total_score = zeros((test_size, 1), dtype = float32 )
sample_score = zeros((test_size, 1), dtype = float32 )
test_key = fnn
for tn in set_list:
start_tset = clock()
test_set_num = tn
valid_set_index = [1,2,3,4,5,6,7,8,9,10]
valid_set_num = num10(tn+1)
# X_train, X_test, X_valid, y_train, y_test, y_valid, D_train, D_test, D_valid, jump_num, FN, nb_classes, open_data = set_file_func.set_data(fnn, test_set_num, valid_set_num, speak)
X_train, X_valid, y_train, y_valid, k_train, k_valid, D_train, D_valid = set_file_func_novalid.set_data_onlytrain(fnn, test_set_num, valid_set_num, speak)
X_test, X_valid, y_test, y_valid, k_test, k_valid, D_test, D_valid = set_file_func_novalid.set_data_onlytest(fnn_test, test_set_num, valid_set_num, speak)
def most_common(lst):
return max(((item, lst.count(item)) for item in set(lst)), key=lambda a: a[0])[0]
print ('Data file : '+ fnn, 'and', 'test set - ', tn)
print ('Patience : ', patience)
## Confusion Matrix
confusion_matrix = zeros((nb_classes, nb_classes))
confusion_matrix_mv = zeros((nb_classes, nb_classes))
total_score = zeros((test_size, 1), dtype=float32)
sample_score = zeros((test_size, 1), dtype=float32)
test_key = fnn
for tn in set_list:
start_tset = clock()
test_set_num = tn
valid_set_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
valid_set_num = num10(tn + 1)
# X_train, X_test, X_valid, y_train, y_test, y_valid, D_train, D_test, D_valid, jump_num, FN, nb_classes, open_data = set_file_func.set_data(fnn, test_set_num, valid_set_num, speak)
X_train, X_valid, y_train, y_valid, D_train, D_valid, jump_num, FN, nb_classes, open_data = set_file_func_novalid.set_data_onlytrain(
fnn, test_set_num, valid_set_num, speak
)
X_test, X_valid, y_test, y_valid, D_test, D_valid, jump_num, FN, nb_classes, open_data = set_file_func_novalid.set_data_onlytest(
fnn_test, test_set_num, valid_set_num, speak
)
def most_common(lst):
return max(((item, lst.count(item)) for item in set(lst)), key=lambda a: a[0])[0]
print("Data file : " + fnn, "and", "test set - ", tn)
print("Patience : ", patience)
## Confusion Matrix
confusion_matrix = zeros((nb_classes, nb_classes))
confusion_matrix_mv = zeros((nb_classes, nb_classes))
