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
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