Comments (1)
Hi Carlos,
sorry for taking so long to get back to you.
The reason the diagonal is not zero is that we do also average product correction on the pseudo_frob
feature:
pseudo_frob = frobenius_norm_no_gaps(pseudolikelihood)
pseudo_frob -= add_apc(pseudo_frob)
The frobenius_norm_no_gaps
function is:
def frobenius_norm_no_gaps(matrix, q=22):
n = len(matrix) // q
frob = np.zeros((n, n))
for i in range(n):
for j in range(i + 1, n):
# We only take the frobenius norm of everything apart from the gaps.
frob[i, j] = 0.5 * (np.linalg.norm(matrix[i*q:i*q+(q-1), j*q:j*q+(q-1)]) +
np.linalg.norm(matrix[j*q:j*q+(q-1), i*q:i*q+(q-1)]))
frob[j, i] = frob[i, j]
return frob
The add_apc
function is:
def add_apc(matrix):
row_mean = (matrix.sum(axis=1) - np.diag(matrix)) / (len(matrix) - 1.0)
if row_mean.mean() != 0.0:
return np.outer(row_mean, row_mean) / row_mean.mean()
else:
return np.zeros(len(matrix), dtype=np.float32)
I hope this helps.
from deepmind-research.
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