Open daniel-dodd opened 1 year ago
As an aside to the above, it would be nice to add a high-level sklearn function to train and quickly "update" (or fantasise) new data for use in active learning.
from tunegp import SVGP
svgp = SVGP()
svgp.fit(X_train, y_train) # train with natural gradients.
svgp.update(X_new,y_new) # fantasise new data.
svgp.predict(X_test) # predict
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Though mentioned in meeting minutes, making my development on this public. Will tick things off as and when they are completed.
Checklist
Add:
Rethink:
Refactor:
StochasticVI
object, and clean up training abstractions.StochasticVI
to override the variational expectation rule.Updates
19-Jan: