def test_load_data(self):
from build import load_data
res = load_data()
self.assertEqual(13, res['# Summer']['Afghanistan'])
self.assertEqual(152, res['Silver']['Australia'])
def test_get_points(self):
from build import get_points, load_data
res = load_data()
res = get_points(res)
self.assertEqual(2, res['Afghanistan'])
self.assertEqual(130, res['Argentina'])
self.assertEqual(923, res['Australia'])
self.assertEqual(569, res['Austria'])
self.assertEqual(276, res['Belgium'])
def main():
parser = OptionParser()
opt = parser.parse_args()
filename = opt.filename
step = opt.step
epochs = opt.epochs
x_train, y_train, x_val, y_val, x_test, y_test, gt_test, max_data, min_data = load_data(
filename, step)
opt.max_data = max_data
opt.min_data = min_data
opt.train_len = x_train.shape[1]
opt.val_len = x_val.shape[1] - x_train.shape[1]
opt.test_len = x_test.shape[1] - x_val.shape[1]
lstm = LSTM(1, 50, 1)
sfm = SFM(1, 50, 20, 1)
net = sfm
train_function = train_sfm
test_function = test_sfm
if opt.net not in ["lstm", "sfm"]:
raise NameError("Undefined net!!")
if opt.net == "lstm":
net = lstm
train_function = train_lstm
test_function = test
print("Using net: %s" % (opt.net))
if opt.train:
train_function(net, x_train, y_train, epochs=epochs)
if opt.test:
test_function(net, x_test, y_test, opt)
type=str,
default='../dataset/data.npy')
# visualization
parser.add_argument('-v',
'--visualization',
type=distutils.util.strtobool,
default='false')
args = parser.parse_args()
step = args.step
global_start_time = time.time()
print '> Loading data... '
data_file = args.data_file
X_train, y_train, X_val, y_val, X_test, y_test, gt_test, max_data, min_data = build.load_data(
data_file, step)
test_len = X_test.shape[1] - X_val.shape[1]
print 'test length:', test_len
print '> Data Loaded. Compiling...'
#dimension of hidden states
if step == 1:
hidden_dim = 10
elif step == 3:
hidden_dim = 50
elif step == 5:
hidden_dim = 50
else:
raise Exception(
"Don't have the model pretrained with the n-step prediction.")
#number of frequencies
import build as b df = b.load_data() print(df) print(b.first_country(df)) print(b.gold_medal(df)) print(b.biggest_difference_in_gold_medal(df)) p = b.get_points(df) print(p) print(b.k_means(df))
def test_biggest_difference_in_gold_medal(self):
from build import biggest_difference_in_gold_medal, load_data
res = load_data()
res = biggest_difference_in_gold_medal(res)
self.assertEqual('United States', res)
def test_gold_medal(self):
from build import gold_medal, load_data
res = load_data()
res = gold_medal(res)
self.assertEqual('United States', res)
def test_first_country(self):
from build import first_country, load_data
res = load_data()
res = first_country(res)
self.assertEqual(13, res["# Summer"])