perceptron_test.py

# Imports
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import datasets
from Perceptron import Perceptron

def accuracy(y_true, y_pred):
    '''
    Define a function to test accuracy of the model
    '''
    accuracy = np.sum(y_true == y_pred) / len(y_true)
    return accuracy

# Get some dummy data from Sci-kit learn
X, y = datasets.make_blobs(n_samples=100, n_features=2, centers=2, cluster_std=1.06, random_state=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)

# Train and predict from the data
p = Perceptron(learning_rate=0.01, n_iters=1000)
p.fit(X_train, y_train)
predictions = p.predict(X_test)
print("Perceptron classification accuracy", accuracy(y_test, predictions))

# Plot the results
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.scatter(X_train[:, 0], X_train[:, 1], marker="o", c=y_train)

x0_1 = np.amin(X_train[:, 0])
x0_2 = np.amax(X_train[:, 0])
x1_1 = (-p.weights[0] * x0_1 - p.bias) / p.weights[1]
x1_2 = (-p.weights[0] * x0_2 - p.bias) / p.weights[1]
ax.plot([x0_1, x0_2], [x1_1, x1_2], "k")
ymin = np.amin(X_train[:, 1])
ymax = np.amax(X_train[:, 1])
ax.set_ylim([ymin - 3, ymax + 3])
plt.show()