def __init__(self, graph, init_embedding=None, radius=None, fit_radius=False):
self.graph = graph
n = self.graph.number_of_nodes()
self.fit_radius = fit_radius
if self.fit_radius:
self.__expected_vector_size = 2 * n + 1
else:
self.__expected_vector_size = 2 * n
# initialize embedding
if radius is None:
radius = 2 * np.log(n)
self.embedding = {
'vertices': {},
'radius': radius,
}
def gen_random_coordinates(v):
r = 2*np.log(float(n) / self.graph.degree(v))
phi = np.random.uniform(0.0, 2*np.pi)
return (r, phi)
def delta_phi(r, R):
# compute \Delta\phi from equality
# \cosh(R) = \cosh^2(r) - \sinh^2(r) * \cos(\Delta\phi)
cos_delta_phi = (np.cosh(r)**2 - np.cosh(R)) / np.sinh(r)**2
cos_delta_phi = max(-1., min(1., cos_delta_phi))
return np.arccos(cos_delta_phi)
if init_embedding is not None:
init_embedding_fringe = fringe(self.graph, init_embedding.embedding['vertices'])
for v in self.graph.nodes():
if v in init_embedding.embedding['vertices']:
self.embedding['vertices'][v] = init_embedding.embedding['vertices'][v]
#elif any([_n in init_embedding.embedding['vertices'] for _n in self.graph.neighbors(v)]):
elif v in init_embedding_fringe:
neigh = list(set(self.graph.neighbors(v)) & set(init_embedding.embedding['vertices']))[0]
r_n, phi_n = init_embedding.embedding['vertices'][neigh]
delta_phi_n = delta_phi(r_n, radius)
r_random, phi_zzz = gen_random_coordinates(v)
phi_random_with_delta = np.random.uniform(4*np.pi + phi_n - delta_phi_n, 4*np.pi + phi_n + delta_phi_n)
self.embedding['vertices'][v] = (r_random, phi_random_with_delta)
else:
self.embedding['vertices'][v] = gen_random_coordinates(v)
else:
for v in self.graph.nodes():
self.embedding['vertices'][v] = gen_random_coordinates(v)
self._vertex2index = {}
for i, v in enumerate(sorted(self.embedding['vertices'])):
self._vertex2index[v] = i
def graph_specific(graph, proper=False, fringe=1):
""" Returns a list of hyponym nodes in the fringe (all if None).
"""
if fringe:
nodes = graph.fringe(fringe)
else:
nodes = graph.nodes
if graph.root:
nodes = filter(lambda n: n.is_a(graph.root, direct=False), nodes)
else:
nodes = filter(lambda n: n.has_rule("is-a", direct=True), nodes)
if proper:
nodes = filter(lambda n: n in graph.proper_nouns(), nodes)
return nodes
def graph_specific(graph, proper=False, fringe=1):
""" Returns a list of hyponym nodes in the fringe (all if None).
"""
if fringe:
nodes = graph.fringe(fringe)
else:
nodes = graph.nodes
if graph.root:
nodes = filter(lambda n: n.is_a(graph.root, direct=False), nodes)
else:
nodes = filter(lambda n: n.has_rule("is-a", direct=True), nodes)
if proper:
nodes = filter(lambda n: n in graph.proper_nouns(), nodes)
return nodes