gudhi.representations.metrics.WassersteinDistance Class Reference

Public Member Functions

def __init__ (self, order=1, internal_p=np.inf, mode="hera", delta=0.01, 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 Wasserstein distance matrix from a list of persistence diagrams. 

Constructor & Destructor Documentation

◆ __init__()

def gudhi.representations.metrics.WassersteinDistance.__init__ (   self,
  order = 1,
  internal_p = np.inf,
  mode = "hera",
  delta = 0.01,
  n_jobs = None 
)
Constructor for the WassersteinDistance class.

Parameters:
    order (int): exponent for Wasserstein, default value is 1., see :func:`gudhi.wasserstein.wasserstein_distance`.
    internal_p (int): ground metric on the (upper-half) plane (i.e. norm l_p in R^2), default value is `np.inf`, see :func:`gudhi.wasserstein.wasserstein_distance`.
    mode (str): method for computing Wasserstein distance. Either "pot" or "hera". Default set to "hera".
    delta (float): relative error 1+delta. Used only if mode == "hera".
    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.WassersteinDistance.__call__ (   self,
  diag1,
  diag2 
)
Apply WassersteinDistance 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: Wasserstein distance.

◆ fit()

def gudhi.representations.metrics.WassersteinDistance.fit (   self,
  X,
  y = None 
)
Fit the WassersteinDistance 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.WassersteinDistance.transform (   self,
  X 
)
Compute all Wasserstein 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 Wasserstein 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