Eric, Implementing either of your suggestions (swapping the lines or using an intermediate variable) worked fine under the latest Numpy (v1.16.1)!
Thanks a lot for your help! Best regards, Em ter, 12 de fev de 2019 às 23:06, Eric Wieser <[email protected]> escreveu: > It looks like your code is wrong, and numpy 1.12 happened to let you get > away with it > > This line: > > evals = evals[evals > tolerance] > > Reduces the eigenvalues to only those which are greater than the tolerance > > When you do U[:, evals > tolerance], evals > tolerance is just going to > be [True, True, ...]. > > You need to swap the last two lines, to > > U = U[:, evals > tolerance] > evals = evals[evals > tolerance] > > Or better yet, introduce an intermediate variable: > > keep = evals > tolerance > evals = evals[keep] > U = U[:, keep] > > Eric > > > On Tue, 12 Feb 2019 at 15:16 Mauro Cavalcanti <[email protected]> wrote: > >> Dear ALL, >> >> I am trying to port an eigenalysis function that runs smoothly on Numpy >> 1.12 but fail miserably on Numpy 1.13 or higher with the dreadful error >> "boolean index did not match indexed array along dimension 1". >> >> Here is a fragment of the code, where the error occurrs: >> >> evals, evecs = np.linalg.eig(Syy) >> idx = evals.argsort()[::-1] >> evals = np.real(evals[idx]) >> U = np.real(evecs[:, idx]) >> evals = evals[evals > tolerance] >> U = U[:, evals > tolerance] # Here is where the error occurs >> >> So, I ask: is there a way out of this? >> >> Thanks in advance for any assistance you can provide. >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > https://mail.python.org/mailman/listinfo/numpy-discussion > -- Dr. Mauro J. Cavalcanti E-mail: [email protected] Web: http://sites.google.com/site/maurobio "Life is complex. It consists of real and imaginary parts."
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