def test_get_variables():
gpar = GPARRegressor()
gpar.vs.get(init=1.0, name='variable')
yield eq, list(gpar.get_variables().items()), [('variable', 1.0)]
def test_scale_tying():
reg = GPARRegressor(scale_tie=True)
reg.sample(np.linspace(0, 10, 20), p=2) # Instantiate variables.
vs = reg.get_variables()
yield ok, (0, 'I/scales') in vs
yield ok, (1, 'I/scales') not in vs
def test_get_variables():
gpar = GPARRegressor()
gpar.vs.get(init=1.0, name='variable')
assert list(gpar.get_variables().items()) == [('variable', 1.0)]
def test_scale_tying():
reg = GPARRegressor(scale_tie=True)
reg.sample(np.linspace(0, 10, 20), p=2) # Instantiate variables.
vs = reg.get_variables()
assert '0/input/scales' in vs
assert '1/input/scales' not in vs
# model = GPARRegressor(scale=args.synthetic_scales, scale_tie=True,
# linear=True, linear_scale=10., input_linear=False,
# nonlinear=False,
# markov=1,
# replace=True,
# noise=args.noise)
# time the fitting
start = time.time()
# Fit model and print hyperparameters.
model.fit(transform_x(x), y, iters=args.iters, trace=False, fix=True)
if args.joint:
model.fit(transform_x(x), y, iters=args.iters, trace=False, fix=False)
print('Hyperparameters:')
for k, v in model.get_variables().items():
print(k)
print(' ', v)
# Predict.
# preds = []
# for i, x_test in enumerate(x_tests):
# print('Predicting {}/{}'.format(i + 1, max_layers - min_layers + 1))
# preds.append(model.predict(transform_x(x_test),
# num_samples=50,
# latent=True,
# credible_bounds=True))
end = time.time()
samples = []
def test_scale_tying(x, w):
reg = GPARRegressor(scale_tie=True)
reg.sample(x, w, p=2) # Instantiate variables.
vs = reg.get_variables()
assert "0/input/scales" in vs
assert "1/input/scales" not in vs