Exactly, I was talking about predict and about the state of the estimator. It seemed much more difficult before I thought about it better :)
On Thu, Apr 25, 2013 at 10:54 AM, Mathieu Blondel <[email protected]> wrote: > > On Thu, Apr 25, 2013 at 10:26 AM, Vlad Niculae <[email protected]> wrote: >> >> If we are talking about the same thing, you are returning clusters of >> samples and features together (ie rows and columns). So if in K-means >> we return a 1D array with cluster labels, here the output would be two >> arrays, one of (n_samples,) and one of (n_features,). Another >> alternative would be a list of length `n_clusters` where each element >> is a pair of lists of row, respectively column indices. But I believe >> the first one can be uniform enough wrt our current API. > > > I think you are talking about the predict method. In the case of the fit > method, I think we only need fit(X). Then the fitted attributes could be > row_clusters_ where row_clusters_[i, k] = 1 means that the row i belongs to > cluster k and col_clusters_ where col_clusters_[j, k] = 1 means that column > j belongs to cluster k. > > Mathieu ------------------------------------------------------------------------------ Try New Relic Now & We'll Send You this Cool Shirt New Relic is the only SaaS-based application performance monitoring service that delivers powerful full stack analytics. Optimize and monitor your browser, app, & servers with just a few lines of code. Try New Relic and get this awesome Nerd Life shirt! http://p.sf.net/sfu/newrelic_d2d_apr _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
