SVD decomposition #57
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Jul 21, 2020
63: WIP: add SVD functionality r=odunbar a=odunbar Resolves #57 . We add the ability for the Gaussian process to learn non - diagonal covariance matrix for the observational noise by applying and SVD. Resolve #58 We add functionality to toggle whether we wish to learn the observational noise. and add (mathematically correct) default values when we are not. We also have 2 examples: - [x] Gaussian process plot. We train a GP on some training points in 2D space, and plot the mean and variance (compared with the underlying model and observational noise) - in the untransformed space - [x] Noise learning test. We train a GP with known noise, with `learn_noise = false` and with `learn_noise = true` we compare the learned `WhiteKernel` parameters to the true unlearned parameters. Coauthored with @bielim Co-authored-by: odunbar <odunbar@caltech.edu> Co-authored-by: Melanie Bieli <melanie.bieli@bluewin.ch> Co-authored-by: bielim <bielim@users.noreply.github.com> Co-authored-by: odunbar <47412152+odunbar@users.noreply.github.com>
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In the papers to be released on the GCM use of CES we use an SVD decomposition to transform the space in which we learn with GP. I suggest we implement this here.
Primarily this allows the learning of noise to be exact and reguralization to be precise, as we transform into a space where the observational noise is not only diagonal but we can normalize it to be exactly the identity. Then the built in
alphaorobservational_noiseparameters in the GP are more naturally chosen.This will involve firstly an SVD implementation within the
GPEmulatorclass, and secondly a modifiedMCMCclass to work in the transformed variables.The text was updated successfully, but these errors were encountered: