Hey Rajat, > After perusing the docs on the Mahout site, it seems like the > following algorithms havent been implemented yet- > Locally-Weighted Linear Regression > Linear Regression
Implementing LWLR was an initial goal of the project since LWLR is also mentioned in the Stanford paper that talks about doing machine learning in a map-reduce way. I said I would look into implementing it a long time ago (maybe a year or even one and a half) but so far just haven't gotten around to actually do it. I don't think that it would be too much work, maybe a weekend and some evening. I probably just should try to get my shit together and just implement it. Now there would be a bit more motivation with knowing that there's someone who would actually use it. Linear Regression is just a degenerated LWLR where all weights are equal to 1. > Basically, there is a stock market phenomenon which I'm trying to > predict. It is called a short squeeze. I have about 16,000 data points > - stocks and a point in time where the phenomenon has occurred. I'm > trying to develop a predictive model in a hadoop cluster. As others have already pointed out, you wouldn't see a noticable difference when using Mahout to do this. It could easily be done on a single machine. However, if it's not about this particular problem but about a principle implementation and showing that a speedup is possible, it would make sense to implement it using Mahout/Hadoop. But for just solving the regression problem I would just code it in Matlab (one-liner using the \ operator). Alex -- PGP Public Key: http://www.tu-ilmenau.de/~alha-in/ahans.asc Fingerprint: E110 4CA3 288A 93F3 5237 E904 A85B 4B18 CFDC 63E3
