def generate_graphs(sim_thresholds,word_groups,lexicons,
plot_graphs = True,
show_pos = True,
write_graphs = True,
connect_to_animal = True,
animal_sim = 0.4,
overwrite = False):
all_graphs = {}
for sim in sim_thresholds:
g = WordsGraph(20,sim,'hub-categories-prob-random',0,0.00001,20,0,1,'map')
#print 'this part loaded'
for w in word_groups:
for _l in lexicons:
name = '_'.join([_l,w,str(sim)])
if connect_to_animal:
name = ''.join([name,'_A',str(animal_sim)])
fpath = "graphs/" + name + '.graphml'
if not overwrite:
if os.path.isfile(fpath):
print(name + " exists, skipping")
continue
print(name)
graph = g.create_final_graph(word_groups[w],lexicons[_l],8500,'COS')
#connect to word animal for learned data
if connect_to_animal:
for word in word_groups[w]:
#don't connect animal to itself
if word == 'animal:N':
continue
a_sim = evaluate.calculate_similarity(8500,lexicons[_l].meaning(word),lexicons[_l].meaning('animal:N'),'COS')
#learner.acquisition_score(word)
if a_sim >= animal_sim:
e = graph.add_edge(graph.vs.find(label='animal:N').index,graph.vs.find(label=word).index)
graph.edge_properties['distance'][e] = 1-a_sim
all_graphs[name] = graph
if plot_graphs: plot_graph(graph,name,show_pos)
if write_graphs: write_graphxl(graph,fpath)
return all_graphs
def get_animal_sim(graph,l1,l2):
return evaluate.calculate_similarity(8500,l1.meaning(word),l2.meaning('animal:N'),'COS')
