message: Apply suggestions from code review
  (thank you Marc!)
diff: @@ -586,10 +586,8 @@ def _gaus_contrast(measure, centers, inertias):
  def _indicator_contrast(diags, centers, inertias):
  """contrast function for vectorising `measure` in ATOL"""
  - pair_dist = pairwise.pairwise_distances(diags, Y=centers)
  - flat_circ = (pair_dist < inertias).astype(int)
  - robe_curve = np.clip(2-pair_dist/inertias, 0, 1)
  - return flat_circ + robe_curve
  + robe_curve = np.clip(2-pairwise.pairwise_distances(diags, Y=centers)/inertias, 0, 1)
  + return robe_curve
  def _cloud_weighting(measure):
  """automatic uniform weighting with mass 1 for `measure` in ATOL"""
  @@ -603,7 +601,7 @@ class Atol(BaseEstimator, TransformerMixin):
  """
  This class allows to vectorise measures (e.g. point clouds, persistence diagrams, etc) after a quantisation step.
  - ATOL paper: https://arxiv.org/abs/1909.13472
  + ATOL paper: :cite:`royer2019atol`
  Example
  --------
  @@ -632,7 +630,7 @@ class Atol(BaseEstimator, TransformerMixin):
  Parameters:
  quantiser (Object): Object with `fit` (sklearn API consistent) and `cluster_centers` and `n_clusters`
  - attributes. This object will be fitted by the function `fit`.
  + attributes, e.g. sklearn.cluster.KMeans. It will be fitted when the Atol object function `fit` is called.
  weighting_method (string): constant generic function for weighting the measure points
  choose from {"cloud", "iidproba"}
  (default: constant function, i.e. the measure is seen as a point cloud by default).
  @@ -647,8 +645,6 @@ class Atol(BaseEstimator, TransformerMixin):
  "laplacian": _lapl_contrast,
  "indicator": _indicator_contrast,
  }.get(contrast, _gaus_contrast)
  - self.centers = np.ones(shape=(self.quantiser.n_clusters, 2))*np.inf
  - self.inertias = np.full(self.quantiser.n_clusters, np.nan)
  self.weighting_method = {
  "cloud" : _cloud_weighting,
  "iidproba": _iidproba_weighting,
  @@ -670,11 +666,6 @@ class Atol(BaseEstimator, TransformerMixin):
  """
  if not hasattr(self.quantiser, 'fit'):
  raise TypeError("quantiser %s has no `fit` attribute." % (self.quantiser))
  - if np.sum([measure.shape[0] for measure in X]) < self.quantiser.n_clusters:
  - # in case there are not enough observations for fitting the quantiser, we add random points in [0, 1]^2
  - # @Martin: perhaps this behaviour is to be externalised and a warning should be raised instead
  - random_points = np.random.rand(self.quantiser.n_clusters-len(X), X[0].shape[1])
  - X.append(random_points)
  if sample_weight is None:
  sample_weight = np.concatenate([self.weighting_method(measure) for measure in X])
  