Method transform

ot/da.py:2733–2787

r""" Computes the images of the new source samples :code:`Xs` of classes :code:`ys` by the fitted Smooth Strongly Convex Nearest Brenier Potentials (SSNB) :ref:`[58]`. The output is the images of two SSNB optimal maps, called 'lower' and 'upper' potentials (from :ref:`[59]`,

(self, Xs, ys=None)

        return self

    def transform(self, Xs, ys=None):
        r"""
        Computes the images of the new source samples :code:`Xs` of classes :code:`ys` by the fitted
        Smooth Strongly Convex Nearest Brenier Potentials (SSNB) :ref:`[58]`. The output is the images of two SSNB optimal
        maps, called 'lower' and 'upper' potentials (from :ref:`[59]`, Theorem 3.14).

        Wrapper for :code:`nearest_brenier_potential_predict_bounds`.

        .. warning:: This function requires the CVXPY library
        .. warning:: Accepts any backend but will convert to Numpy then back to the backend.

        Parameters
        ----------
        Xs : array-like (m, d)
            input source points
        ys : : array_like (m,), optional
            classes of the input source points, defaults to a single class

        Returns
        -------
        G_lu : array-like (2, m, d)
            gradients of the lower and upper bounding potentials at Y (images of the source inputs)

        References
        ----------

        .. [58] François-Pierre Paty, Alexandre d’Aspremont, and Marco Cuturi. Regularity as regularization:
                Smooth and strongly convex brenier potentials in optimal transport. In International Conference
                on Artificial Intelligence and Statistics, pages 1222–1232. PMLR, 2020.

        .. [59] Adrien B Taylor. Convex interpolation and performance estimation of first-order methods for
                convex optimization. PhD thesis, Catholic University of Louvain, Louvain-la-Neuve, Belgium,
                2017.

        See Also
        --------
        ot.mapping.nearest_brenier_potential_predict_bounds : Predicting SSNB images on new source data

        """
        returned = nearest_brenier_potential_predict_bounds(
            self.fit_Xs,
            self.phi,
            self.G,
            Xs,
            X_classes=self.fit_ys,
            Y_classes=ys,
            strongly_convex_constant=self.strongly_convex_constant,
            gradient_lipschitz_constant=self.gradient_lipschitz_constant,
            log=self.log,
        )
        if self.log:
            _, G_lu, self.predict_log = returned
        else:
            _, G_lu = returned
        return G_lu