├── sklearn_rvm
    ├── tests
    │   ├── __init__.py
    │   ├── performance.py
    │   ├── test_regressor.py
    │   └── test_classifier.py
    ├── _version.py
    ├── __init__.py
    └── em_rvm.py
├── MANIFEST.in
├── requirements.txt
├── doc
    ├── sphinxext
    │   ├── MANIFEST.in
    │   └── github_link.py
    ├── complexity.rst
    ├── kernel.rst
    ├── logos
    │   ├── favicon.ico
    │   └── sklearn-rvm-logo-small.png
    ├── tips.rst
    ├── mathematics.rst
    ├── requirements.txt
    ├── themes
    │   └── scikit-learn-modern
    │   │   ├── images
    │   │       └── logo.png
    │   │   ├── theme.conf
    │   │   ├── search.html
    │   │   ├── nav.html
    │   │   ├── layout.html
    │   │   ├── javascript.html
    │   │   └── static
    │   │       ├── js
    │   │           ├── searchtools.js
    │   │           └── vendor
    │   │           │   └── bootstrap.min.js
    │   │       └── css
    │   │           └── theme.css
    ├── .readthedocs.yml
    ├── regression.rst
    ├── classification.rst
    ├── templates
    │   ├── function.rst
    │   ├── numpydoc_docstring.py
    │   └── class.rst
    ├── user_guide.rst
    ├── introduction.rst
    ├── api.rst
    ├── about.rst
    ├── install.rst
    ├── index.rst
    ├── make.bat
    ├── Makefile
    └── conf.py
├── environment.yml
├── examples
    ├── README.txt
    ├── simple_example.py
    ├── simple_example_precomputed.py
    ├── rvm_for_regression.py
    ├── plot_rvm_for_classification.py
    ├── plot_compare_rvr_ard.py
    ├── plot_compare_rvr_svr.py
    └── plot_iris_rvc.py
├── setup.cfg
├── .coveragerc
├── .readthedocs.yml
├── AUTHORS.rst
├── .travis.yml
├── .gitignore
├── README.rst
├── .circleci
    └── config.yml
├── LICENSE
├── setup.py
└── CODE_OF_CONDUCT.md


/sklearn_rvm/tests/__init__.py:
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1 | 


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/MANIFEST.in:
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1 | include requirements.txt
2 | 


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/requirements.txt:
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1 | numpy
2 | scipy
3 | scikit-learn


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/sklearn_rvm/_version.py:
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1 | __version__ = "0.1.1"
2 | 


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/doc/sphinxext/MANIFEST.in:
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1 | recursive-include tests *.py
2 | include *.txt
3 | 


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/doc/complexity.rst:
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1 | .. _complexity:
2 | 
3 | ===========
4 | Complexity
5 | ===========
6 | 


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/doc/kernel.rst:
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1 | .. _kernel:
2 | 
3 | ================
4 | Kernel Functions
5 | ================
6 | 


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/doc/logos/favicon.ico:
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https://raw.githubusercontent.com/Mind-the-Pineapple/sklearn-rvm/HEAD/doc/logos/favicon.ico


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/environment.yml:
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1 | name: sklearn_rvm
2 | dependencies:
3 |   - numpy
4 |   - scipy
5 |   - scikit-learn
6 | 


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/doc/tips.rst:
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1 | .. _tips:
2 | 
3 | =====================
4 | Tips on Practical Use
5 | =====================
6 | 


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/examples/README.txt:
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1 | .. _general_examples:
2 | 
3 | General examples
4 | ================
5 | 
6 | Introductory examples.
7 | 


--------------------------------------------------------------------------------
/doc/mathematics.rst:
--------------------------------------------------------------------------------
1 | .. _mathematics:
2 | 
3 | ========================
4 | Mathematical Formulation
5 | ========================
6 | 


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/doc/requirements.txt:
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1 | sphinx==2.3.1
2 | sphinx-gallery
3 | sphinx_rtd_theme
4 | numpydoc
5 | sphinxcontrib-fulltoc
6 | scikit-learn


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/doc/logos/sklearn-rvm-logo-small.png:
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https://raw.githubusercontent.com/Mind-the-Pineapple/sklearn-rvm/HEAD/doc/logos/sklearn-rvm-logo-small.png


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/setup.cfg:
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1 | [metadata]
2 | description-file = README.rst
3 | 
4 | [aliases]
5 | test = pytest
6 | 
7 | [tool:pytest]
8 | addopts = --doctest-modules
9 | 


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/images/logo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Mind-the-Pineapple/sklearn-rvm/HEAD/doc/themes/scikit-learn-modern/images/logo.png


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/sklearn_rvm/__init__.py:
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1 | from .em_rvm import EMRVR, EMRVC
2 | 
3 | from ._version import __version__
4 | 
5 | __all__ = ['EMRVR', 'EMRVC', '__version__']
6 | 


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/doc/.readthedocs.yml:
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1 | formats:
2 |   - none
3 | requirements_file: requirements.txt
4 | python:
5 |   pip_install: true
6 |   extra_requirements:
7 |     - tests
8 |     - docs


--------------------------------------------------------------------------------
/doc/regression.rst:
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1 | .. _regression:
2 | 
3 | ===========
4 | Regression
5 | ===========
6 | 
7 | EMRVR is a class able to solve regression problems.
8 | 
9 | * Present example on regression problem.


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/theme.conf:
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1 | [theme]
2 | inherit = basic
3 | pygments_style = default
4 | stylesheet = css/theme.css
5 | 
6 | [options]
7 | google_analytics = true
8 | mathjax_path =
9 | 


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/doc/classification.rst:
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 1 | .. _classification:
 2 | 
 3 | ===============
 4 | Classification
 5 | ===============
 6 | 
 7 | EMRVC is a class able to solve classification problems.
 8 | 
 9 | * Present example on 2 class classification.
10 | 
11 | * Present example on multiple class classification.


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/doc/templates/function.rst:
--------------------------------------------------------------------------------
 1 | :mod:`{{module}}`.{{objname}}
 2 | {{ underline }}====================
 3 | 
 4 | .. currentmodule:: {{ module }}
 5 | 
 6 | .. autofunction:: {{ objname }}
 7 | 
 8 | .. include:: {{module}}.{{objname}}.examples
 9 | 
10 | .. raw:: html
11 | 
12 |     <div class="clearer"></div>
13 | 


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/doc/templates/numpydoc_docstring.py:
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 1 | {{index}}
 2 | {{summary}}
 3 | {{extended_summary}}
 4 | {{parameters}}
 5 | {{returns}}
 6 | {{yields}}
 7 | {{other_parameters}}
 8 | {{attributes}}
 9 | {{raises}}
10 | {{warns}}
11 | {{warnings}}
12 | {{see_also}}
13 | {{notes}}
14 | {{references}}
15 | {{examples}}
16 | {{methods}}
17 | 


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/doc/user_guide.rst:
--------------------------------------------------------------------------------
 1 | .. title:: User guide: contents
 2 | 
 3 | .. _user_guide:
 4 | 
 5 | ==========
 6 | User Guide
 7 | ==========
 8 | 
 9 | .. toctree::
10 |    :numbered:
11 | 
12 |    introduction.rst
13 |    regression.rst
14 |    classification.rst
15 |    complexity.rst
16 |    tips.rst
17 |    kernel.rst
18 |    mathematics.rst
19 | 


--------------------------------------------------------------------------------
/doc/introduction.rst:
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 1 | .. _introduction:
 2 | 
 3 | ============
 4 | Introduction
 5 | ============
 6 | 
 7 | The **Relevance Vector Machines (RVMs)** are a set of supervised learning methods for regression and classification problems that only require a sparse kernel representation.
 8 | 
 9 | Advantages of the RVM are:
10 | 
11 | Disadvantages of the RVM are:
12 | 


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/doc/templates/class.rst:
--------------------------------------------------------------------------------
 1 | :mod:`{{module}}`.{{objname}}
 2 | {{ underline }}==============
 3 | 
 4 | .. currentmodule:: {{ module }}
 5 | 
 6 | .. autoclass:: {{ objname }}
 7 | 
 8 |    {% block methods %}
 9 |    .. automethod:: __init__
10 |    {% endblock %}
11 | 
12 | .. include:: {{module}}.{{objname}}.examples
13 | 
14 | .. raw:: html
15 | 
16 |     <div class="clearer"></div>
17 | 


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/doc/api.rst:
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 1 | ####################
 2 | sklearn-api API
 3 | ####################
 4 | 
 5 | This is the full API documentation of the `sklearn-rvm` toolbox.
 6 | 
 7 | RVM using expectation maximization
 8 | ==============================================================
 9 | 
10 | .. currentmodule:: sklearn_rvm
11 | 
12 | .. autosummary::
13 |    :toctree: generated/
14 |    :template: class.rst
15 | 
16 |    em_rvm.EMRVR
17 |    em_rvm.EMRVC
18 | 


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/.coveragerc:
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 1 | # Configuration for coverage.py
 2 | 
 3 | [run]
 4 | branch = True
 5 | source = sklearn_rvm
 6 | include = */sklearn_rvm/*
 7 | omit =
 8 |     */setup.py
 9 | 
10 | [report]
11 | exclude_lines =
12 |     pragma: no cover
13 |     def __repr__
14 |     if self.debug:
15 |     if settings.DEBUG
16 |     raise AssertionError
17 |     raise NotImplementedError
18 |     if 0:
19 |     if __name__ == .__main__.:
20 |     if self.verbose:
21 | show_missing = True
22 | 


