原始感知机算法实现

import numpy as np
from matplotlib import pyplot as plt


# S1-->随机生成训练集并标注

# 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

# S2-->原始感知机模型的训练及做图

# 感知机模型:f(x) = sign(w*x+b)
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，更新 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:
        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))


# S3-->随机生成测试集，并测试模型

# 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


def plot_n_rate(rate_l):
    # plot n-rate
    n_l = sorted([float(x) for x in rate_l.keys()])
    y = [float(rate_l[n_l[i]]) for i in range(len(n_l))]
    print(n_l, '\n', y)
    plt.plot(n_l, y, 'ro-')
    plt.title("n-accuracy")
    plt.show()



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
    # rate_l 记录学习率的更新
    rate_l = {}

    # 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, 1000):
        n = i
        w, b = train(MA, w, b)
        # print(w,b)
        rate = test(w, b, test_data)
        if rate >= rate0:
            rate_l[n] = rate
            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("n=", n)
            print('---------------------------------------------')

    # 在选定最优的学习率后，作图
    plot_classify(w0, b0, x, rate0)
    plt.show()

    # 作出学习率——准确率的图
    plot_n_rate(rate_l)