Nice.


On Tue, Dec 10, 2013 at 9:11 PM, Arnaud Joly <notifications@github.com>wrote:

>  In sklearn/multiclass.py:
>
> > +        Parameters
> > +        ----------
> > +        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
> > +            Input data.
> > +
> > +        Returns
> > +        -------
> > +        y : array-like, shape = [n_samples] or [n_samples, n_outputs]
> > +            Predicted multilabel target.
> > +        """
> > +        y_coded = self.estimator.predict(X)
> > +        n_classes = len(self.label_binarizer_.classes_)
> > +        n_samples = X.shape[0]
> > +
> > +        y_decoded = np.empty((n_samples, n_classes), dtype=np.int)
> > +        for i in range(n_samples):
>
> Finally, I have found a simple way to perform the decoding with only numpy
> operation and without bounding the maximal number of classes.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/scikit-learn/scikit-learn/pull/2461/files#r8227308>
> .
>