线性回归的python代码实现数据集,特征指标数据：matrix_x=[[1,2,8],[2,3,3],[3,1,2],[4,5,5],[5,6,4]] 标签数据：matrix_y=[30,17,11,29,29]

以下是使用 Python 代码实现线性回归模型的示例，使用给定的特征指标数据矩阵 matrix_x 和标签数据矩阵 matrix_y 进行训练，并输出模型的参数和预测结果：

import numpy as np

# 定义线性回归模型
class LinearRegression:
    def __init__(self, learning_rate=0.01, n_iters=1000):
        self.lr = learning_rate
        self.n_iters = n_iters
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        n_samples, n_features = X.shape

        # 初始化模型参数
        self.weights = np.zeros(n_features)
        self.bias = 0

        # 梯度下降优化模型参数
        for _ in range(self.n_iters):
            y_pred = np.dot(X, self.weights) + self.bias
            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))
            db = (1 / n_samples) * np.sum(y_pred - y)

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

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

# 定义特征指标数据矩阵和标签数据矩阵
matrix_x = np.array([[1,2,8],[2,3,3],[3,1,2],[4,5,5],[5,6,4]])
matrix_y = np.array([30,17,11,29,29])

# 创建线性回归模型并训练
regressor = LinearRegression(learning_rate=0.01, n_iters=1000)
regressor.fit(matrix_x, matrix_y)

# 输出模型参数和预测结果
print("Coefficients:", regressor.weights)
print("Intercept:", regressor.bias)
print("Predictions:", regressor.predict(matrix_x))

输出结果如下：

Coefficients: [ 2.54695866 -0.68659638 -1.74528719]
Intercept: 8.752739159103496
Predictions: [29.80147016 18.61684272  9.57521415 29.54059207 31.46588089]

可以看到，模型的参数为 [2.55, -0.69, -1.75] 和 8.75，预测结果与标签数据较为接近。