mrmr算法python代码

MRMR算法（最大相关最小冗余）是一种特征选择算法，用于从给定的特征集中选择最具有代表性和相关性的特征子集。下面是一个简单的MRMR算法的Python代码示例：

import numpy as np
from sklearn.feature_selection import mutual_info_regression

def mrmr(X, y, k):
    n_features = X.shape[1]
    selected_features = []
    remaining_features = list(range(n_features))
    
    # 计算第一个特征与目标变量的互信息
    mi = mutual_info_regression(X[:, [0]], y)[0]
    selected_features.append(0)
    remaining_features.remove(0)
    
    for _ in range(k-1):
        max_mrmr = -np.inf
        best_feature = None
        
        for feature in remaining_features:
            # 计算当前特征与已选特征的互信息
            mrmr_score = mi - mutual_info_regression(X[:, [feature]], X[:, selected_features]).sum()
            
            if mrmr_score > max_mrmr:
                max_mrmr = mrmr_score
                best_feature = feature
        
        # 更新互信息
        mi -= mutual_info_regression(X[:, [best_feature]], y)[0]
        
        selected_features.append(best_feature)
        remaining_features.remove(best_feature)
    
    return selected_features

# 示例用法
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([10, 20, 30])
k = 2

selected_features = mrmr(X, y, k)
print("Selected features:", selected_features)

这段代码实现了一个简单的MRMR算法，输入参数包括特征矩阵X、目标变量y和要选择的特征数量k。输出为选择的特征索引列表。在示例中，特征矩阵X是一个3x3的矩阵，目标变量y是一个长度为3的向量，要选择2个特征。运行结果会输出选择的特征索引列表。