def cov_embedding(covariances):
"""Returns for a list of matrices a list of transformed
matrices in matrix form, with 1. on the diagonal
"""
ce = CovEmbedding()
covariances = ce.fit_transform(covariances) # shape n(n+1)/2
if covariances is None:
return None
# changed from k=1 to k=0 by Salma
return np.asarray([untri(c, k=0, fill=1.) for c in covariances])
def cov_embedding(covariances):
ce = CovEmbedding()
covariances = ce.fit_transform(covariances)
if covariances is None:
return None
return np.asarray([untri(c, k=1, fill=1.0) for c in covariances])
