线性回归——scikit-learn代码实现

最新推荐文章于 2025-06-09 01:35:21 发布

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最新推荐文章于 2025-06-09 01:35:21 发布 · 1k 阅读

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#线性回归 #scikit-learn #算法

最小二乘法代数求解

def least_squares_algebraic(x: np.ndarray, y: np.ndarray):
    """最小二乘法代数求解"""
    n = x.shape[0]
    w1 = (n * sum(x * y) - sum(x) * sum(y)) / (n * sum(x * x) - sum(x) * sum(x))
    w0 = (sum(x * x) * sum(y) - sum(x) * sum(x * y)) / (
        n * sum(x * x) - sum(x) * sum(x)
    )
    return w0, w1
"""绘图"""
w0 = least_squares_algebraic(x, y)[0]
w1 = least_squares_algebraic(x, y)[1]
plt.scatter(x, y)
plt.plot(x_temp, x_temp * w1 + w0, "r")

最小二乘法矩阵求解

def least_squares_matrix(x: np.matrix, y: np.matrix):
    """最小二乘法矩阵求解"""
    w = (x.T * x).I * x.T * y
    return w
"""使用np.hstack方法水平对原 x 数据添加截距项系数 1"""
x_matrix = np.