有谁能帮助我如何集成这个python代码到matlab,我想在matlab中运行这个python程序…
#Load dependencies import pandas as pd import numpy as np from sklearn。预处理import StandardScaler from matplotlibPyplot作为PLT来自matplotlib。Cm从matplotlib导入register_cmap。mlab import PCA from learning .decomposition import PCA
从scipy import stats #from wpca import PCA from sklearn.decomposition import PCA as sklearnPCA import seaborn
#加载电影名称和电影评级movies = pd.read_csv('movies.csv') ratings = pd.read_csv('ratings.csv') ratings. pngdrop(['timestamp'], axis=1, inplace=True) # def replace_name(x): return movies[movies['movieId']==x].title。值[0]#评级。# M = ratings.movieId.map(replace_name) # M = ratings.movieId.map(replace_name)数据透视表(index=['userId'], columns=['movieId'], values='rating') m = m .shape #posesall = pd.read_csv('FileName_Poses.csv')#Step 2:协方差矩阵和特征分解mean_vec = np。cov_mat = (X_std - mean_vec). t。点((X_std - mean_vec)) / (X_std.shape[0] 1)打印(协方差矩阵\ n % s的% cov_mat)打印(NumPy协方差矩阵:\ n % s的% np.cov (X_std.T)) #对协方差矩阵进行eigendecomposition cov_mat = np.cov (X_std.T) eig_vals eig_vecs = np.linalg.eig (cov_mat)打印(特征向量\ n % s的% eig_vecs)打印(‘\ nEigenvalues \ n % s % eig_vals) # 3步:eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))] print(' eigenvalues in descending order:') for i in eig_pairs:print(i[0]) pca = pca (n_components = 93) All_poses_pca = pca.fit_transform(movies)方差= (pca.explained_variance_ratio_)
#解释方差pca = pca ().fit(X_std) plt.plot(np.cumsum(pca.explained_variance_ratio_)) plt。标题(“小石子阴谋”)plt。xlabel('主成分数量')ylabel('累计解释方差')