Public Member Functions | |
def | __init__ (self, resolution=100, sample_range=[np.nan, np.nan]) |
def | fit (self, X, y=None) |
def | transform (self, X) |
def | __call__ (self, diag) |
This is a class for computing Betti curves from a list of persistence diagrams. A Betti curve is a 1D piecewise-constant function obtained from the rank function. It is sampled evenly on a given range and the vector of samples is returned. See https://www.researchgate.net/publication/316604237_Time_Series_Classification_via_Topological_Data_Analysis for more details.
def gudhi.representations.vector_methods.BettiCurve.__init__ | ( | self, | |
resolution = 100 , |
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sample_range = [np.nan, np.nan] |
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) |
Constructor for the BettiCurve class. Parameters: resolution (int): number of sample for the piecewise-constant function (default 100). sample_range ([double, double]): minimum and maximum of the piecewise-constant function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
def gudhi.representations.vector_methods.BettiCurve.__call__ | ( | self, | |
diag | |||
) |
Apply BettiCurve on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): input persistence diagram. Returns: numpy array with shape (**resolution**): output Betti curve.
def gudhi.representations.vector_methods.BettiCurve.fit | ( | self, | |
X, | |||
y = None |
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) |
Fit the BettiCurve class on a list of persistence diagrams: if any of the values in **sample_range** is numpy.nan, replace it with the corresponding value computed on the given list of persistence diagrams. Parameters: X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused).
def gudhi.representations.vector_methods.BettiCurve.transform | ( | self, | |
X | |||
) |
Compute the Betti curve for each persistence diagram individually and concatenate the results. Parameters: X (list of n x 2 numpy arrays): input persistence diagrams. Returns: numpy array with shape (number of diagrams) x (**resolution**): output Betti curves.
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 |