def test_equivalence(self):
"""
Make sure chaining with identity doesn't lead to different values.
"""
with self.test_context() as session:
x, x_np, transforms = self.prepare()
for transform in transforms:
equiv_transforms = [transform,
Chain(transform, Identity()),
Chain(Identity(), transform)]
ys_np = [t.forward(x_np) for t in equiv_transforms]
self.assertEqualElements(ys_np)
y_np = ys_np[0]
xs_np = [t.backward(y_np) for t in equiv_transforms]
self.assertEqualElements(xs_np)
ys = [t.forward_tensor(x) for t in equiv_transforms]
ys_tf = [session.run(y) for y in ys]
self.assertEqualElements(ys_tf)
logjs = [t.log_jacobian_tensor(x) for t in equiv_transforms]
logjs_tf = [session.run(logj) for logj in logjs]
self.assertEqualElements(logjs_tf)
def prepare(self):
x_np = np.random.randn(10).astype(settings.float_type)
transforms = []
for transform_class in gpflow.transforms.Transform.__subclasses__():
if transform_class == Chain:
continue # Chain transform cannot be tested on its own
if transform_class == gpflow.transforms.LowerTriangular:
transforms.append(transform_class(4))
elif transform_class == gpflow.transforms.DiagMatrix:
transforms.append(transform_class(5))
else:
transform = transform_class()
transforms.append(transform)
transforms.append(Chain(Identity(), transform))
transforms.append(Chain(transform, Identity()))
transforms.append(gpflow.transforms.Logistic(7.3, 19.4))
# test __call__() and chaining:
transforms.append(gpflow.transforms.positive(gpflow.transforms.Rescale(7.5)))
transforms.append(gpflow.transforms.Rescale(9.5)(gpflow.transforms.positive))
# test helper:
transforms.append(gpflow.transforms.positiveRescale(9.5))
return tf.convert_to_tensor(x_np), x_np, transforms
def prepare(self):
x = tf.placeholder(settings.np_float, 10)
x_np = np.random.randn(10).astype(settings.np_float)
transforms = []
for transform_class in gpflow.transforms.Transform.__subclasses__():
if transform_class == Chain:
continue # Chain transform cannot be tested on its own
if transform_class == gpflow.transforms.LowerTriangular:
transforms.append(transform_class(4))
else:
transform = transform_class()
transforms.append(transform)
transforms.append(Chain(Identity(), transform))
transforms.append(Chain(transform, Identity()))
#self.transforms = [C() for C in gpflow.transforms.Transform.__subclasses__()]
transforms.append(gpflow.transforms.Logistic(7.3, 19.4))
transforms.append(
gpflow.transforms.positive(gpflow.transforms.Rescale(
9.5))) # test __call__() and chaining
transforms.append(
gpflow.transforms.Rescale(9.5)(
gpflow.transforms.positive)) # test __call__() and chaining
return x, x_np, transforms