From 74155081bb8b3330c562d5c40d7f0a32fc188012 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Mon, 30 Mar 2020 18:02:43 +0200
Subject: Add density estimator

---
 src/python/gudhi/point_cloud/dtm.py | 66 +++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 23c36b88..e12eefa1 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -8,6 +8,7 @@
 #   - YYYY/MM Author: Description of the modification
 
 from .knn import KNN
+import numpy as np
 
 
 class DTM:
@@ -54,3 +55,68 @@ class DTM:
         # 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
+
+
+class DTMDensity:
+    """
+    Density estimator based on the distance to the empirical measure defined by a point set, as defined in :cite:`dtmdensity`. Note that this implementation does not renormalize so the total measure is not 1, see the reference for suitable normalization factors in the Euclidean case.
+    """
+
+    def __init__(self, k=None, weights=None, q=None, dim=None, **kwargs):
+        """
+        Args:
+            k (int): number of neighbors (possibly including the point itself).
+            weights (numpy.array): weights of each of the k neighbors, optional.
+            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 when the dimension cannot be read from the input (metric="neighbors" or metric="precomputed").
+            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.
+        """
+        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)
+        else:
+            self.weights = weights
+            self.k = len(weights)
+            assert k is None or k == self.k, "k differs from the length of weights"
+        self.q = q
+        self.dim = dim
+        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 = KNN(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).
+        """
+        q = self.q
+        dim = self.dim
+        if dim is None:
+            assert self.params["metric"] not in {
+                "neighbors",
+                "precomputed",
+            }, "dim not specified and cannot guess the dimension"
+            dim = len(X[0])
+        if q is None:
+            q = dim
+        if self.params["metric"] == "neighbors":
+            distances = X[:, : self.k]
+        else:
+            distances = self.knn.transform(X)
+        distances = distances ** q
+        dtm = (distances * weights).sum(-1)
+        return dtm ** (-dim / q)
+        # We compute too many powers, 1/p in knn then q in dtm, d/q in dtm then whatever in the caller.
+        # Add option to skip the final root?
-- 
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