Inhibition #26
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Inhibition #26
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dasmy
commented
May 25, 2021
dasmy
commented
May 25, 2021
tnmf/TransformInvariantNMF.py
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| self._inhibition_range = tuple(np.ceil(a / 2) for a in atom_shape) if inhibition_range is None else inhibition_range | ||
| assert len(self._inhibition_range) == len(atom_shape) | ||
| inhibition_kernels_1D = list((1 - ((np.arange(-i, i + 1) / i) ** 2) for i in self._inhibition_range)) | ||
| self._inhibition_kernel = np.prod(np.array(np.meshgrid(*inhibition_kernels_1D)), axis=0) |
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atom.ndim separate 1D-convolutions are more efficient than this multi-D convolution
dasmy
commented
May 25, 2021
tnmf/TransformInvariantNMF.py
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| # TODO: maybe also add cross-channel/cross-atom inhibition? | ||
| convolve_axes = range(-len(self.atom_shape), 0) | ||
| inhibition_gradient = convolvend( | ||
| self.H, self._inhibition_kernel[np.newaxis, np.newaxis, :], mode='same', axes=convolve_axes) |
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This has to be moved into the backend.
|
Fixed all comments we talked about. Btw.: This fixes #25 |
Both reach the same level of sparsity with considerably different reconstruction quality
i.e. no inhibition, to fix existing unit tests
ususally, it does not make sense to set inhibition and sparsity_H > 0. Thus, setting both to default 0 is more intuitive
…convolutions unfortunately, there is no oaconvolve1D
I'd rather not destroy the tabular layout by adding line breaks here
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Hi @AdrianSosic,
This PR adds inhibition essentially identical to how it was done in the old implementation (except for that this one is supporting different inhibition kernel sizes in the different directions, which is in line with support for non-square atoms).
What do you think? - Maybe, adding cross-atom inhibition might make sense, but this could also be a follow-up project as parametrization, etc. would still have to be discussed...