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_1(self):
# empty fsa
array_size = k2host.IntArray2Size(0, 0)
fsa = k2host.Fsa.create_fsa_with_size(array_size)
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)
self.assertTrue(k2host.is_empty(fsa_out))
self.assertTrue(arc_map.empty())
# test without arc_map
sorter.get_output(fsa_out)
self.assertTrue(k2host.is_empty(fsa_out))
def test_case_3(self):
# non-connected fsa (not accessible)
s = r'''
0 2 -1 0
1 0 1 0
1 2 0 0
2
'''
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)
state_map = k2host.IntArray1.create_array_with_size(array_size.size1)
status = sorter.get_output(fsa_out, state_map)
self.assertFalse(status)
self.assertTrue(k2host.is_empty(fsa_out))
self.assertTrue(state_map.empty())
def test_case_3(self):
# non-connected fsa (not accessible)
s = r'''
0 2 -1 0
1 0 1 0
1 2 0 0
2
'''
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)
self.assertFalse(k2host.is_empty(fsa_out))
expected_arc_map = torch.IntTensor([0])
self.assertTrue(torch.equal(arc_map.data, expected_arc_map))