def test_arc_sort(self):
s = r'''
0 1 2 1
0 4 0 2
0 2 0 3
1 2 1 4
1 3 0 5
2 1 0 6
4
'''
fsa = k2host.str_to_fsa(s)
sorter = k2host.ArcSorter(fsa)
array_size = k2host.IntArray2Size()
sorter.get_sizes(array_size)
fsa_out = k2host.Fsa.create_fsa_with_size(array_size)
arc_map = k2host.IntArray1.create_array_with_size(array_size.size2)
sorter.get_output(fsa_out, arc_map)
expected_arc_indexes = torch.IntTensor([0, 3, 5, 6, 6, 6])
expected_arcs = torch.IntTensor([[0, 2, 0, float_to_int(3)],
[0, 4, 0, float_to_int(2)],
[0, 1, 2, float_to_int(1)],
[1, 3, 0, float_to_int(5)],
[1, 2, 1, float_to_int(4)],
[2, 1, 0, float_to_int(6)]])
expected_arc_map = torch.IntTensor([2, 1, 0, 4, 3, 5])
self.assertTrue(torch.equal(fsa_out.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(fsa_out.data, expected_arcs))
self.assertTrue(torch.equal(arc_map.data, expected_arc_map))
def test_bad_cases1(self):
s = r'''
0 2 0 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_connected(fsa))
def test_good_case_2(self):
s_a = r'''
0 1 1 0
1 2 3 0
2 3 4 0
3 4 -1 0
4
'''
fsa = k2host.str_to_fsa(s_a)
rand_path = k2host.RandPath(fsa, False)
array_size = k2host.IntArray2Size()
rand_path.get_sizes(array_size)
path = k2host.Fsa.create_fsa_with_size(array_size)
arc_map = k2host.IntArray1.create_array_with_size(array_size.size2)
status = rand_path.get_output(path, arc_map)
self.assertTrue(status)
self.assertFalse(k2host.is_empty(path))
self.assertFalse(arc_map.empty())
expected_arc_indexes = torch.IntTensor([0, 1, 2, 3, 4, 4])
expected_arcs = torch.IntTensor([[0, 1, 1, 0], [1, 2, 3, 0],
[2, 3, 4, 0], [3, 4, -1, 0]])
expected_arc_map = torch.IntTensor([0, 1, 2, 3])
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))
def test_bad_case1(self):
s = r'''
0 1 2 0
1
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_empty(fsa))
def test_fsa(self):
s = r'''
0 1 1 1.25
0 2 2 1.5
1 3 3 1.75
2 3 3 2.25
3 4 -1 2.5
4
'''
fsa = k2host.str_to_fsa(s)
self.assertEqual(fsa.num_states(), 5)
self.assertEqual(fsa.final_state(), 4)
self.assertFalse(fsa.empty())
self.assertIsInstance(fsa, k2host.Fsa)
# test get_data
self.assertEqual(fsa.get_data(0).src_state, 0)
self.assertEqual(fsa.get_data(0).dest_state, 1)
self.assertEqual(fsa.get_data(0).label, 1)
self.assertEqual(fsa.get_data(0).weight, 1.25)
self.assertEqual(fsa.get_data(1).weight, 1.5)
self.assertEqual(fsa.get_data(2).weight, 1.75)
self.assertEqual(fsa.get_data(3).weight, 2.25)
self.assertEqual(fsa.get_data(4).weight, 2.5)
# fsa.data and the corresponding k2host::Fsa object are sharing memory
fsa.data[0] = torch.IntTensor([5, 1, 6, 1])
self.assertEqual(fsa.get_data(0).src_state, 5)
def test_case_4(self):
# connected fsa
s = r'''
0 4 40 0
0 2 20 0
1 6 -1 0
2 3 30 0
3 6 -1 0
3 1 10 0
4 5 50 0
5 2 8 0
6
'''
fsa = k2host.str_to_fsa(s)
sorter = k2host.TopSorter(fsa)
array_size = k2host.IntArray2Size()
sorter.get_sizes(array_size)
fsa_out = k2host.Fsa.create_fsa_with_size(array_size)
arc_map = k2host.IntArray1.create_array_with_size(array_size.size2)
status = sorter.get_output(fsa_out, arc_map)
self.assertTrue(status)
expected_arc_indexes = torch.IntTensor([0, 2, 3, 4, 5, 7, 8, 8])
expected_arcs = torch.IntTensor([[0, 1, 40, 0], [0, 3, 20, 0],
[1, 2, 50, 0], [2, 3, 8, 0],
[3, 4, 30, 0], [4, 6, -1, 0],
[4, 5, 10, 0], [5, 6, -1, 0]])
expected_arc_map = torch.IntTensor([0, 1, 6, 7, 3, 4, 5, 2])
self.assertTrue(torch.equal(fsa_out.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(fsa_out.data, expected_arcs))
self.assertTrue(torch.equal(arc_map.data, expected_arc_map))
def test_case_2(self):
