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:orphan:
.. To get rid of WARNING: document isn't included in any toctree
======================
Representations manual
======================
.. include:: representations_sum.inc
This module, originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning, by providing implementations of most of the vector representations for persistence diagrams in the literature, in a scikit-learn format. More specifically, it provides tools, using the scikit-learn standard interface, to compute distances and kernels on persistence diagrams, and to convert these diagrams into vectors in Euclidean space.
A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time.
A small example is provided
.. only:: builder_html
* :download:`diagram_vectorizations_distances_kernels.py <../example/diagram_vectorizations_distances_kernels.py>`
Preprocessing
-------------
.. automodule:: gudhi.representations.preprocessing
:members:
:special-members:
:show-inheritance:
Vector methods
--------------
.. automodule:: gudhi.representations.vector_methods
:members:
:special-members:
:show-inheritance:
Kernel methods
--------------
.. automodule:: gudhi.representations.kernel_methods
:members:
:special-members:
:show-inheritance:
Metrics
-------
.. automodule:: gudhi.representations.metrics
:members:
:special-members:
:show-inheritance:
Basic example
-------------
This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
.. testcode::
import numpy as np
from gudhi.representations import Landscape
# A single diagram with 4 points
D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
diags = [D]
l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
print(l)
The output is:
.. testoutput::
[[1.02851895 2.05703791 2.57129739 1.54277843 0.89995409 1.92847304
2.95699199 3.08555686 2.05703791 1.02851895 0. 0.64282435
0. 0. 0.51425948 0. 0. 0.
0.77138922 1.02851895]]
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