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Diffstat (limited to 'src/python/gudhi/point_cloud/dtm.py')
-rw-r--r-- | src/python/gudhi/point_cloud/dtm.py | 70 |
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 index 00000000..13e16d24 --- /dev/null +++ b/src/python/gudhi/point_cloud/dtm.py @@ -0,0 +1,70 @@ +# 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 |