def __init__(self, v, mat_dim, shift=0.001, upper=False, learnable=False):
self.v = v
tri_matrix = BF.triangular_form(v)
if upper:
tri_matrix = BF.transpose(tri_matrix, 2, 1) #TODO: Dangerous, uses inconsistent indexing
self.tri_matrix = tri_matrix
self.diag_indices = np.diag_indices(mat_dim)
self.shift = shift
self.upper = upper
self.learnable = learnable
def forward_transform(self, x, dim):
return BF.matmul(x, BF.transpose(x, -2, -1))
optimizer=optimizer,
lr=lr)
loss_list3 = model.diagnostics["loss curve"]
ELBO3 = model.estimate_log_model_evidence(1000)
# Structured Gaussian distribution (low rank) #
rank = 1
cov_factor = RootVariable(np.random.normal(0, 0.1, (out_size, rank)),
"cov_factor")
cov_shift = RootVariable(0.01 * np.identity(out_size),
"cov_shift",
learnable=False)
mean_shift = RootVariable(np.zeros((out_size, )), "mean_shift", learnable=True)
QV = MultivariateNormal(loc=mean_shift,
covariance_matrix=cov_shift +
BF.matmul(cov_factor, BF.transpose(cov_factor, 2, 1)),
name="V",
learnable=True)
Qgroup_means = [
Normal(QV[n], 4., "group_mean_{}".format(n), learnable=True)
for n in range(N_groups)
]
Qpeople_means = [
Normal(QV[N_groups + m], 0.1, "person_{}".format(m), learnable=True)
for m, assignment_list in enumerate(assignment_matrix)
]
model.set_posterior_model(ProbabilisticModel(Qpeople_means + Qgroup_means))
# Inference #