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comparison tests/test_logistic_regression.py @ 31:fa7a51df0d90

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[logistic regression] test gradient descent
author Jeff Hammel <k0scist@gmail.com>
date Mon, 04 Sep 2017 12:37:45 -0700
parents cf7584f0a29f
children 0f29b02f4806
comparison
equal deleted inserted replaced
30:ae0c345ea09d 31:fa7a51df0d90
9 import unittest 9 import unittest
10 from tvii import logistic_regression 10 from tvii import logistic_regression
11 11
12 12
13 class LogisticRegresionTests(unittest.TestCase): 13 class LogisticRegresionTests(unittest.TestCase):
14
15 def compare_arrays(self, a, b):
16 assert a.shape == b.shape
17 for x, y in zip(a.flatten(),
18 b.flatten()):
19 self.assertAlmostEqual(x, y)
20
14 21
15 def test_cost(self): 22 def test_cost(self):
16 """test cost function""" 23 """test cost function"""
17 24
18 w, b, X, Y = (np.array([[1],[2]]), 25 w, b, X, Y = (np.array([[1],[2]]),
46 assert grads['dw'].shape == dw_expected.shape 53 assert grads['dw'].shape == dw_expected.shape
47 for a, b in zip(grads['dw'].flatten(), 54 for a, b in zip(grads['dw'].flatten(),
48 dw_expected.flatten()): 55 dw_expected.flatten()):
49 self.assertAlmostEqual(a, b) 56 self.assertAlmostEqual(a, b)
50 57
58 def test_optimize(self):
59 """test gradient descent method"""
60
61 # test examples
62 w, b, X, Y = np.array([[1],[2]]), 2, np.array([[1,2],[3,4]]), np.array([[1,0]])
63
64 params, grads, costs = logistic_regression.optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)
65
66 # expected output
67 w_expected = np.array([[0.1124579 ],
68 [0.23106775]])
69 dw_expected = np.array([[ 0.90158428],
70 [ 1.76250842]])
71 b_expected = 1.55930492484
72 db_expected = 0.430462071679
73
74 # compare output
75 self.assertAlmostEqual(params['b'], b_expected)
76 self.assertAlmostEqual(grads['db'], db_expected)
77 self.compare_arrays(w_expected, params['w'])
78 self.compare_arrays(dw_expected, grads['dw'])
79
51 80
52 if __name__ == '__main__': 81 if __name__ == '__main__':
53 unittest.main() 82 unittest.main()