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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 KNN
class DTM:
"""
Class to compute the distance to the empirical measure defined by a point set.
"""
def __init__(self, k, q=None, **kwargs):
"""
Args:
k (int): number of neighbors (possibly including the point itself).
q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if metric is "neighbors" or "distance_matrix".
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.
"""
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":
# KNN gives sorted distances, which is unnecessary here.
# Maybe add a parameter to say we don't need sorting?
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).
"""
q = self.q
if q is None:
if self.params["metric"] in {"neighbors", "precomputed"}:
q = 2
else:
q = len(X[0])
if self.params["metric"] == "neighbors":
distances = X[:, : self.k]
else:
distances = self.knn.transform(X)
distances = distances ** q
dtm = distances.sum(-1) / self.k
dtm = dtm ** (1.0 / q)
return dtm
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