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annotate tests/test_logistic_regression.py @ 41:8c1648921827

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[activation functions] placeholder
author Jeff Hammel <k0scist@gmail.com>
date Mon, 04 Sep 2017 14:31:25 -0700
parents 0f29b02f4806
children
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1 #!/usr/bin/env python
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2
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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 compare_arrays(self, a, b):
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16 assert a.shape == b.shape
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17 for x, y in zip(a.flatten(),
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18 b.flatten()):
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19 self.assertAlmostEqual(x, y)
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22 def test_cost(self):
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23 """test cost function"""
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24
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25 w, b, X, Y = (np.array([[1],[2]]),
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26 2,
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27 np.array([[1,2],[3,4]]),
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28 np.array([[1,0]]))
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30 expected_cost = 6.000064773192205
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31 cost = logistic_regression.cost_function(w, b, X, Y)
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32 assert abs(cost - expected_cost) < 1e-6
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34 def test_propagate(self):
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35 """test canned logistic regression example"""
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36
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37 # sample variables
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38 w = np.array([[1],[2]])
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39 b = 2
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40 X = np.array([[1,2],[3,4]])
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41 Y = np.array([[1,0]])
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42
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43 # calculate gradient and cost
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44 grads, cost = logistic_regression.propagate(w, b, X, Y)
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45
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46 # compare to expected,
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47 dw_expected = np.array([[ 0.99993216], [ 1.99980262]])
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48 db_expected = 0.499935230625
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49 cost_expected = 6.000064773192205
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51 self.assertAlmostEqual(cost_expected, cost)
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52 self.assertAlmostEqual(grads['db'], db_expected)
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53 assert grads['dw'].shape == dw_expected.shape
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54 for a, b in zip(grads['dw'].flatten(),
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55 dw_expected.flatten()):
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56 self.assertAlmostEqual(a, b)
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58 def test_optimize(self):
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59 """test gradient descent method"""
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60
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61 # test examples
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62 w, b, X, Y = (np.array([[1],[2]]), 2, np.array([[1,2],[3,4]]), np.array([[1,0]]))
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64 params, grads, costs = logistic_regression.optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)
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65
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66 # expected output
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67 w_expected = np.array([[0.1124579 ],
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68 [0.23106775]])
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69 dw_expected = np.array([[ 0.90158428],
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70 [ 1.76250842]])
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71 b_expected = 1.55930492484
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72 db_expected = 0.430462071679
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74 # compare output
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75 self.assertAlmostEqual(params['b'], b_expected)
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76 self.assertAlmostEqual(grads['db'], db_expected)
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77 self.compare_arrays(w_expected, params['w'])
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78 self.compare_arrays(dw_expected, grads['dw'])
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80 def test_predict(self):
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82 w, b, X, Y = (np.array([[1],[2]]), 2, np.array([[1,2],[3,4]]), np.array([[1,0]]))
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84 predictions = logistic_regression.predict(w, b, X)
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86 assert predictions[0][0] == 1
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87 assert predictions[0][1] == 1
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89 if __name__ == '__main__':
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90 unittest.main()