cmake

1 files changed, 40 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
index 00000000..08f9ea60
--- /dev/null
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -0,0 +1,40 @@
+from .knn import KNN
+
+
+class DTM:
+    def __init__(self, k, q=2, **kwargs):
+        """
+        Args:
+            q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'.
+            kwargs: Same parameters as KNN, except that metric="neighbors" means that 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 = KNN(self.k, return_index=False, return_distance=True, **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).
+        """
+        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)
+        return dtm