初始提交

classification_model/WaveSelect/Pca.py

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+"""
+    -*- coding: utf-8 -*-
+    @Time   :2022/04/12 17:10
+    @Author : Pengyou FU
+    @blogs  : https://blog.csdn.net/Echo_Code?spm=1000.2115.3001.5343
+    @github : https://github.com/FuSiry/OpenSA
+    @WeChat : Fu_siry
+    @License：Apache-2.0 license
+
+"""
+
+from sklearn.decomposition import PCA
+
+def Pca(X, nums=20):
+    """
+       :param X: raw spectrum data, shape (n_samples, n_features)
+       :param nums: Number of principal components retained
+       :return: X_reduction：Spectral data after dimensionality reduction
+    """
+    pca = PCA(n_components=nums)  # 保留的特征数码
+    pca.fit(X)
+    X_reduction = pca.transform(X)
+
+    return X_reduction