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/doc/themes/scikit-learn-modern/search.html:
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1 | {%- extends "basic/search.html" %}
2 | {% block extrahead %}
3 |   <script type="text/javascript" src="{{ pathto('searchindex.js', 1) }}" defer></script>
4 |   <script src="{{ pathto('_static/underscore.js', 1) }}"></script>
5 |   <script src="{{ pathto('_static/doctools.js', 1) }}"></script>
6 |   <script src="{{ pathto('_static/language_data.js', 1) }}"></script>
7 |   <script src="{{ pathto('_static/js/searchtools.js', 1) }}"></script>
8 | {% endblock %}
9 | 


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/doc/about.rst:
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 1 | About us
 2 | ========
 3 | 
 4 | .. include:: ../AUTHORS.rst
 5 | 
 6 | .. _citing-sklearn-rvm:
 7 | 
 8 | Citing sklearn-rvm
 9 | -------------------
10 | 
11 | If you use sklearn-rvm in a scientific publication, we would appreciate
12 | citations to the following paper::
13 | 
14 |  @inproceedings{tipping2000relevance,
15 |   title={The relevance vector machine},
16 |   author={Tipping, Michael E},
17 |   booktitle={Advances in neural information processing systems},
18 |   pages={652--658},
19 |   year={2000}
20 | }


--------------------------------------------------------------------------------
/.readthedocs.yml:
--------------------------------------------------------------------------------
 1 | # .readthedocs.yml
 2 | # Read the Docs configuration file
 3 | # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
 4 | 
 5 | # Required
 6 | version: 2
 7 | 
 8 | # Build documentation in the docs/ directory with Sphinx
 9 | sphinx:
10 |   configuration: doc/conf.py
11 | 
12 | # Build documentation with MkDocs
13 | #mkdocs:
14 | #  configuration: mkdocs.yml
15 | 
16 | # Optionally set the version of Python and requirements required to build your docs
17 | python:
18 |   version: 3.7
19 |   system_packages: true
20 |   install:
21 |     - requirements: doc/requirements.txt
22 | #    - requirements: requirements.txt
23 |     - method: pip
24 |       path: .
25 | 


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/examples/simple_example.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =========================================================
 6 | Simple example
 7 | =========================================================
 8 | """
 9 | print(__doc__)
10 | 
11 | import numpy as np
12 | from sklearn_rvm import EMRVC
13 | 
14 | # General a toy dataset:s it's just a straight line with some Gaussian noise:
15 | n_samples = 100
16 | np.random.seed(0)
17 | 
18 | X = np.random.normal(size=n_samples)
19 | y = (X > 0).astype(np.float)
20 | 
21 | X[X > 0] *= 4
22 | X += .3 * np.random.normal(size=n_samples)
23 | 
24 | X = X[:, np.newaxis]
25 | 
26 | # Fit the classifier
27 | clf = EMRVC(kernel="linear")
28 | clf.fit(X, y)
29 | 
30 | print(clf.predict(X))
31 | print(clf.predict_proba(X))
32 | print(clf.score(X, y))
33 | 


--------------------------------------------------------------------------------
/examples/simple_example_precomputed.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =========================================================
 6 | Simple example precomputed
 7 | ========================================================="""
 8 | print(__doc__)
 9 | 
10 | import numpy as np
11 | from sklearn.metrics.pairwise import pairwise_kernels
12 | 
13 | from sklearn_rvm import EMRVC
14 | 
15 | # General a toy dataset:s it's just a straight line with some Gaussian noise:
16 | n_samples = 100
17 | np.random.seed(0)
18 | X = np.random.normal(size=n_samples)
19 | y = (X > 0).astype(np.float)
20 | X[X > 0] *= 4
21 | X += .3 * np.random.normal(size=n_samples)
22 | 
23 | X = X[:, np.newaxis]
24 | 
25 | K = pairwise_kernels(X)
26 | # Fit the classifier
27 | clf = EMRVC(kernel="precomputed")
28 | clf.fit(K, y)
29 | 
30 | print(clf.predict(K))
31 | print(clf.predict_proba(K))
32 | print(clf.score(K, y))
33 | 


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/sklearn_rvm/tests/performance.py:
--------------------------------------------------------------------------------
 1 | # import timeit
 2 | #
 3 | #
 4 | # # Time classification with the iris dataset.
 5 | #
 6 | # setup_c = """
 7 | # from sklearn.datasets import load_iris
 8 | # from sklearn_rvm import EMRVC
 9 | # iris = load_iris()
10 | # X = iris.data
11 | # y = iris.target
12 | # clf = EMRVC()
13 | # """
14 | #
15 | # time = timeit.timeit("clf.fit(X, y)", setup=setup_c, number=10)
16 | #
17 | # print("10 runs of Iris classification fitting took {} seconds.".format(time))
18 | #
19 | # # Time regression with the boston ds.
20 | #
21 | # setup_r = """
22 | # from sklearn.datasets import load_boston
23 | # from sklearn_rvm import EMRVR
24 | # boston = load_boston()
25 | # X = boston.data
26 | # y = boston.target
27 | # clf = EMRVR()
28 | # """
29 | #
30 | # time = timeit.timeit("clf.fit(X, y)", setup=setup_r, number=10)
31 | #
32 | # print("10 runs of boston refression fitting took {} seconds.".format(time))


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/sklearn_rvm/tests/test_regressor.py:
--------------------------------------------------------------------------------
 1 | import pytest
 2 | import numpy as np
 3 | from sklearn_rvm import EMRVR
 4 | from sklearn.metrics.pairwise import pairwise_kernels
 5 | from sklearn import datasets
 6 | 
 7 | boston = datasets.load_boston()
 8 | rng = np.random.RandomState(0)
 9 | perm = rng.permutation(boston.target.size)
10 | boston.data = boston.data[perm]
11 | boston.target = boston.target[perm]
12 | 
13 | def test_simple_fit_predict():
14 |     X = np.array([[0, 0], [2, 2]])
15 |     y = np.array([0.0, 2.5 ])
16 |     clf = EMRVR()
17 |     X_test = np.array([[5,5]])
18 |     clf.fit(X,y)
19 |     pred = clf.predict(X_test)
20 |     assert pred != None
21 | 
22 | def test_precomputed_fit_predict():
23 |     kernel = pairwise_kernels(boston.data, metric='linear')
24 |     clf = EMRVR(kernel = "precomputed")
25 |     clf.fit(kernel, boston.target)
26 |     pred = clf.predict(kernel)
27 |     assert pred.shape == boston.target.shape
28 | 


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/doc/install.rst:
--------------------------------------------------------------------------------
 1 | #####################################
 2 | Installation sklearn-rvm
 3 | #####################################
 4 | 
 5 | Prerequisites
 6 | =============
 7 | 
 8 | The sklearn-rvm package requires the following dependencies:
 9 | 
10 | * numpy (>=1.11)
11 | * scipy (>=0.17)
12 | * scikit-learn (>=0.22)
13 | 
14 | Installing the latest release
15 | =============================
16 | 
17 | sklearn-rvm is currently available on the PyPi's reporitories and you can
18 | install it via `pip`::
19 | 
20 |   pip install -U sklearn-rvm
21 | 
22 | 
23 | If you prefer, you can clone it and run the setup.py file. Use the following
24 | commands to get a copy from Github and install all dependencies::
25 | 
26 |   git clone https://github.com/Mind-the-Pineapple/sklearn-rvm.git
27 |   cd sklearn-rvm
28 |   pip install .
29 | 
30 | Or install using pip and GitHub::
31 | 
32 |   pip install -U git+https://github.com/Mind-the-Pineapple/sklearn-rvm.git


--------------------------------------------------------------------------------
/AUTHORS.rst:
--------------------------------------------------------------------------------
 1 | 
 2 | This project was initiated in October 2019 with the mission of creating a Relevance Vector Machine implementation in Python that would be fully compatible with the scikit-learn_ framework. The project was helmed by `Pedro Ferreira da Costa
 3 | <https://pedroferreiradacosta.github.io/>`_, `Walter Hugo Lopez Pinaya <https://warvito.github.io/>`_, and `Jessica Dafflon <https://www.researchgate.net/profile/Jessica_Dafflon>`_, researchers from King's College London.
 4 | 
 5 | Mind The Pinapple
 6 | -----------------
 7 | 
 8 | Mind the Pinapple is a spin-off group from King's College London focused on Machine Learning applied to Neuroscience and Health.
 9 |  Our aim  is to make Machine Learning and AI research more open.
10 | 
11 | 
12 | Contributors
13 | ------------
14 | 
15 | Refers to GitHub contributors page_.
16 | 
17 | .. _scikit-learn: http://scikit-learn.org
18 | .. _page: https://github.com/Mind-the-Pineapple/sklearn-rvm/graphs/contributors
19 | 


