sklearn调用逻辑回归模块，自编程实现逻辑回归算法复杂的完整代码

好的，以下是一个简单的逻辑回归算法的Python代码实现，使用了sklearn库中的LogisticRegression模块作为参考：

import numpy as np
import matplotlib.pyplot as plt

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

# 定义逻辑回归模型
class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=100):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        # 初始化参数
        m, n = X.shape
        self.weights = np.zeros((n, 1))
        self.bias = 0

        # 梯度下降
        for i in range(self.num_iterations):
            z = np.dot(X, self.weights) + self.bias
            A = sigmoid(z)
            cost = -1/m * np.sum(y*np.log(A) + (1-y)*np.log(1-A))
            dz = A - y
            dw = 1/m * np.dot(X.T, dz)
            db = 1/m * np.sum(dz)

            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

            if i % 1000 == 0:
                print("Cost after iteration %i: %f" % (i, cost))

    def predict(self, X):
        z = np.dot(X, self.weights) + self.bias
        A = sigmoid(z)
        y_pred = np.round(A)
        return y_pred

# 测试
np.random.seed(0)
X = np.random.randn(100, 2)
y = np.random.randint(0, 2, size=(100, 1))

model = LogisticRegression(learning_rate=0.1, num_iterations=10000)
model.fit(X, y)

y_pred = model.predict(X)

plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.show()

这段代码实现了一个简单的逻辑回归算法，包括sigmoid函数、梯度下降、模型拟合和预测等步骤。可以通过调整learning_rate和num_iterations参数来优化模型的性能。