原始感知机入门——python3实现

import numpy as np
from matplotlib import pyplot as plt


# train matrix
def get_train_data():		
	M1 = np.random.random((100,2))
	M11 = np.column_stack((M1,np.ones(100)))
	M2 = np.random.random((100,2)) - 0.7
	M22 = np.column_stack((M2,np.ones(100)*(-1)))
	MA = np.vstack((M11,M22))

	plt.plot(M1[:,0],M1[:,1], ‘ro‘)
	plt.plot(M2[:,0],M2[:,1], ‘go‘)
	min_x = np.min(M2)
	max_x = np.max(M1)
	# 此处返回 x 是为了之后作图方便
	x = np.linspace(min_x, max_x, 100)

	return MA,x

# 方便在train函数中识别误分类点
def func(w,b,xi,yi):
	num = yi*(np.dot(w,xi)+b)
	return num

# 训练training data
def train(MA, w, b):
	# M 存储每次处理后依旧处于误分类的原始数据
	M = []
	for sample in MA:
		xi = sample[0:2]
		yi = sample[-1]
		# 如果为误分类，改变w, b
		# n 为学习率
		if func(w,b,xi,yi) <= 0:
			w += n*yi*xi
			b += n*yi
			M.append(sample)
	if len(M) > 0:
		# print(‘迭代...‘)
		train(M, w, b)
	return w,b

# 作出分类线的图
def plot_classify(w,b,x, rate0):
	y = (w[0]*x+b)/((-1)*w[1])
	plt.plot(x,y)
	plt.title(‘Accuracy = ‘+str(rate0))

# 随机生成testing data 并作图
def get_test_data():
	M = np.random.random((50,2))
	plt.plot(M[:,0],M[:,1],‘*y‘)
	return M
# 对传入的testing data 的单个样本进行分类
def classify(w,b,test_i):
	if np.sign(np.dot(w,test_i)+b) == 1:
		return 1
	else:
		return 0

# 测试数据，返回正确率
def test(w,b,test_data):
	right_count = 0
	for test_i in test_data:
		classx = classify(w,b,test_i)
		if classx == 1:
			right_count += 1
	rate  = right_count/len(test_data)
	return rate

if __name__=="__main__":
	MA,x= get_train_data()
	test_data = get_test_data()
	# 定义初始的w,b
	w = [0,0]
	b = 0
	# 初始化最优的正确率
	rate0 = 0
	# 循环不同的学习率n,寻求最优的学习率，即最终的rate0
	# w0,b0为对应的最优参数
	for i in np.linspace(0.01,1,100):
		n = i
		w,b = train(MA,w,b)
		# print(w,b)
		rate = test(w,b,test_data)
		if rate >= rate0:
			rate0 = rate
			w0 = w
			b0 = b
			print(‘Until now, the best result of the accuracy on test data is ‘+str(rate))
			print(‘with w=‘+str(w0)+‘ b=‘+str(b0))
			print(‘---------------------------------------------‘)
	# 在选定最优的学习率后，作图
	plot_classify(w0,b0,x,rate0)
	plt.show()