课堂笔记 - 深度学习 Deep Learning (6) Lab2

使用读档方式用python实作PLA

Lab2作业需求：

基本上和Lab大同小异，唯一要改的地方就是读取档案的部分：

from matplotlib import markersimport numpy as npimport matplotlib.pyplot as plt# 分割资料def getDataSet(filename):    dataSet = open(filename, 'r')    dataSet = dataSet.readlines()    num = len(dataSet)    x1 = np.zeros((num, 1))    x2 = np.zeros((num, 1))    y = np.zeros((num, 1))    for i in range(num):        data = dataSet[i].strip().split(",")        x1[i] = float(data[0])        x2[i] = float(data[1])        y[i] = float(data[2])    return num, x1, x2, ydef pla_with_data(num, x1, x2, y):    # 初始值 >> w=[0,0] b=0    w = np.zeros((2, 1))    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 -1# 画图def draw(x1, x2, y, prex1, prex2):    # 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')    for i in range(prenum):        ax.scatter(prex1[i], prex2[i], color='green', marker="x")    plt.show()# 先读取训练资料filename = r"Iris_training.txt"num, x1, x2, y = getDataSet(filename)# 把资料带入模型w, b = pla_with_data(num, x1, x2, y)# 再读取要预测的资料filename = r"Iris_test.txt"prenum, prex1, prex2, prey = getDataSet(filename)# 输出预测结果predict = 0for i in range(prenum):    pre = np.sign((prex1[i]*w[0]+prex2[i]*w[1])+b)    if pre != prey[i]:        predict += 1    print('predict example %s = %s' % (i+1, pre))print('error = %s / %s ' % (predict, prenum))print('w1 = %s , w2 = %s , b = %s' % (w[0], w[1], b))draw(x1, x2, y, prex1, prex2)

画图真的是弱项...另一半是因为偷懒 :)

结果图：

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