本文整理匯總了Python中nltk.parse.dependencygraph.DependencyGraph.contains_cycle方法的典型用法代碼示例。如果您正苦於以下問題：Python DependencyGraph.contains_cycle方法的具體用法？Python DependencyGraph.contains_cycle怎麽用？Python DependencyGraph.contains_cycle使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類nltk.parse.dependencygraph.DependencyGraph的用法示例。
在下文中一共展示了DependencyGraph.contains_cycle方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: parse
# 需要導入模塊: from nltk.parse.dependencygraph import DependencyGraph [as 別名]
# 或者: from nltk.parse.dependencygraph.DependencyGraph import contains_cycle [as 別名]
def parse(self, tokens, tags):
"""
Parses a list of tokens in accordance to the MST parsing algorithm
for non-projective dependency parses. Assumes that the tokens to
be parsed have already been tagged and those tags are provided. Various
scoring methods can be used by implementing the ``DependencyScorerI``
interface and passing it to the training algorithm.
:type tokens: list(str)
:param tokens: A list of words or punctuation to be parsed.
:type tags: list(str)
:param tags: A list of tags corresponding by index to the words in the tokens list.
:return: An iterator of non-projective parses.
:rtype: iter(DependencyGraph)
"""
self.inner_nodes = {}
# Initialize g_graph
g_graph = DependencyGraph()
for index, token in enumerate(tokens):
g_graph.nodes[index + 1].update(
{
'word': token,
'tag': tags[index],
'rel': 'NTOP',
'address': index + 1,
}
)
#print (g_graph.nodes)
# Fully connect non-root nodes in g_graph
g_graph.connect_graph()
original_graph = DependencyGraph()
for index, token in enumerate(tokens):
original_graph.nodes[index + 1].update(
{
'word': token,
'tag': tags[index],
'rel': 'NTOP',
'address': index+1,
}
)
b_graph = DependencyGraph()
c_graph = DependencyGraph()
for index, token in enumerate(tokens):
c_graph.nodes[index + 1].update(
{
'word': token,
'tag': tags[index],
'rel': 'NTOP',
'address': index + 1,
}
)
# Assign initial scores to g_graph edges
self.initialize_edge_scores(g_graph)
logger.debug(self.scores)
# Initialize a list of unvisited vertices (by node address)
unvisited_vertices = [
vertex['address'] for vertex in c_graph.nodes.values()
]
# Iterate over unvisited vertices
nr_vertices = len(tokens)
betas = {}
while unvisited_vertices:
# Mark current node as visited
current_vertex = unvisited_vertices.pop(0)
logger.debug('current_vertex: %s', current_vertex)
# Get corresponding node n_i to vertex v_i
current_node = g_graph.get_by_address(current_vertex)
logger.debug('current_node: %s', current_node)
# Get best in-edge node b for current node
best_in_edge = self.best_incoming_arc(current_vertex)
betas[current_vertex] = self.original_best_arc(current_vertex)
logger.debug('best in arc: %s --> %s', best_in_edge, current_vertex)
# b_graph = Union(b_graph, b)
for new_vertex in [current_vertex, best_in_edge]:
b_graph.nodes[new_vertex].update(
{
'word': 'TEMP',
'rel': 'NTOP',
'address': new_vertex,
}
)
b_graph.add_arc(best_in_edge, current_vertex)
# Beta(current node) = b - stored for parse recovery
# If b_graph contains a cycle, collapse it
cycle_path = b_graph.contains_cycle()
if cycle_path:
# Create a new node v_n+1 with address = len(nodes) + 1
new_node = {
'word': 'NONE',
'rel': 'NTOP',
'address': nr_vertices + 1,
}
# c_graph = Union(c_graph, v_n+1)
c_graph.add_node(new_node)
#.........這裏部分代碼省略.........