def transform(self, X, y=None):
        """Transform X to a cluster-distance space.
        In the new space, each dimension is the cosine distance to the cluster
        centers.  Note that even if X is sparse, the array returned by
        `transform` will typically be dense.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            New data to transform.

        Returns
        -------
        X_new : array, shape [n_samples, k]
            X transformed in the new space.
        """
        if self.normalize:
            X = normalize(X)

        check_is_fitted(self, "cluster_centers_")
        X = self._check_test_data(X)
        return self._transform(X)
def transform ( self , X , y = None ) : if self . normalize : X = normalize ( X ) check_is_fitted ( self , "cluster_centers_" ) X = self . _check_test_data ( X ) return self . _transform ( X )
Transform X to a cluster-distance space.
        In the new space, each dimension is the cosine distance to the cluster
        centers.  Note that even if X is sparse, the array returned by
        `transform` will typically be dense.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            New data to transform.

        Returns
        -------
        X_new : array, shape [n_samples, k]
            X transformed in the new space.