sensou0 - 経済統計練習帳

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
 
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 += int(update != 0.0)
            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)
 
 
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
y = df.iloc[0:100, 4].values
x = df.iloc[0:100, [0, 2]].values
y = np.where(y == 'Iris-setosa', -1, 1)
ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(x, y)
plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_,marker='o')
plt.show()