def count_loners_sen():
res = top_k(nnc_k, sen_nnc_mps, nnc_internal, sen_agreement_matrix, nnc_arbit, sen_mps)
loners = 0
for i in range(len(res)):
if res[i].count(1) == 0:
if [res[j][i] for j in range(len(res))].count(1) == 0:
loners += 1
return loners
def visualize_sen_nnc():
"""
"""
adj_matrix = top_k(nnc_k, sen_nnc_mps, nnc_internal, sen_agreement_matrix, nnc_arbit, sen_mps)
# XXX dangerous: this will break if we consider a part of the sen members.
names = [mp[0] for mp in sen_nnc_mps]
groups = [mp[1][0] for mp in sen_nnc_mps]
write_json_graph_general(adj_matrix,
names,
groups,
filename = sen_filename_viz)
def visualize_bg_newman():
adj_matrix = top_k(nnc_k, nnc_mps, nnc_internal, agreement_matrix, nnc_arbit, mps)
names = [mp[0] for mp in nnc_mps]
groups = [mp[1][0] for mp in nnc_mps]
adj_list = []
for i in range(len(adj_matrix)):
for j in range(len(adj_matrix)):
if adj_matrix[i][j] == 1:
adj_matrix[j][i] = 1
for i in range(len(adj_matrix)):
adj_list.append([j for j in range(len(adj_matrix)) if adj_matrix[i][j] == 1])
components = cluster(adj_list)
result = [(nnc_mps[i], components[i]) for i in range(len(nnc_mps))]
open(filename_newman, 'w').close()
f = open(filename_newman, 'wb')
pickle.dump(result, f)
f.close()
def visualize_nnc():
"""
Visualize the NNC graph.
As usual, the filename reflects the method used for the adjacency matrix.
"""
adj_matrix = top_k(nnc_k, nnc_mps, nnc_internal, agreement_matrix, nnc_arbit, mps)
write_json_graph(weights_matrix=adj_matrix,
thres=0,
rescale=lambda x: x,
graph_mps=nnc_mps,
mps=mps,
filename= "viz/nnc/" + agr_method + '_' + str(start_session) +
'_' + str(end_session) + 'minsess' +
str(min_sessions) + '_' + 'k=' + str(nnc_k) +
'_' + str(nnc_arbit) + ".json")
