Public Member Functions | |
def | __init__ (self, num_landscapes=5, 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 persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled evenly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details.
def gudhi.representations.vector_methods.Landscape.__init__ | ( | self, | |
num_landscapes = 5 , |
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resolution = 100 , |
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sample_range = [np.nan, np.nan] |
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) |
Constructor for the Landscape class. Parameters: num_landscapes (int): number of piecewise-linear functions to output (default 5). resolution (int): number of sample for all piecewise-linear functions (default 100). sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, 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.Landscape.__call__ | ( | self, | |
diag | |||
) |
Apply Landscape on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): input persistence diagram. Returns: numpy array with shape (number of samples = **num_landscapes** x **resolution**): output persistence landscape.
def gudhi.representations.vector_methods.Landscape.fit | ( | self, | |
X, | |||
y = None |
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) |
Fit the Landscape 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.Landscape.transform | ( | self, | |
X | |||
) |
Compute the persistence landscape 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 (number of samples = **num_landscapes** x **resolution**): output persistence landscapes.
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 |