nltk.parse.DependencyGraph.contains_cycle¶

DependencyGraph.contains_cycle()[source]¶

Check whether there are cycles.

>>> dg = DependencyGraph(treebank_data)
>>> dg.contains_cycle()
False

>>> cyclic_dg = DependencyGraph()
>>> top = {'word': None, 'deps': [1], 'rel': 'TOP', 'address': 0}
>>> child1 = {'word': None, 'deps': [2], 'rel': 'NTOP', 'address': 1}
>>> child2 = {'word': None, 'deps': [4], 'rel': 'NTOP', 'address': 2}
>>> child3 = {'word': None, 'deps': [1], 'rel': 'NTOP', 'address': 3}
>>> child4 = {'word': None, 'deps': [3], 'rel': 'NTOP', 'address': 4}
>>> cyclic_dg.nodes = {
...     0: top,
...     1: child1,
...     2: child2,
...     3: child3,
...     4: child4,
... }
>>> cyclic_dg.root = top

>>> cyclic_dg.contains_cycle()
[3, 1, 2, 4]