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Add support for sigma=0 in normally distributed hyperparameters #285
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Thanks a lot for pulling out this change. Could you please add some comments in this PR on the changes (so it will be easy later to understand this PR) and add some comments (at least where I requested them)?
Codecov ReportBase: 67.97% // Head: 67.97% // No change to project coverage 👍
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Hi, this PR is out of date and will entirely be so once #346 is merged. As a result, I will close this but thank you for the PR! However I was still wondering why there's a need for a dirac delta function, given this is equivalent to a |
The idea was to autotune with ytopt using certain fixed values that might change later on. For example, autotuning the multiplication of a 32 x 32 matrix and a 32 x 1000 matrix at first and then later autotuning the multiplication of a 32 x 32 and 32 x 5000 matrix (but using the data from the first case to inform initial model). I think I tried using |
Thanks for the use case! Would it ha e helped if you could easily copy and modify a Configuration space between executions? A lot of ConfigSpace relies on hyperparameter's attributes remaining fixed but there's no real API for mutating spaces or hyperparameter's which I guess might have helped here |
Yeah, that likely would have been helpful. |
Moving this to separate pull request per #280
This pull request does mainly two things:
mu
. For instancemu=4.5
might be chosen if one desires four and five to be chosen with equal probability. This required changingdefault_value
to bemu
rounded to the nearest integer when not otherwise specified.sigma=0
in NormalIntegerHyperparameter objects. This means the distribution is a Dirac delta function and the only value returned from the distribution is the mean (mu
). If themu
is not an integer then an error is raised during instantiation.