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annotate tests/test_logistic_regression.py @ 29:cf7584f0a29f

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test linear regression
author Jeff Hammel <k0scist@gmail.com>
date Mon, 04 Sep 2017 12:01:57 -0700
parents 77f68c241b37
children fa7a51df0d90
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1 #!/usr/bin/env python
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3 """
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4 test logistic regression
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5 """
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7 import numpy as np
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8 import os
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9 import unittest
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10 from tvii import logistic_regression
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13 class LogisticRegresionTests(unittest.TestCase):
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15 def test_cost(self):
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16 """test cost function"""
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18 w, b, X, Y = (np.array([[1],[2]]),
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19 2,
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20 np.array([[1,2],[3,4]]),
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21 np.array([[1,0]]))
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23 expected_cost = 6.000064773192205
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24 cost = logistic_regression.cost_function(w, b, X, Y)
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25 assert abs(cost - expected_cost) < 1e-6
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27 def test_propagate(self):
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28 """test canned logistic regression example"""
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30 # sample variables
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31 w = np.array([[1],[2]])
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32 b = 2
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33 X = np.array([[1,2],[3,4]])
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34 Y = np.array([[1,0]])
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36 # calculate gradient and cost
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37 grads, cost = logistic_regression.propagate(w, b, X, Y)
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39 # compare to expected,
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40 dw_expected = np.array([[ 0.99993216], [ 1.99980262]])
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41 db_expected = 0.499935230625
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42 cost_expected = 6.000064773192205
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44 self.assertAlmostEqual(cost_expected, cost)
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45 self.assertAlmostEqual(grads['db'], db_expected)
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46 assert grads['dw'].shape == dw_expected.shape
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47 for a, b in zip(grads['dw'].flatten(),
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48 dw_expected.flatten()):
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49 self.assertAlmostEqual(a, b)
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52 if __name__ == '__main__':
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53 unittest.main()