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 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.
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