Untitled Python Project

import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import PCA from sklearn import preprocessing %matplotlib notebook

data = np.load("time-seriesPCAnew.npy") X = np.column_stack((data[:,1], data[:,2], data[:,3], data[:,4], data[:,5])) pca = PCA(n_components=5) pca.fit(X)

print("explained variance:", pca.explained_variance_)

X_pca = pca.transform(X) print("original shape : ", X.shape) print("transformed shape :", X_pca.shape) # do PCA and plot principal components fig = plt.figure() ax1 = fig.add_subplot(1,1,1) ax1.plot(data[:,0],X_pca[:,1]) ax1.plot(data[:,0],X_pca[:,2]) plt.show()