hi guys,

could you open an issue with a small test script with one X and y that
produce a different result using both implementations?

Alex

On Tue, Oct 18, 2011 at 1:22 PM, Alejandro Weinstein
<[email protected]> wrote:
> 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.
>
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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
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