def transform(self, X):
        '''
        Compute kernels from X to :attr:`features_`.

        Parameters
        ----------
        X : list of arrays or :class:`skl_groups.features.Features`
            The bags to compute "from". Must have same dimension as
            :attr:`features_`.

        Returns
        -------
        K : array of shape ``[len(X), len(features_)]``
            The kernel evaluations from X to :attr:`features_`.
        '''

        X = as_features(X, stack=True, bare=True)
        Y = self.features_

        if X.dim != Y.dim:
            raise ValueError("MMK transform got dimension {} but had {} at fit"
                             .format(X.dim, Y.dim))

        pointwise = pairwise_kernels(X.stacked_features, Y.stacked_features,
                                     metric=self.kernel,
                                     filter_params=True,
                                     **self._get_kernel_params())

        # TODO: is there a way to do this without a Python loop?
        K = np.empty((len(X), len(Y)))
        for i in range(len(X)):
            for j in range(len(Y)):
                K[i, j] = pointwise[X._boundaries[i]:X._boundaries[i+1],
                                    Y._boundaries[j]:Y._boundaries[j+1]].mean()

        return K
def transform(self, X):
        

        X = as_features(X, stack=True, bare=True)
        Y = self.features_

        if X.dim != Y.dim:
            raise ValueError("MMK transform got dimension {} but had {} at fit"
                             .format(X.dim, Y.dim))

        pointwise = pairwise_kernels(X.stacked_features, Y.stacked_features,
                                     metric=self.kernel,
                                     filter_params=True,
                                     **self._get_kernel_params())

        
        K = np.empty((len(X), len(Y)))
        for i in range(len(X)):
            for j in range(len(Y)):
                K[i, j] = pointwise[X._boundaries[i]:X._boundaries[i+1],
                                    Y._boundaries[j]:Y._boundaries[j+1]].mean()

        return K
def transform ( self , X ) : X = as_features ( X , stack = True , bare = True ) Y = self . features_ if X . dim != Y . dim : raise ValueError ( "MMK transform got dimension {} but had {} at fit" . format ( X . dim , Y . dim ) ) pointwise = pairwise_kernels ( X . stacked_features , Y . stacked_features , metric = self . kernel , filter_params = True , * * self . _get_kernel_params ( ) ) # TODO: is there a way to do this without a Python loop? K = np . empty ( ( len ( X ) , len ( Y ) ) ) for i in range ( len ( X ) ) : for j in range ( len ( Y ) ) : K [ i , j ] = pointwise [ X . _boundaries [ i ] : X . _boundaries [ i + 1 ] , Y . _boundaries [ j ] : Y . _boundaries [ j + 1 ] ] . mean ( ) return K
Compute kernels from X to :attr:`features_`.

        Parameters
        ----------
        X : list of arrays or :class:`skl_groups.features.Features`
            The bags to compute "from". Must have same dimension as
            :attr:`features_`.

        Returns
        -------
        K : array of shape ``[len(X), len(features_)]``
            The kernel evaluations from X to :attr:`features_`.
Compute kernels from X to : attr : features_ .