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/sklearn_rvm/tests/test_classifier.py:
--------------------------------------------------------------------------------
 1 | #import pytest
 2 | import numpy as np
 3 | from sklearn_rvm import EMRVC
 4 | from sklearn import datasets
 5 | from sklearn.multiclass import OneVsRestClassifier
 6 | 
 7 | iris = datasets.load_iris()
 8 | rng = np.random.RandomState(0)
 9 | perm = rng.permutation(iris.target.size)
10 | iris.data = iris.data[perm]
11 | iris.target = iris.target[perm]
12 | n_classes = 3
13 | 
14 | def test_simple_fit_predict():
15 |     X = np.array([-1, -1, -1,-1, 1, 1, 1, 1]).reshape(-1,1)
16 |     y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
17 |     clf = EMRVC()
18 |     pred = clf.fit(X,y).predict(X)
19 |     assert pred.all() == y.all()
20 | 
21 | 
22 | def test_multiclass_fit_predict():
23 |     ovr = OneVsRestClassifier(EMRVC())
24 |     pred = ovr.fit(iris.data, iris.target).predict(iris.data)
25 |     assert len(ovr.estimators_) == n_classes
26 | 
27 |     clf = EMRVC()
28 |     pred2 = clf.fit(iris.data, iris.target).predict(iris.data)
29 |     assert np.mean(iris.target == pred) == np.mean(iris.target == pred2)
30 | 
31 |     ovr = OneVsRestClassifier(EMRVC())
32 |     pred = ovr.fit(iris.data, iris.target).predict(iris.data)
33 |     assert np.mean(iris.target == pred) > 0.65
34 | 
35 | 


--------------------------------------------------------------------------------
/.travis.yml:
--------------------------------------------------------------------------------
 1 | dist: trusty
 2 | sudo: false
 3 | 
 4 | language: python
 5 | 
 6 | cache:
 7 |   directories:
 8 |   - $HOME/.cache/pip
 9 | 
10 | matrix:
11 |   include:
12 |     - env: PYTHON_VERSION="3.5" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1"
13 |            SKLEARN_VERSION="0.19.1"
14 |     - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*"
15 |            SKLEARN_VERSION="*"
16 | 
17 | 
18 | install:
19 |   # install miniconda
20 |   - deactivate
21 |   - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
22 |   - MINICONDA_PATH=/home/travis/miniconda
23 |   - chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH
24 |   - export PATH=$MINICONDA_PATH/bin:$PATH
25 |   - conda update --yes conda
26 |   # create the testing environment
27 |   - conda create -n testenv --yes python=$PYTHON_VERSION pip
28 |   - source activate testenv
29 |   - conda install --yes numpy==$NUMPY_VERSION scipy==$SCIPY_VERSION scikit-learn==$SKLEARN_VERSION cython nose pytest pytest-cov
30 |   - pip install codecov
31 |   - pip install .
32 | 
33 | script:
34 |   - mkdir for_test
35 |   - cd for_test
36 |   - pytest -v --cov=sklearn_rvm --pyargs sklearn_rvm
37 | 
38 | after_success:
39 |   - codecov
40 | 


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/examples/rvm_for_regression.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =========================================================
 6 | RVM for regression
 7 | =========================================================
 8 | Based on https://github.com/ctgk/PRML/blob/master/notebooks/ch07_Sparse_Kernel_Machines.ipynb
 9 | """
10 | print(__doc__)
11 | 
12 | import matplotlib.pyplot as plt
13 | import numpy as np
14 | 
15 | from sklearn_rvm import EMRVR
16 | 
17 | 
18 | def create_toy_data(n=20):
19 |     x = np.linspace(0, 1, n)
20 |     t = np.sin(2 * np.pi * x) + np.random.normal(scale=0.1, size=n)
21 |     return x, t
22 | 
23 | 
24 | x_train, y_train = create_toy_data(n=50)
25 | x = np.linspace(0, 1, 100)
26 | 
27 | model = EMRVR(kernel="rbf")
28 | model.fit(x_train[:, None], y_train)
29 | 
30 | y, y_std = model.predict(x[:, None], return_std=True)
31 | 
32 | plt.scatter(x_train, y_train, facecolor="none", edgecolor="g", label="training")
33 | plt.scatter(x[model.relevance_], y[model.relevance_], s=100, facecolor="none", edgecolor="b", label="relevance vector")
34 | plt.plot(x[:, None], y, color="r", label="predict mean")
35 | plt.fill_between(x, y - y_std, y + y_std, color="pink", alpha=0.2, label="predict std.")
36 | plt.legend(loc="best")
37 | plt.show()
38 | 


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/.gitignore:
--------------------------------------------------------------------------------
 1 | # Byte-compiled / optimized / DLL files
 2 | __pycache__/
 3 | *.py[cod]
 4 | *$py.class
 5 | 
 6 | # C extensions
 7 | *.so
 8 | 
 9 | # scikit-learn specific
10 | doc/_build/
11 | doc/auto_examples/
12 | doc/modules/generated/
13 | doc/datasets/generated/
14 | 
15 | # Distribution / packaging
16 | 
17 | .Python
18 | env/
19 | build/
20 | develop-eggs/
21 | dist/
22 | downloads/
23 | eggs/
24 | .eggs/
25 | lib/
26 | lib64/
27 | parts/
28 | sdist/
29 | var/
30 | *.egg-info/
31 | .installed.cfg
32 | *.egg
33 | 
34 | # PyInstaller
35 | #  Usually these files are written by a python script from a template
36 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
37 | *.manifest
38 | *.spec
39 | 
40 | # Installer logs
41 | pip-log.txt
42 | pip-delete-this-directory.txt
43 | 
44 | # Unit test / coverage reports
45 | htmlcov/
46 | .tox/
47 | .coverage
48 | .coverage.*
49 | .cache
50 | nosetests.xml
51 | coverage.xml
52 | *,cover
53 | .hypothesis/
54 | 
55 | # Translations
56 | *.mo
57 | *.pot
58 | 
59 | # Django stuff:
60 | *.log
61 | 
62 | # Sphinx documentation
63 | doc/_build/
64 | doc/generated/
65 | 
66 | # PyBuilder
67 | target/
68 | 
69 | # Pycharm
70 | .idea
71 | 
72 | # General MacOS
73 | .DS_Store
74 | 
75 | # Environments
76 | .env
77 | .venv
78 | env/
79 | venv/
80 | ENV/
81 | env.bak/
82 | venv.bak/


--------------------------------------------------------------------------------
/README.rst:
--------------------------------------------------------------------------------
 1 | .. -*- mode: rst -*-
 2 | 
 3 | |Travis|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_
 4 | 
 5 | .. |Travis| image:: https://travis-ci.com/Mind-the-Pineapple/sklearn-rvm.svg?branch=master
 6 | .. _Travis: https://travis-ci.com/Mind-the-Pineapple/sklearn-rvm
 7 | 
 8 | .. |Codecov| image:: https://codecov.io/gh/Mind-the-Pineapple/sklearn-rvm/branch/master/graph/badge.svg
 9 | .. _Codecov: https://codecov.io/gh/Mind-the-Pineapple/sklearn-rvm
10 | 
11 | .. |CircleCI| image:: https://circleci.com/gh/Mind-the-Pineapple/sklearn-rvm.svg?style=shield&circle-token=:circle-token
12 | .. _CircleCI: https://circleci.com/gh/Mind-the-Pineapple/sklearn-rvm
13 | 
14 | .. |ReadTheDocs| image:: https://readthedocs.org/projects/sklearn-rvm/badge/?version=latest
15 | .. _ReadTheDocs: https://sklearn-rvm.readthedocs.io/en/latest/?badge=latest
16 | 
17 | Sklearn-RVM
18 | ===========
19 | 
20 | 
21 | Installation
22 | ------------
23 | 
24 | .. code:: bash
25 | 
26 |     pip install sklearn-rvm
27 | 
28 | 
29 | Requirements
30 | ------------
31 | .. _Scikit-Learn: https://scikit-learn.org/
32 | 
33 | - Python (>= 3.5)
34 | - Scikit-Learn_ (>= 0.21)
35 | 
36 | Documentation
37 | -------------
38 | 
39 | .. _documentation: https://sklearn-rvm.readthedocs.io/en/latest/index.html
40 | 
41 | Refer to the documentation_ to modify the template for your own scikit-learn
42 | contribution.


