Doc tweaks, default DTM exponent

2 files changed, 15 insertions, 8 deletions

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index e4096c5e..520cbea8 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -15,10 +15,11 @@ class DTM:
     Class to compute the distance to the empirical measure defined by a point set.
     """
 
-    def __init__(self, k, q=2, **kwargs):
+    def __init__(self, k, q=None, **kwargs):
         """
         Args:
-            q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'.
+            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".
             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
@@ -31,7 +32,7 @@ class DTM:
     def fit(self, X, y=None):
         """
         Args:
-            X (numpy.array): coordinates for mass points
+            X (numpy.array): coordinates for mass points.
         """
         if self.params.setdefault("metric", "euclidean") != "neighbors":
             # KNN gives sorted distances, which is unnecessary here.
@@ -45,11 +46,17 @@ 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 ** self.q
+        distances = distances ** q
         dtm = distances.sum(-1) / self.k
-        dtm = dtm ** (1.0 / self.q)
+        dtm = dtm ** (1.0 / q)
         return dtm
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index 02448530..31e4fc9f 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -18,7 +18,7 @@ class KNN:
     def __init__(self, k, return_index=True, return_distance=False, metric="euclidean", **kwargs):
         """
         Args:
-            k (int): number of neighbors (including the point itself).
+            k (int): number of neighbors (possibly including the point itself).
             return_index (bool): if True, return the index of each neighbor.
             return_distance (bool): if True, return the distance to each neighbor.
             implementation (str): Choice of the library that does the real work.
@@ -68,7 +68,7 @@ class KNN:
     def fit(self, X, y=None):
         """
         Args:
-            X (numpy.array): coordinates for reference points
+            X (numpy.array): coordinates for reference points.
         """
         self.ref_points = X
         if self.params["implementation"] == "ckdtree":
@@ -105,7 +105,7 @@ class KNN:
     def transform(self, X):
         """
         Args:
-            X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed"
+            X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed".
         """
         metric = self.metric
         k = self.k