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hunse
reviewed
Mar 28, 2018
| class DalesL2(Dales): | ||
| """Solves for weights subject to Dale's principle with regularisation.""" | ||
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| def __call__(self, A, Y, rng=None, E=None, sigma=0.): |
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sigma shouldn't be a parameter that's passed in here. Rather, the instance should have a reg parameter, which is then used to compute sigma using the max value in A. Just like in LstsqL2.
hunse
reviewed
Mar 28, 2018
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| # assert that weights themselves are close (this is true for L2 weights) | ||
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| assert np.allclose(W1, W2) |
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You might want to check that the signs of the weights are as requested (i.e. the correct number positive/negative).
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Based off the code described in this Nengo issue. Equivalent in effect to the Parisien transform, but has better performance.
Still to do:
nnlssolver in SciPy has problems andcvxoptshould be used instead, as shown in this forum post.