def test_qrnn(self, backend):
"""
Test training of QRNNs using numpy arrays as input.
"""
set_backend(backend)
qrnn = QRNN(self.x_train.shape[1], np.linspace(0.05, 0.95, 10))
qrnn.train((self.x_train, self.y_train), maximum_epochs=1)
qrnn.predict(self.x_train)
x, qs = qrnn.cdf(self.x_train[:2, :])
assert qs[0] == 0.0
assert qs[-1] == 1.0
x, y = qrnn.pdf(self.x_train[:2, :])
assert x.shape == y.shape
mu = qrnn.posterior_mean(self.x_train[:2, :])
assert len(mu.shape) == 1
r = qrnn.sample_posterior(self.x_train[:4, :], n=2)
assert r.shape == (4, 2)
r = qrnn.sample_posterior_gaussian_fit(self.x_train[:4, :], n=2)
assert r.shape == (4, 2)
def test_save_qrnn(self, backend):
"""
Test saving and loading of QRNNs.
"""
set_backend(backend)
qrnn = QRNN(self.x_train.shape[1], np.linspace(0.05, 0.95, 10))
f = tempfile.NamedTemporaryFile()
qrnn.save(f.name)
qrnn_loaded = QRNN.load(f.name)
x_pred = qrnn.predict(self.x_train)
x_pred_loaded = qrnn.predict(self.x_train)
if not type(x_pred) == np.ndarray:
x_pred = x_pred.detach()
assert np.allclose(x_pred, x_pred_loaded)
