Github user takuti commented on the issue:
https://github.com/apache/incubator-hivemall/pull/79
I supported `-mini_batch` option for
[regressor](https://github.com/takuti/incubator-hivemall/blob/c3b89f8a671a1ccf7a0c19e9f061d61c6e0c2807/core/src/main/java/hivemall/regression/GeneralRegressionUDTF.java#L121-L181)
and
[classifier](https://github.com/takuti/incubator-hivemall/blob/c3b89f8a671a1ccf7a0c19e9f061d61c6e0c2807/core/src/main/java/hivemall/classifier/GeneralClassifierUDTF.java#L122-L182)
(same code).
The idea is just accumulating `new_weight` obtained from
`optimizer.update()`. Once `miniBatchSize` samples are observed, a mean value
of the accumulated `new_weight` will be set to a model via `model.setWeight`.
For SGD, it's clearly equivalent to [what RegressorBaseUDTF
does](https://github.com/takuti/incubator-hivemall/blob/5dc6f4eb5a8d8532201f6706673e2381d47d7e70/core/src/main/java/hivemall/regression/RegressionBaseUDTF.java#L247-L251).
However, I'm a little bit afraid if I can do the same thing for Adagrad, Adam,
Adadelta and AdagradRDA. (Currently, doing the same thing for Adagrad, Adam and
Adadelta are allowed. By contrast, AdagradRDA + `-mini_batch` option is not
supported.)
BTW, practically, I observed that the naive Adagrad + `-mini_batch`
implementation seems to work correctly as shown in the next comment:
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