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* migrate useful functions from previous PR (cherry picked from commit 9f61ab4) * opvi draft (cherry picked from commit d0997ff) * made some test work (cherry picked from commit b1a87d5) * refactored approximation to support aevb (without test) * refactor opvi delete unnecessary methods from operator, change method order * change log_q_local computation * add full rank approximation * add more_params argument to ObjectiveFunction.updates (aevb case) * refactor density computation in full rank approximation * typo: cast dict values to list * typo: cast dict values to list * typo: undefined T in dist_math * refactor gradient scaling as suggested in approximateinference.org/accepted/RoederEtAl2016.pdf * implement Langevin-Stein (LS) operator * fix docstring * add blank line in docs * refactor ObjectiveFunction * add not working LS Op test * experiments with not working LS Op * change activations * refactor networks * add step_function * remove Langevin Stein, done refactoring * remove Langevin Stein, done refactoring * change optimizers * refactor init params * implement tests * implement Inference * code style * test fix * add minibatch test (fails now) * add more tests for minibatch training * add logdet to FullRank approximation * add conversion of arrays to floatX * tiny changes * change number of iterations * fix test and pylint check * memoize functions in Objective function * Optimize code a lot * a bit more efficient pickling * add docs * Add MeanField -> FullRank parameter transfer * refactor MeanField and FullRank a bit * fix FullRank bug with shapes in random * refactor Model.flatten (CC @taku-y) * add `approximate` to inference * rename approximate->fit * change abbreviations * Fix bug with scaling input variable in aevb * fix theano bottleneck in graph * more efficient scaling for local vars * fix typo in local Q * add aevb test * refactor memoize to work with my objects * add tests for numpy view usage * pickle-hash fix * pickle-hash fix again * add node sampling + make up some code * add notebook with example * sample_proba explained
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