Public Member Functions | |
def | __init__ (self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs) |
def | fit (self, X, y=None) |
def | transform (self, X) |
Density estimator based on the distance to the empirical measure defined by a point set, as defined in :cite:`dtmdensity`. Note that this implementation only renormalizes when asked, and the renormalization only works for a Euclidean metric, so in other cases the total measure may not be 1. .. note:: When the dimension is high, using it as an exponent can quickly lead to under- or overflows. We recommend using a small fixed value instead in those cases, even if it won't have the same nice theoretical properties as the dimension.
def gudhi.point_cloud.dtm.DTMDensity.__init__ | ( | self, | |
k = None , |
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weights = None , |
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q = None , |
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dim = None , |
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normalize = False , |
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n_samples = None , |
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** | kwargs | ||
) |
Args: k (int): number of neighbors (possibly including the point itself). Optional if it can be guessed from weights or metric="neighbors". weights (numpy.array): weights of each of the k neighbors, optional. They are supposed to sum to 1. 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 is "neighbors" or "precomputed"). normalize (bool): normalize the density so it corresponds to a probability measure on ℝᵈ. Only available for the Euclidean metric, defaults to False. n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and metric is "neighbors". 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.
def gudhi.point_cloud.dtm.DTMDensity.fit | ( | self, | |
X, | |||
y = None |
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) |
Args: X (numpy.array): coordinates for mass points.
def gudhi.point_cloud.dtm.DTMDensity.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).
GUDHI Version 3.3.0 - C++ library for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding. - Copyright : MIT | Generated on Tue Aug 11 2020 11:58:59 for GUDHI by Doxygen 1.8.18 |