def order_dps(name2dp, connections):
""" Returns a total order consistent with the partial order """
names = set(name2dp)
# List the ones that have no functions or no resources
# a >= 10 g (no functions)
# b <= 10 s (no resources)
# # no_functions = set()
# # no_resources = set()
# #
# # for name, ndp in name2dp.items():
# # if not ndp.get_fnames():
# # no_functions.add(name)
# # if not ndp.get_rnames():
# # no_resources.add(name)
#
# print('no_functions: %s' % no_functions)
# print('no_resources: %s' % no_resources)
G = get_connection_graph(names, connections)
# I should probably think more about this
# for nf in no_functions:
# for nr in no_resources:
# G.add_edge(nr, nf)
Gu = G.to_undirected()
if not is_connected(Gu):
msg = 'The graph is not weakly connected. (missing constraints?)'
msg += '\nNames: %s' % names
msg += '\nconnections: %s' % connections
raise DPSemanticError(msg)
l = list(topological_sort(G))
if not (set(l) == names):
msg = 'names = %s\n returned = %s\n connections: %s' % (names, l,
connections)
msg += '\n graph: %s %s' % (list(Gu.nodes()), list(Gu.edges()))
raise DPInternalError(msg)
return l
def order_dps(name2dp, connections):
""" Returns a total order consistent with the partial order """
names = set(name2dp)
# List the ones that have no functions or no resources
# a >= 10 g (no functions)
# b <= 10 s (no resources)
# # no_functions = set()
# # no_resources = set()
# #
# # for name, ndp in name2dp.items():
# # if not ndp.get_fnames():
# # no_functions.add(name)
# # if not ndp.get_rnames():
# # no_resources.add(name)
#
# print('no_functions: %s' % no_functions)
# print('no_resources: %s' % no_resources)
G = get_connection_graph(names, connections)
# I should probably think more about this
# for nf in no_functions:
# for nr in no_resources:
# G.add_edge(nr, nf)
Gu = G.to_undirected()
if not is_connected(Gu):
msg = 'The graph is not weakly connected. (missing constraints?)'
msg += '\nNames: %s' % names
msg += '\nconnections: %s' % connections
raise DPSemanticError(msg)
l = topological_sort(G)
if not (set(l) == names):
msg = 'names = %s\n returned = %s\n connections: %s' % (names, l, connections)
msg += '\n graph: %s %s' % (list(Gu.nodes()), list(Gu.edges()))
raise DPInternalError(msg)
return l
[u'聞', u'聽'] # Moon - Hear, make known, Cheong - Hear, listen
# [u'事', #Sa - Affair, business
# [u'今', # Geum - now, today
# Extra: 冷-Neng,cold | 溫-On,warm | 0.170588235294
# Shik - formula, Gyu - regulation
# Hyeong - shape, Mo - standard, model
# Chong - General, all, whole
]
# The 2-hanja graph projection is not connected, thus we must get the largest
# connected component of the graph.
gen = nxa.connected_components(G)
mainLst = next(gen)
G = G.subgraph(mainLst)
if nxa.is_connected(G) == False:
print("We have a PROBLEM, Houston.")
import scipy as sp
import numpy as np
M = nx.adjacency_matrix(G)
N = M.toarray()
Nlog = np.log2(N + 1)
import scipy.stats as sps
entropy = sps.entropy(Nlog)
entropy = entropy + 1
res = np.divide(N, entropy)
U, s, V = sp.linalg.svd(res)
s[20:] = 0
# encoding: utf-8
import networkx as nx
import networkx.algorithms as nxa
G = nx.read_graphml('hanja_unip.graphml',unicode)
# The 2-hanja graph projection is not connected, thus we must get the largest
# connected component of the graph.
gen = nxa.connected_components(G)
mainLst = gen.next()
G = G.subgraph(mainLst)
if nxa.is_connected(G) == False:
print("We have a PROBLEM, Houston.")
import number_of_walks as now
import numpy as np
num_2step_walks = now.all_pairs_number_of_walks(G,2)
num_2step_walk_dist = {}
normalized_2step_w = []
for h1 in num_2step_walks:
for h2 in num_2step_walks[h1]:
if h1 == h2: continue
if num_2step_walks[h1][h2] not in num_2step_walk_dist:
num_2step_walk_dist[num_2step_walks[h1][h2]] = 0
num_2step_walk_dist[num_2step_walks[h1][h2]] += 1
if num_2step_walks[h1][h2] == 0: continue
