逻辑回归分类python实现模板

算法的思路我就不说了，我就提供一个万能模板，适用于任何纬度数据集。
虽然代码类似于梯度下降，但他是个分类算法
定义sigmoid函数

def sigmoid(x):
    return 1/(1+np.exp(-x))

进行逻辑回归的参数设置以及迭代

def weights(x,y,alpha,thershold):
    #初始化参数
    m,n = x_train.shape
    theta = np.random.rand(n)  #参数
    cnt = 0 # 迭代次数
    max_iter = 50000
    #开始迭代
    while cnt < max_iter:
        cnt += 1
        diff = np.full(n,0)
        for i in range(m):
            diff = (y[i]-sigmoid(theta.T @ x[i]))*x[i]
            theta = theta + alpha * diff
        if(abs(diff)<thershold).all():
            break
    return theta

预测函数

def predict(x_test,theta):
    if sigmoid(theta.T @ x_test)>0.5:
        return 1
    else:return 0

调用函数

x_train = np.array([[1,2.697,6.254],
                   [1,1.872,2.014],
                   [1,2.312,0.812],
                   [1,1.983,4.990],
                   [1,0.932,3.920],
                   [1,1.321,5.583],
                   [1,2.215,1.560],
                   [1,1.659,2.932],
                   [1,0.865,7.362],
                   [1,1.685,4.763],
                   [1,1.786,2.523]])
y_train = np.array([1,0,0,1,0,1,0,0,1,0,1])
alpha = 0.001  # 学习率
thershold = 0.01  # 指定一个阈值，用于检查两次误差
print(weights(x_train,y_train,alpha,thershold))