WitnessComplex constructor. :param landmarks: A list of landmarks (in the point cloud). :type landmarks (Sequence[Sequence[float]]):

Args: points (numpy.ndarray): array of points. distance_matrix (numpy.ndarray): full distance matrix. k (

PeriodicCubicalComplex constructor from dimensions and cells (either vertices or top dimensional) or from a Perseus-style file name.

Constructor for the RipsPersistence class. Parameters: homology_dimensions: The returned persistence diagrams dimension(s).

Constructor for the CechPersistence class. Parameters: homology_dimensions: The returned persistence diagrams dimension(s).

Constructor for the WeightedCechPersistence class. Parameters: homology_dimensions: The returned persistence diagrams dimension(s

Constructor for the CubicalPersistence class. Parameters: homology_dimensions: The returned persistence diagrams dimensi

Args: graph_type (str): 'manual', 'knn' or 'radius'. Default is 'knn'. density_type (str): 'manual', 'DTM', 'logDTM',

Constructor for the TimeDelayEmbedding class. Parameters: dim (int): `d` of R^d to be embedded. Optional (default=3).

Args: k (int): number of neighbors (possibly including the point itself). q (float): order used to compute the distan

Args: k (int): number of neighbors (possibly including the point itself). Optional if it can be guessed from weig

Args: k (int): number of neighbors (possibly including the point itself). return_index (bool): if True, return the in

Constructor for the RipsLayer class Parameters: maximum_edge_length (float): maximum edge length for the Rips complex

Constructor for the LowerStarSimplexTreeLayer class Parameters: simplextree (gudhi.SimplexTree): underlying simplex tree

Constructor for the CubicalLayer class Parameters: homology_dimensions (List[int]): list of homology dimensions

Constructor for the GridPerslayWeight class. Parameters: grid (n x n numpy array): grid of values. grid_bnds

Constructor for the GridPerslayWeight class. Parameters: gaussians (4 x n numpy array): parameters of the n Gaussian fun

Constructor for the PowerPerslayWeight class. Parameters: constant (float): constant value. power (float): p

Constructor for the GaussianPerslayPhi class. Parameters: image_size (int numpy array): number of grid elements on each

Constructor for the GaussianPerslayPhi class. Parameters: samples (float numpy array): grid elements on which to evaluat

Constructor for the FlatPerslayPhi class. Parameters: samples (float numpy array): grid elements on which to evaluate th

Constructor for the Clamping class. Parameters: limit (float): clamping value (default np.inf).

Constructor for BirthPersistenceTransform class.

Constructor for the DiagramScaler class. Parameters: use (bool): whether to use the class or not (default False).

Constructor for the Padding class. Parameters: use (bool): whether to use the class or not (default False).

Constructor for the ProminentPoints class. Parameters: use (bool): whether to use the class or not (default False).

Constructor for the DiagramSelector class. Parameters: use (bool): whether to use the class or not (default False).

Constructor for the DimensionSelector class. Parameters: index (int): The returned persistence diagrams dimension index.

Constructor for the SlicedWassersteinKernel class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel appl

Constructor for the PersistenceWeightedGaussianKernel class. Parameters: bandwidth (double): bandwidth of the Gaussian k

Constructor for the PersistenceScaleSpaceKernel class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel

Constructor for the PersistenceFisherKernel class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel appl

Constructor for the PersistenceImage class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel (default 1.

Constructor for the Landscape class. Parameters: num_landscapes (int): number of piecewise-linear functions to output (d

Constructor for the Silhouette class. Parameters: weight (function): weight function for the persistence diagram points

Constructor for the BettiCurve class. Parameters: resolution (int): number of samples for the piecewise-constant functio

Constructor for the Entropy class. Parameters: mode (string): what entropy to compute: either "scalar" for computing the

Constructor for the TopologicalVector class. Parameters: threshold (int): number of distances to keep (default 10). This

Constructor for the ComplexPolynomial class. Parameters: polynomial_type (char): either "R", "S" or "T" (default "R"). Ty

Constructor for the Atol measure vectorisation class. Parameters: quantiser (Object): Object with `fit` (sklearn API con

Constructor for the PersistenceLengths class. Parameters: num_lengths (int): number of persistence lengths to return (de

Constructor for the SlicedWassersteinDistance class. Parameters: num_directions (int): number of lines evenly sampled fr

Constructor for the BottleneckDistance class. Parameters: epsilon (double): **[deprecated]** consider using `e` instead.

Constructor for the PersistenceFisherDistance class. Parameters: bandwidth (double): bandwidth of the Gaussian kernel us

Constructor for the WassersteinDistance class. Parameters: order (int): exponent for Wasserstein, default value is 1., s

* @brief Applies the surjective reflection diamond principle to the current filtration. * * @details The vector chainsInF is sorted by decreasin

For compatibility with older versions, we want to support e=None.

automatic uniform weighting with mass 1 for `measure` in ATOL

Compute the angle formed by two points of a PD

Compute the integral of |cos()| between alpha and beta, valid only if alpha is in [-pi,pi] and beta-alpha is in [0,pi]

Evaluation of the Sliced Wasserstein Distance between a pair of diagrams. TODO: decompose it in smaller methods if some modifications have to be done

contrast function for vectorising `measure` in ATOL we use cdist so as to accept 'inf' values instead of raising ValueError with sklearn.pairwise