""" Principal components analysis,PCA,COV,eig,eig vector Parameters¶ : path：数据集的存放路径 dim : 数据降维后的维度数 Attribute: means_data : 原数据- 原数据均值化 m : 数据集的行数 n ：数据集的列数 cov_matrix : 协方差矩阵 eig_vectors : 协方差矩阵的特征向量 eig_value : 协方差矩阵求得的特征值 cum_P : 排序后的特征值, 累积百分比计算 Method: PCA.dataset():数据集导入, return：means_data,array(m,n) PCA.cov_matrix:协方差矩阵计算, retrn:cov_matrix,array(n,n) PCA.eig_vector:特征值和特征向量计算, return:(eig_value, eig_vectors),(array(1xn),array(n,n)) PCA.pc:降维后的数据集计算, return:data_rescaled,array(m,dim), defaut:dim=2

PCA_test.py
PCA_test.txt

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