def test_rbf_with_dataset():
'''Tests the RBF model with TimeSeriesDataset'''
rbf_model = RidgeRBFModel(10, .5, alpha=.01)
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-6)
y = D.np_like_firstn().sum(axis=1)
model = rbf_model.fit(D, y)
y_pred = model.predict(D)
mrse = (((y - y_pred) / y)**2).mean()
assert_almost_equal(0, mrse, 4)
def test_rbf_with_dataset():
'''Tests the RBF model with TimeSeriesDataset'''
rbf_model = RidgeRBFModel(10, .5, alpha=.01)
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-6)
y = D.np_like_firstn().sum(axis=1)
model = rbf_model.fit(D, y)
y_pred = model.predict(D)
mrse = (((y - y_pred) / y)**2).mean()
assert_almost_equal(0, mrse, 4)
def test_rbf_model():
'''Tests the RBF model'''
rbf_model = RidgeRBFModel(100, 50, alpha=.00001)
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-6).np_like_firstn()
X_train = D[:500, :7]
X_test = D[500:, :7]
y_train = D.sum(axis=1)[:500]
y_test = D.sum(axis=1)[500:]
model = rbf_model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mrse = (((y_test - y_pred) / y_test)**2).mean()
print(mrse)
assert_equal(1, mrse > 0)
assert_equal(1, mrse < 1)
def test_rbf_model():
'''Tests the RBF model'''
rbf_model = RidgeRBFModel(100, 50, alpha=.00001)
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-6).np_like_firstn()
X_train = D[:500, :7]
X_test = D[500:, :7]
y_train = D.sum(axis=1)[:500]
y_test = D.sum(axis=1)[500:]
model = rbf_model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mrse = (((y_test - y_pred) / y_test)**2).mean()
print(mrse)
assert_equal(1, mrse > 0)
assert_equal(1, mrse < 1)
