tsne visualization

Author: lazyprogrammer

unsupervised_class2/tsne_visualization.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 mpl_toolkits.mplot3d import Axes3D
+from sklearn.manifold import TSNE
+
+
+if __name__ == '__main__':
+  # define the centers of each Gaussian cloud
+  centers = np.array([
+    [ 1,  1,  1],
+    [ 1,  1, -1],
+    [ 1, -1,  1],
+    [ 1, -1, -1],
+    [-1,  1,  1],
+    [-1,  1, -1],
+    [-1, -1,  1],
+    [-1, -1, -1],
+  ])*3
+
+  # create the clouds, Gaussian samples centered at
+  # each of the centers we just made
+  data = []
+  pts_per_cloud = 100
+  for c in centers:
+    cloud = np.random.randn(pts_per_cloud, 3) + c
+    data.append(cloud)
+  data = np.concatenate(data)
+
+  # visualize the clouds in 3-D
+  # add colors / labels so we can track where the points go
+  colors = np.array([[i]*pts_per_cloud for i in range(len(centers))]).flatten()
+  fig = plt.figure()
+  ax = fig.add_subplot(111, projection='3d')
+  ax.scatter(data[:,0], data[:,1], data[:,2], c=colors)
+  plt.show()
+
+
+  # perform dimensionality reduction
+  tsne = TSNE()
+  transformed = tsne.fit_transform(data)
+
+  # visualize the clouds in 2-D
+  plt.scatter(transformed[:,0], transformed[:,1], c=colors)
+  plt.show()
+
+
+