def lower_half(mat):
# Takes the lower half of the matrix, and half the diagonal.
# Necessary since numpy only uses lower half of covariance matrix.
if len(mat.shape) == 2:
return 0.5 * (np.tril(mat) + np.triu(mat, 1).T)
elif len(mat.shape) == 3:
return 0.5 * (np.tril(mat) + np.swapaxes(np.triu(mat, 1), 1,2))
else:
raise ArithmeticError
def unpack_params(params):
"""Unpacks parameter vector into the proportions, means and covariances
of each mixture component. The covariance matrices are parametrized by
their Cholesky decompositions."""
log_proportions = parser.get(params, 'log proportions')
normalized_log_proportions = log_proportions - logsumexp(log_proportions)
means = parser.get(params, 'means')
lower_tris = np.tril(parser.get(params, 'lower triangles'), k=-1)
diag_chols = np.exp( parser.get(params, 'log diagonals'))
chols = []
for lower_tri, diag in zip(lower_tris, diag_chols):
chols.append(np.expand_dims(lower_tri + np.diag(diag), 0))
chols = np.concatenate(chols, axis=0)
return normalized_log_proportions, means, chols
