They are useful when you use the RBM as a feature extractor. transform is
the method that extracts the features, then you can use an SVM on top of
them.

On Sun, Dec 30, 2012 at 2:56 AM, Vlad Niculae <notifications@github.com>wrote:

> In sklearn/neural_networks/rbm.py:
>
> > +
> > +    def transform(self, X):
> > +        """
> > +        Computes the probabilities ``P({\bf h}_j=1|{\bf v}={\bf X})``.
> > +
> > +        Parameters
> > +        ----------
> > +        X: array-like, shape (n_samples, n_features)
> > +
> > +        Returns
> > +        -------
> > +        h: array-like, shape (n_samples, n_components)
> > +        """
> > +        return self.mean_hiddens(X)
> > +
> > +    def mean_hiddens(self, v):
>
> This function along with many of the ones below seem internal, I don't see
> how someone applying an RBM would use them. Am I missing a possible utility
> or should they be underscored? Apart from free_energy and
> pseudo_likelihood I mean.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/scikit-learn/scikit-learn/pull/1200/files#r2520763>.
>
>