From dd9457649d0d197bbed6402200e0f2f55655680e Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Sat, 28 Mar 2020 15:39:15 +0100
Subject: Default param of 2 for DTM

---
 src/python/gudhi/point_cloud/dtm.py | 14 ++++----------
 src/python/test/test_dtm.py         |  2 +-
 2 files changed, 5 insertions(+), 11 deletions(-)

(limited to 'src/python')

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 678524f2..c26ba844 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -15,11 +15,11 @@ class DTM:
     Class to compute the distance to the empirical measure defined by a point set.
     """
 
-    def __init__(self, k, q=None, **kwargs):
+    def __init__(self, k, q=2, **kwargs):
         """
         Args:
             k (int): number of neighbors (possibly including the point itself).
-            q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if metric is "neighbors" or "distance_matrix".
+            q (float): order used to compute the distance to measure. Defaults to 2.
             kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors.
         """
         self.k = k
@@ -44,19 +44,13 @@ class DTM:
         Args:
             X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored).
         """
-        q = self.q
-        if q is None:
-            if self.params["metric"] in {"neighbors", "precomputed"}:
-                q = 2
-            else:
-                q = len(X[0])
         if self.params["metric"] == "neighbors":
             distances = X[:, : self.k]
         else:
             distances = self.knn.transform(X)
-        distances = distances ** q
+        distances = distances ** self.q
         dtm = distances.sum(-1) / self.k
-        dtm = dtm ** (1.0 / q)
+        dtm = dtm ** (1.0 / self.q)
         # We compute too many powers, 1/p in knn then q in dtm, 1/q in dtm then q or some log in the caller.
         # Add option to skip the final root?
         return dtm
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 33b2f3a2..93b13e1a 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -30,7 +30,7 @@ def test_dtm_compare_euclidean():
     from scipy.spatial.distance import cdist
 
     d = cdist(pts, pts)
-    dtm = DTM(k, q=4, metric="precomputed")
+    dtm = DTM(k, metric="precomputed")
     r4 = dtm.fit_transform(d)
     assert r4 == pytest.approx(r0)
     dtm = DTM(k, implementation="keops")
-- 
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