import numpy as np
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.colors import ListedColormap
class Perceptron(object):
def __init__(self,lr, n_iter): self.lr = lr self.n_iter = n_iterdef train(self,X,y): #这个向量蕴含了两重信息，第一w_[0]代表了偏置项b，w_[1:]代表了权重向量 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): #这里的target - self.predict(xi)对应公式（5）中的yi， update = self.lr * (target - self.predict(xi)) self.w_[1:] += update * xi self.w_[0] += update errors += int(update != 0.0) self.errors_.append(errors) return self#这个函数实现了感知机定义外面的w·x+b这个操作def net_input(self, X): return np.dot(X, self.w_[1:])+self.w_[0]#依照学习算法的第三步，[货币代码](https://www.gendan5.com/currencycode.html)对式子判断其是否小于0def predict(self, X): return np.where(self.net_input(X) >= 0.0, 1, -1)将bunch格局的数据集转化为pandas的dataframe
def sklearn_to_df(datasets):
df = pd.DataFrame(datasets.data, columns=datasets.feature_names)df['target'] = pd.Series(datasets.target)return dfiris = load_iris()
df_iris = sklearn_to_df(iris)
y = df_iris.iloc[0:100,4].values
y = np.where(y == 0,-1,1)
X = df_iris.iloc[0:100,[0,2]].values
鸢尾花数据集的可视化
plt.scatter(X[:50,0],X[:50,1],
color='r',marker='o',label='setosa')
plt.scatter(X[50:100,0],X[50:100,1],
color='b',marker='x',label='versicolor')
plt.xlabel('petal length')
plt.ylabel('sepal length')
plt.legend(loc='upper left')
plt.show()
利用鸢尾花数据集来训练感知机
pn = Perceptron(lr=0.01,n_iter=10)
pn.train(X, y)
plt.plot(range(1,len(pn.errors_)+1),pn.errors_,marker='o')
plt.xlabel('Epoch')
plt.ylabel('Number of miscalssifications')
plt.show()
画出决策边界
def plot_decision_regions(X, y, classifier, resolution=0.02):
markers = ('s','x','o','^','v')colors = ('red','blue','lightgreen','gray','cyan')cmap = ListedColormap(colors[:len(np.unique(y))])x1_min, x1_max = X[:,0].min() - 1, X[:,0].max() + 1x2_min, x2_max = X[:,1].min() - 1, X[:,1].max() + 1xx1, 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)z = z.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)plot_decision_regions(X, y, classifier=pn)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc = 'upper left')
plt.show()