On Fri, Dec 14, 2012 at 05:51:08PM +0100, federico vaggi wrote: > In this case, X is:
> (n_samples, n_features, M_bootstrapped_iterations): > So I thought I could get a better estimate by taking each M-length vector and > calculating the covariance against every other vector, which would result in a > (n_samples * n_features, n_samples* n_features) sized matrix, not a > (n_samples, > n_features) sized matrix. Compute an (n_features, n_features) covariance matrix for each bootsrapped iteration and average them. G ------------------------------------------------------------------------------ LogMeIn Rescue: Anywhere, Anytime Remote support for IT. Free Trial Remotely access PCs and mobile devices and provide instant support Improve your efficiency, and focus on delivering more value-add services Discover what IT Professionals Know. Rescue delivers http://p.sf.net/sfu/logmein_12329d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
