课堂笔记 - 深度学习 Deep Learning (5) Lab1

用python实作PLA (直接把训练资料写进程式里)

昨天才发现之前就交出去的作业code不小心写错一个地方.... 希望扣分不要扣太重...

import numpy as npimport matplotlib.pyplot as pltdataset = np.array([    [1, 0, 1],    [1, 3, -1],    [2, -6, 1],    [-1, -3, 1],    [-5, 5, -1],    [5, 2, 1],    [-2, 2, -1],    [-7, 2, -1],    [4, -4, 1],    [-5, -1, -1]])num = 10# 切割dataset的数据x1 = dataset[:, 0]x2 = dataset[:, 1]y = dataset[:, 2]def pla_with_data():    # 初始值 >> w=[0,0] b=0    w = np.zeros((2, 1))    dot = 0    b = 0    flag = 1    for k in range(100):   # 限制无穷迴圈 >> 次数设定100次        flag = 1        for i in range(num):     # 看每个点是否为正确            dot = x1[i]*int(w[0])+x2[i]*int(w[1])   # 将一个点的座标带入 跟w作内积            if sign(dot, b) != y[i]:  # 与参考资料y不相符 >> 线划分错误                flag = 0                w[0] += y[i] * x1[i]    # 矫正 w = w + y*x                w[1] += y[i] * x2[i]                b = b + y[i]            # 矫正 b = b + y                # print(w, b)            else:                continue  # 与参考资料y相符 >> 下一个点        if flag == 1:            break  # 全部的点都与参考资料y相符 >> 划分完成    return w, bdef sign(dot, b):    if dot+b >= 0:        return 1    else:        return -1def draw(x1, x2, y):    # 製作figure    fig = plt.figure()    # 图表的设定    ax = fig.add_subplot(1, 1, 1)    # 散布图    for i in range(num):        if y[i] == 1:            ax.scatter(x1[i], x2[i], color='red')        else:            ax.scatter(x1[i], x2[i], color='black')    # data    x = [2, -5, -2]    y = [-4, 1, -2]    ax.scatter(x, y, c='g', marker="x")    plt.show()prex1 = [2, -5, -2]prex2 = [-4, 1, -2]w, b = pla_with_data()for i in range(3):    pre = np.sign((prex1[i]*w[0]+prex2[i]*w[1])+b)    print('predict example %s = %s' % (i+1, pre))print('w1 = %s , w2 = %s , b = %s' % (w[0], w[1], b))draw(x1, x2, y)

该写的注解都在上面了，下面是结果：

github连结：
https://github.com/Minimindy/PLA-numpy-only-/tree/main