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")