--------------------------------------------------------------------------------
/.circleci/config.yml:
--------------------------------------------------------------------------------
 1 | version: 2
 2 | 
 3 | jobs:
 4 |   python3:
 5 |     docker:
 6 |       - image: circleci/python:3.6.1
 7 |     steps:
 8 |       - checkout
 9 |       - run:
10 |           command: |
11 |             wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
12 |             chmod +x miniconda.sh && ./miniconda.sh -b -p ~/miniconda
13 |             export PATH="~/miniconda/bin:$PATH"
14 |             conda update --yes --quiet conda
15 |             conda create -n testenv --yes --quiet python=3
16 |             source activate testenv
17 |             conda install --yes pip numpy scipy scikit-learn matplotlib sphinx sphinx_rtd_theme numpydoc pillow
18 |             pip install sphinx-gallery sphinxcontrib-fulltoc
19 |             pip install .
20 |             cd doc
21 |             make html
22 |       - store_artifacts:
23 |           path: doc/_build/html
24 |           destination: doc
25 |       - store_artifacts:
26 |           path: ~/log.txt
27 |       - persist_to_workspace:
28 |           root: doc/_build/html
29 |           paths: .
30 |       - attach_workspace:
31 |           at: doc/_build/html
32 |       - run: ls -ltrh doc/_build/html
33 |     filters:
34 |       branches:
35 |         ignore: gh-pages
36 | 
37 | workflows:
38 |   version: 2
39 |   build-doc-and-deploy:
40 |     jobs:
41 |       - python3
42 | 


--------------------------------------------------------------------------------
/examples/plot_rvm_for_classification.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =========================================================
 6 | RVM for classification
 7 | =========================================================
 8 | Based on https://github.com/ctgk/PRML/blob/master/notebooks/ch07_Sparse_Kernel_Machines.ipynb
 9 | """
10 | print(__doc__)
11 | 
12 | import matplotlib.pyplot as plt
13 | import numpy as np
14 | from sklearn_rvm import EMRVC
15 | 
16 | 
17 | def create_toy_data():
18 |     x0 = np.random.normal(size=100).reshape(-1, 2) - 1.
19 |     x1 = np.random.normal(size=100).reshape(-1, 2) + 1.
20 |     x = np.concatenate([x0, x1])
21 |     y = np.concatenate([np.zeros(50), np.ones(50)]).astype(np.int)
22 |     return x, y
23 | 
24 | 
25 | x_train, y_train = create_toy_data()
26 | 
27 | model = EMRVC(kernel="rbf")
28 | model.fit(x_train, y_train)
29 | 
30 | x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))
31 | x = np.array([x0, x1]).reshape(2, -1).T
32 | plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker="x")
33 | plt.scatter(model.relevance_vectors_[:, 0], model.relevance_vectors_[:, 1], s=100, facecolor="none", edgecolor="g")
34 | plt.contourf(x0, x1, model.predict_proba(x)[:, 1].reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)
35 | plt.colorbar()
36 | plt.xlim(-4, 4)
37 | plt.ylim(-4, 4)
38 | plt.gca().set_aspect("equal", adjustable="box")
39 | 


--------------------------------------------------------------------------------
/doc/index.rst:
--------------------------------------------------------------------------------
 1 | ============================================
 2 | Welcome to sklearn-rvm's documentation!
 3 | ============================================
 4 | 
 5 | .. toctree::
 6 |    :maxdepth: 2
 7 |    :hidden:
 8 |    :caption: Getting Started
 9 | 
10 |    install
11 | 
12 | .. toctree::
13 |    :maxdepth: 2
14 |    :hidden:
15 |    :caption: Documentation
16 | 
17 |    user_guide
18 |    api
19 | 
20 | .. toctree::
21 |    :maxdepth: 2
22 |    :hidden:
23 |    :caption: Tutorial - Examples
24 | 
25 |    auto_examples/index
26 | 
27 | .. toctree::
28 |    :maxdepth: 2
29 |    :hidden:
30 | 
31 |    about
32 | 
33 | `Getting started <install.html>`_
34 | ---------------------------------
35 | 
36 | Information to install, test, and contribute to the package.
37 | 
38 | `User Guide <user_guide.html>`_
39 | -------------------------------
40 | 
41 | The main documentation. This contains an in-depth description of all
42 | algorithms and how to apply them.
43 | 
44 | `API Documentation <api.html>`_
45 | -------------------------------
46 | 
47 | The exact API of all functions and classes, as given in the
48 | doctring. The API documents expected types and allowed features for
49 | all functions, and all parameters available for the algorithms.
50 | 
51 | `Examples <auto_examples/index.html>`_
52 | --------------------------------------
53 | 
54 | A set of examples illustrating the use of the different algorithms. It
55 | complements the `User Guide <user_guide.html>`_.
56 | 
57 | `About us <about.html>`_
58 | --------------------------------------
59 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | BSD 3-Clause License
 2 | 
 3 | Copyright (c) 2019, Pedro Ferreira da Costa and Walter Hugo Lopez Pinaya
 4 | All rights reserved.
 5 | 
 6 | Redistribution and use in source and binary forms, with or without
 7 | modification, are permitted provided that the following conditions are met:
 8 | 
 9 | 1. Redistributions of source code must retain the above copyright notice, this
10 |    list of conditions and the following disclaimer.
11 | 
12 | 2. Redistributions in binary form must reproduce the above copyright notice,
13 |    this list of conditions and the following disclaimer in the documentation
14 |    and/or other materials provided with the distribution.
15 | 
16 | 3. Neither the name of the copyright holder nor the names of its
17 |    contributors may be used to endorse or promote products derived from
18 |    this software without specific prior written permission.
19 | 
20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


--------------------------------------------------------------------------------
/examples/plot_compare_rvr_ard.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =====================================================================
 6 | Comparison of relevance vector regression and ARDRegression
 7 | =====================================================================
 8 | 
 9 | Based on https://scikit-learn.org/stable/auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py
10 | """
11 | print(__doc__)
12 | 
13 | import matplotlib.pyplot as plt
14 | import numpy as np
15 | from scipy import stats
16 | from sklearn.linear_model import ARDRegression
17 | from sklearn_rvm import EMRVR
18 | 
19 | # #############################################################################
20 | # Generating simulated data with Gaussian weights
21 | 
22 | # Parameters of the example
23 | np.random.seed(0)
24 | 
25 | n_samples, n_features = 100, 100
26 | 
27 | # Create Gaussian data
28 | X = np.random.randn(n_samples, n_features)
29 | # Create weights with a precision lambda_ of 4.
30 | lambda_ = 4.
31 | w = np.zeros(n_features)
32 | # Only keep 10 weights of interest
33 | relevant_features = np.random.randint(0, n_features, 10)
34 | for i in relevant_features:
35 |     w[i] = stats.norm.rvs(loc=0, scale=1. / np.sqrt(lambda_))
36 | # Create noise with a precision alpha of 50.
37 | alpha_ = 50.
38 | noise = stats.norm.rvs(loc=0, scale=1. / np.sqrt(alpha_), size=n_samples)
39 | # Create the target
40 | y = np.dot(X, w) + noise
41 | 
42 | # #############################################################################
43 | # Fit the ARD Regression
44 | clf = ARDRegression(compute_score=True)
45 | clf.fit(X, y)
46 | 
47 | rvr = EMRVR(kernel="linear")
48 | rvr.fit(X, y)
49 | 
50 | # #############################################################################
51 | # Plot the true weights, the estimated weights, the histogram of the
52 | # weights, and predictions with standard deviations
53 | plt.figure(figsize=(6, 5))
54 | plt.title("Weights of the model")
55 | plt.plot(rvr.coef_, color="darkblue", linestyle="-", linewidth=2, label="RVR estimate")
56 | plt.plot(clf.coef_, color="yellowgreen", linestyle=":", linewidth=2, label="ARD estimate")
57 | plt.plot(w, color="orange", linestyle="-", linewidth=2, label="Ground truth")
58 | plt.xlabel("Features")
59 | plt.ylabel("Values of the weights")
60 | plt.legend(loc=1)
61 | plt.show()
62 | 


--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
 1 | #! /usr/bin/env python
 2 | """A template for scikit-learn compatible packages."""
 3 | 
 4 | import codecs
 5 | import os
 6 | 
 7 | from setuptools import find_packages, setup
 8 | 
 9 | # get __version__ from _version.py
10 | ver_file = os.path.join('sklearn_rvm', '_version.py')
11 | with open(ver_file) as f:
12 |     exec(f.read())
13 | 
14 | DISTNAME = 'sklearn_rvm'
15 | DESCRIPTION = 'An scikit-learn style implementation of Relevance Vector Machines (RVM).'
16 | with codecs.open('README.rst', encoding='utf-8-sig') as f:
17 |     LONG_DESCRIPTION = f.read()
18 | MAINTAINER = 'Pedro F. da Costa, Walter Hugo Lopez Pinaya'
19 | MAINTAINER_EMAIL = 'pedro.ferreira_da_costa@kcl.ac.uk, walter.diaz_sanz@kcl.ac.uk'
20 | 
21 | URL = 'https://github.com/Mind-the-Pineapple/sklearn-rvm'
22 | LICENSE = 'new BSD'
23 | DOWNLOAD_URL = 'https://github.com/Mind-the-Pineapple/sklearn-rvm'
24 | VERSION = __version__
25 | INSTALL_REQUIRES = ['numpy', 'scipy', 'scikit-learn']
26 | CLASSIFIERS = ['Intended Audience :: Science/Research',
27 |                'Intended Audience :: Developers',
28 |                'License :: OSI Approved',
29 |                'Programming Language :: Python',
30 |                'Topic :: Software Development',
31 |                'Topic :: Scientific/Engineering',
32 |                'Operating System :: Microsoft :: Windows',
33 |                'Operating System :: POSIX',
34 |                'Operating System :: Unix',
35 |                'Operating System :: MacOS',
36 |                'Programming Language :: Python :: 3.5',
37 |                'Programming Language :: Python :: 3.6',
38 |                'Programming Language :: Python :: 3.7']
39 | EXTRAS_REQUIRE = {
40 |     'tests': [
41 |         'pytest',
42 |         'pytest-cov'],
43 |     'docs': [
44 |         'sphinx',
45 |         'sphinx-gallery',
46 |         'sphinx_rtd_theme',
47 |         'numpydoc',
48 |         'matplotlib'
49 |     ]
50 | }
51 | 
52 | setup(name=DISTNAME,
53 |       maintainer=MAINTAINER,
54 |       maintainer_email=MAINTAINER_EMAIL,
55 |       description=DESCRIPTION,
56 |       license=LICENSE,
57 |       url=URL,
58 |       version=VERSION,
59 |       download_url=DOWNLOAD_URL,
60 |       long_description=LONG_DESCRIPTION,
61 |       zip_safe=False,  # the package can run out of an .egg file
62 |       classifiers=CLASSIFIERS,
63 |       packages=find_packages(),
64 |       install_requires=INSTALL_REQUIRES,
65 |       extras_require=EXTRAS_REQUIRE)


