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From Tommaso Teofili <tommaso.teof...@gmail.com>
Subject Re: [ML] - data storage and basic design approach
Date Tue, 10 Jul 2012 08:58:42 GMT
Ok, sure, I'll just add the writables along with DoubleMatrix/Vector with
the AL2 headers on top.
Thanks Thomas for the contribution and feedback.
Tommaso

2012/7/10 Thomas Jungblut <thomas.jungblut@gmail.com>

> Feel free to commit this, but take care to add the apache license headers.
> Also I wanted to add a few testcases over the next few weekends.
>
> 2012/7/10 Tommaso Teofili <tommaso.teofili@gmail.com>
>
> > nice idea, quickly thinking to it it looks to me that (C)GD is a good fit
> > for BSP.
> > Also I was trying to implement some easy meta learning algorithm like the
> > weighed majority algorithm where each peer as a proper learning algorithm
> > and gest penalized for each mistaken prediction.
> > Regarding your math library do you plan to commit it yourself? Otherwise
> I
> > can do it.
> > Regards,
> > Tommaso
> >
> >
> > 2012/7/10 Thomas Jungblut <thomas.jungblut@gmail.com>
> >
> > > Maybe a first good step towards algorithms would be to try to evaluate
> > how
> > > we can implement some non-linear optimizers in BSP. (BFGS or conjugate
> > > gradient method)
> > >
> > > 2012/7/9 Tommaso Teofili <tommaso.teofili@gmail.com>
> > >
> > > > 2012/7/9 Thomas Jungblut <thomas.jungblut@gmail.com>
> > > >
> > > > > For the matrix/vector I would propose my library interface: (quite
> > like
> > > > > mahouts math, but without boundary checks)
> > > > >
> > > > >
> > > >
> > >
> >
> https://github.com/thomasjungblut/tjungblut-math/blob/master/src/de/jungblut/math/DoubleVector.java
> > > > >
> > > > >
> > > > >
> > > >
> > >
> >
> https://github.com/thomasjungblut/tjungblut-math/blob/master/src/de/jungblut/math/DoubleMatrix.java
> > > > > Full Writable for Vector and basic Writable for Matrix:
> > > > >
> > > > >
> > > >
> > >
> >
> https://github.com/thomasjungblut/thomasjungblut-common/tree/master/src/de/jungblut/writable
> > > > >
> > > > > It is an enough to make all machine learning algorithms I've seen
> > until
> > > > now
> > > > > and the builder pattern allows really nice chaining of commands to
> > > easily
> > > > > code equations or translate code from matlab/octave.
> > > > > See for example logistic regression cost function
> > > > >
> > > > >
> > > >
> > >
> >
> https://github.com/thomasjungblut/thomasjungblut-common/blob/master/src/de/jungblut/regression/LogisticRegressionCostFunction.java
> > > >
> > > >
> > > > very nice, +1!
> > > >
> > > >
> > > > >
> > > > >
> > > > > For the interfaces of the algorithms:
> > > > > I guess we need to get some more experience, I can not tell how the
> > > > > interfaces for them should look like, mainly because I don't know
> how
> > > the
> > > > > BSP version of them will call the algorithm logic.
> > > > >
> > > >
> > > > you're right, it's more reasonable to just proceed bottom - up with
> > this
> > > as
> > > > we're going to have a clearer idea while developing the different
> > > > algorithms.
> > > > So for now I'd introduce your library Writables and then proceed 1
> step
> > > at
> > > > a time with the more common API.
> > > > Thanks,
> > > > Tommaso
> > > >
> > > >
> > > >
> > > >
> > > > >
> > > > > But having stable math interfaces is the key point.
> > > > >
> > > > > 2012/7/9 Tommaso Teofili <tommaso.teofili@gmail.com>
> > > > >
> > > > > > Ok, so let's sketch up here what these interfaces should look
> like.
> > > > > > Any proposal is more than welcome.
> > > > > > Regards,
> > > > > > Tommaso
> > > > > >
> > > > > > 2012/7/7 Thomas Jungblut <thomas.jungblut@gmail.com>
> > > > > >
> > > > > > > Looks fine to me.
> > > > > > > The key are the interfaces for learning and predicting
so we
> > should
> > > > > > define
> > > > > > > some vectors and matrices.
> > > > > > > It would be enough to define the algorithms via the interfaces
> > and
> > > a
> > > > > > > generic BSP should just run them based on the given input.
> > > > > > >
> > > > > > > 2012/7/7 Tommaso Teofili <tommaso.teofili@gmail.com>
> > > > > > >
> > > > > > > > Hi all,
> > > > > > > >
> > > > > > > > in my spare time I started writing some basic BSP
based
> machine
> > > > > > learning
> > > > > > > > algorithms for our ml module, now I'm wondering, from
a
> design
> > > > point
> > > > > of
> > > > > > > > view, where it'd make sense to put the training data
/ model.
> > I'd
> > > > > > assume
> > > > > > > > the obvious answer would be HDFS so this makes me
think we
> > should
> > > > > come
> > > > > > > with
> > > > > > > > (at least) two BSP jobs for each algorithm: one for
learning
> > and
> > > > one
> > > > > > for
> > > > > > > > "predicting" each to be run separately.
> > > > > > > > This would allow to read the training data from HDFS,
and
> > > > > consequently
> > > > > > > > create a model (also on HDFS) and then the created
model
> could
> > be
> > > > > read
> > > > > > > > (again from HDFS) in order to predict an output for
a new
> > input.
> > > > > > > > Does that make sense?
> > > > > > > > I'm just wondering what a general purpose design for
Hama
> based
> > > ML
> > > > > > stuff
> > > > > > > > would look like so this is just to start the discussion,
any
> > > > opinion
> > > > > is
> > > > > > > > welcome.
> > > > > > > >
> > > > > > > > Cheers,
> > > > > > > > Tommaso
> > > > > > > >
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
>

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