Add update_moments function to Dataset

    """

    if not len(self.metadata_df):

      raise ValueError("No data in dataset.")

    sample_X = load_from_disk(self.metadata_df.iterrows().next()[1]['X'])[0]

    sample_X = load_from_disk(

        self.metadata_df.iterrows().next()[1]['X-transformed'])[0]

    return np.shape(sample_X)

  def _get_metadata_filename(self):

    X_means, X_stds, y_means, y_stds = compute_mean_and_std(df)

    return X_means, X_stds, y_means, y_stds

  def update_moments(self):

    """Re-compute statistics of this dataset during transformation"""

    df = self.metadata_df

    X_means, X_stds, y_means, y_stds = update_mean_and_std(df)

    return X_means, X_stds, y_means, y_stds

def compute_sums_and_nb_sample(tensor, W=None):

  """

  Computes sums, squared sums of tensor along axis 0.

  y_means = np.sum(y_sums, axis=0)/y_n

  y_vars = np.sum(y_sum_squares, axis=0)/y_n - np.square(y_means)

  return overall_X_means, np.sqrt(X_vars), y_means, np.sqrt(y_vars)

def update_mean_and_std(df):

  """

  Compute means/stds of X/y from sums/sum_squares of tensors.

  """

  X_n = list(df['X_n']) 

  X_transform = []

  for _, row in df.iterrows():

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

    X_transform.append(np.array(Xt))

  # Re-calculate X_sums and X_sum_squares 

  X_sums = []

  X_sum_squares = []

  for i, row in df.iterrows():

    Xs = np.sum(X_transform[i],axis=0)

    Xss = np.sum(np.square(X_transform[i]),axis=0)

    X_sums.append(Xs)

    X_sum_squares.append(Xss)

  n = float(np.sum(X_n))

  X_sums = np.vstack(X_sums)

  X_sum_squares = np.vstack(X_sum_squares)

  overall_X_sums = np.sum(X_sums, axis=0)

  overall_X_means = overall_X_sums / n

  overall_X_sum_squares = np.sum(X_sum_squares, axis=0)

  X_vars = (overall_X_sum_squares - np.square(overall_X_sums)/n)/(n)

  y_n = list(df['y_n'])

  y_transform = []

  for _, row in df.iterrows():

    yt = load_from_disk(row['y-transformed'])

    y_transform.append(np.array(yt))

  y_sums = []

  y_sum_squares = []

  for i, row in df.iterrows():

    ys = np.sum(y_transform[i],axis=0)

    yss = np.sum(np.square(y_transform[i]),axis=0)

    y_sums.append(ys)

    y_sum_squares.append(yss)

  y_n = np.sum(y_n, axis=0)

  y_sums = np.vstack(y_sums)

  y_sum_squares = np.vstack(y_sum_squares)

  y_means = np.sum(y_sums, axis=0)/y_n

  y_vars = np.sum(y_sum_squares, axis=0)/y_n - np.square(y_means)

  return overall_X_means, np.sqrt(X_vars), y_means, np.sqrt(y_vars)