Merge pull request #7 from lscheinkman/add_test

Add duty_cycle_metrics_test

Author: lscheinkman

tests/duty_cycle_metrics_test.py

@@ -0,0 +1,87 @@
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2019, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero Public License for more details.
+#
+# You should have received a copy of the GNU Affero Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+
+import unittest
+
+import torch
+
+from nupic.torch.duty_cycle_metrics import \
+     binaryEntropy, maxEntropy
+
+
+class DutyCycleMetricsTest(unittest.TestCase):
+  """
+  Simplistic tests of duty cycle entropy metrics
+  """
+
+  def testBinaryEntropy(self):
+
+    p = torch.tensor([0.1, 0.02, 0.99, 0.5, 0.75, 0.8, 0.3, 0.4, 0.0, 1.0])
+    entropy, entropySum = binaryEntropy(p)
+    self.assertAlmostEqual(entropySum.item(), 5.076676985, places=4)
+    self.assertAlmostEqual(entropySum.item(), entropy.sum(), places=4)
+    self.assertAlmostEqual(entropy[0].item(), 0.468995594, places=4)
+    self.assertAlmostEqual(entropy[1].item(), 0.141440543, places=4)
+    self.assertAlmostEqual(entropy[2].item(), 0.080793136, places=4)
+    self.assertEqual(entropy[8].item(), 0.0)
+    self.assertEqual(entropy[9].item(), 0.0)
+
+    p = torch.tensor([0.25, 0.25, 0.25, 0.25])
+    entropy, entropySum = binaryEntropy(p)
+    self.assertAlmostEqual(entropySum, 3.245112498, places=4)
+    self.assertAlmostEqual(entropySum, entropy.sum(), places=4)
+
+    p = torch.tensor([0.5, 0.5, 0.5, 0.5])
+    entropy, entropySum = binaryEntropy(p)
+    self.assertAlmostEqual(entropySum, 4.0, places=4)
+    self.assertAlmostEqual(entropySum, entropy.sum(), places=4)
+    self.assertAlmostEqual(entropy[0], 1.0, places=4)
+    self.assertAlmostEqual(entropy[1], 1.0, places=4)
+    self.assertAlmostEqual(entropy[2], 1.0, places=4)
+    self.assertAlmostEqual(entropy[3], 1.0, places=4)
+
+
+  def testMaxEntropy(self):
+
+    entropy = maxEntropy(1,1)
+    self.assertAlmostEqual(entropy, 0.0, places=4)
+
+    entropy = maxEntropy(1,0)
+    self.assertAlmostEqual(entropy, 0.0, places=4)
+
+    entropy = maxEntropy(4,1)
+    self.assertAlmostEqual(entropy, 3.245112498, places=4)
+
+    entropy = maxEntropy(4,2)
+    self.assertAlmostEqual(entropy, 4.0, places=4)
+
+    entropy = maxEntropy(100,1)
+    self.assertAlmostEqual(entropy, 8.07931359, places=4)
+
+    entropy = maxEntropy(100,10)
+    self.assertAlmostEqual(entropy, 46.89955936, places=4)
+
+    entropy = maxEntropy(2048, 40)
+    self.assertAlmostEqual(entropy, 284.2634199, places=4)
+
+
+if __name__ == "__main__":
+  unittest.main()

tests/k_winners_test.py

@@ -104,7 +104,7 @@ def testOne(self):
     self.assertEqual(
       (grad_x[indices] == self.gradient.reshape(-1)[indices]).sum(), 4)
     self.assertAlmostEqual(
-      grad_x.sum(), self.gradient.reshape(-1)[indices].sum(), places=4)
+      grad_x.sum().item(), self.gradient.reshape(-1)[indices].sum().item(), places=4)
     self.assertEqual(len(grad_x.nonzero()), 4)