Merge tag 'tags/gudhi-release-3.2.0' into dfsg/latest

1 files changed, 70 insertions, 0 deletions

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
new file mode 100644
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+++ b/src/python/gudhi/point_cloud/dtm.py
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+# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+# Author(s):       Marc Glisse
+#
+# Copyright (C) 2020 Inria
+#
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
+from .knn import KNearestNeighbors
+
+__author__ = "Marc Glisse"
+__copyright__ = "Copyright (C) 2020 Inria"
+__license__ = "MIT"
+
+
+class DistanceToMeasure:
+    """
+    Class to compute the distance to the empirical measure defined by a point set, as introduced in :cite:`dtm`.
+    """
+
+    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 2.
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that
+                metric="neighbors" means that :func:`transform` expects an array with the distances
+                to the k nearest neighbors.
+        """
+        self.k = k
+        self.q = q
+        self.params = kwargs
+
+    def fit_transform(self, X, y=None):
+        return self.fit(X).transform(X)
+
+    def fit(self, X, y=None):
+        """
+        Args:
+            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.fit(X)
+        return self
+
+    def transform(self, X):
+        """
+        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).
+
+        Returns:
+            numpy.array: a 1-d array with, for each point of X, its distance to the measure defined
+            by the argument of :func:`fit`.
+        """
+        if self.params["metric"] == "neighbors":
+            distances = X[:, : self.k]
+        else:
+            distances = self.knn.transform(X)
+        distances = distances ** self.q
+        dtm = distances.sum(-1) / self.k
+        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