mrmr特征选择 python代码

以下是使用Python进行mRMR特征选择的示例代码：

import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectKBest, mutual_info_classif

def mrmr_feature_selection(X, y, k):
    # 计算互信息
    mi = mutual_info_classif(X, y)
    
    # 初始化特征索引列表和已选择特征集合
    feature_indices = list(range(X.shape[1]))
    selected_features = []
    
    # 选择第一个特征（最大互信息）
    max_mi_index = np.argmax(mi)
    selected_features.append(feature_indices[max_mi_index])
    feature_indices.pop(max_mi_index)
    
    # 依次选择剩余的特征
    for _ in range(1, k):
        max_mrmr = float('-inf')
        selected_feature = None
        
        # 计算每个特征的mRMR值
        for feature in feature_indices:
            mRMR = mi[feature] - np.mean([mi[feature] for feature in selected_features])
            if mRMR > max_mrmr:
                max_mrmr = mRMR
                selected_feature = feature
        
        # 将mRMR值最大的特征添加到已选择特征集合中
        selected_features.append(selected_feature)
        feature_indices.remove(selected_feature)
    
    # 返回选择的特征
    return X[:, selected_features]

# 示例用法
# 加载数据集
data = pd.read_csv('data.csv')
X = data.drop('target', axis=1).values
y = data['target'].values

# 进行mRMR特征选择，选择4个特征
selected_X = mrmr_feature_selection(X, y, k=4)

# 打印选择的特征
print(selected_X)

请确保安装所需的依赖库（如pandas和scikit-learn）并将数据集替换为您自己的数据。此代码将根据互信息计算mRMR值，并选择具有最高mRMR值的k个特征。