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 <[email protected]> > 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 <[email protected]> > > > 2012/7/9 Thomas Jungblut <[email protected]> > > > > > 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 <[email protected]> > > > > > > > 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 <[email protected]> > > > > > > > > > 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 <[email protected]> > > > > > > > > > > > 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 > > > > > > > > > > > > > > > > > > > > >
