def logdet_dense_chol(R,U,rows,cols):
if not is_psd_dense(R, U, rows, cols):
return -np.Inf
X = build_dense(R, U, rows, cols)
C = linalg.cholesky(X).diagonal()
return np.sum(np.log(C))*2
def logdet_dense(R,U,rows,cols):
if not is_psd_dense(R, U, rows, cols):
return -np.Inf
X = build_dense(R, U, rows, cols)
eis = linalg.eigvalsh(X)
return np.sum(np.log(eis))