# a cyclic input fsa
# after trimming, the cycle is removed;
# so the output fsa should be topsorted.
s = r'''
0 1 1 0
0 2 2 1
1 3 3 -2
1 6 6 -3
2 4 2 4
2 6 3 5
2 6 -1 6
5 0 1 7
5 7 -1 8
7
'''
fsa = k2host.str_to_fsa(s)
connection = k2host.Connection(fsa)
array_size = k2host.IntArray2Size()
connection.get_sizes(array_size)
fsa_out = k2host.Fsa.create_fsa_with_size(array_size)
arc_map = k2host.IntArray1.create_array_with_size(array_size.size2)
status = connection.get_output(fsa_out, arc_map)
self.assertTrue(status)
self.assertTrue(k2host.is_empty(fsa_out))
self.assertTrue(arc_map.empty())
def test_case_1(self):
# a non-connected, non-topsorted, acyclic input fsa;
# the output fsa is topsorted.
s = r'''
0 1 1 0
0 2 2 0
1 3 3 0
1 6 -1 0
2 4 2 0
2 6 -1 0
2 1 1 0
5 0 1 0
6
'''
fsa = k2host.str_to_fsa(s)
connection = k2host.Connection(fsa)
array_size = k2host.IntArray2Size()
connection.get_sizes(array_size)
fsa_out = k2host.Fsa.create_fsa_with_size(array_size)
arc_map = k2host.IntArray1.create_array_with_size(array_size.size2)
status = connection.get_output(fsa_out, arc_map)
self.assertTrue(status)
expected_arc_indexes = torch.IntTensor([0, 2, 4, 5, 5])
expected_arcs = torch.IntTensor([[0, 2, 1, 0], [0, 1, 2, 0],
[1, 3, -1, 0], [1, 2, 1, 0],
[2, 3, -1, 0]])
expected_arc_map = torch.IntTensor([0, 1, 5, 6, 3])
self.assertTrue(torch.equal(fsa_out.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(fsa_out.data, expected_arcs))
self.assertTrue(torch.equal(arc_map.data, expected_arc_map))
def test_case_4(self):
# a cyclic input fsa
# after trimming, the cycle remains (it is not a self-loop);
# so the output fsa is NOT topsorted.
s = r'''
0 3 3 1
0 2 2 2
1 0 1 3
2 6 -1 4
3 5 5 5
3 2 2 6
3 5 5 7
4 4 4 8
5 3 3 9
5 4 4 10
6
'''
fsa = k2host.str_to_fsa(s)
connection = k2host.Connection(fsa)
array_size = k2host.IntArray2Size()
connection.get_sizes(array_size)
fsa_out = k2host.Fsa.create_fsa_with_size(array_size)
status = connection.get_output(fsa_out)
self.assertFalse(status)
self.assertFalse(k2host.is_top_sorted(fsa_out))
def test_good_case_1(self):
# both fsas will be empty after trimming
s_a = r'''
0 1 1 0
0 2 2 0
1 2 3 0
3
'''
fsa_a = k2host.str_to_fsa(s_a)
s_b = r'''
0 1 1 0
0 2 2 0
3
'''
fsa_b = k2host.str_to_fsa(s_b)
self.assertTrue(k2host.is_rand_equivalent(fsa_a, fsa_b))
def test_arc_sort(self):
s = r'''
0 1 2 1
0 4 0 2
0 2 0 3
1 2 1 4
1 3 0 5
2 1 0 6
4
'''
fsa = k2host.str_to_fsa(s)
arc_map = k2host.IntArray1.create_array_with_size(fsa.size2)
k2host.arc_sort(fsa, arc_map)
expected_arc_indexes = torch.IntTensor([0, 3, 5, 6, 6, 6])
expected_arcs = torch.IntTensor([[0, 2, 0, float_to_int(3)],
[0, 4, 0, float_to_int(2)],
[0, 1, 2, float_to_int(1)],
[1, 3, 0, float_to_int(5)],
[1, 2, 1, float_to_int(4)],
[2, 1, 0, float_to_int(6)]])
expected_arc_map = torch.IntTensor([2, 1, 0, 4, 3, 5])
self.assertTrue(torch.equal(fsa.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(fsa.data, expected_arcs))
self.assertTrue(torch.equal(arc_map.data, expected_arc_map))
def test_case_2(self):
s_a = r'''
0 1 1 0
1 2 0 0
1 3 1 0
1 4 2 0
2 2 1 0
2 3 1 0
2 3 2 0
3 3 0 0
3 4 1 0
4
'''
fsa_a = k2host.str_to_fsa(s_a)
s_b = r'''
0 1 1 0
1 3 1 0
1 2 2 0
2 3 1 0
3
'''
fsa_b = k2host.str_to_fsa(s_b)
intersection = k2host.Intersection(fsa_a, fsa_b)
array_size = k2host.IntArray2Size()
intersection.get_sizes(array_size)
fsa_out = k2host.Fsa.create_fsa_with_size(array_size)
arc_map_a = k2host.IntArray1.create_array_with_size(array_size.size2)
