gudhi.representations.metrics.BottleneckDistance Class Reference

Public Member Functions

def __init__ (self, epsilon=None, n_jobs=None)
 
def fit (self, X, y=None)
 
def transform (self, X)
 
def __call__ (self, diag1, diag2)
 

Detailed Description

This is a class for computing the bottleneck distance matrix from a list of persistence diagrams.

:Requires: `CGAL <installation.html#cgal>`_ :math:`\geq` 4.11.0

Constructor & Destructor Documentation

◆ __init__()

def gudhi.representations.metrics.BottleneckDistance.__init__ (   self,
  epsilon = None,
  n_jobs = None 
)
Constructor for the BottleneckDistance class.

Parameters:
    epsilon (double): absolute (additive) error tolerated on the distance (default is the smallest positive float), see :func:`gudhi.bottleneck_distance`.
    n_jobs (int): number of jobs to use for the computation. See :func:`pairwise_persistence_diagram_distances` for details.

Member Function Documentation

◆ __call__()

def gudhi.representations.metrics.BottleneckDistance.__call__ (   self,
  diag1,
  diag2 
)
Apply BottleneckDistance on a single pair of persistence diagrams and outputs the result.

Parameters:
    diag1 (n x 2 numpy array): first input persistence diagram.
    diag2 (n x 2 numpy array): second input persistence diagram.

Returns:
    float: bottleneck distance.

◆ fit()

def gudhi.representations.metrics.BottleneckDistance.fit (   self,
  X,
  y = None 
)
Fit the BottleneckDistance class on a list of persistence diagrams: persistence diagrams are stored in a numpy array called **diagrams**.

Parameters:
    X (list of n x 2 numpy arrays): input persistence diagrams.
    y (n x 1 array): persistence diagram labels (unused).

◆ transform()

def gudhi.representations.metrics.BottleneckDistance.transform (   self,
  X 
)
Compute all bottleneck distances between the persistence diagrams that were stored after calling the fit() method, and a given list of (possibly different) persistence diagrams.

Parameters:
    X (list of n x 2 numpy arrays): input persistence diagrams.

Returns:
    numpy array of shape (number of diagrams in **diagrams**) x (number of diagrams in X): matrix of pairwise bottleneck distances.

The documentation for this class was generated from the following file:
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