Corrected regularization and rmse in non-negative solvers #839
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arvoelke
changed the title
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This doesn't effect the standard solver, does it? |
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Nope. It just makes the non-negative solvers comparable in regularization/rmse to the standard solver. |
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| logger = logging.getLogger(__name__) | |||
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| def _rmses(A, Y, X): | |||
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Not a big deal, but I would prefer the order of arguments here to be A, X, Y or Y, A, X, probably the first since the system is typically written AX = Y.
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Made suggested changes. |
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This looks good to me. |
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Me too, just needs some squashin'. You want to merge @hunse? |
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sure |
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Previously, the
'rmses' for the non-negative solvers were off by an order of magnitude, because they were with respect to the Gram system instead of the original system. This has been fixed to make these values now comparable to the other solvers.This fix has been tested offline using
nengo.utils.connection.eval_point_decodingon the same set of eval points (now gives an exact match). If desired, I could also add a unit test to check that each solver's rmse is correct with respect to the given system.Also, the regularization wasn't scaled consistently with the other solvers. This was causing drastically different performance results when using one solver over another (I also wonder if this contributed in part to the performance improvement in #321). I verified the fix (again offline) by comparing the RMSE when decoding a positive constant function -- they were roughly the same across a variety of regularization coefficients. Again, this can be made into a unit test if desired.