def main(config):
assert config is not None, ValueError
tf.random.set_seed(config.seed)
gpflow_config.set_default_float(config.floatx)
gpflow_config.set_default_jitter(config.jitter)
X = tf.random.uniform([config.num_test, config.input_dims],
dtype=floatx())
Z_shape = config.num_cond, config.input_dims
for cls in SupportedBaseKernels:
minval = config.rel_lengthscales_min * (config.input_dims**0.5)
maxval = config.rel_lengthscales_max * (config.input_dims**0.5)
lenscales = tf.random.uniform(shape=[config.input_dims],
minval=minval,
maxval=maxval,
dtype=floatx())
q_sqrt = tf.zeros([1] + 2 * [config.num_cond], dtype=floatx())
kern = cls(lengthscales=lenscales,
variance=config.kernel_variance)
Z = InducingPoints(tf.random.uniform(Z_shape, dtype=floatx()))
const = tf.random.normal([1], dtype=floatx())
model = SVGP(kernel=kern,
likelihood=None,
inducing_variable=Z,
mean_function=mean_functions.Constant(c=const),
q_sqrt=q_sqrt)
mf, Sff = subroutine(config, model, X)
mg, Sgg = model.predict_f(X, full_cov=True)
tol = config.error_tol
assert allclose(mf, mg, tol, tol)
assert allclose(Sff, Sgg, tol, tol)
def main(config):
assert config is not None, ValueError
tf.random.set_seed(config.seed)
gpflow_config.set_default_float(config.floatx)
gpflow_config.set_default_jitter(config.jitter)
X = tf.random.uniform([config.num_test, config.input_dims],
dtype=floatx())
allK = []
allZ = []
Z_shape = config.num_cond, config.input_dims
for cls in SupportedBaseKernels:
minval = config.rel_lengthscales_min * (config.input_dims**0.5)
maxval = config.rel_lengthscales_max * (config.input_dims**0.5)
lenscales = tf.random.uniform(shape=[config.input_dims],
minval=minval,
maxval=maxval,
dtype=floatx())
rel_variance = tf.random.uniform(shape=[],
minval=0.9,
maxval=1.1,
dtype=floatx())
allK.append(
cls(lengthscales=lenscales,
variance=config.kernel_variance * rel_variance))
allZ.append(
InducingPoints(tf.random.uniform(Z_shape, dtype=floatx())))
kern = kernels.SeparateIndependent(allK)
Z = SeparateIndependentInducingVariables(allZ)
Kuu = covariances.Kuu(Z,
kern,
jitter=gpflow_config.default_jitter())
q_sqrt = tf.linalg.cholesky(Kuu)\
* tf.random.uniform(shape=[kern.num_latent_gps, 1, 1],
minval=0.0,
maxval=0.5,
dtype=floatx())
const = tf.random.normal([len(kern.kernels)], dtype=floatx())
model = SVGP(kernel=kern,
likelihood=None,
inducing_variable=Z,
mean_function=mean_functions.Constant(c=const),
q_sqrt=q_sqrt,
whiten=False,
num_latent_gps=len(allK))
mf, Sff = subroutine(config, model, X)
mg, Sgg = model.predict_f(X, full_cov=True)
tol = config.error_tol
assert allclose(mf, mg, tol, tol)
assert allclose(Sff, Sgg, tol, tol)
def main(config):
assert config is not None, ValueError
tf.random.set_seed(config.seed)
gpflow_config.set_default_float(config.floatx)
gpflow_config.set_default_jitter(config.jitter)
X = tf.random.uniform([config.num_test, config.input_dims],
dtype=floatx())
Z_shape = config.num_cond, config.input_dims
for cls in SupportedBaseKernels:
minval = config.rel_lengthscales_min * (config.input_dims**0.5)
maxval = config.rel_lengthscales_max * (config.input_dims**0.5)
lenscales = tf.random.uniform(shape=[config.input_dims],
minval=minval,
maxval=maxval,
dtype=floatx())
base = cls(lengthscales=lenscales,
variance=config.kernel_variance)
kern = kernels.SharedIndependent(base, output_dim=2)
Z = SharedIndependentInducingVariables(
InducingPoints(tf.random.uniform(Z_shape, dtype=floatx())))
Kuu = covariances.Kuu(Z,
kern,
jitter=gpflow_config.default_jitter())
q_sqrt = tf.stack([
tf.zeros(2 * [config.num_cond], dtype=floatx()),
tf.linalg.cholesky(Kuu)
])
const = tf.random.normal([2], dtype=floatx())
model = SVGP(kernel=kern,
likelihood=None,
inducing_variable=Z,
mean_function=mean_functions.Constant(c=const),
q_sqrt=q_sqrt,
whiten=False,
num_latent_gps=2)
mf, Sff = subroutine(config, model, X)
mg, Sgg = model.predict_f(X, full_cov=True)
tol = config.error_tol
assert allclose(mf, mg, tol, tol)
assert allclose(Sff, Sgg, tol, tol)
def main(config):
assert config is not None, ValueError
tf.random.set_seed(config.seed)
gpflow_config.set_default_float(config.floatx)
gpflow_config.set_default_jitter(config.jitter)
X = tf.random.uniform([config.num_cond, config.input_dims],
dtype=floatx())
Xnew = tf.random.uniform([config.num_test, config.input_dims],
