def test_transforming():
k = TEST_CASES[6][0]
def vscaling_fn(x):
return x.sum(dim=1)
def iwarping_fn(x):
return x**2
owarping_coef = [2, 0, 1, 3, 0]
K = k(X, Z)
K_iwarp = k(iwarping_fn(X), iwarping_fn(Z))
K_owarp = 2 + K**2 + 3 * K**3
K_vscale = vscaling_fn(X).unsqueeze(1) * K * vscaling_fn(Z).unsqueeze(0)
assert_equal(K_iwarp.data, Warping(k, iwarping_fn=iwarping_fn)(X, Z).data)
assert_equal(K_owarp.data,
Warping(k, owarping_coef=owarping_coef)(X, Z).data)
assert_equal(K_vscale.data,
VerticalScaling(k, vscaling_fn=vscaling_fn)(X, Z).data)
assert_equal(K.exp().data, Exponent(k)(X, Z).data)
# test get_subkernel
k1 = Sum(Warping(k, iwarping_fn=iwarping_fn), TEST_CASES[7][0])
assert k1.get_subkernel(k.name) is k
def __init__(self,
input_dim,
feature_dim=None,
mean_fn=None,
embed_fn=None):
super(DMEGP, self).__init__()
# store params
self.input_dim = input_dim
self.feature_dim = feature_dim
# define mean function and embedding function
self.mean_fn = mean_fn
self.embed_fn = embed_fn
# define kernel function
if embed_fn == None:
feature_dim = input_dim
kernel = RBF(feature_dim, lengthscale=torch.ones(feature_dim))
else:
kernel = RBF(feature_dim, lengthscale=torch.ones(feature_dim))
kernel = Warping(kernel, iwarping_fn=self.embed_fn)
if mean_fn != None:
self.mean_fn = Warping_mean(self.mean_fn, self.embed_fn)
# define gaussian process regression model
self.gp_model = GPRegression(
X=torch.ones(1, feature_dim), # dummy
y=None,
kernel=kernel,
mean_function=self.mean_fn)
def define_new_GP(self):
# define kernel function
if self.embed_fn == None:
feature_dim = self.input_dim
kernel = RBF(feature_dim, lengthscale=torch.ones(feature_dim))
else:
feature_dim = self.feature_dim
embed_fn = copy.deepcopy(self.embed_fn)
kernel = RBF(feature_dim, lengthscale=torch.ones(feature_dim))
kernel = Warping(kernel, iwarping_fn=embed_fn)
if self.mean_fn != None:
mean_fn = copy.deepcopy(self.mean_fn)
# define gaussian process regression model
gp_model = GPRegression(
X=torch.ones(1, feature_dim), # dummy
y=None,
kernel=kernel,
mean_function=mean_fn)
return gp_model