6.3,2.8,5.1,1.5,class-3 6.1,2.6,5.6,1.4,class-3 7.7,3.0,6.1,2.3,class-3 6.3,3.4,5.6,2.4,class-3 6.4,3.1,5.5,1.8,class-3 6.0,3.0,4.8,1.8,class-3 6.9,3.1,5.4,2.1,class-3