(self, X, y=None)
return Xt
For each image in `X`, compute the relevant persistence diagram as
        an array of triples [b, d, q]. Each triple represents a persistent
        topological feature in dimension q (belonging to `homology_dimensions`)
        which is born at b and dies at d. Only triples in which b < d are
        meaningful. Triples in which b and d are equal ("diagonal elements")
        may be artificially introduced during the computation for padding
        purposes, since the number of non-trivial persistent topological
        features is typically not constant across samples. They carry no
        information and hence should be effectively ignored by any further
        computation.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_pixels_1, ..., n_pixels_d)
            Input data. Array of d-dimensional images.

        y : None
            There is no need of a target in a transformer, yet the pipeline API
            requires this parameter.

        Returns
        -------
        Xt : ndarray of shape (n_samples, n_features, 3)
            Array of persistence diagrams computed from the feature arrays or
            distance matrices in `X`. ``n_features`` equals
            :math:`\\sum_q n_q`, where :math:`n_q` is the maximum number of
            topological features in dimension :math:`q` across all samples in
            `X`.
For each image in `X`, compute the relevant persistence diagram as
        an array of triples [b, d, q]. Each triple represents a persistent
        topological feature in dimension q (belonging to `homology_dimensions`)
        which is born at b and dies at d. Only triples in which b < d are
        meaningful. Triples in which b and d are equal ("diagonal elements")
        may be artificially introduced during the computation for padding
        purposes, since the number of non-trivial persistent topological
        features is typically not constant across samples. They carry no
        information and hence should be effectively ignored by any further
        computation.
For each image in X compute the relevant persistence diagram as an array of triples [ b d q ] . Each triple represents a persistent topological feature in dimension q ( belonging to homology_dimensions ) which is born at b and dies at d . Only triples in which b < d are meaningful . Triples in which b and d are equal ( diagonal elements ) may be artificially introduced during the computation for padding purposes since the number of non - trivial persistent topological features is typically not constant across samples . They carry no information and hence should be effectively ignored by any further computation .
def transform(self, X, y=None):
        """For each image in `X`, compute the relevant persistence diagram as
        an array of triples [b, d, q]. Each triple represents a persistent
        topological feature in dimension q (belonging to `homology_dimensions`)
        which is born at b and dies at d. Only triples in which b < d are
        meaningful. Triples in which b and d are equal ("diagonal elements")
        may be artificially introduced during the computation for padding
        purposes, since the number of non-trivial persistent topological
        features is typically not constant across samples. They carry no
        information and hence should be effectively ignored by any further
        computation.

        Parameters
        ----------
        X : ndarray of shape (n_samples, n_pixels_1, ..., n_pixels_d)
            Input data. Array of d-dimensional images.

        y : None
            There is no need of a target in a transformer, yet the pipeline API
            requires this parameter.

        Returns
        -------
        Xt : ndarray of shape (n_samples, n_features, 3)
            Array of persistence diagrams computed from the feature arrays or
            distance matrices in `X`. ``n_features`` equals
            :math:`\\sum_q n_q`, where :math:`n_q` is the maximum number of
            topological features in dimension :math:`q` across all samples in
            `X`.

        """
        check_is_fitted(self)
        Xt = check_collection(X, force_all_finite=False)

        Xt = Parallel(n_jobs=self.n_jobs)(delayed(self._gudhi_diagram)(x)
                                          for x in Xt)

        Xt = _postprocess_diagrams(
            Xt, "gudhi", self._homology_dimensions, self.infinity_values_,
            self.reduced_homology
            )

        return Xt
def transform ( self , X , y = None ) : check_is_fitted ( self ) Xt = check_collection ( X , force_all_finite = False ) Xt = Parallel ( n_jobs = self . n_jobs ) ( delayed ( self . _gudhi_diagram ) ( x ) for x in Xt ) Xt = _postprocess_diagrams ( Xt , "gudhi" , self . _homology_dimensions , self . infinity_values_ , self . reduced_homology ) return Xt