增加模块；增加主调用命令

Feature_Selection_method/ReliefF.py

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+
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+class ReliefF:
+    def __init__(self, n_neighbors=20, n_features_to_keep=20):
+        """
+        初始化 ReliefF 算法参数。
+        :param n_neighbors: 每个样本的近邻数量。
+        :param n_features_to_keep: 每次保留的特征数量。
+        """
+        self.n_neighbors = n_neighbors
+        self.n_features_to_keep = n_features_to_keep
+        self.feature_scores = None  # 用于存储每个特征的评分
+        self.top_features = None  # 用于存储评分最高的特征索引
+
+    def fit(self, X, y):
+        """
+        根据给定的数据 X 和标签 y 计算特征评分。
+        :param X: 输入特征矩阵。
+        :param y: 类别标签。
+        :return: 返回选择的特征索引。
+        """
+        m, n = X.shape  # m 是样本数，n 是特征数
+
+        self.feature_scores = np.zeros(n)  # 初始化特征评分为 0
+
+        # 寻找每个样本的 n_neighbors 个近邻
+        nbrs = NearestNeighbors(n_neighbors=self.n_neighbors + 1).fit(X)
+        distances, indices = nbrs.kneighbors(X)
+
+        # 遍历每个样本，更新特征评分
+        for i in range(m):
+            y_i = y[i]  # 当前样本的类别标签
+
+            # 初始化同类和异类邻居
+            hit_neighbors = []
+            miss_neighbors = []
+
+            for j in indices[i][1:]:  # indices[i][0] 是样本自身，跳过
+                if y[j] == y_i:
+                    hit_neighbors.append(X[j])
+                else:
+                    miss_neighbors.append(X[j])
+
+            # 更新每个特征的评分
+            for f in range(n):
+                for hit in hit_neighbors:
+                    self.feature_scores[f] -= (X[i, f] - hit[f]) ** 2 / (self.n_neighbors * m)
+                for miss in miss_neighbors:
+                    self.feature_scores[f] += (X[i, f] - miss[f]) ** 2 / (self.n_neighbors * m)
+
+        # 选择评分最高的 n_features_to_keep 个特征的索引
+        self.top_features = np.argsort(self.feature_scores)[-self.n_features_to_keep:]
+
+        return self.top_features  # 返回选择的特征索引
+
+    def fit_transform(self, X, y):
+        """一步完成拟合和转换，返回选择的特征索引。"""
+        return self.fit(X, y)
+
+def multi_scale_relieff_stratified(X, y, segment_size=100, n_subsegments=20, n_features_per_subsegment=5):
+    """
+    分层多尺度特征选择，确保每个波长段都能被覆盖。
+    :param X: 输入特征矩阵。
+    :param y: 类别标签。
+    :param segment_size: 每个波长段的大小。
+    :param n_subsegments: 每个段内的子区域数量。
+    :param n_features_per_subsegment: 每个子区域选择的特征数量。
+    :return: 分层选择的特征索引。
+    """
+    selected_features = []
+
+    # 遍历每个波长段
+    for i in range(0, X.shape[1], segment_size):
+        segment_X = X[:, i:i + segment_size]
+        subsegment_size = segment_size // n_subsegments  # 子区域大小
+
+        # 在每个子区域内进行特征选择
+        for j in range(0, segment_size, subsegment_size):
+            subsegment_X = segment_X[:, j:j + subsegment_size]
+            relief = ReliefF(n_neighbors=10, n_features_to_keep=n_features_per_subsegment)
+            subsegment_selected = relief.fit_transform(subsegment_X, y)
+
+            # 将局部索引转换为全局索引并添加到结果中
+            selected_features.extend(subsegment_selected + i + j)
+
+    # 返回去重后的特征索引
+    return np.unique(selected_features)