def test_data_manipulation(self):
tol = 1e-1
x = np.linspace(0, 1, 101).reshape((-1, 1))
y = np.exp(-x**2).reshape(-1)
x_ = np.linspace(1.5, 2, 51).reshape((-1, 1))
y_ = 1 + np.exp(-x_**2).reshape(-1)
gp = MaternGP(x, y, noise_prior=(0.1, 0.1))
tmp = gp.empty_data()
self.assertEqual(tmp, gp)
self.assertTrue(tuple(gp.train_x.shape), (0, 1))
# GPyTorch fails when predicting with an empty dataset, so the following line fails if uncommented
# gp.predict(x)
gp.set_data(x, y)
self.assertEqual(tuple(gp.train_x.shape), (len(x), 1))
gp.optimize_hyperparameters(epochs=10)
gp_pred = gp.predict(x_).mean.cpu().numpy()
self.assertFalse(np.all(np.abs(gp_pred - y_) < tol))
tmp = gp.append_data(x_, y_)
# self.assertTrue(gp != tmp)
# self.assertEqual(tuple(gp.train_x.shape), (len(x), 1))
self.assertEqual(tuple(tmp.train_x.shape), (len(x) + len(x_), 1))
tmp.optimize_hyperparameters(epochs=10)
tmp_pred = tmp.predict(x_).mean.cpu().numpy()
self.assertTrue(np.all(np.abs(tmp_pred - y_) < tol))
def test_timeforgetting_dataset(self):
x = np.linspace(0, 1, 100, dtype=np.float32).reshape((-1, 1))
y = np.exp(-x**2).reshape(-1)
gp = MaternGP(x,
y,
noise_constraint=(0, 1e-3),
dataset_type='timeforgetting',
dataset_params={'keep': 50})
self.assertTrue((gp.train_x.cpu().numpy() == x[-50:]).all())
self.assertTrue((gp.train_y.cpu().numpy() == y[-50:]).all())
gp.append_data(x[:10], y[:10])
self.assertTrue((gp.train_x.cpu().numpy() == np.vstack(
(x[-40:], x[:10]))).all())
self.assertTrue((gp.train_y.cpu().numpy() == np.hstack(
(y[-40:], y[:10]))).all())
gp.set_data(x[:75], y[:75])
self.assertTrue((gp.train_x.cpu().numpy() == x[25:75]).all())
self.assertTrue((gp.train_y.cpu().numpy() == y[25:75]).all())