Infer k when we pass the distances to the nearest neighbors

1 files changed, 17 insertions, 6 deletions

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 88f197e7..d836c28d 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -85,7 +85,8 @@ class DTMDensity:
     def __init__(self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs):
         """
         Args:
-            k (int): number of neighbors (possibly including the point itself).
+            k (int): number of neighbors (possibly including the point itself). Optional if it can be guessed
+                from weights or metric="neighbors".
             weights (numpy.array): weights of each of the k neighbors, optional. They are supposed to sum to 1.
             q (float): order used to compute the distance to measure. Defaults to dim.
             dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
@@ -98,9 +99,12 @@ class DTMDensity:
                 :func:`transform` expects an array with the distances to the k nearest neighbors.
         """
         if weights is None:
-            assert k is not None, "Must specify k or weights"
             self.k = k
-            self.weights = np.full(k, 1.0 / k)
+            if k is None:
+                assert kwargs.get("metric") == "neighbors", 'Must specify k or weights, unless metric is "neighbors"'
+                self.weights = None
+            else:
+                self.weights = np.full(k, 1.0 / k)
         else:
             self.weights = weights
             self.k = len(weights)
@@ -145,14 +149,21 @@ class DTMDensity:
             dim = len(X[0])
         if q is None:
             q = dim
+        k = self.k
+        weights = self.weights
         if self.params["metric"] == "neighbors":
-            distances = np.asarray(X)[:, : self.k]
+            distances = np.asarray(X)
+            if weights is None:
+                k = distances.shape[1]
+                weights = np.full(k, 1.0 / k)
+            else:
+                distances = distances[:, :k]
         else:
             distances = self.knn.transform(X)
         distances = distances ** q
-        dtm = (distances * self.weights).sum(-1)
+        dtm = (distances * weights).sum(-1)
         if self.normalize:
-            dtm /= (np.arange(1, self.k + 1) ** (q / dim) * self.weights).sum()
+            dtm /= (np.arange(1, k + 1) ** (q / dim) * weights).sum()
         density = dtm ** (-dim / q)
         if self.normalize:
             import math