--------------------------------------------------------------------------------
/examples/plot_compare_rvr_svr.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =====================================================================
 6 | Comparison of relevance vector machine and support vector machine
 7 | =====================================================================
 8 | """
 9 | print(__doc__)
10 | 
11 | import time
12 | 
13 | import matplotlib.pyplot as plt
14 | import numpy as np
15 | from sklearn.svm import SVR
16 | from sklearn_rvm import EMRVR
17 | 
18 | np.random.seed(8)
19 | rng = np.random.RandomState(0)
20 | 
21 | # Generate sample data
22 | X = 4 * np.pi * np.random.random(100) - 2 * np.pi
23 | y = np.sinc(X)
24 | y += 0.25 * (0.5 - rng.rand(X.shape[0]))  # add noise
25 | 
26 | X = X[:, None]
27 | 
28 | # Fit SVR
29 | svr = SVR(kernel="rbf", gamma="auto")
30 | stime = time.time()
31 | svr.fit(X, y)
32 | print("Time for SVR fitting: %.3f" % (time.time() - stime))
33 | 
34 | # Fit RVR
35 | rvr = EMRVR(kernel="rbf")
36 | stime = time.time()
37 | rvr.fit(X, y)
38 | print("Time for RVR fitting: %.3f" % (time.time() - stime))
39 | 
40 | X_plot = np.linspace(-2 * np.pi, 2 * np.pi, 10000)[:, None]
41 | # Predict using SVR
42 | stime = time.time()
43 | y_svr = svr.predict(X_plot)
44 | print("Time for SVR prediction: %.3f" % (time.time() - stime))
45 | 
46 | # Predict using Rvm
47 | stime = time.time()
48 | y_rvr = rvr.predict(X_plot, return_std=False)
49 | print("Time for RVR prediction: %.3f" % (time.time() - stime))
50 | 
51 | stime = time.time()
52 | y_rvr, y_std = rvr.predict(X_plot, return_std=True)
53 | print("Time for RVR prediction with standard-deviation: %.3f" % (time.time() - stime))
54 | 
55 | # Plot results
56 | fig = plt.figure(figsize=(10, 5))
57 | lw = 2
58 | fig.suptitle("RVR versus SVR", fontsize=16)
59 | 
60 | plt.subplot(121)
61 | plt.scatter(X, y, marker=".", c="k", label="data")
62 | plt.plot(X_plot, np.sinc(X_plot), color="navy", lw=lw, label="True")
63 | 
64 | plt.plot(X_plot, y_svr, color="turquoise", lw=lw, label="SVR")
65 | support_vectors_idx = svr.support_
66 | plt.scatter(X[support_vectors_idx], y[support_vectors_idx], s=80, facecolors="none", edgecolors="r",
67 |             label="support vectors")
68 | plt.ylabel("target")
69 | plt.xlabel("data")
70 | plt.legend(loc="best", scatterpoints=1, prop={"size": 8})
71 | 
72 | plt.subplot(122)
73 | plt.scatter(X, y, marker=".", c="k", label="data")
74 | plt.plot(X_plot, np.sinc(X_plot), color="navy", lw=lw, label="True")
75 | 
76 | plt.plot(X_plot, y_rvr, color="darkorange", lw=lw, label="RVR")
77 | plt.fill_between(X_plot[:, 0], y_rvr - y_std, y_rvr + y_std, color="darkorange", alpha=0.2)
78 | relevance_vectors_idx = rvr.relevance_
79 | plt.scatter(X[relevance_vectors_idx], y[relevance_vectors_idx], s=80, facecolors="none", edgecolors="r",
80 |             label="relevance vectors")
81 | 
82 | plt.xlabel("data")
83 | plt.legend(loc="best", scatterpoints=1, prop={"size": 8})
84 | plt.show()
85 | 


--------------------------------------------------------------------------------
/examples/plot_iris_rvc.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | """
 5 | =====================================================================
 6 | Example of a Multiple Layer Classifier using the Iris Dataset
 7 | =====================================================================
 8 | 
 9 | Based on https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html
10 | """
11 | print(__doc__)
12 | 
13 | import matplotlib.pyplot as plt
14 | import numpy as np
15 | from sklearn import datasets
16 | from sklearn_rvm import EMRVC
17 | 
18 | 
19 | def make_meshgrid(x, y, h=.02):
20 |     """Create a mesh of points to plot in
21 | 
22 |     Parameters
23 |     ----------
24 |     x: data to base x-axis meshgrid on
25 |     y: data to base y-axis meshgrid on
26 |     h: stepsize for meshgrid, optional
27 | 
28 |     Returns
29 |     -------
30 |     xx, yy : ndarray
31 |     """
32 |     x_min, x_max = x.min() - 1, x.max() + 1
33 |     y_min, y_max = y.min() - 1, y.max() + 1
34 |     xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
35 |                          np.arange(y_min, y_max, h))
36 |     return xx, yy
37 | 
38 | 
39 | def plot_contours(ax, clf, xx, yy, **params):
40 |     """Plot the decision boundaries for a classifier.
41 | 
42 |     Parameters
43 |     ----------
44 |     ax: matplotlib axes object
45 |     clf: a classifier
46 |     xx: meshgrid ndarray
47 |     yy: meshgrid ndarray
48 |     params: dictionary of params to pass to contourf, optional
49 |     """
50 |     #Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
51 |     Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
52 |     Z = Z.reshape(xx.shape)
53 |     out = ax.contourf(xx, yy, Z, **params)
54 |     return out
55 | 
56 | # import some data to play with
57 | iris = datasets.load_iris()
58 | # Take the first two features.
59 | X = iris.data[:, :2]
60 | y = iris.target
61 | 
62 | 
63 | models = (EMRVC(kernel='linear'),
64 |           EMRVC(kernel='rbf'),
65 |           EMRVC(kernel='sigmoid'))
66 | models = (clf.fit(X, y) for clf in models)
67 | 
68 | # title for the plots
69 | titles = ('RVC with linear kernel',
70 |           'RVC with RBF kernel')
71 | 
72 | # Set-up 2x2 grid for plotting.
73 | fig, sub = plt.subplots(1, 2)
74 | 
75 | plt.subplots_adjust(wspace=0.4, hspace=0.4)
76 | X0, X1 = X[:, 0], X[:, 1]
77 | xx, yy = make_meshgrid(X0, X1)
78 | 
79 | for clf, title, ax in zip(models, titles, sub.flatten()):
80 |     plot_contours(ax, clf, xx, yy,
81 |                   cmap=plt.cm.coolwarm, alpha=0.8)
82 |     ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
83 |     #ax.scatter(clf.relevance_vectors_[:, 0], clf.relevance_vectors_[:, 1], s=100, facecolor="none", edgecolor="g")
84 |     #ax.colorbar()
85 |     ax.set_xlim(xx.min(), xx.max())
86 |     ax.set_ylim(yy.min(), yy.max())
87 |     ax.set_xlabel('Sepal length')
88 |     ax.set_ylabel('Sepal width')
89 |     ax.set_xticks(())
90 |     ax.set_yticks(())
91 |     ax.set_title(title)
92 | 
93 | plt.show()