arc_map_b = k2host.IntArray1.create_array_with_size(array_size.size2)
status = intersection.get_output(fsa_out, arc_map_a, arc_map_b)
self.assertTrue(status)
expected_arc_indexes = torch.IntTensor([0, 1, 4, 7, 8, 8, 8, 10, 10])
expected_arcs = torch.IntTensor([[0, 1, 1, 0], [1, 2, 0, 0],
[1, 3, 1, 0], [1, 4, 2, 0],
[2, 5, 1, 0], [2, 3, 1, 0],
[2, 6, 2, 0], [3, 3, 0, 0],
[6, 6, 0, 0], [6, 7, 1, 0]])
expected_arc_map_a = torch.IntTensor([0, 1, 2, 3, 4, 5, 6, 7, 7, 8])
expected_arc_map_b = torch.IntTensor([0, -1, 1, 2, 1, 1, 2, -1, -1, 3])
self.assertTrue(torch.equal(fsa_out.indexes, expected_arc_indexes))
self.assertTrue(torch.equal(fsa_out.data, expected_arcs))
self.assertTrue(torch.equal(arc_map_a.data, expected_arc_map_a))
self.assertTrue(torch.equal(arc_map_b.data, expected_arc_map_b))
def test_good_case2(self):
s = r'''
0 1 0 0
0 2 0 0
1 2 0 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.is_top_sorted(fsa))
def test_good_case3(self):
s = r'''
0 1 0 0
0 2 -1 0
1 2 -1 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.is_valid(fsa))
def test_good_case2(self):
s = r'''
0 1 2 0
0 2 1 0
1 2 1 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.is_epsilon_free(fsa))
def test_bad_cases2(self):
# same label on two arcs
s = r'''
0 2 0 0
0 1 0 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_arc_sorted(fsa))
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
0 2 0 0
1 2 1 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_epsilon_free(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_case2(self):
s = r'''
0 1 0 0
1 2 0 0
1 1 0 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.has_self_loops(fsa))
def test_bad_cases1(self):
s = r'''
0 1 0 0
0 2 0 0
2 1 0 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_top_sorted(fsa))
def test_good_case2(self):
s = r'''
0 1 0 0
0 2 0 0
2 3 -1 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.is_valid(fsa))
def test_bad_cases1(self):
s = r'''
0 1 0 0
0 2 0 0
1 2 0 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.has_self_loops(fsa))
def test_bad_case_1(self):
# just set arc.weight as 0 since we won't use it here
s_a = r'''
0 1 1 0
0 2 2 0
1 2 3 0
1 3 4 0
2 3 5 0
3
'''
fsa_a = k2host.str_to_fsa(s_a)
s_b = r'''
0 1 1 0
0 2 2 0
1 2 3 0
3
'''
fsa_b = k2host.str_to_fsa(s_b)
self.assertFalse(k2host.is_rand_equivalent(fsa_a, fsa_b))
def test_bad_cases1(self):
s = r'''
0 1 1 0
0 2 2 0
1 2 2 0
1 3 1 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_arc_sorted(fsa))
def test_bad_case2(self):
# only kFinalSymbol arcs enter the final state
s = r'''
0 1 0 0
0 2 1 0
1 2 0 0
2
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_valid(fsa))
def test_bad_case_2(self):
s_a = r'''
0 1 1 0
0 2 2 0
1 2 3 0
1 3 4 0
2 3 5 0
3
'''
fsa_a = k2host.str_to_fsa(s_a)
s_b = r'''
0 1 1 0
0 2 2 0
1 2 3 0
1 3 4 0
2 3 6 0
3
'''
fsa_b = k2host.str_to_fsa(s_b)
self.assertFalse(k2host.is_rand_equivalent(fsa_a, fsa_b, 100))
def test_good_case2(self):
s = r'''
0 1 2 0
0 2 1 0
1 2 0 0
1 3 5 0
2 3 6 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertTrue(k2host.is_acyclic(fsa))
def test_bad_case_2(self):
s_a = r'''
0 1 1 0
0 2 2 0
0 3 8 0
1 4 4 0
2 4 5 0
4
'''
fsa_a = k2host.str_to_fsa(s_a)
s_b = r'''
0 2 1 0
0 1 2 0
0 3 9 0
1 4 5 0
2 4 4 0
4
'''
fsa_b = k2host.str_to_fsa(s_b)
self.assertTrue(k2host.is_rand_equivalent(fsa_a, fsa_b))
def test_bad_cases1(self):
s = r'''
0 1 2 0
0 4 0 0
0 2 0 0
1 2 1 0
1 3 0 0
2 1 0 0
3
'''
fsa = k2host.str_to_fsa(s)
self.assertFalse(k2host.is_acyclic(fsa))