def get_prop_sim_matrix(graph, graph1):
centra = Centrality()
aifsim = Aifsim()
g_copy = graph.copy()
g1_copy = graph1.copy()
g_inodes = centra.get_i_node_list(g_copy)
g1_inodes = centra.get_i_node_list(g1_copy)
relsi, valsi, switched = aifsim.text_sim_matrix(g_inodes, g1_inodes)
#if switched the relations have been switched order so they need reversed when creating the dictionary
rels_dict = aifsim.rels_to_dict(relsi, switched)
return rels_dict
def get_graph_sim(aif_id1, aif_id2):
centra = Centrality()
aifsim = Aifsim()
graph, json = aifsim.get_graph(aif_id1, centra)
graph1, json1 = aifsim.get_graph(aif_id2, centra)
graph = centra.remove_iso_analyst_nodes(graph)
graph1 = centra.remove_iso_analyst_nodes(graph1)
rep_form = aifsim.is_iat(graph, graph1, centra)
g_copy = graph.copy()
g1_copy = graph1.copy()
graph_mean = 0
text_mean = 0
overall_mean = 0
if rep_form == 'diff':
return 'Error'
else:
graph_mean = aifsim.call_diagram_parts_and_sum(
g_copy, g1_copy, rep_form)
if rep_form == 'aif':
g_inodes = centra.get_i_node_list(g_copy)
g1_inodes = centra.get_i_node_list(g1_copy)
relsi, valsi, switched = aifsim.text_sim_matrix(
g_inodes, g1_inodes)
i_mean = aifsim.get_mean_of_list(valsi)
text_mean = i_mean
else:
g_inodes = centra.get_i_node_list(g_copy)
g1_inodes = centra.get_i_node_list(g1_copy)
g_lnodes = centra.get_l_node_list(g_copy)
g1_lnodes = centra.get_l_node_list(g1_copy)
relsi, valsi, switched = aifsim.text_sim_matrix(
g_inodes, g1_inodes)
relsl, valsl, switched = aifsim.text_sim_matrix(
g_lnodes, g1_lnodes)
i_mean = aifsim.get_mean_of_list(valsi)
l_mean = aifsim.get_mean_of_list(valsl)
text_mean = aifsim.get_l_i_mean(l_mean, i_mean)
overall_score = aifsim.get_l_i_mean(text_mean, graph_mean)
return overall_score, text_mean, graph_mean