2011/11/9 Alexandre Gramfort <[email protected]>: > to complexify a bit the pb note that in the SVM/Lasso/... case the > precomputed gram > is np.dot(X, X.T) which means that the cross-val can be done just with it > while for the covariance estimation, like GraphLassoCV, the empirical > covariance is np.dot(X.T, X) hence the fit needs X as input. > > so it seems to me we have 3 cases: > > - kernel / similarity, shape (n_samples, n_features) > - distance, shape (n_samples, n_features) > - cov, shape (n_features, n_features)
I would rather say: - data / design matrix, shape (n_samples, n_features) - kernel / Gram / similarity / affinity / connectivity, shape (n_samples, n_samples) - distance, shape (n_samples, n_samples) (same shape as kernel but opposite semantics) - covariance, shape (n_features, n_features) - precision, shape (n_features, n_features) (same shape as covariance but inverse semantics) -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
