def test_bad_case_1(self):
# empty fsa
array_size = k2.IntArray2Size(0, 0)
fsa = k2.Fsa.create_fsa_with_size(array_size)
rand_path = k2.RandPath(fsa, False)
array_size = k2.IntArray2Size()
rand_path.get_sizes(array_size)
path = k2.Fsa.create_fsa_with_size(array_size)
arc_map = k2.IntArray1.create_array_with_size(array_size.size2)
status = rand_path.get_output(path, arc_map)
self.assertFalse(status)
self.assertTrue(k2.is_empty(path))
self.assertTrue(arc_map.empty())
def test_bad_case_2(self):
# non-connected fsa
s_a = r'''
0 1 1
0 2 2
1 3 4
3
'''
fsa = k2.str_to_fsa(s_a)
rand_path = k2.RandPath(fsa, False)
array_size = k2.IntArray2Size()
rand_path.get_sizes(array_size)
path = k2.Fsa.create_fsa_with_size(array_size)
arc_map = k2.IntArray1.create_array_with_size(array_size.size2)
status = rand_path.get_output(path, arc_map)
self.assertFalse(status)
self.assertTrue(k2.is_empty(path))
self.assertTrue(arc_map.empty())
def test_good_case_1(self):
s_a = r'''
0 1 1
0 2 2
1 2 3
2 3 4
2 4 5
3 4 7
4 5 9
5
'''
fsa = k2.str_to_fsa(s_a)
rand_path = k2.RandPath(fsa, False)
array_size = k2.IntArray2Size()
rand_path.get_sizes(array_size)
path = k2.Fsa.create_fsa_with_size(array_size)
status = rand_path.get_output(path)
self.assertTrue(status)
self.assertFalse(k2.is_empty(path))
def test_eps_arc_1(self):
s_a = r'''
0 1 1
0 2 0
1 2 3
2 3 0
2 4 5
3 4 7
4 5 9
5
'''
fsa = k2.str_to_fsa(s_a)
rand_path = k2.RandPath(fsa, True)
array_size = k2.IntArray2Size()
rand_path.get_sizes(array_size)
path = k2.Fsa.create_fsa_with_size(array_size)
arc_map = k2.IntArray1.create_array_with_size(array_size.size2)
status = rand_path.get_output(path, arc_map)
self.assertTrue(status)
self.assertFalse(k2.is_empty(path))
self.assertFalse(arc_map.empty())
def test_good_case_2(self):
s_a = r'''
0 1 1
1 2 3
2 3 4
3
'''
fsa = k2.str_to_fsa(s_a)
rand_path = k2.RandPath(fsa, False)
array_size = k2.IntArray2Size()
rand_path.get_sizes(array_size)
path = k2.Fsa.create_fsa_with_size(array_size)
arc_map = k2.IntArray1.create_array_with_size(array_size.size2)
status = rand_path.get_output(path, arc_map)
self.assertTrue(status)
self.assertFalse(k2.is_empty(path))
self.assertFalse(arc_map.empty())
expected_arc_indexes = torch.IntTensor([0, 1, 2, 3, 3])
expected_arcs = torch.IntTensor([[0, 1, 1], [1, 2, 3], [2, 3, 4]])
expected_arc_map = torch.IntTensor([0, 1, 2])
self.assertTrue(torch.equal(path.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(path.data, expected_arcs))
self.assertTrue(torch.equal(arc_map.data, expected_arc_map))