pca impl

unsupervised_class2/pca_impl.py

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+# 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 util import getKaggleMNIST
+
+# get the data
+Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST()
+
+# decompose covariance
+covX = np.cov(Xtrain.T)
+lambdas, Q = np.linalg.eigh(covX)
+
+
+# lambdas are sorted from smallest --> largest
+# some may be slightly negative due to precision
+idx = np.argsort(-lambdas)
+lambdas = lambdas[idx] # sort in proper order
+lambdas = np.maximum(lambdas, 0) # get rid of negatives
+Q = Q[:,idx]
+
+
+# plot the first 2 columns of Z
+Z = Xtrain.dot(Q)
+plt.scatter(Z[:,0], Z[:,1], s=100, c=Ytrain, alpha=0.3)
+plt.show()
+
+
+# plot variances
+plt.plot(lambdas)
+plt.title("Variance of each component")
+plt.show()
+
+# cumulative variance
+plt.plot(np.cumsum(lambdas))
+plt.title("Cumulative variance")
+plt.show()