On 2011-12-05, at 5:50 PM, Ian Goodfellow wrote: > > I think I was mostly confused by the terminology-- I don't consider the code > to be part of a sparse coding model, nor to be estimated (I am aware that > sparse coding involves iterative optimization but I don't consider the > optimizer > to be solving an estimation problem). > > I don't understand exactly what interface Alexandre is saying to use. > > To use the sparse_encode interface, should I pass a dictionary of shape > (num_data_features, num_code_elements) for X and a data matrix of shape > (num_data_features, num_examples) for Y? > > I have tried doing that, but for alpha = 1. or alpha = 0.1 it returns > a matrix of > all zeros, and for alpha = .01 it returns a code with NaNs in it.
This actually gets at something I've been meaning to fiddle with and report but haven't had time: I'm not sure I completely trust the coordinate descent implementation in scikit-learn, because it seems to give me bogus answers a lot (i.e., the optimality conditions necessary for it to be an actual solution are not even approximately satisfied). Are you guys using something weird for the termination condition? David ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