--------------------------------------------------------------------------------
/doc/sphinxext/github_link.py:
--------------------------------------------------------------------------------
 1 | from operator import attrgetter
 2 | import inspect
 3 | import subprocess
 4 | import os
 5 | import sys
 6 | from functools import partial
 7 | 
 8 | REVISION_CMD = 'git rev-parse --short HEAD'
 9 | 
10 | 
11 | def _get_git_revision():
12 |     try:
13 |         revision = subprocess.check_output(REVISION_CMD.split()).strip()
14 |     except (subprocess.CalledProcessError, OSError):
15 |         print('Failed to execute git to get revision')
16 |         return None
17 |     return revision.decode('utf-8')
18 | 
19 | 
20 | def _linkcode_resolve(domain, info, package, url_fmt, revision):
21 |     """Determine a link to online source for a class/method/function
22 | 
23 |     This is called by sphinx.ext.linkcode
24 | 
25 |     An example with a long-untouched module that everyone has
26 |     >>> _linkcode_resolve('py', {'module': 'tty',
27 |     ...                          'fullname': 'setraw'},
28 |     ...                   package='tty',
29 |     ...                   url_fmt='http://hg.python.org/cpython/file/'
30 |     ...                           '{revision}/Lib/{package}/{path}#L{lineno}',
31 |     ...                   revision='xxxx')
32 |     'http://hg.python.org/cpython/file/xxxx/Lib/tty/tty.py#L18'
33 |     """
34 | 
35 |     if revision is None:
36 |         return
37 |     if domain not in ('py', 'pyx'):
38 |         return
39 |     if not info.get('module') or not info.get('fullname'):
40 |         return
41 | 
42 |     class_name = info['fullname'].split('.')[0]
43 |     if type(class_name) != str:
44 |         # Python 2 only
45 |         class_name = class_name.encode('utf-8')
46 |     module = __import__(info['module'], fromlist=[class_name])
47 |     obj = attrgetter(info['fullname'])(module)
48 | 
49 |     try:
50 |         fn = inspect.getsourcefile(obj)
51 |     except Exception:
52 |         fn = None
53 |     if not fn:
54 |         try:
55 |             fn = inspect.getsourcefile(sys.modules[obj.__module__])
56 |         except Exception:
57 |             fn = None
58 |     if not fn:
59 |         return
60 | 
61 |     fn = os.path.relpath(fn,
62 |                          start=os.path.dirname(__import__(package).__file__))
63 |     try:
64 |         lineno = inspect.getsourcelines(obj)[1]
65 |     except Exception:
66 |         lineno = ''
67 |     return url_fmt.format(revision=revision, package=package,
68 |                           path=fn, lineno=lineno)
69 | 
70 | 
71 | def make_linkcode_resolve(package, url_fmt):
72 |     """Returns a linkcode_resolve function for the given URL format
73 | 
74 |     revision is a git commit reference (hash or name)
75 | 
76 |     package is the name of the root module of the package
77 | 
78 |     url_fmt is along the lines of ('https://github.com/USER/PROJECT/'
79 |                                    'blob/{revision}/{package}/'
80 |                                    '{path}#L{lineno}')
81 |     """
82 |     revision = _get_git_revision()
83 |     return partial(_linkcode_resolve, revision=revision, package=package,
84 |                    url_fmt=url_fmt)
85 | 


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/nav.html:
--------------------------------------------------------------------------------
 1 | {%- if pagename != 'index' and pagename != 'documentation' %}
 2 |   {%- set nav_bar_class = "sk-docs-navbar" %}
 3 |   {%- set top_container_cls = "sk-docs-container" %}
 4 | {%- else %}
 5 |   {%- set nav_bar_class = "sk-landing-navbar" %}
 6 |   {%- set top_container_cls = "sk-landing-container" %}
 7 | {%- endif %}
 8 | 
 9 | {%- set drop_down_navigation = [
10 |   ('Getting Started', pathto('install')),
11 |   ('About us', pathto('about')),
12 |   ('GitHub', 'https://github.com/Mind-the-Pineapple/sklearn-rvm'),
13 |   ('Other Versions', 'https://pypi.org/project/sklearn-rvm/#history')]
14 | -%}
15 | 
16 | <nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
17 |   <div class="container-fluid {{ top_container_cls }} px-0">
18 |     {%- if logo %}
19 |       <a class="navbar-brand py-0" href="{{ pathto('index') }}">
20 |         <img
21 |           class="sk-brand-img"
22 |           src="{{ pathto('_static/' + logo, 1) }}"
23 |           alt="logo"/>
24 |       </a>
25 |     {%- endif %}
26 |     <button
27 |       id="sk-navbar-toggler"
28 |       class="navbar-toggler"
29 |       type="button"
30 |       data-toggle="collapse"
31 |       data-target="#navbarSupportedContent"
32 |       aria-controls="navbarSupportedContent"
33 |       aria-expanded="false"
34 |       aria-label="Toggle navigation"
35 |     >
36 |       <span class="navbar-toggler-icon"></span>
37 |     </button>
38 | 
39 |     <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
40 |       <ul class="navbar-nav mr-auto">
41 |         <li class="nav-item">
42 |           <a class="sk-nav-link nav-link" href="{{ pathto('index') }}">Home</a>
43 |         </li>
44 |         <li class="nav-item">
45 |           <a class="sk-nav-link nav-link" href="{{ pathto('install') }}">Install</a>
46 |         </li>
47 |         <li class="nav-item">
48 |           <a class="sk-nav-link nav-link" href="{{ pathto('user_guide') }}">User Guide</a>
49 |         </li>
50 |         <li class="nav-item">
51 |           <a class="sk-nav-link nav-link" href="{{ pathto('api') }}">API</a>
52 |         </li>
53 |         <li class="nav-item">
54 |           <a class="sk-nav-link nav-link" href="{{ pathto('auto_examples/index') }}">Examples</a>
55 |         </li>
56 |         <li class="nav-item dropdown nav-more-item-dropdown">
57 |           <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
58 |           <div class="dropdown-menu" aria-labelledby="navbarDropdown">
59 |             {%- for title, link in drop_down_navigation %}
60 |               <a class="sk-nav-dropdown-item dropdown-item" href="{{ link }}">{{ title}}</a>
61 |             {%- endfor %}
62 |           </div>
63 |         </li>
64 |       </ul>
65 |       {%- if pagename != "search"%}
66 |       <div id="searchbox" role="search">
67 |           <div class="searchformwrapper">
68 |           <form class="search" action="{{ pathto('search') }}" method="get">
69 |             <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
70 |             <input class="sk-search-text-btn" type="submit" value="{{ _('Go') }}" />
71 |           </form>
72 |           </div>
73 |       </div>
74 |       {%- endif %}
75 |     </div>
76 |   </div>
77 | </nav>
78 | 


--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
--------------------------------------------------------------------------------
 1 | # Contributor Covenant Code of Conduct
 2 | 
 3 | ## Our Pledge
 4 | 
 5 | In the interest of fostering an open and welcoming environment, we as
 6 | contributors and maintainers pledge to making participation in our project and
 7 | our community a harassment-free experience for everyone, regardless of age, body
 8 | size, disability, ethnicity, sex characteristics, gender identity and expression,
 9 | level of experience, education, socio-economic status, nationality, personal
10 | appearance, race, religion, or sexual identity and orientation.
11 | 
12 | ## Our Standards
13 | 
14 | Examples of behavior that contributes to creating a positive environment
15 | include:
16 | 
17 | * Using welcoming and inclusive language
18 | * Being respectful of differing viewpoints and experiences
19 | * Gracefully accepting constructive criticism
20 | * Focusing on what is best for the community
21 | * Showing empathy towards other community members
22 | 
23 | Examples of unacceptable behavior by participants include:
24 | 
25 | * The use of sexualized language or imagery and unwelcome sexual attention or
26 |  advances
27 | * Trolling, insulting/derogatory comments, and personal or political attacks
28 | * Public or private harassment
29 | * Publishing others' private information, such as a physical or electronic
30 |  address, without explicit permission
31 | * Other conduct which could reasonably be considered inappropriate in a
32 |  professional setting
33 | 
34 | ## Our Responsibilities
35 | 
36 | Project maintainers are responsible for clarifying the standards of acceptable
37 | behavior and are expected to take appropriate and fair corrective action in
38 | response to any instances of unacceptable behavior.
39 | 
40 | Project maintainers have the right and responsibility to remove, edit, or
41 | reject comments, commits, code, wiki edits, issues, and other contributions
42 | that are not aligned to this Code of Conduct, or to ban temporarily or
43 | permanently any contributor for other behaviors that they deem inappropriate,
44 | threatening, offensive, or harmful.
45 | 
46 | ## Scope
47 | 
48 | This Code of Conduct applies both within project spaces and in public spaces
49 | when an individual is representing the project or its community. Examples of
50 | representing a project or community include using an official project e-mail
51 | address, posting via an official social media account, or acting as an appointed
52 | representative at an online or offline event. Representation of a project may be
53 | further defined and clarified by project maintainers.
54 | 
55 | ## Enforcement
56 | 
57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be
58 | reported by contacting the project team at pedro.hpf.costa@gmail.com. All
59 | complaints will be reviewed and investigated and will result in a response that
60 | is deemed necessary and appropriate to the circumstances. The project team is
61 | obligated to maintain confidentiality with regard to the reporter of an incident.
62 | Further details of specific enforcement policies may be posted separately.
63 | 
64 | Project maintainers who do not follow or enforce the Code of Conduct in good
65 | faith may face temporary or permanent repercussions as determined by other
66 | members of the project's leadership.
67 | 
68 | ## Attribution
69 | 
70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
72 | 
73 | [homepage]: https://www.contributor-covenant.org
74 | 
75 | For answers to common questions about this code of conduct, see
76 | https://www.contributor-covenant.org/faq
77 | 


