Apply suggestions from code review #456

(thank you Marc!)

1 files changed, 5 insertions, 14 deletions

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index ede1087f..49c05c51 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -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
     --------
@@ -632,9 +630,9 @@ 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"}
+                choose from {"cloud", "iidproba"}
                 (default: constant function, i.e. the measure is seen as a point cloud by default).
                 This will have no impact if weights are provided along with measures all the way: `fit` and `transform`.
             contrast (string): constant function for evaluating proximity of a measure with respect to centers
@@ -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])