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1 from random_transformation import row_random_transformation
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2
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3 import unittest
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4 from theano import compile
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5 from theano import gradient
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6
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7 from theano.sparse import _is_dense, _is_sparse, _is_dense_result, _is_sparse_result
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8 from theano.sparse import _mtypes, _mtype_to_str
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9 from theano.sparse import as_sparse
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10
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11 from theano.tensor import as_tensor
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12 from theano.scalar import as_scalar
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13
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14 import random
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15 import numpy.random
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16
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17 class T_RowRandomTransformation(unittest.TestCase):
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18 def setUp(self):
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19 random.seed(44)
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20 numpy.random.seed(44)
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21
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22 def test_basic(self):
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23 rows = 4
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24 cols = 20
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25 fakeseed = 0
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26 length = 3
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27 md = numpy.random.rand(rows, cols)
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28 for mtype in _mtypes:
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29 m = as_sparse(mtype(md))
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30 o = row_random_transformation(m, length, initial_seed=fakeseed)
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31 y = compile.eval_outputs([o])
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32 expected = "[[ 0.88239119 1.03244463 -1.29297503]\n [ 0.02644961 1.50119695 -0.025081 ]\n [-0.60741013 1.25424625 0.30119422]\n [-1.08659967 -0.35531544 -1.38915467]]"
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33 self.failUnless(str(y) == expected)
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34
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35 def test_length(self):
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36 """ Test that if length is increased, we obtain the same results
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37 (except longer). """
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38
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39 for i in range(10):
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40 mtype = random.choice(_mtypes)
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41 rows = random.randint(1, 20)
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42 cols = random.randint(1, 20)
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43 fakeseed = random.randint(0, 100)
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44 length = random.randint(1, 10)
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45 extralength = random.randint(1, 10)
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46
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47 m = as_sparse(mtype(numpy.random.rand(rows, cols)))
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48 o1 = row_random_transformation(m, length, initial_seed=fakeseed)
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49 o2 = row_random_transformation(m, length + extralength, initial_seed=fakeseed)
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50
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51 y1 = compile.eval_outputs([o1])
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52 y2 = compile.eval_outputs([o2])
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53
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54 self.failUnless((y1 == y2[:,:length]).all())
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55
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56 def test_permute(self):
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57 """ Test that if the order of the rows is permuted, we obtain the same results. """
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58 for i in range(10):
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59 mtype = random.choice(_mtypes)
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60 rows = random.randint(2, 20)
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61 cols = random.randint(1, 20)
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62 fakeseed = random.randint(0, 100)
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63 length = random.randint(1, 10)
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64
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65 permute = numpy.random.permutation(rows)
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66
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67
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68 m1 = numpy.random.rand(rows, cols)
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69 m2 = m1[permute]
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70 for r in range(rows):
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71 self.failUnless((m2[r] == m1[permute[r]]).all())
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72 s1 = as_sparse(mtype(m1))
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73 s2 = as_sparse(mtype(m2))
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74 o1 = row_random_transformation(s1, length, initial_seed=fakeseed)
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75 o2 = row_random_transformation(s2, length, initial_seed=fakeseed)
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76 y1 = compile.eval_outputs([o1])
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77 y2 = compile.eval_outputs([o2])
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78
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79 self.failUnless(y1.shape == y2.shape)
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80 for r in range(rows):
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81 self.failUnless((y2[r] == y1[permute[r]]).all())
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82
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83 if __name__ == '__main__':
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84 unittest.main()
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