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

from perceptron import Perceptron


def accuracy(y_true, y_pred):
    accuracy = np.sum(y_true == y_pred) / len(y_true)
    return accuracy

X, y = datasets.make_blobs(n_samples=150,n_features=2,centers=2,cluster_std=1.05,random_state=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

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))

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()