tdads.kernel
============

.. py:module:: tdads.kernel


Classes
-------

.. autoapisummary::

   tdads.kernel.kernel


Module Contents
---------------

.. py:class:: kernel(dim: int = 0, sigma: float = 1, t: float = 1, inf_replace_val: float = None, n_cores: int = cpu_count() - 1)

   .. py:method:: __str__()

      Describe a persistence Fisher kernel by its `sigma` and `t` parameters.



   .. py:method:: compute(D1, D2)

      Compute the kernel value between two persistence diagrams.

      :param `D1`: The first persistence diagram (computed from either the ripser, gph, flagser, gudhi or cechmate packages).
      :type `D1`: any
      :param `D2`: The second persistence diagram.
      :type `D2`: any

      :returns: The numeric kernel calculation value.
      :rtype: float

      .. rubric:: Examples

      >>> from tdads import kernel
      >>> from ripser import ripser
      >>> import numpy as np
      >>> # create 2 datasets
      >>> data1 = np.random((100,2))
      >>> data2 = np.random((100,2))
      >>> # compute persistence diagrams with ripser
      >>> diagram1 = ripser(data1)
      >>> diagram2 = ripser(data2)
      >>> # create kernel object
      >>> k = kernel()
      >>> # compute kernel value
      >>> k.compute(diagram1, diagrams2)

      Citations
      ---------
      Le T, Yamada M (2018). "Persistence fisher kernel: a riemannian manifold kernel for persistence diagrams." https://proceedings.neurips.cc/paper/2018/file/959ab9a0695c467e7caf75431a872e5c-Paper.pdf.



   .. py:method:: compute_matrix(diagrams, other_diagrams=None)

      Compute a Gram (kernel) matrix between one or two lists of persistence diagrams.
      :param `diagrams`: The first first of persistence diagram (computed from either the ripser, gph, flagser, gudhi or cechmate packages).
      :type `diagrams`: list
      :param `other_diagrams`: The optional second list of persistence diagram for computing a cross-Gram matrix. Default `None`.
      :type `other_diagrams`: any

      :returns: The (cross) Gram matrix.
      :rtype: numpy.ndarray

      .. rubric:: Examples

      >>> from tdads import kernel
      >>> from ripser import ripser
      >>> import numpy as np
      >>> # create 2 datasets
      >>> data1 = np.random((100,2))
      >>> data2 = np.random((100,2))
      >>> # compute persistence diagrams with ripser
      >>> diagram1 = ripser(data1)
      >>> diagram2 = ripser(data2)
      >>> # create kernel object
      >>> k = kernel()
      >>> # compute Gram matrix
      >>> k.compute_matrix([diagram1, diagram2])
      >>> # this is the same as:
      >>> k.compute_matrix([diagram1, diagram2], [diagram1, diagram2])