[WIP] Expanding the work on augmentations in a tutorial #81
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theogf
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Oh it looks like the new manifest format is not appreciated? |
st--
reviewed
Dec 1, 2021
src/expected_loglik.jl
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| @@ -75,7 +75,7 @@ function expected_loglik( | |||
| # size(fs): (length(y), n_points) | |||
| fs = √2 * std.(q_f) .* xs' .+ mean.(q_f) | |||
| lls = loglikelihood.(lik.(fs), y) | |||
| return sum((1 / √π) * lls * ws) | |||
| return (1 / √π) * mapreduce(dot, +, eachrow(lls), ws) | |||
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| return (1 / √π) * mapreduce(dot, +, eachrow(lls), ws) | |
| return mapreduce(dot, +, eachrow(lls), ws) / √π |
But also, at this point it's getting really hard to glance at the code and understand what's going on.... what do you think of adding a comment to explaining that?
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How about sticking to julia 1.6 for the docs in this PR, and we can sort out the upgrade separately ? |
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This introduces a new tutorial similar to the classification one using augmentation as in AGP.jl.
It shows both the solution with and without hyperparameters optimization.
I still need to give a brief explanation on how the augmentation works but things are working well otherwise