From c87a1f10e048477d210ae0abd657da87bba1102a Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Tue, 12 May 2020 20:36:38 +0200
Subject: test + reformat

---
 src/python/gudhi/point_cloud/dtm.py |  9 ++++++---
 src/python/test/test_dtm.py         | 11 ++++++++++-
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index f8cca2c1..4454d8a2 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -108,8 +108,8 @@ class DTMDensity:
         self.q = q
         self.dim = dim
         self.params = kwargs
-        self.normalize=normalize
-        self.n_samples=n_samples
+        self.normalize = normalize
+        self.n_samples = n_samples
 
     def fit_transform(self, X, y=None):
         return self.fit(X).transform(X)
@@ -120,7 +120,9 @@ class DTMDensity:
             X (numpy.array): coordinates for mass points.
         """
         if self.params.setdefault("metric", "euclidean") != "neighbors":
-            self.knn = KNearestNeighbors(self.k, return_index=False, return_distance=True, sort_results=False, **self.params)
+            self.knn = KNearestNeighbors(
+                self.k, return_index=False, return_distance=True, sort_results=False, **self.params
+            )
             self.knn.fit(X)
             if self.params["metric"] != "precomputed":
                 self.n_samples = len(X)
@@ -154,6 +156,7 @@ class DTMDensity:
         density = dtm ** (-dim / q)
         if self.normalize:
             import math
+
             if self.params["metric"] == "precomputed":
                 self.n_samples = len(X[0])
             # Volume of d-ball
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index bff4c267..34d28d4d 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -8,10 +8,11 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.point_cloud.dtm import DistanceToMeasure
+from gudhi.point_cloud.dtm import DistanceToMeasure, DTMDensity
 import numpy
 import pytest
 import torch
+import math
 
 
 def test_dtm_compare_euclidean():
@@ -66,3 +67,11 @@ def test_dtm_precomputed():
     dtm = DistanceToMeasure(2, q=2, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01)
+
+
+def test_density_normalized():
+    sample = numpy.random.normal(0, 1, (1000000, 2))
+    queries = numpy.array([[0.0, 0.0], [-0.5, 0.7], [0.4, 1.7]])
+    expected = numpy.exp(-(queries ** 2).sum(-1) / 2) / (2 * math.pi)
+    estimated = DTMDensity(k=150, normalize=True).fit(sample).transform(queries)
+    assert estimated == pytest.approx(expected, rel=0.4)
-- 
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