自学教程：详解如何用Python实现感知器算法

'''20210610 Julyer 感知器'''import numpy as npimport matplotlib.pyplot as pltdef get_zgxl(xn, a):    '''    获取增广向量    :param x: 数组    :param a: 1或-1    :return:    '''    temp = []    if a == 1:        xn.append(1)    if a == -1:        for i in range(len(xn)):            temp.append(xn[i]*(-1))        temp.append(-1)        xn = temp    # print('xn:'+ str(np.array(x).reshape(-1, 1)))    return np.array(xn).reshape(-1, 1)def calculate_w(w, xn):    '''    已知xn和初始值，计算w    :param w: 列向量 --> wT：行向量    :param xn: 列向量    :return:    '''    # wT = w.reshape(1, -1)  # 列向量转变为行向量，改变w    wT = w.T   # 列向量转变为行向量，不改变w    wTx = np.dot(wT, xn).reshape(-1)  # 行向量乘以列向量, 维度降为1。    #wTx = wT@xn  # 行向量乘以列向量    if wTx > 0:        w_value = w    else:        w_value = np.add(w, xn)    # print("w_update的shape" + str(w_update.shape))    #print("wTx:" + str(wTx))    return w_value, wTx     # w_value为列向量, wTx为一个数def fit_one(w1, x1, x2, x3, x4):    '''    完成一轮迭代，遍历一次数据，更新到w5。    :param w1: 初始值    :param x1:    :param x2:    :param x3:    :param x4:    :return: 返回w5和wTx的列表。    '''    wTx_list = []    update_w = w1    for i in range(0, len(x_data)): #len计算样本个数，通过循环更新w        update_w, wTx = calculate_w(update_w, x_data[i])        wTx_list.append(wTx)    #print(wTx_list)    return update_w, wTx_listdef draw_plot(class1, class2, update_w):    plt.figure()    x_coordinate = []    y_coordinate = []    for i in range(len(class1)):        x_coordinate.append(class1[i][0])        y_coordinate.append(class1[i][1])    plt.scatter(x_coordinate, y_coordinate, color='orange', label='class1')    x_coordinate = []    y_coordinate = []    for i in range(len(class2)):        x_coordinate.append(class2[i][0])        y_coordinate.append(class2[i][1])    plt.scatter(x_coordinate, y_coordinate, color='green', label='class2')    w_reshape = update_w.reshape(-1)    #print    x = np.linspace(0, 2, 5)    if w_reshape[1] == 0:        plt.axvline(x = (-1) * w_reshape[2]/w_reshape[0])    else:        plt.plot(x, (x*w_reshape[0]*(-1) + w_reshape[2]*(-1))/w_reshape[1])    plt.title('result of perception')    plt.xlabel('x1')    plt.ylabel('x2')    plt.legend()    plt.show()if __name__ == '__main__':    x1 = [0, 0]    x2 = [0, 1]    x3 = [1, 0]    x4 = [1, 1]    class1 = [x1, x2]    class2 = [x3, x4]    x1 = get_zgxl(x1, 1)    x2 = get_zgxl(x2, 1)    x3 = get_zgxl(x3, -1)    x4 = get_zgxl(x4, -1)    x_data = [x1, x2, x3, x4]    # print(x_data)    w1 = np.zeros((3, 1))  # 初始值w1为列向量    #print('w1:' + str(w1) + '/n')    update_w = w1    update_w, wTx_list = fit_one(update_w, x1, x2, x3, x4)    count = 0    iter_number = 0    for wTx in wTx_list:        if wTx > 0:            count += 1        if count < 4:            update_w, wTx_list = fit_one(update_w, x1, x2, x3, x4)            iter_number += 1        else:            break    print('迭代次数为：' + str(iter_number))    print('迭代终止时的w：'+'/n' + str(update_w))    #print(wTx_list)    draw_plot(class1, class2, update_w)