python实现神经网络感知器算法

# -*- coding: utf-8 -*-import numpy as npclass Perceptron(object): """ eta:学习率 n_iter:权重向量的训练次数 w_：神经分叉权重向量 errors_：用于记录神经元判断出错次数 """ def __init__(self, eta=0.01, n_iter=2):  self.eta = eta  self.n_iter = n_iter  pass def fit(self, X, y):  """  输入训练数据培训神经元  X:神经元输入样本向量  y: 对应样本分类  X:shape[n_samples,n_features]  x:[[1,2,3],[4,5,6]]  n_samples = 2 元素个数  n_features = 3 子向量元素个数  y:[1,-1]  初始化权重向量为0  加一是因为前面算法提到的w0,也就是步调函数阈值  """  self.w_ = np.zeros(1 + X.shape[1])  self.errors_ = []  for _ in range(self.n_iter):   errors = 0   """   zip(X,y) = [[1,2,3,1]，[4,5,6,-1]]   xi是前面的[1,2,3]   target是后面的1   """   for xi, target in zip(X, y):    """    predict(xi)是计算出来的分类    """    update = self.eta * (target - self.predict(xi))    self.w_[1:] += update * xi    self.w_[0] += update    print update    print xi    print self.w_    errors += int(update != 0.0)    self.errors_.append(errors)    pass def net_input(self, X):  """  z = w0*1+w1*x1+....Wn*Xn  """  return np.dot(X, self.w_[1:]) + self.w_[0] def predict(self, X):  return np.where(self.net_input(X) >= 0, 1, -1)if __name__ == '__main__': datafile = '../data/iris.data.csv' import pandas as pd df = pd.read_csv(datafile, header=None) import matplotlib.pyplot as plt import numpy as np y = df.loc[0:100, 4].values y = np.where(y == "Iris-setosa", 1, -1) X = df.iloc[0:100, [0, 2]].values # plt.scatter(X[:50, 0], X[:50, 1], color="red", marker='o', label='setosa') # plt.scatter(X[50:100, 0], X[50:100, 1], color="blue", marker='x', label='versicolor') # plt.xlabel("hblength") # plt.ylabel("hjlength") # plt.legend(loc='upper left') # plt.show() pr = Perceptron() pr.fit(X, y)