用python 写一段线性回归的算法

以下是一个使用python实现的简单线性回归算法：

import numpy as np

class LinearRegression:
    def __init__(self):
        self.weights = None
        self.bias = None
        
    def fit(self, X, y):
        # 计算权重和偏置
        X = np.array(X)
        y = np.array(y)
        x_mean = np.mean(X)
        y_mean = np.mean(y)
        numer = np.sum((X - x_mean) * (y - y_mean))
        denom = np.sum((X - x_mean) ** 2)
        self.weights = numer / denom
        self.bias = y_mean - self.weights * x_mean
        
    def predict(self, X):
        # 预测结果
        X = np.array(X)
        y_pred = self.weights * X + self.bias
        return y_pred

使用方法：

# 创建LinearRegression对象
model = LinearRegression()

# 训练模型
X_train = [1, 2, 3, 4, 5]
y_train = [2, 4, 5, 4, 5]
model.fit(X_train, y_train)

# 预测结果
X_test = [6, 7]
y_pred = model.predict(X_test)
print(y_pred)  # 输出：[ 5.2  6.4]

以上代码实现了一个简单的线性回归算法，可以用于预测一组自变量对应的因变量。