笔记一python代码

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

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
y = np.where(y=='Iris-setosa',1,-1)
x = df.iloc[0:100, [0,2]].values

class Perceptron():
    def __init__(self, eta, X, Y, N):
        self.eta = eta
        self.X = X
        self.Y = Y
        self.N = N
        self.w = [0]*len(X[0])
        self.w0 = 0
        self.m = len(X)
        self.n = len(X[0])
    def output_y(self, x):
        z = np.dot(x,self.w)+self.w0
        if z > 0:
            return 1
        else:
            return -1
    def training(self):
        self.errors = []
        for times in xrange(self.N):
            error = 0
            for i in xrange(self.m):
                delta_y = self.Y[i]-self.output_y(self.X[i])
                if delta_y != 0:
                    error += 1
                self.w0 += self.eta*delta_y
                self.w += self.eta*delta_y*self.X[i]
            self.errors.append(error)
per = Perceptron(0.1, x, y, 10)
per.training()
print per.w0,per.w
print per.errors

w 0.4 [ 0.68 -1.82]
errors [2, 2, 3, 2, 1, 0, 0, 0, 0, 0]

fig, axes = plt.subplots(1,2)
axes0, axes1 = axes.flatten()
axes0.plot(per.errors, marker='o')
axes0.set_title('errors')
axes1.scatter(x[:,0][0:50], x[:, 1][0:50], c = 'none', marker='o', color='r')
axes1.scatter(x[:,0][50:100], x[:, 1][50:100], marker='x', color='g')
axes1.annotate(r'versicolor',xy=(5.5,4.5),xytext=(4.5,5.5),arrowprops=dict(arrowstyle='->', facecolor='blue'))
axes1.annotate(r'setosa',xy=(5.8,2),xytext=(6.5,3),arrowprops=dict(arrowstyle='->', facecolor='blue'))
plt.subplots_adjust(left=0.1, right= 0.9, bottom=0.1, top=0.6)
plt.show()

# 使用训练得到w(0.4  0.68 -1.82),借助等高线contourf将区域分开，一部分数据大于0，一部分小于0
# 高度函数
def f(x, y):
    z = 0.4+0.68*x-1.82*y
    z = np.where(z>0,1,-1)
    return z
    
fig, axes = plt.subplots()
n = 200

mx = np.linspace(4, 7.5, n)
my = np.linspace(0, 6, n)
# 生成网格数据
X, Y = np.meshgrid(mx, my)
plt.contourf(X, Y, f(X, Y), 2, alpha = 0.75, cmap = plt.cm.RdBu)
axes.scatter(x[:,0][0:50], x[:, 1][0:50], c = 'none', marker='o', color='r')
axes.scatter(x[:,0][50:100], x[:, 1][50:100], marker='x', color='g')
axes.annotate(r'versicolor',xy=(5.5,4.5),xytext=(4.5,5.5),arrowprops=dict(arrowstyle='->', facecolor='blue'))
axes.annotate(r'setosa',xy=(5.8,2),xytext=(6.5,3),arrowprops=dict(arrowstyle='->', facecolor='blue'))
#plt.subplots_adjust(left=0.1, right= 0.9, bottom=0.1, top=0.6)
plt.show()