2-2.py

#encoding=utf-8

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets

class Perceptron(object):
    def __init__(self, eta=0.01, n_iter=10):
        self.eta = eta
        self.n_iter = n_iter

    def fit(self, X, y):
        self.w_ = np.zeros(1 + X.shape[1])
        self.errors_ = []

        for _ in range(self.n_iter):
            errors = 0
            for xi, target in zip(X, y):
                update = self.eta * (target - self.predict(xi))
                self.w_[1:] += update * xi
                self.w_[0] += update
                errors += np.where(update == 0.0, 0, 1)
            self.errors_.append(errors)
        return self

    def net_input(self, X):
        return np.dot(X, self.w_[1:]) + self.w_[0]

    def predict(self, X):
        return np.where(self.net_input(X) >= 0.0, 1, -1)


def plot_decision_regions(X, y, classifier, resolution=0.02):

    markers = ('s', 'x')
    colors = ('red', 'blue')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T).reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl)

def main():
    iris = datasets.load_iris()
    X = iris.data[:100, [0,2]]
    y = iris.target[:100]
    for i in range(len(y)):
        if y[i] == 0:
            y[i] = -1

    plt.scatter(X[:50, 0], X[:50, 1], color="red", marker="x", label="A")
    plt.scatter(X[50:100, 0], X[50:100, 1], color="blue", marker="*", label="B")
    plt.xlabel("x")
    plt.ylabel("y")
    plt.legend(loc="upper left")
    plt.show()

    ppn = Perceptron(eta = 0.1, n_iter = 10).fit(X,y)

    plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker="o")
    plt.xlabel("Epochs")
    plt.ylabel("Number of misclassifications")
    plt.show()

    plot_decision_regions(X, y, classifier=ppn)
    plt.xlabel("x")
    plt.ylabel("y")
    plt.legend(loc="upper left")
    plt.show()

if __name__== "__main__":
    main()