def transform(self, X, batch_size=100, show_progressbar=False):
        """
        Transform input to a distance matrix by measuring the L2 distance.

        Parameters
        ----------
        X : numpy array.
            The input data.
        batch_size : int, optional, default 100
            The batch size to use in transformation. This may affect the
            transformation in stateful, i.e. sequential SOMs.
        show_progressbar : bool
            Whether to show a progressbar during transformation.

        Returns
        -------
        transformed : numpy array
            A matrix containing the distance from each datapoint to all
            neurons. The distance is normally expressed as euclidean distance,
            but can be any arbitrary metric.

        """
        X = self._check_input(X)

        batched = self._create_batches(X, batch_size, shuffle_data=False)

        activations = []
        prev = self._init_prev(batched)

        for x in tqdm(batched, disable=not show_progressbar):
            prev = self.forward(x, prev_activation=prev)[0]
            activations.extend(prev)

        activations = np.asarray(activations, dtype=np.float64)
        activations = activations[:X.shape[0]]
        return activations.reshape(X.shape[0], self.num_neurons)
Transform input to a distance matrix by measuring the L2 distance.

        Parameters
        ----------
        X : numpy array.
            The input data.
        batch_size : int, optional, default 100
            The batch size to use in transformation. This may affect the
            transformation in stateful, i.e. sequential SOMs.
        show_progressbar : bool
            Whether to show a progressbar during transformation.

        Returns
        -------
        transformed : numpy array
            A matrix containing the distance from each datapoint to all
            neurons. The distance is normally expressed as euclidean distance,
            but can be any arbitrary metric.