Public Member Functions | |
def | __init__ (self, weight=lambda x:1, 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 silhouettes from a list of persistence diagrams. A persistence silhouette is computed by taking a weighted average of the collection of 1D piecewise-linear functions given by the persistence landscapes, and then by evenly sampling this average on a given range. Finally, the corresponding vector of samples is returned. See https://arxiv.org/abs/1312.0308 for more details.
def gudhi.representations.vector_methods.Silhouette.__init__ | ( | self, | |
weight = lambda x: 1 , |
|||
resolution = 100 , |
|||
sample_range = [np.nan, np.nan] |
|||
) |
Constructor for the Silhouette class. Parameters: weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie on lists or numpy arrays of the form [p_x,p_y]. resolution (int): number of samples for the weighted average (default 100). sample_range ([double, double]): minimum and maximum for the weighted average 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.Silhouette.__call__ | ( | self, | |
diag | |||
) |
Apply Silhouette 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 persistence silhouette.
def gudhi.representations.vector_methods.Silhouette.fit | ( | self, | |
X, | |||
y = None |
|||
) |
Fit the Silhouette 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.Silhouette.transform | ( | self, | |
X | |||
) |
Compute the persistence silhouette 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 persistence silhouettes.
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 |