2012/5/16 Mathieu Blondel <[email protected]>:
>
>
> On Wed, May 16, 2012 at 5:31 AM, Andreas Mueller <[email protected]>
> wrote:
>>
>>
>> The SequentialDataset was made for vector x vector operations. Depending
>> on whether we
>> do mini-batch or online learning, the MLP needs vector x matrix or
>> matrix x matrix operations.
>> In particular matrix x matrix is probably not feasible with the
>> SequentialDataset, though I think
>> even vector x matrix might be ugly and possibly slow, though I'm not
>> sure there.
>>
>> What do you think Mathieu (and the others)?
>
>
> I think that it is worth investigating the separation between the core
> algorithm logic and the data representation dependent parts. SGD used to be
> implemented separately for dense and sparse inputs but the rewrite based on
> SequentialDataset significantly simplified the source code (but Peter is the
> best person to comment on this). David could start by getting the numpy
> array based implementation right, then before implementing the sparse
> version, investigate how to abstract away the data representation dependent
> parts either by using/extending SequentialDataset/WeightVector or by
> creating his own utility classes.
>
> Mathieu
>
> PS: When it makes sense, it would be nice if we could strive to add sparse
> matrix support whenever we add a new estimator.

I totally agree

>
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-- 
Peter Prettenhofer

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