def map_wordnet_EVD():
print "loading EVD"
dict_EVD = EVDParser.readEVDFile()
print "loading WN"
dict_wn = WordnetProcessForEVD.read_nouns()
for key, values in dict_wn.items():
key_lemmas = key.split("=")[1]
key_lemmas = key_lemmas.split("-")
key = key.split("=")[0]
key_definition = key.split("-")[1]
key = key.split("-")[0]
test_flag = 0
for lemma in key_lemmas:
if lemma[:1] == "b" :
test_flag = 1
if test_flag == 0:
continue
print "map_wordnet_EVD " + key
vi_means = get_EVD_means(key, key_lemmas, values, dict_EVD)
ox_means = get_Ox_means(key, key_lemmas)
means = get_best_mean(vi_means, ox_means, 2)
################################################################################
# get greatest duplicated mean
# if len(values) == 1:
# means = vi_means
# item_count = [(item,count) for item, count in collections.Counter(vi_means).items() if count > 1]
# if len(item_count) > 0:
# means = [max(item_count,key = itemgetter(1))[0]]
#
# items_2 = [item for item, count in collections.Counter(vi_means).items() if count > 2]
# for item in items_2:
# means.append(item)
# means = list(set(means))
#
# else:
# item_count = [(item,count) for item, count in collections.Counter(vi_means).items() if count > 1]
# if len(item_count) == 0:
# continue
# means = [max(item_count,key = itemgetter(1))[0]]
#
# items_2 = [item for item, count in collections.Counter(vi_means).items() if count > 2]
# for item in items_2:
# means.append(item)
# means = list(set(means))
################################################################################
if len(means) > 0:
means = [means[0]]
means.insert(0,key + "-" + key_definition)
filename = "Results/EVD/wn_evd_b_0_1.csv"
FileProcess.append_result_to_excel_file(filename, means)
def append_params_and_result_to_file(values): FileProcess.append_result_to_excel_file(result_file_name,values)
