Safe normalization for CoulombBinarizationTransformer

  def untransform(self, z):

    print("Cannot undo CoulombRandomizationTransformer.")

class CoulombBinarizationTransformer(CoulombRandomizationTransformer):

class CoulombBinarizationTransformer(Transformer):

  def __init__(self, transform_X=False, transform_y=False, dataset=None,

               theta=1):

      X = load_from_disk(row['X-transformed'])

      self.feature_max = np.maximum(self.feature_max,X.max(axis=0))

  def transform(self, dataset, parallel=False):

    super(CoulombBinarizationTransformer, self).transform(dataset,

          parallel=parallel)

    df = dataset.metadata_df

    Xt = []

    for _, row in df.iterrows():

      X_t = load_from_disk(row['X-transformed'])

      Xt.append(np.array(X_t))

    X = np.vstack(Xt)

    print(X.shape)

    X_means = X.mean(axis=0)

    X_stds = (X-X_means).std()

    for i, row in df.iterrows():

      X_t = (Xt[i]-X_means)/X_stds

      save_to_disk(X_t, row['X-transformed'])

  def transform_row(self, i, df):

    """

    Binarizes data in dataset with sigmoid function