Perceptron Data Plotting

PerceptronDataPlot.py

from toolkit.matrix import Matrix
import numpy as np
import matplotlib.pyplot as plt
import math

# plots the decision line and data points
    def plot(self, features, labels):
        # Prepare data
        xVec0 = []
        yVec0 = []
        xVec1 = []
        yVec1 = []
      
        # split data into outputs of 0 and 1
        for (i, row) in enumerate(features.data):
            if labels.data[i][0] == 1:
                xVec1.append(row[0])
                yVec1.append(row[1])
            else:
                xVec0.append(row[0])
                yVec0.append(row[1])

        plt.scatter(xVec0, yVec0, color='red')
        plt.scatter(xVec1, yVec1, color='blue')


        x = np.linspace(-1, 1, 100)

        # Plot the decision line
        plt.plot(x, (-self.weights[0]/self.weights[1])*x - self.weights[2]/self.weights[1], label='not linear')

        # Add a legend - not really necessary
        plt.legend()

        plt.xlabel('Feature A')
        plt.ylabel('Feature B')
        plt.title('Linearly Separable Decision Boundary')

        # Show the plot
        plt.show()