Implement CoulombRandomizationFitTransformer

      w_balanced[one_indices, ind] = self.weights[ind][1]

    return (X, y, w_balanced)

class CoulombRandomizationFitTransformer():

  def __init__(self, seed=None):

    """Iniitialize coulomb matrix randomization transformation. """

    self.seed=seed

  def unpad_randomize_and_flatten(self, cm):

    """

    1. Remove zero padding on Coulomb Matrix

    2. Randomly permute the rows and columns for n_samples

    3. Flatten each sample to upper triangular portion

    Returns list of feature vectors

    """

    max_atom_number = len(cm) 

    atom_number = 0

    for i in cm[0]:

        if atom_number == max_atom_number: break

        elif i != 0.: atom_number += 1

        else: break

    upcm = cm[0:atom_number,0:atom_number]

    row_norms = np.asarray(

        [np.linalg.norm(row) for row in upcm], dtype=float)

    rng = np.random.RandomState(self.seed)

    e = rng.normal(size=row_norms.size)

    p = np.argsort(row_norms+e)

    rcm = upcm[p][:,p]

    rcm = pad_array(rcm, len(cm))

    rcm = rcm[np.triu_indices_from(rcm)]

    return rcm

  def X_transform(self, X):

    return unpad_randomize_and_flatten(X)

  def transform(self, dataset):

    raise NotImplementedError(

      "Cannot transform datasets with FitTransformer")

  def untransform(self, z):

    raise NotImplementedError(

      "Cannot untransform datasets with FitTransformer.")

class CoulombRandomizationTransformer(Transformer):

  def __init__(self, transform_X=False, transform_y=False,