写一个地理空间回归算法程序

下面是一个用 Python 实现的简单的地理空间回归算法：

import numpy as np
import pandas as pd
from sklearn import linear_model

def spatial_regression(X, y, spatial_weights):
    """
    Perform a spatial regression
    
    Parameters:
    - X (array-like, shape (n_samples, n_features)): independent variables
    - y (array-like, shape (n_samples,)): dependent variable
    - spatial_weights (array-like, shape (n_samples, n_samples)): spatial weights matrix
    
    Returns:
    - coef_ (array-like, shape (n_features,)): coefficients of the regression
    - intercept_ (float): intercept of the regression
    """
    n_samples = X.shape[0]
    n_features = X.shape[1]
    
    # Add a spatial lag term to X
    X_with_lag = np.zeros((n_samples, n_features + 1))
    X_with_lag[:, :-1] = X
    X_with_lag[:, -1] = np.dot(spatial_weights, y)
    
    # Perform regression
    reg = linear_model.LinearRegression()
    reg.fit(X_with_lag, y)
    
    return reg.coef_, reg.intercept_

这个算法接受三个参数：独立变量 `X`，因变量 `y` 和空间权重矩阵 `spatial_weights`。它返回回归的系数和截路。

示例使用：

# Example data
n_samples = 100
n_features = 5
X = np.random.randn(n_samples, n_features)
y = np.random.randn(n_samples,)
spatial_weights = np.random.randn(n_samples, n_samples)

# Perform regression
coef_, intercept_ = spatial_regression(X, y, spatial_weights)

请注意，这只是一个简单的示例，在实际应用中可能需要更多的处理和验证。