def test_pruned_logsum(self):
forward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_logsum_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kLogSumWeight,
forward_logsum_weights,
backward_logsum_weights)
beam = 10.0
determinizer = k2host.DeterminizerPrunedLogSum(
wfsa, beam, 100, k2host.FbWeightType.kNoWeight)
fsa_size = k2host.IntArray2Size()
arc_derivs_size = k2host.IntArray2Size()
determinizer.get_sizes(fsa_size, arc_derivs_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
arc_derivs = k2host.LogSumArcDerivs.create_arc_derivs_with_size(
arc_derivs_size)
arc_weights_out = k2host.FloatArray1.create_array_with_size(
fsa_size.size2)
determinizer.get_output(fsa_out, arc_derivs)
self.assertTrue(k2host.is_deterministic(fsa_out))
self.assertEqual(fsa_out.size1, 7)
self.assertEqual(fsa_out.size2, 9)
self.assertEqual(arc_derivs.size1, 9)
self.assertEqual(arc_derivs.size2, 15)
self.assertTrue(
k2host.is_rand_equivalent_logsum_weight(self.fsa, fsa_out, beam))
# cast float to int
arc_ids = k2host.StridedIntArray1.from_float_tensor(arc_derivs.data[:,
0])
def test_pruned_max(self):
forward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
backward_max_weights = k2host.DoubleArray1.create_array_with_size(
self.num_states)
wfsa = k2host.WfsaWithFbWeights(self.fsa,
k2host.FbWeightType.kMaxWeight,
forward_max_weights,
backward_max_weights)
beam = 10.0
determinizer = k2host.DeterminizerPrunedMax(
wfsa, beam, 100, k2host.FbWeightType.kNoWeight)
fsa_size = k2host.IntArray2Size()
arc_derivs_size = k2host.IntArray2Size()
determinizer.get_sizes(fsa_size, arc_derivs_size)
fsa_out = k2host.Fsa.create_fsa_with_size(fsa_size)
arc_derivs = k2host.IntArray2.create_array_with_size(arc_derivs_size)
arc_weights_out = k2host.FloatArray1.create_array_with_size(
fsa_size.size2)
determinizer.get_output(fsa_out, arc_derivs)
self.assertTrue(k2host.is_deterministic(fsa_out))
self.assertEqual(fsa_out.size1, 7)
self.assertEqual(fsa_out.size2, 9)
self.assertEqual(arc_derivs.size1, 9)
self.assertEqual(arc_derivs.size2, 12)
self.assertTrue(
k2host.is_rand_equivalent_max_weight(self.fsa, fsa_out, beam))
def test_good_case2(self):
s = r'''
0 1 2 0
1 2 0 0
1 3 2 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.is_deterministic(fsa))
def test_bad_cases1(self):
s = r'''
0 1 2 0
1 2 0 0
1 3 0 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_deterministic(fsa))
def test_good_cases1(self):
# empty fsa
array_size = k2host.IntArray2Size(0, 0)
fsa = k2host.Fsa.create_fsa_with_size(array_size)
self.assertTrue(k2host.is_deterministic(fsa))