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/layout.html:
--------------------------------------------------------------------------------
  1 | {# TEMPLATE VAR SETTINGS #}
  2 | {%- set url_root = pathto('', 1) %}
  3 | {%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
  4 | {%- if not embedded and docstitle %}
  5 |   {%- set titlesuffix = " &mdash; "|safe + docstitle|e %}
  6 | {%- else %}
  7 |   {%- set titlesuffix = "" %}
  8 | {%- endif %}
  9 | {%- set lang_attr = 'en' %}
 10 | 
 11 | <!DOCTYPE html>
 12 | <!--[if IE 8]><html class="no-js lt-ie9" lang="{{ lang_attr }}" > <![endif]-->
 13 | <!--[if gt IE 8]><!--> <html class="no-js" lang="{{ lang_attr }}" > <!--<![endif]-->
 14 | <head>
 15 |   <meta charset="utf-8">
 16 |   {{ metatags }}
 17 |   <meta name="viewport" content="width=device-width, initial-scale=1.0">
 18 |   <meta name="Description" content="scikit-learn: machine learning in Python">
 19 | 
 20 |   {% block htmltitle %}
 21 |   <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
 22 |   {% endblock %}
 23 |   <link rel="canonical" href="http://scikit-learn.org/stable/{{pagename}}.html" />
 24 | 
 25 |   {% if favicon %}
 26 |   <link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
 27 |   {% endif %}
 28 | 
 29 |   <link rel="stylesheet" href="{{ pathto('_static/css/vendor/bootstrap.min.css', 1) }}" type="text/css" />
 30 |   {%- for css in css_files %}
 31 |     {%- if css|attr("rel") %}
 32 |   <link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
 33 |     {%- else %}
 34 |   <link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
 35 |     {%- endif %}
 36 |   {%- endfor %}
 37 |   <link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
 38 | <script id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
 39 | <script src="{{ pathto('_static/jquery.js', 1) }}"></script>
 40 | {%- block extrahead %} {% endblock %}
 41 | </head>
 42 | <body>
 43 | {% include "nav.html" %}
 44 | {%- block content %}
 45 | <div class="d-flex" id="sk-doc-wrapper">
 46 |     <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
 47 |     <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
 48 |     <div id="sk-sidebar-wrapper" class="border-right">
 49 |       <div class="sk-sidebar-toc-wrapper">
 50 |         <div class="sk-sidebar-toc-logo">
 51 |           {%- if logo %}
 52 |           <a href="{{ pathto('index') }}">
 53 |             <img
 54 |               class="sk-brand-img"
 55 |               src="{{ pathto('_static/' + logo, 1) }}"
 56 |               alt="logo"/>
 57 |           </a>
 58 |           {%- endif %}
 59 |         </div>
 60 |         {%- if pagename != "install" %}
 61 |         <div class="alert alert-danger p-1 mb-2" role="alert">
 62 |           <p class="text-center mb-0">
 63 |             <strong>sklearn-rvm {{ release }}</strong><br/>
 64 |             <a href="https://pypi.org/project/sklearn-rvm/#history">Other versions</a>
 65 |           </p>
 66 |         </div>
 67 |         {%- endif %}
 68 |         <div class="alert alert-warning p-1 mb-2" role="alert">
 69 |           <p class="text-center mb-0">
 70 |             Please <a class="font-weight-bold" href="{{ pathto('about').replace('#', '') }}#citing-sklearn-rvm"><string>cite us</string></a> if you use the software.
 71 |           </p>
 72 |         </div>
 73 |         <div class="sk-sidebar-toc">
 74 |           {{ toc }}
 75 |         </div>
 76 |       </div>
 77 |     </div>
 78 |   <div id="sk-page-content-wrapper">
 79 |       <div class="sk-page-content container-fluid body px-md-3" role="main">
 80 |         {% block body %}{% endblock %}
 81 |       </div>
 82 |     <div class="container">
 83 |       <footer class="sk-content-footer">
 84 |       {%- if show_copyright %}
 85 |         {%- if hasdoc('copyright') %}
 86 |           {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
 87 |         {%- else %}
 88 |           {% trans copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
 89 |         {%- endif %}
 90 |       {%- endif %}
 91 |       {%- if last_updated %}
 92 |         {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
 93 |       {%- endif %}
 94 |       {%- if show_source and has_source and sourcename %}
 95 |         <a href="{{ pathto('_sources/' + sourcename, true)|e }}" rel="nofollow">{{ _('Show this page source') }}</a>
 96 |       {%- endif %}
 97 |       </footer>
 98 |     </div>
 99 |   </div>
100 | </div>
101 | {%- endblock %}
102 | <script src="{{ pathto('_static/js/vendor/bootstrap.min.js', 1) }}"></script>
103 | {% include "javascript.html" %}
104 | </body>
105 | </html>
106 | 


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/javascript.html:
--------------------------------------------------------------------------------
  1 | {% if theme_google_analytics|tobool %}
  2 | <script>
  3 |     window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
  4 |     ga('create', 'UA-22606712-2', 'auto');
  5 |     ga('set', 'anonymizeIp', true);
  6 |     ga('send', 'pageview');
  7 | </script>
  8 | <script async src='https://www.google-analytics.com/analytics.js'></script>
  9 | {% endif %}
 10 | 
 11 | <script>
 12 | $(document).ready(function() {
 13 |     /* Add a [>>>] button on the top-right corner of code samples to hide
 14 |      * the >>> and ... prompts and the output and thus make the code
 15 |      * copyable. */
 16 |     var div = $('.highlight-python .highlight,' +
 17 |                 '.highlight-python3 .highlight,' +
 18 |                 '.highlight-pycon .highlight,' +
 19 | 		'.highlight-default .highlight')
 20 |     var pre = div.find('pre');
 21 | 
 22 |     // get the styles from the current theme
 23 |     pre.parent().parent().css('position', 'relative');
 24 |     var hide_text = 'Hide prompts and outputs';
 25 |     var show_text = 'Show prompts and outputs';
 26 | 
 27 |     // create and add the button to all the code blocks that contain >>>
 28 |     div.each(function(index) {
 29 |         var jthis = $(this);
 30 |         if (jthis.find('.gp').length > 0) {
 31 |             var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
 32 |             button.attr('title', hide_text);
 33 |             button.data('hidden', 'false');
 34 |             jthis.prepend(button);
 35 |         }
 36 |         // tracebacks (.gt) contain bare text elements that need to be
 37 |         // wrapped in a span to work with .nextUntil() (see later)
 38 |         jthis.find('pre:has(.gt)').contents().filter(function() {
 39 |             return ((this.nodeType == 3) && (this.data.trim().length > 0));
 40 |         }).wrap('<span>');
 41 |     });
 42 | 
 43 |     // define the behavior of the button when it's clicked
 44 |     $('.copybutton').click(function(e){
 45 |         e.preventDefault();
 46 |         var button = $(this);
 47 |         if (button.data('hidden') === 'false') {
 48 |             // hide the code output
 49 |             button.parent().find('.go, .gp, .gt').hide();
 50 |             button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
 51 |             button.css('text-decoration', 'line-through');
 52 |             button.attr('title', show_text);
 53 |             button.data('hidden', 'true');
 54 |         } else {
 55 |             // show the code output
 56 |             button.parent().find('.go, .gp, .gt').show();
 57 |             button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
 58 |             button.css('text-decoration', 'none');
 59 |             button.attr('title', hide_text);
 60 |             button.data('hidden', 'false');
 61 |         }
 62 |     });
 63 | 
 64 | 	/*** Add permalink buttons next to glossary terms ***/
 65 | 	$('dl.glossary > dt[id]').append(function() {
 66 | 		return ('<a class="headerlink" href="#' +
 67 | 			    this.getAttribute('id') +
 68 | 			    '" title="Permalink to this term">¶</a>');
 69 | 	});
 70 | 
 71 | {%- if pagename != 'index' and pagename != 'documentation' %}
 72 |   /*** Hide navbar when scrolling down ***/
 73 |   // Returns true when headerlink target matches hash in url
 74 |   (function() {
 75 |     hashTargetOnTop = function() {
 76 |         var hash = window.location.hash;
 77 |         if ( hash.length < 2 ) { return false; }
 78 | 
 79 |         var target = document.getElementById( hash.slice(1) );
 80 |         if ( target === null ) { return false; }
 81 | 
 82 |         var top = target.getBoundingClientRect().top;
 83 |         return (top < 2) && (top > -2);
 84 |     };
 85 | 
 86 |     // Hide navbar on load if hash target is on top
 87 |     var navBar = document.getElementById("navbar");
 88 |     var navBarToggler = document.getElementById("sk-navbar-toggler");
 89 |     var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
 90 |     var $window = $(window);
 91 | 
 92 |     hideNavBar = function() {
 93 |         navBar.style.top = navBarHeightHidden;
 94 |     };
 95 | 
 96 |     showNavBar = function() {
 97 |         navBar.style.top = "0";
 98 |     }
 99 | 
100 |     if (hashTargetOnTop()) {
101 |         hideNavBar()
102 |     }
103 | 
104 |     var prevScrollpos = window.pageYOffset;
105 |     hideOnScroll = function(lastScrollTop) {
106 |         if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
107 |             return;
108 |         }
109 |         if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
110 |             hideNavBar()
111 |         } else {
112 |             showNavBar()
113 |         }
114 |         prevScrollpos = lastScrollTop;
115 |     };
116 | 
117 |     /*** high performance scroll event listener***/
118 |     var raf = window.requestAnimationFrame ||
119 |         window.webkitRequestAnimationFrame ||
120 |         window.mozRequestAnimationFrame ||
121 |         window.msRequestAnimationFrame ||
122 |         window.oRequestAnimationFrame;
123 |     var lastScrollTop = $window.scrollTop();
124 | 
125 |     if (raf) {
126 |         loop();
127 |     }
128 | 
129 |     function loop() {
130 |         var scrollTop = $window.scrollTop();
131 |         if (lastScrollTop === scrollTop) {
132 |             raf(loop);
133 |             return;
134 |         } else {
135 |             lastScrollTop = scrollTop;
136 |             hideOnScroll(lastScrollTop);
137 |             raf(loop);
138 |         }
139 |     }
140 |   })();
141 | {%- endif %}
142 | });
143 | 
144 | </script>
145 | {%- if pagename != 'index' and pagename != 'documentation' %}
146 |     {% if theme_mathjax_path %}
147 | <script id="MathJax-script" async src="{{ theme_mathjax_path }}"></script>
148 |     {% endif %}
149 | {%- endif %}
150 | 


