def setUp(self):
cols = ['GarageArea', 'OverallQual', 'BldgType']
self.dataset = io_tools.read_dataset("data/train.csv")
self.processed_data = data_tools.preprocess_data(self.dataset,
feature_columns=cols)
self.N = self.processed_data[0].shape[0]
self.ndims = self.processed_data[0].shape[1]
self.model = linear_regression.LinearRegression(self.ndims, "zeros")
def sarcos_linear_regression():
X_train, Y_train, X_cv, Y_cv, X_test, Y_test = data.get_data_split(
[0.6, 0.2, 0.2], normalize=True)
print('Linear regression, sarcos:')
print('\tThis will take about 5 seconds, please wait...')
model = linear_regression.LinearRegression(X_train, Y_train)
model.train(10000, 0.1, print_iterations=False)
rmse, prediction = model.test(X_test, Y_test)
rmse_train, _ = model.test(X_train, Y_train)
print('\tRMSE: ' + str(rmse))
def test_total_loss(self):
self.model = linear_regression.LinearRegression(2,
'zeros',
w_decay_factor=0.1)
self.model.w = np.array([[2], [-2]])
y = np.array([[1], [1], [1], [-1], [-1], [-1]])
f = np.array([[0.5], [0], [0.5], [-1], [-1], [-1]])
loss = self.model.total_loss(f, y)
self.assertEqual(round(loss, 5),
0.5 * (0.5**2. + 1 + 0.5**2. + 0 + 0 + 8 * 0.1))
def setUp(self):
self.model = linear_regression.LinearRegression(5, 'zeros')
def test_init_ones(self):
self.model = linear_regression.LinearRegression(5, 'ones')
np.testing.assert_array_equal(np.ones((6, 1)), self.model.w)
def test_uniform_init(self):
self.model = linear_regression.LinearRegression(5, 'uniform')
np.testing.assert_equal(
np.any(np.equal(np.zeros((6, 1)), self.model.w)), False)
def setUp(self):
self.model = linear_regression.LinearRegression(5, 'uniform')