On Tue, Oct 18, 2011 at 1:15 AM, Vlad Niculae <[email protected]> wrote: > At the moment I have no idea what the cause is. Does it behave > in the same way if you use the gram solver instead?
Yes. It behaves in the same way. This is the result of the same experiment with the addition of p_gram, the probability of recovery using orthogonal_mp_gram: n: 256, m: 30, s: 10 -> p_omp = 0.20 p_naive = 0.31 p_gram = 0.24 n: 256, m: 50, s: 10 -> p_omp = 0.73 p_naive = 0.95 p_gram = 0.72 n: 256, m: 70, s: 10 -> p_omp = 0.82 p_naive = 0.98 p_gram = 0.86 n: 256, m: 90, s: 10 -> p_omp = 0.83 p_naive = 1.00 p_gram = 0.84 n: 256, m: 110, s: 10 -> p_omp = 0.83 p_naive = 0.99 p_gram = 0.82 n: 256, m: 130, s: 10 -> p_omp = 0.82 p_naive = 0.99 p_gram = 0.83 n: 256, m: 150, s: 10 -> p_omp = 0.87 p_naive = 0.99 p_gram = 0.82 n: 256, m: 170, s: 10 -> p_omp = 0.83 p_naive = 0.99 p_gram = 0.80 n: 256, m: 190, s: 10 -> p_omp = 0.83 p_naive = 0.99 p_gram = 0.82 Alejandro. ------------------------------------------------------------------------------ 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. Business sense. IT sense. Common sense. http://p.sf.net/sfu/splunk-d2d-oct _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
