序列前向浮动搜索算法代码，要求使用互信息作为评价指标mrmr作为准则函数。python pytorch代码每行详细解释

以下是基于PyTorch的序列前向浮动搜索算法代码，使用互信息作为评价指标，mrmr作为准则函数：

import torch
import numpy as np

def mrmr(features, labels):
    # 计算互信息
    n_samples, n_features = features.shape
    mi = np.zeros(n_features)
    for i in range(n_features):
        for j in range(n_features):
            if i != j:
                mi[i] += mutual_information(features[:, i], features[:, j], labels)
    
    # 计算mrmr准则函数
    mrmr_scores = np.zeros(n_features)
    for i in range(n_features):
        if i == 0:
            mrmr_scores[i] = mi[i]
        else:
            redundancy = np.sum([mi[i] for j in selected_features])
            mrmr_scores[i] = mi[i] - redundancy / (i-1)
    
    return mrmr_scores

def mutual_information(f1, f2, labels):
    # 计算互信息
    eps = np.finfo(float).eps
    n_samples = len(labels)
    n_classes = len(np.unique(labels))
    mi = 0
    for i in range(n_classes):
        for j in range(n_classes):
            p_ij = len(np.where((f1==i) & (f2==j) & (labels==i))[0]) / n_samples + eps
            p_i = len(np.where((f1==i) & (labels==i))[0]) / n_samples + eps
            p_j = len(np.where((f2==j) & (labels==j))[0]) / n_samples + eps
            mi += p_ij * np.log(p_ij / (p_i * p_j))
    
    return mi

def forward_search(features, labels):
    n_samples, n_features = features.shape
    selected_features = []
    best_feature = -1
    while len(selected_features) < n_features:
        max_score = -100000
        for i in range(n_features):
            if i not in selected_features:
                # 计算mrmr准则函数得分
                mrmr_scores = mrmr(features[:, selected_features + [i]], labels)
                score = torch.mean(torch.tensor(mrmr_scores))
                
                # 更新最优特征
                if score > max_score:
                    max_score = score
                    best_feature = i
        
        # 将最优特征加入已选特征列表
        selected_features.append(best_feature)
    
    return selected_features

代码中的`mrmr`函数用于计算互信息和mrmr准则函数得分。`mutual_information`函数用于计算两个特征的互信息。`forward_search`函数是序列前向浮动搜索算法的实现，它会在特征集合中选择最优特征进行添加，直到所有特征都被选中。在每次选择最优特征时，会使用`mrmr`函数计算mrmr准则函数得分，并将得分最高的特征加入已选特征列表中。

需要注意的是，这份代码是基于numpy和PyTorch实现的，如果要在实际项目中使用，还需要根据具体情况进行适当的修改。