def main():
args = arg.preprocess_args()
rand = 0
data, _ = helper.pre_processed_data_all(args, rand, dry=False)
if args.mean:
rand = random.randint(0, 9999999)
label, _ = helper.pre_processed_label_all(args, rand, dry=False)
data = preprocess.mean_image(label, data)
helper.create_images_from_rows((args.name or 'img'), data)
def server():
args = rForest_args()
rand = np.random.randint(10000000)
data_train, data_test = pre_processed_data_all(args, rand)
label_train, label_test = pre_processed_label_all(args, rand)
res = []
for i in range(2, 6):
print('===\,n=====Epochs: %d=====\n===' % i)
res.append(
run_function(random_forest,
args.cross_validate,
data_train,
label_train,
data_test,
label_test,
max_depth=i + 1))
print(res)
res = extract_measures(res)
print(res)
plot_experiment('random_forest_test_9000', 'n estimator', res)
def server():
args = rForest_args()
rand = np.random.randint(10000000)
data_train, data_test = pre_processed_data_all(args, rand)
label_train, label_test = pre_processed_label_all(args, rand)
res = []
for i in range(5):
print('===\n=====Epochs: %d=====\n===' % i)
res.append(
run_function(linear_classifier,
args.cross_validate,
data_train,
label_train,
data_test,
label_test,
max_iter=(i + 1)))
print(res)
res = extract_measures(res)
print(res)
plot_experiment('linear_classifier_test', 'max iteration ( x 100)', res)
def main():
args = keras_args()
rand = np.random.randint(10000000)
data_train, data_test = helper.pre_processed_data_all(args, rand)
label_train, label_test = helper.pre_processed_label_all(args, rand)
res = []
for i in range(10, 25):
print('===\n=====Epochs: %d=====\n===' % i)
resa = []
for j in range(5):
resa.append(helper.run_function(keras_build_and_predict,
args.cross_validate,
data_train, label_train,
data_test, label_test,
epochs=i))
res.append(helper.mean_measures(helper.extract_measures(resa)))
print(res)
res = helper.extract_measures(res)
print(res)
helper.plot_experiment_server('research', 'epochs', res)
def main():
args = arg.preprocess_args()
rand = random.randint(0, 9999999)
data, _ = helper.pre_processed_data_all(args, rand, dry=False)
if args.mean:
label, _ = helper.pre_processed_label_all(args, rand, dry=False)
data = preprocess.mean_image(label, data)
elif args.split is not None or args.randomize:
label, _ = helper.pre_processed_label(args, rand, dry=False)
helper.write_data_to_file(
(args.folder or helper.FOLDER) + (args.name or 'processed_data') +
'_l' + helper.EXT,
label,
fmt='%d',
h='0')
# for i in range(4):
# for j in range(4):
helper.write_data_to_file((args.folder or helper.FOLDER) +
(args.name or 'processed_data') + helper.EXT,
data,
h=', '.join(str(i) for i in range(len(data[0]))))
def j48_depth(options):
args = j48_args(options).parse_args(options[1:])
rand = np.random.randint(10000000)
data_train, data_test = pre_processed_data_all(args, rand)
label_train, label_test = pre_processed_label_all(args, rand)
res = []
for i in range(24):
print('===\n=====Epochs: %d=====\n===' % i)
res.append(
run_function(j48,
args.cross_validate,
data_train,
label_train,
data_test,
label_test,
depth=i,
min_split=2,
min_leaf=1,
min_weight=0))
print(res)
res = extract_measures(res)
print(res)
plot_experiment_server("j48_" + options[0] + "max_depth", 'max depth', res)