update urls unsupervised

Author: lazyprogrammer

unsupervised_class/books.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import networkx as nx
 import nltk

unsupervised_class/choose_k.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import numpy as np
 import matplotlib.pyplot as plt

unsupervised_class/gmm.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import numpy as np
 import matplotlib.pyplot as plt
@@ -29,6 +30,11 @@ def gmm(X, K, max_iter=20, smoothing=10e-3):
             for n in xrange(N):
                 R[n,k] = weighted_pdfs[n,k] / weighted_pdfs[n,:].sum()
 
+        # a faster way to do step 1: "vectorization"
+        # for k in xrange(K):
+        #     weighted_pdfs[:,k] = pi[k]*multivariate_normal.pdf(X, M[k], C[k])
+        # R = weighted_pdfs / weighted_pdfs.sum(axis=1, keepdims=True)
+
         # step 2: recalculate params
         for k in xrange(K):
             Nk = R[:,k].sum()

unsupervised_class/hcluster.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import numpy as np
 import matplotlib.pyplot as plt

unsupervised_class/kmeans.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import numpy as np
 import matplotlib.pyplot as plt
@@ -25,7 +26,8 @@ def cost(X, R, M):
 def plot_k_means(X, K, max_iter=20, beta=1.0, show_plots=True):
     N, D = X.shape
     M = np.zeros((K, D))
-    R = np.zeros((N, K))
+    # R = np.zeros((N, K))
+    exponents = np.empty((N, K))
 
     # initialize M to random
     for k in xrange(K):
@@ -37,7 +39,11 @@ def plot_k_means(X, K, max_iter=20, beta=1.0, show_plots=True):
         # is this inefficient?
         for k in xrange(K):
             for n in xrange(N):
-                R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in xrange(K) )
+                # R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in xrange(K) )
+                exponents[n,k] = np.exp(-beta*d(M[k], X[n]))
+
+        R = exponents / exponents.sum(axis=1, keepdims=True)
+        # assert(np.abs(R - R2).sum() < 10e-10)
 
         # step 2: recalculate means
         for k in xrange(K):

unsupervised_class/kmeans_fail.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import numpy as np
 from kmeans import plot_k_means

unsupervised_class/kmeans_mnist.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 
 # data is from https://www.kaggle.com/c/digit-recognizer

unsupervised_class/kmeans_visualize.py

@@ -1,3 +1,4 @@
+# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python
 # https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python
 import numpy as np
 import matplotlib.pyplot as plt