python使用逻辑回归代码

以下是使用Python实现逻辑回归的代码示例：

import numpy as np
import matplotlib.pyplot as plt

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

def logistic_regression(x, label, learning_rate, num_iterations):
    m, n = x.shape
    x = np.hstack((np.ones((m, 1)), x))
    omega = np.zeros(n + 1)

    for i in range(num_iterations):
        z = np.dot(x, omega)
        h = sigmoid(z)
        gradient = np.dot(x.T, (h - label)) / m
        omega -= learning_rate * gradient

    return omega

# 示例数据
x = np.array([[2, 3], [4, 1], [6, 7], [8, 5]])
label = np.array([0, 0, 1, 1])

# 调用逻辑回归函数
omega = logistic_regression(x, label, learning_rate=0.1, num_iterations=1000)

# 画出决策边界
x1 = np.linspace(0, 10, 100)
y1 = (-omega - omega * x1) / omega
plt.scatter(x[:, 0], x[:, 1], c=label)
plt.plot(x1, y1)
plt.show()

这段代码实现了逻辑回归算法，其中包括sigmoid函数的定义、梯度下降法的实现以及画出决策边界的代码[^1]。