def __init__(self,
input_dim,
locs_poi,
typeIndicator,
active_dims=None,
name=None,
typIdx=1,
lengthscale=None,
effects=None,
locs=None,
mindist=0.5,
kernel_type="linear"):
super().__init__(input_dim, active_dims, name=name)
effects = np.random.uniform(low=0.5,
high=1.0) if effects is None else effects
MeanFunction.__init__(self)
self.locs_poi = locs_poi.astype(np.float64)
self.typeIndicator = typeIndicator.astype(np.float64)
self.typIdx = typIdx
self.locs_poi_j = self.locs_poi[self.typeIndicator[:, self.typIdx] ==
1, :]
self.kernel_type = kernel_type
self.lengthscale = 0 #Parameter(2, transform=transforms.Logistic(a=mindist, b=10), dtype=settings.float_type)
self.effects = Parameter(effects,
transform=transforms.Logistic(a=0.01, b=1),
dtype=settings.float_type)
self.distmax = Parameter(0.5,
transform=transforms.Logistic(a=0, b=1.5),
dtype=settings.float_type)
def __init__(self,
input_dim,
variance=1.0,
frequency=np.array([1.0, 1.0]),
lengthscale=1.0,
correlation=0.0,
max_freq=1.0,
active_dims=None):
assert (input_dim == 1
) # the derivations are valid only for one dimensional input
Kernel.__init__(self, input_dim=input_dim, active_dims=active_dims)
self.variance = Param(variance, transforms.positive)
self.frequency = Param(frequency, transforms.Logistic(0.0, max_freq))
self.lengthscale = Param(lengthscale, transforms.positive)
correlation = np.clip(correlation, 1e-4,
1 - 1e-4) # clip for numerical reasons
self.correlation = Param(correlation, transforms.Logistic())
def __init__(self, num_classes, epsilon=1e-3, **kwargs):
super().__init__(**kwargs)
self.epsilon = Parameter(epsilon,
transforms.Logistic(),
trainable=False,
dtype=settings.float_type,
prior=priors.Beta(0.2, 5.))
self.num_classes = num_classes
def __init__(self, delta=1e-3, a=0., **kwargs):
super().__init__(**kwargs)
self.delta = Parameter(delta,
transforms.Logistic(),
trainable=False,
dtype=settings.float_type,
prior=priors.Beta(0.2, 5.))
self.a = Parameter(a,
transforms.positive,
trainable=False,
dtype=settings.float_type)
def initialize_model(x_s,
y,
m,
q,
mu=None,
s=None,
joint=False,
infer_type="diag",
xi_kern="RBF",
x_st_infer=None,
x_st_test=None):
"""
:param use_kronecker:
:param t: Inputs (i.e. simulator inputs)
:param x_s: spatiotemporal inputs (just temporal for KO...)
:type x_s: Iterable of 2D np.ndarrays
:param y: outputs
:param m:
:param q:
:param joint: if true then we're training a joint model with 2 channels
(output dimensions). Theya re assumed to be provided as
column-concatenated in y.
:param infer_type: How to infer latent variables. Options:
* "diag": VI with diagonal Gaussian variational posterior
* "full": VI will full Gaussian variational posterior
* "mcmc": MCMC particle inference
:return:
"""
n_s = np.prod([x_si.shape[0] for x_si in x_s])
# Providing transformed variables...
if mu is None:
if joint:
y_pca = y[:, :n_s] # Inputs only
else:
y_pca = y
mu = pca(y_pca, q)
s = 0.1 * np.ones(mu.shape)
supervised = False
train_kl = True
else:
supervised = True
train_kl = False
if m == mu.shape[0]:
z = mu
else:
z = None
x = [mu] + x_s
d_in = [x_i.shape[1] for x_i in x]
with gpflow.defer_build():
# X -> Y kernels
if xi_kern == "Linear":
kern_list = [Linear(d_in[0], variance=0.01, ARD=True)]
kern_list[-1].variance.transform = transforms.Logistic(
1.0e-12, 1.0)
elif xi_kern == "RBF":
kern_list = [RBF(d_in[0], lengthscales=np.sqrt(d_in[0]), ARD=True)]
kern_list[-1].lengthscales.transform = transforms.Logistic(
1.0e-12, 1000.0)
kern_list[-1].lengthscales.prior = Gamma(mu=2.0, var=1.0)
elif xi_kern == "Sum":
kern_list = [
gpflow.kernels.Sum([
RBF(d_in[0], lengthscales=np.sqrt(d_in[0]), ARD=True),
Linear(d_in[0], variance=0.01, ARD=True)
])
]
kern_list[-1].kernels[0].lengthscales.transform = \
transforms.Logistic(1.0e-12, 1000.0)
kern_list[-1].kernels[0].lengthscales.prior = Gamma(mu=2.0,
var=1.0)
kern_list[-1].kernels[1].variance.transform = \
transforms.Logistic(1.0e-12, 1.0)
else:
raise NotImplementedError("Unknown xi kernel {}".format(xi_kern))
for d_in_i in d_in[1:]:
kern_list.append(
Exponential(d_in_i, lengthscales=np.sqrt(d_in_i), ARD=True))
for kern in kern_list[1:]:
kern.lengthscales = 0.1
# Restructure the inputs for the SGPLVM:
if joint:
y_structured = np.concatenate((y[:, :n_s].reshape(
(-1, 1)), y[:, n_s:].reshape((-1, 1))), 1)
else:
y_structured = y.reshape((-1, 1))
# Initialize model:
model_types = {"diag": Sgplvm, "full": SgplvmFullCovInfer}
if infer_type not in model_types:
raise NotImplementedError(
"No suport for infer_type {}".format(infer_type))
else:
kgplvm = model_types[infer_type]
model = kgplvm(x,
s,
y_structured,
kern_list,
m,
z,
train_kl=train_kl,
x_st_infer=x_st_infer,
x_st_test=x_st_test)
model.likelihood.variance = 1.0e-2 * np.var(y)
if supervised:
# Lock provided inputs
model.X0.trainable = False
model.h_s.trainable = False
model.compile()
return model
