{"push_id":2428411188,"size":2,"distinct_size":2,"ref":"refs/heads/feature/minibatch-preprocessing-grouping","head":"41afa92102767c32e17e622a81c1a968bda8772f","before":"cbc826c5f307afae82ab4557a5aaf2851bb87b03","commits":[{"sha":"a595e2218c95685d73dc51f28e9fc1f0193abce9","author":{"name":"Nandish Jayaram","email":"4dde1d31662c83dc3647b0f4cce7af527b1138c1@apache.org"},"message":"MLP: Add minibatch gradient descent solver\n\nJIRA: MADLIB-1206\n\nThis commit adds support for mini-batch based gradient descent for MLP.\nIf the input table contains a 2D matrix for independent variable,\nminibatch is automatically used as the solver. Two minibatch specific\noptimizers are also introduced: batch_size and n_epochs.\n- batch_size is defaulted to min(200, buffer_size), where buffer_size is\nequal to the number of original input rows packed into a single row in\nthe matrix.\n- n_epochs is the number of times all the batches in a buffer are\niterated over (default 1).\n\nOther changes include:\n- dependent variable in the minibatch solver is also a matrix now. It\nwas initially a vector.\n- Randomize the order of processing a batch within an epoch.\n- MLP minibatch currently doesn't support weights param, an error is\nthrown now.\n- Delete an unused type named mlp_step_result.\n- Add unit tests for newly added functions in python file.\n\nCloses #243\n\nCo-authored-by: Rahul Iyer <riyer@apache.org>\nCo-authored-by: Nikhil Kak <nkak@pivotal.io>","distinct":true,"url":"https://api.github.com/repos/madlib/madlib/commits/a595e2218c95685d73dc51f28e9fc1f0193abce9"},{"sha":"41afa92102767c32e17e622a81c1a968bda8772f","author":{"name":"Nikhil Kak","email":"83ab3f4ad51991c4170b6be4fdd82970805fcb50@pivotal.io"},"message":"Initial commit WIP","distinct":true,"url":"https://api.github.com/repos/madlib/madlib/commits/41afa92102767c32e17e622a81c1a968bda8772f"}]}
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