Class to compute the distance to the empirical measure defined by a point set, as introduced in :cite:`dtm`.
◆ __init__()
def gudhi.point_cloud.dtm.DistanceToMeasure.__init__ |
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self, |
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k, |
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q = 2 , |
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** |
kwargs |
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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.
◆ fit()
def gudhi.point_cloud.dtm.DistanceToMeasure.fit |
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self, |
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X, |
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y = None |
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Args:
X (numpy.array): coordinates for mass points.
◆ transform()
def gudhi.point_cloud.dtm.DistanceToMeasure.transform |
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self, |
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X |
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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`.
The documentation for this class was generated from the following file:
- src/python/gudhi/point_cloud/dtm.py