用python语言自编程实现逻辑回归算法

好的，下面是用Python语言自编程实现逻辑回归算法的步骤：

步骤1：导入必要的库

import numpy as np
import pandas as pd

步骤2：定义sigmoid函数

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

步骤3：定义代价函数

def costFunction(X, y, theta):
    m = len(y)
    h = sigmoid(X @ theta)
    J = (-1/m) * (y.T @ np.log(h) + (1 - y).T @ np.log(1 - h))
    return J

步骤4：定义梯度下降函数

def gradientDescent(X, y, theta, alpha, num_iters):
    m = len(y)
    J_history = np.zeros((num_iters, 1))
    for i in range(num_iters):
        h = sigmoid(X @ theta)
        theta = theta - (alpha/m) * (X.T @ (h - y))
        J_history[i] = costFunction(X, y, theta)
    return (theta, J_history)

步骤5：读取数据并进行预处理

data = pd.read_csv('data.csv')
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
X = np.c_[np.ones((X.shape[0],1)), X]
y = y[:, np.newaxis]
theta = np.zeros((X.shape[1], 1))

步骤6：运行梯度下降算法

alpha = 0.01
num_iters = 1500
theta, J_history = gradientDescent(X, y, theta, alpha, num_iters)

最后可以通过J_history查看代价函数的变化情况，也可以对新的数据进行预测。