def train_sim_definition(): Parameters.reset_params_zero() (ch_1_n_f_score, ch_1_n_paramas) = choice_1_COL_MIN_FIRST() (ch_n_n_f_score, ch_n_n_paramas) = choice_N_N_MIN_FIRST() Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_1_MIN = 0 Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_MIN_FIRST = ch_1_n_paramas[1] Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST = ch_1_n_paramas[2] Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_MIN_FIRST = ch_n_n_paramas[3] Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_RANGE_FIRST = ch_n_n_paramas[4] # # Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_1_MIN = 0 # Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_MIN_FIRST = 0.0 # Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST = 1.0 # Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_MIN_FIRST = 1 # Parameters.PARAMETERS_CHOICE_0_1.CHOICE_N_N_RANGE_FIRST = 1.25 # sim_ox_wn_via_svm()
def sim_ox_wn_via_svm():
total_tp = 0.00001
total_tn = 0.00001
total_fn = 0.00001
total_fp = 0.00001
total_pair = 0
dict_ox = OxfordParser.get_dict_nouns()
flag_can_go = False
for word in dict_ox:
# if word == "brook":
# flag_can_go = True
#
# if flag_can_go == False:
# continue
word_syns_ox = dict_ox[word]
wn_synsets = WordnetHandler.get_synsets_for_word(word, "n")
m2d_sim = [[0 for x in range(len(word_syns_ox))] for x in range(len(wn_synsets))]
if len(word_syns_ox) == 1 and len(wn_synsets) == 1:
m2d_sim[0][0] = 1
else:
m2d_sim = get_m2d_sim_for_word_from_svm_result(word)
if m2d_sim == None:
continue
# DebugHandler.print_2d_matrix(m2d_sim)
m2d_sim = choose_pair_0_1(m2d_sim, len(m2d_sim), len(m2d_sim[0]))
# DebugHandler.print_2d_matrix(m2d_sim)
pair = count_pair(m2d_sim)
total_pair += pair
(tp, tn, fn, fp) = CompareWithGold.compareGoldWithResult_without_cal_result(m2d_sim,word)
if tp != -1:
total_tp += tp
total_tn += tn
total_fn += fn
total_fp += fp
precision = total_tp / (total_tp + total_fp)
recall = total_tp / (total_tp + total_fn)
accuracy = (total_tp + total_tn) / (total_tp + total_tn + total_fp + total_fn)
f_score = 0
if precision != 0 or recall != 0:
f_score = 2*(precision*recall)/(precision + recall)
print "total:"
print total_pair
print total_tp
print total_tn
print total_fn
print total_fp
print precision
print recall
print f_score
print accuracy
Parameters.append_result_to_file( precision, recall, f_score, accuracy)
current_params = Parameters.get_current_params()
current_params = copy.deepcopy(current_params)
return f_score, current_params
