手写感知机并进行训练测试

代码部分

# -*- encoding:utf-8 -*-

"""
create by 邪老黄大 on 20220814

"""

import numpy as np
import matplotlib.pyplot as plt

# 1、创建数据集
def createdata():
    samples = np.array([[3, -3], [4, -3], [1, 1], [1, 2]])
    labels = [-1, -1, 1, 1]
    return samples, labels

# 训练感知机模型
class Perceptron:
    def __init__(self, x, y, a=1):
        self.x = x
        self.y = y
        self.w = np.zeros((x.shape[1], 1))  # 初始化权重，w1,w2均为0
        self.b = 0
        self.a = 1  # 学习率
        self.numsamples = self.x.shape[0]
        self.numfeatures = self.x.shape[1]

    def sign(self, w, b, x):
        y = np.dot(x, w) + b
        return int(y)

    def update(self, label_i, data_i):
        tmp = label_i * self.a * data_i
        tmp = tmp.reshape(self.w.shape)
        # 更新w和b
        self.w = tmp + self.w
        self.b = self.b + label_i * self.a

    def train(self):
        isFind = False
        while not isFind:
            count = 0
            for i in range(self.numsamples):
                print("i = ", i)
                tmpY = self.sign(self.w, self.b, self.x[i, :])
                if tmpY * self.y[i] <= 0:  # 如果是一个误分类实例点  ==> y(训练)*y(真) = 不同类则进行更新权值
                    print("tmpY = ", tmpY)
                    print("self.y[i] =",self.y[i])
                    print('误分类点为：', self.x[i, :], '此时的w和b为：', self.w, self.b)
                    print("---------------------------------------------------------------")
                    count += 1
                    t1 = self.y[i]
                    t2 = self.x[i, :]
                    self.update(t1, t2)
            if count == 0:
                print('最终训练得到的w和b为：', self.w, self.b)
                isFind = True
        return self.w, self.b

# 画图描绘
class Picture:
    def __init__(self, data, w, b):
        self.b = b
        self.w = w
        plt.figure(1)
        plt.title('Perceptron Learning Algorithm', size=14)
        plt.xlabel('x0-axis', size=14)
        plt.ylabel('x1-axis', size=14)

        xData = np.linspace(0, 5, 100)
        yData = self.expression(xData)
        plt.plot(xData, yData, color='r', label='sample data')

        plt.scatter(data[0][0], data[0][1], s=50)
        plt.scatter(data[1][0], data[1][1], s=50)
        plt.scatter(data[2][0], data[2][1], s=50, marker='x')
        plt.scatter(data[3][0], data[3][1], s=50, marker='x')
        plt.savefig('2d.png', dpi=75)

    def expression(self, x):
        y = (-self.b - self.w[0] * x) / self.w[1]  # 注意在此，把x0，x1当做两个坐标轴，把x1当做自变量，x2为因变量
        return y

    def Show(self):
        plt.show()

if __name__ == '__main__':
    samples, labels = createdata()
    myperceptron = Perceptron(x=samples, y=labels)
    weights, bias = myperceptron.train()
    Picture = Picture(samples, weights, bias)
    Picture.Show()

效果

i =  0
tmpY =  0
self.y[i] = -1
误分类点为： [ 3 -3] 此时的w和b为： [[0.]
 [0.]] 0
---------------------------------------------------------------
i =  1
i =  2
tmpY =  -1
self.y[i] = 1
误分类点为： [1 1] 此时的w和b为： [[-3.]
 [ 3.]] -1
---------------------------------------------------------------
i =  3
i =  0
i =  1
i =  2
i =  3
最终训练得到的w和b为： [[-2.]
 [ 4.]] 0