def build_prior_KL(self):
"""
We return the KL for all latent funtions
"""
KL = 0
for q in np.arange(self.rank): # q is the group id.
for i in np.arange(self.num_latent_list[q]):
lat_id = np.sum(self.num_latent_list[:q],dtype = np.int64) + i #id of latent function
if self.whiten_list[lat_id]:
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_white_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], self.dim)
else:
KL += kullback_leiblers.gauss_kl_white(self.q_mu_list[lat_id],self.q_sqrt_list[lat_id], self.dim)
else:
K = self.kern_list[q].K(self.Z[lat_id]) + eye(self.num_inducing_list[lat_id]) * 1e-6
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], K, self.dim)
else:
KL += kullback_leiblers.gauss_kl(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], K, self.dim)
if self.tsk:
for d in np.arange(self.num_tasks):
lat_id = np.sum(self.num_latent_list,dtype = np.int64) + d#id of latent function
if self.whiten_list[lat_id]:
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_white_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], self.dim)
else:
KL += kullback_leiblers.gauss_kl_white(self.q_mu_list[lat_id],self.q_sqrt_list[lat_id], self.dim)
else:
K = self.tskern_list[d].K(self.Z[lat_id]) + eye(self.num_inducing_list[lat_id]) * 1e-6 ## compute with the ith kernel
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id],K, self.dim)
else:
KL += kullback_leiblers.gauss_kl(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id],K, self.dim)
return KL
def build_prior_KL(self):
"""
We return the KL for all latent funtions
"""
KL = 0
for i in np.arange(self.rank): # i is the group id.
for j in np.arange(self.num_latent_list[i]):
lat_id = np.sum(self.num_latent_list[:i],dtype = np.int64) + j #id of latent function
if self.whiten_list[lat_id]:
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_white_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], self.dim)#rotates the coordinate system to make it independent
else:
KL += kullback_leiblers.gauss_kl_white(self.q_mu_list[lat_id],self.q_sqrt_list[lat_id], self.dim)
else:
K = self.kern_list[i].K(self.Z[lat_id]) + eye(self.num_inducing_list[lat_id]) * 1e-6 ## compute with the ith kernel
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], K, self.dim)
else:
KL += kullback_leiblers.gauss_kl(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], K, self.dim)
if self.tsk:
for task_id in np.arange(self.num_tasks):
lat_id = np.sum(self.num_latent_list,dtype = np.int64) + task_id#id of latent function
if self.whiten_list[lat_id]:
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_white_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id], self.dim)
#rotates the coordinate system to make it independent
else:
KL += kullback_leiblers.gauss_kl_white(self.q_mu_list[lat_id],self.q_sqrt_list[lat_id], self.dim)
else:
K = self.tskern_list[task_id].K(self.Z[lat_id]) + eye(self.num_inducing_list[lat_id]) * 1e-6 ## compute with the ith kernel
if self.q_diag_list[lat_id]:
KL += kullback_leiblers.gauss_kl_diag(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id],K, self.dim)
else:
KL += kullback_leiblers.gauss_kl(self.q_mu_list[lat_id], self.q_sqrt_list[lat_id],K, self.dim)
return KL
def build_prior_KL(self):
KL = None
for d in xrange(self.X.shape[1]):
q_mu_d = self.__getattribute__('q_mu_%d' % d)
q_sqrt_d = self.__getattribute__('q_sqrt_%d' % d)
Z_d = self.__getattribute__('Z_%d' % d)
if self.whiten:
if self.q_diag:
KL_d = kullback_leiblers.gauss_kl_white_diag(q_mu_d, q_sqrt_d, self.num_latent)
else:
KL_d = kullback_leiblers.gauss_kl_white(q_mu_d, q_sqrt_d, self.num_latent)
else:
K = self.kern[d].K(Z_d) + eye(self.num_inducing[d]) * 1e-6
if self.q_diag:
KL_d = kullback_leiblers.gauss_kl_diag(q_mu_d, q_sqrt_d, K, self.num_latent)
else:
KL_d = kullback_leiblers.gauss_kl(q_mu_d, q_sqrt_d, K, self.num_latent)
# add things up, we were too lazy to check the type of KL_d
if KL is None:
KL = KL_d
else:
KL += KL_d
return KL
def build_prior_KL(self):
if self.whiten:
if self.q_diag:
KL = kullback_leiblers.gauss_kl_white_diag(self.q_mu, self.q_sqrt, self.num_latent)
else:
KL = kullback_leiblers.gauss_kl_white(self.q_mu, self.q_sqrt, self.num_latent)
else:
K = self.kern.K(self.Z) + eye(self.num_inducing) * 1e-6
if self.q_diag:
KL = kullback_leiblers.gauss_kl_diag(self.q_mu, self.q_sqrt, K, self.num_latent)
else:
KL = kullback_leiblers.gauss_kl(self.q_mu, self.q_sqrt, K, self.num_latent)
return KL
def build_prior_KL(self):
if self.whiten:
if self.q_diag:
KL = kullback_leiblers.gauss_kl_white_diag(
self.q_mu, self.q_sqrt)
else:
KL = kullback_leiblers.gauss_kl_white(self.q_mu, self.q_sqrt)
else:
K = self.kern.K(self.Z) + tf.eye(
self.num_inducing,
dtype=_settings.tf_float) * _settings.jitter_level
if self.q_diag:
KL = kullback_leiblers.gauss_kl_diag(self.q_mu, self.q_sqrt, K)
else:
KL = kullback_leiblers.gauss_kl(self.q_mu, self.q_sqrt, K)
return KL
def build_prior_KL(self):
KL = tf.Variable(0, name='KL', trainable=False, dtype=float_type)
for i in range(self.D):
if self.whiten:
if self.q_diag:
KL += gauss_kl_white_diag(self.q_mu[i], self.q_sqrt[i])
else:
KL += gauss_kl_white(self.q_mu[i], self.q_sqrt[i])
else:
K = self.kerns[i].K(self.Zs[self.f_indices[i]]) + eye(
self.num_inducing[i]) * jitter_level
if self.q_diag:
KL += gauss_kl_diag(self.q_mu[i], self.q_sqrt[i], K)
else:
KL += gauss_kl(self.q_mu[i], self.q_sqrt[i], K)
return KL
def build_prior_KL(self):
if self.whiten:
if self.q_diag:
KL = kullback_leiblers.gauss_kl_white_diag(
self.q_mu, self.q_sqrt, self.num_latent)
else:
KL = kullback_leiblers.gauss_kl_white(self.q_mu, self.q_sqrt,
self.num_latent)
else:
K = self.kern.K(self.Z) + eye(self.num_inducing) * 1e-6
if self.q_diag:
KL = kullback_leiblers.gauss_kl_diag(self.q_mu, self.q_sqrt, K,
self.num_latent)
else:
KL = kullback_leiblers.gauss_kl(self.q_mu, self.q_sqrt, K,
self.num_latent)
return KL
def build_prior_KL(self):
S = np.square(self.s) * eye(self.D) # diagonal prior v*I
KL = gauss_kl_diag(self.q_A_mu, self.q_A_sqrt, S)
return KL