compare pca svd

unsupervised_class2/compare_pca_svd.py

@@ -0,0 +1,31 @@
+# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python
+# https://www.udemy.com/unsupervised-deep-learning-in-python
+from __future__ import print_function, division
+from builtins import range, input
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.decomposition import PCA, TruncatedSVD
+from util import getKaggleMNIST
+
+
+X, Y, _, _ = getKaggleMNIST()
+m = X.mean(axis=0)
+s = X.std(axis=0)
+np.place(s, s == 0, 1)
+X = (X - m) / s
+
+pca = PCA()
+svd = TruncatedSVD()
+
+Z1 = pca.fit_transform(X)
+Z2 = svd.fit_transform(X)
+
+plt.subplot(1,2,1)
+plt.scatter(Z1[:,0], Z1[:,1], c=Y)
+plt.subplot(1,2,2)
+plt.scatter(Z2[:,0], Z2[:,1], c=Y)
+plt.show()