blob: c870f8349b6a39d792d9ba99653536c3b68d4b71 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
|
:orphan:
.. To get rid of WARNING: document isn't included in any toctree
======================
Representations manual
======================
.. include:: representations_sum.inc
This module, originally named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning tools, in particular scikit-learn. It provides tools, using the scikit-learn standard interface, to compute distances and kernels on diagrams, and to convert diagrams into vectors.
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:
|
