any end() iterator

*@brief constructs a simplicial complex with a given number of vertices and a visitor. */

* @brief Constructor with default policies. * * @details The default cost, placement, valid and visitor policies * are respectively : the edg

* name_file : file to read * read_complex : complex that will receive the file content * read_only_points : specify true if only the points must

* name_file : file where the off will be written * save_complex : complex that be outputted in the file * for now only save triangles. */

* \brief Sliced Wasserstein kernel constructor. * * @param[in] diagram persistence diagram. * @param[in] sigma bandwidth parameter. *

\brief Constructor * @param[in] exact Option for exact filtration values computation. Not exact if `Kernel` is not <a target="_blank" * href="ht

`prefer_max` means that we prefer outputting more points at squared distance between r2min and r2max, while `!prefer_max` means we prefer fewer.

* \brief Constructor of the translated function. * * @param[in] function The function to be translated. * @param[in] off The offset vector. T

* @remark assume that the complex is non-empty */

* The default constructor. **/

* @brief Modify complex to be the expansion of the k-nearest neighbor * symmetric graph. */

* @brief Default constructor. */

* @brief Constructor of the Zigzag_persistence class. * @details After construction of the class, the zigzag filtration should be given in a stream

* @brief Default constructor. Sets the element to 0. */

* @brief Default constructor. If a non-zero characteristic is given, initializes the field with it. * The characteristic can later be changed again

Transform method for single time-series data. Parameters ---------- ts : Iterable[float] or Iterable[Iterable[float]]

Apply BirthPersistenceTransform on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy ar

Apply DiagramScaler on a single persistence diagram and outputs the result. If :func:`fit` hasn't been run, this uses `fit_transform`

Apply Padding on a single persistence diagram and outputs the result. If :func:`fit` hasn't been run, this uses `fit_transform` on a

Apply ProminentPoints on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): inpu

Apply DiagramSelector on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): inpu

Apply SlicedWassersteinKernel on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x 2

Apply PersistenceWeightedGaussianKernel on a single pair of persistence diagrams and outputs the result. Parameters: dia

Apply PersistenceScaleSpaceKernel on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n

Apply PersistenceFisherKernel on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x 2

Apply PersistenceImage on a single persistence diagram and outputs the result. If :func:`fit` hasn't been run, this uses `fit_transfo

Apply Landscape on a single persistence diagram and outputs the result. If :func:`fit` hasn't been run, this uses `fit_transform` on

Apply Silhouette on a single persistence diagram and outputs the result. If :func:`fit` hasn't been run, this uses `fit_transform` on

Shorthand for transform on a single persistence diagram. If :func:`fit` hasn't been run, this uses `fit_transform` on a clone of the

Apply Entropy on a single persistence diagram and outputs the result. If :func:`fit` hasn't been run, this uses `fit_transform` on a

Apply TopologicalVector on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): in

Apply ComplexPolynomial on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): in

Apply measure vectorisation on a single measure. Only available after `fit` has been called. Parameters: measure (n x d

Apply PersistenceLengths on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): i

Apply SlicedWassersteinDistance on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x

Apply BottleneckDistance on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x 2 numpy

Apply PersistenceFisherDistance on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x

Apply WassersteinDistance on a single pair of persistence diagrams and outputs the result. Parameters: diag1 (n x 2 nump

WitnessComplex constructor. :param nearest_landmark_table: A list of lists of nearest landmarks and their distances. `nearest_lan

Args: points (Sequence[Sequence[float]]): A list of points in d-Dimension. precision (str): Complex precision can be

StrongWitnessComplex constructor. :param nearest_landmark_table: A list of lists of nearest landmarks and their distances. `neare

RipsComplex constructor. :param points: A list of points in d-Dimension. :type points: Sequence[Sequence[float]] or any array like ob

CubicalComplex constructor from the filtration values of either the top-dimensional cells, or the vertices. Note that in case `top_di

SimplexTree constructor. :param other: If `other` is `None` (default value), an empty `SimplexTree` is created. If `other` is a `

Args: distance_matrix (Sequence[Sequence[float]]): distance matrix (full square or lower triangular). weights (Sequen

WitnessComplex constructor. :param landmarks: A list of landmarks (in the point cloud). :type landmarks: list of list of double

CoverComplex constructor.

TangentialComplex constructor. :param intrinsic_dim: Intrinsic dimension of the manifold. :type intrinsic_dim: integer :para

Constructor for the CoverComplexPy class. Parameters ----------

Constructor for the MapperComplex class. Parameters ---------- input_type : string type of input data. E

Constructor for the GraphInducedComplex class. Parameters: input_type (string): type of input data. Either "point cloud"