dtype=floatx())
for cls in SupportedBaseKernels:
minval = config.rel_lengthscales_min * (config.input_dims**0.5)
maxval = config.rel_lengthscales_max * (config.input_dims**0.5)
lenscales = tf.random.uniform(shape=[config.input_dims],
minval=minval,
maxval=maxval,
dtype=floatx())
kern = cls(lengthscales=lenscales,
variance=config.kernel_variance)
const = tf.random.normal([1], dtype=floatx())
K = kern(X, full_cov=True)
K = tf.linalg.set_diag(
K,
tf.linalg.diag_part(K) + config.noise_variance)
L = tf.linalg.cholesky(K)
y = L @ tf.random.normal([L.shape[-1], 1],
dtype=floatx()) + const
model = GPR(kernel=kern,
noise_variance=config.noise_variance,
data=(X, y),
mean_function=mean_functions.Constant(c=const))
mf, Sff = subroutine(config, model, Xnew)
mg, Sgg = model.predict_f(Xnew, full_cov=True)
tol = config.error_tol
assert allclose(mf, mg, tol, tol)
assert allclose(Sff, Sgg, tol, tol)
def const_mean():
return mean_functions.Constant(c=rng.randn(Data.D_out))
def markov_gauss():
cov_params = rng.randn(num_data + 1, D_in, 2 * D_in) / 2.0 # (N+1)xDx2D
Xcov = cov_params @ np.transpose(cov_params, (0, 2, 1)) # (N+1)xDxD
Xcross = cov_params[:-1] @ np.transpose(cov_params[1:], (0, 2, 1)) # NxDxD
Xcross = np.concatenate((Xcross, np.zeros((1, D_in, D_in))),
0) # (N+1)xDxD
Xcov = np.stack([Xcov, Xcross]) # 2x(N+1)xDxD
return MarkovGaussian(Xmu_markov, ctt(Xcov))
_means = {
"lin": mf.Linear(A=rng.randn(D_in, D_out), b=rng.randn(D_out)),
"identity": mf.Identity(input_dim=D_in),
"const": mf.Constant(c=rng.randn(D_out)),
"zero": mf.Zero(output_dim=D_out),
}
_distrs = {
"gauss":
Gaussian(Xmu, Xcov),
"dirac_gauss":
Gaussian(Xmu, np.zeros((num_data, D_in, D_in))),
"gauss_diag":
DiagonalGaussian(Xmu, rng.rand(num_data, D_in)),
"dirac_diag":
DiagonalGaussian(Xmu, np.zeros((num_data, D_in))),
"dirac_markov_gauss":
MarkovGaussian(Xmu_markov, np.zeros((2, num_data + 1, D_in, D_in))),
"markov_gauss":
def const():
return mean_functions.Constant(rng.rand(Data.D_out))
class Data:
rng = np.random.RandomState(1)
num_data = 5
num_ind = 4
D_in = 2
D_out = 2
Xmu = rng.randn(num_data, D_in)
L = gen_L(rng, num_data, D_in, D_in)
Xvar = np.array([l @ l.T for l in L])
Z = rng.randn(num_ind, D_in)
# distributions don't need to be compiled (No Parameter objects)
# but the members should be Tensors created in the same graph
graph = tf.Graph()
with test_util.session_context(graph) as sess:
gauss = Gaussian(tf.constant(Xmu), tf.constant(Xvar))
dirac = Gaussian(tf.constant(Xmu),
tf.constant(np.zeros((num_data, D_in, D_in))))
gauss_diag = DiagonalGaussian(tf.constant(Xmu),
tf.constant(rng.rand(num_data, D_in)))
dirac_diag = DiagonalGaussian(tf.constant(Xmu),
tf.constant(np.zeros((num_data, D_in))))
dirac_markov_gauss = MarkovGaussian(
tf.constant(Xmu), tf.constant(np.zeros((2, num_data, D_in, D_in))))
# create the covariance for the pairwise markov-gaussian
dummy_gen = lambda rng, n, *shape: np.array(
[rng.randn(*shape) for _ in range(n)])
L_mg = dummy_gen(rng, num_data, D_in, 2 * D_in) # N+1 x D x 2D
LL = np.concatenate((L_mg[:-1], L_mg[1:]), 1) # N x 2D x 2D
Xcov = LL @ np.transpose(LL, (0, 2, 1))
Xc = np.concatenate((Xcov[:, :D_in, :D_in], Xcov[-1:, D_in:, D_in:]),
0) # N+1 x D x D
Xcross = np.concatenate(
(Xcov[:, :D_in, D_in:], np.zeros(
(1, D_in, D_in))), 0) # N+1 x D x D
Xcc = np.stack([Xc, Xcross]) # 2 x N+1 x D x D
markov_gauss = MarkovGaussian(Xmu, Xcc)
with gpflow.decors.defer_build():
# features
ip = features.InducingPoints(Z)
# kernels
rbf_prod_seperate_dims = kernels.Product([
kernels.RBF(1,
variance=rng.rand(),
lengthscales=rng.rand(),
active_dims=[0]),
kernels.RBF(1,
variance=rng.rand(),
lengthscales=rng.rand(),
active_dims=[1])
])
rbf_lin_sum = kernels.Sum([
kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()),
kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand()),
kernels.Linear(D_in, variance=rng.rand())
])
rbf = kernels.RBF(D_in, variance=rng.rand(), lengthscales=rng.rand())
lin_kern = kernels.Linear(D_in, variance=rng.rand())
# mean functions
lin = mean_functions.Linear(rng.rand(D_in, D_out), rng.rand(D_out))
iden = mean_functions.Identity(
D_in) # Note: Identity can only be used if Din == Dout
zero = mean_functions.Zero(output_dim=D_out)
const = mean_functions.Constant(rng.rand(D_out))