import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from mpl_toolkits.mplot3d import Axes3D

def fit_and_plot_3d(data, output_path="fit_surface.png"):
    """
    对两个自变量（文字密度、程序）和一个因变量（NED）进行线性拟合并可视化

    参数:
        data: 二维数组或列表，每行格式为 [文字密度, 程序, NED]
        output_path: 图片保存路径
    """
    data = np.array(data)
    X = data[:, :2]  # 自变量（文字密度、程序）
    y = data[:, 2]   # 因变量（NED）

    # 拟合多元线性回归
    model = LinearRegression()
    model.fit(X, y)
    y_pred = model.predict(X)

    # 计算R²
    r2 = r2_score(y, y_pred)
    print(f"✅ 拟合完成，R² = {r2:.4f}")
    print(f"回归系数: {model.coef_}, 截距: {model.intercept_:.4f}")

    # 创建网格点用于绘制拟合面
    x1_range = np.linspace(X[:,0].min(), X[:,0].max(), 50)
    x2_range = np.linspace(X[:,1].min(), X[:,1].max(), 50)
    X1, X2 = np.meshgrid(x1_range, x2_range)
    X_grid = np.c_[X1.ravel(), X2.ravel()]
    Y_pred = model.predict(X_grid).reshape(X1.shape)

    # 绘制3D散点+拟合平面
    fig = plt.figure(figsize=(8, 6))
    ax = fig.add_subplot(111, projection='3d')

    ax.scatter(X[:,0], X[:,1], y, color='blue', label='Data points')
    ax.plot_surface(X1, X2, Y_pred, color='lightcoral', alpha=0.6, label='Fitted surface')

    ax.set_xlabel('Text Density')
    ax.set_ylabel('Program')
    ax.set_zlabel('NED')
    ax.set_title(f'3D Regression Fit (R²={r2:.3f})')

    # 保存图片
    plt.tight_layout()
    plt.savefig(output_path, dpi=300)
    plt.close(fig)
    print(f"📊 图像已保存到: {output_path}")

# 示例
if __name__ == "__main__":
    # 示例数据 [文字密度, 程序, NED]
    sample_data = [
        [0.1, 1, 0.05],
        [0.2, 1, 0.10],
        [0.3, 2, 0.18],
        [0.4, 2, 0.23],
        [0.5, 3, 0.28],
        [0.6, 3, 0.35],
        [0.7, 4, 0.38],
        [0.8, 4, 0.45],
        [0.9, 5, 0.50],
    ]

    fit_and_plot_3d(sample_data, "ned_fit.png")