--------------------------------------------------------------------------------
/doc/make.bat:
--------------------------------------------------------------------------------
  1 | @ECHO OFF
  2 | 
  3 | REM Command file for Sphinx documentation
  4 | 
  5 | if "%SPHINXBUILD%" == "" (
  6 | 	set SPHINXBUILD=sphinx-build
  7 | )
  8 | set BUILDDIR=_build
  9 | set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .
 10 | set I18NSPHINXOPTS=%SPHINXOPTS% .
 11 | if NOT "%PAPER%" == "" (
 12 | 	set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
 13 | 	set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
 14 | )
 15 | 
 16 | if "%1" == "" goto help
 17 | 
 18 | if "%1" == "help" (
 19 | 	:help
 20 | 	echo.Please use `make ^<target^>` where ^<target^> is one of
 21 | 	echo.  html       to make standalone HTML files
 22 | 	echo.  dirhtml    to make HTML files named index.html in directories
 23 | 	echo.  singlehtml to make a single large HTML file
 24 | 	echo.  pickle     to make pickle files
 25 | 	echo.  json       to make JSON files
 26 | 	echo.  htmlhelp   to make HTML files and a HTML help project
 27 | 	echo.  qthelp     to make HTML files and a qthelp project
 28 | 	echo.  devhelp    to make HTML files and a Devhelp project
 29 | 	echo.  epub       to make an epub
 30 | 	echo.  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter
 31 | 	echo.  text       to make text files
 32 | 	echo.  man        to make manual pages
 33 | 	echo.  texinfo    to make Texinfo files
 34 | 	echo.  gettext    to make PO message catalogs
 35 | 	echo.  changes    to make an overview over all changed/added/deprecated items
 36 | 	echo.  xml        to make Docutils-native XML files
 37 | 	echo.  pseudoxml  to make pseudoxml-XML files for display purposes
 38 | 	echo.  linkcheck  to check all external links for integrity
 39 | 	echo.  doctest    to run all doctests embedded in the documentation if enabled
 40 | 	goto end
 41 | )
 42 | 
 43 | if "%1" == "clean" (
 44 | 	for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
 45 | 	del /q /s %BUILDDIR%\*
 46 | 	goto end
 47 | )
 48 | 
 49 | 
 50 | %SPHINXBUILD% 2> nul
 51 | if errorlevel 9009 (
 52 | 	echo.
 53 | 	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
 54 | 	echo.installed, then set the SPHINXBUILD environment variable to point
 55 | 	echo.to the full path of the 'sphinx-build' executable. Alternatively you
 56 | 	echo.may add the Sphinx directory to PATH.
 57 | 	echo.
 58 | 	echo.If you don't have Sphinx installed, grab it from
 59 | 	echo.http://sphinx-doc.org/
 60 | 	exit /b 1
 61 | )
 62 | 
 63 | if "%1" == "html" (
 64 | 	%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
 65 | 	if errorlevel 1 exit /b 1
 66 | 	echo.
 67 | 	echo.Build finished. The HTML pages are in %BUILDDIR%/html.
 68 | 	goto end
 69 | )
 70 | 
 71 | if "%1" == "dirhtml" (
 72 | 	%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
 73 | 	if errorlevel 1 exit /b 1
 74 | 	echo.
 75 | 	echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
 76 | 	goto end
 77 | )
 78 | 
 79 | if "%1" == "singlehtml" (
 80 | 	%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
 81 | 	if errorlevel 1 exit /b 1
 82 | 	echo.
 83 | 	echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
 84 | 	goto end
 85 | )
 86 | 
 87 | if "%1" == "pickle" (
 88 | 	%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
 89 | 	if errorlevel 1 exit /b 1
 90 | 	echo.
 91 | 	echo.Build finished; now you can process the pickle files.
 92 | 	goto end
 93 | )
 94 | 
 95 | if "%1" == "json" (
 96 | 	%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
 97 | 	if errorlevel 1 exit /b 1
 98 | 	echo.
 99 | 	echo.Build finished; now you can process the JSON files.
100 | 	goto end
101 | )
102 | 
103 | if "%1" == "htmlhelp" (
104 | 	%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
105 | 	if errorlevel 1 exit /b 1
106 | 	echo.
107 | 	echo.Build finished; now you can run HTML Help Workshop with the ^
108 | .hhp project file in %BUILDDIR%/htmlhelp.
109 | 	goto end
110 | )
111 | 
112 | if "%1" == "qthelp" (
113 | 	%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
114 | 	if errorlevel 1 exit /b 1
115 | 	echo.
116 | 	echo.Build finished; now you can run "qcollectiongenerator" with the ^
117 | .qhcp project file in %BUILDDIR%/qthelp, like this:
118 | 	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\sklearn-rvm.qhcp
119 | 	echo.To view the help file:
120 | 	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\sklearn-rvm.ghc
121 | 	goto end
122 | )
123 | 
124 | if "%1" == "devhelp" (
125 | 	%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
126 | 	if errorlevel 1 exit /b 1
127 | 	echo.
128 | 	echo.Build finished.
129 | 	goto end
130 | )
131 | 
132 | if "%1" == "epub" (
133 | 	%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
134 | 	if errorlevel 1 exit /b 1
135 | 	echo.
136 | 	echo.Build finished. The epub file is in %BUILDDIR%/epub.
137 | 	goto end
138 | )
139 | 
140 | if "%1" == "latex" (
141 | 	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
142 | 	if errorlevel 1 exit /b 1
143 | 	echo.
144 | 	echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
145 | 	goto end
146 | )
147 | 
148 | if "%1" == "latexpdf" (
149 | 	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
150 | 	cd %BUILDDIR%/latex
151 | 	make all-pdf
152 | 	cd %BUILDDIR%/..
153 | 	echo.
154 | 	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
155 | 	goto end
156 | )
157 | 
158 | if "%1" == "latexpdfja" (
159 | 	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
160 | 	cd %BUILDDIR%/latex
161 | 	make all-pdf-ja
162 | 	cd %BUILDDIR%/..
163 | 	echo.
164 | 	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
165 | 	goto end
166 | )
167 | 
168 | if "%1" == "text" (
169 | 	%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
170 | 	if errorlevel 1 exit /b 1
171 | 	echo.
172 | 	echo.Build finished. The text files are in %BUILDDIR%/text.
173 | 	goto end
174 | )
175 | 
176 | if "%1" == "man" (
177 | 	%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
178 | 	if errorlevel 1 exit /b 1
179 | 	echo.
180 | 	echo.Build finished. The manual pages are in %BUILDDIR%/man.
181 | 	goto end
182 | )
183 | 
184 | if "%1" == "texinfo" (
185 | 	%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
186 | 	if errorlevel 1 exit /b 1
187 | 	echo.
188 | 	echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
189 | 	goto end
190 | )
191 | 
192 | if "%1" == "gettext" (
193 | 	%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
194 | 	if errorlevel 1 exit /b 1
195 | 	echo.
196 | 	echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
197 | 	goto end
198 | )
199 | 
200 | if "%1" == "changes" (
201 | 	%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
202 | 	if errorlevel 1 exit /b 1
203 | 	echo.
204 | 	echo.The overview file is in %BUILDDIR%/changes.
205 | 	goto end
206 | )
207 | 
208 | if "%1" == "linkcheck" (
209 | 	%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
210 | 	if errorlevel 1 exit /b 1
211 | 	echo.
212 | 	echo.Link check complete; look for any errors in the above output ^
213 | or in %BUILDDIR%/linkcheck/output.txt.
214 | 	goto end
215 | )
216 | 
217 | if "%1" == "doctest" (
218 | 	%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
219 | 	if errorlevel 1 exit /b 1
220 | 	echo.
221 | 	echo.Testing of doctests in the sources finished, look at the ^
222 | results in %BUILDDIR%/doctest/output.txt.
223 | 	goto end
224 | )
225 | 
226 | if "%1" == "xml" (
227 | 	%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
228 | 	if errorlevel 1 exit /b 1
229 | 	echo.
230 | 	echo.Build finished. The XML files are in %BUILDDIR%/xml.
231 | 	goto end
232 | )
233 | 
234 | if "%1" == "pseudoxml" (
235 | 	%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
236 | 	if errorlevel 1 exit /b 1
237 | 	echo.
238 | 	echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
239 | 	goto end
240 | )
241 | 
242 | :end
243 | 


--------------------------------------------------------------------------------
/doc/Makefile:
--------------------------------------------------------------------------------
  1 | # Makefile for Sphinx documentation
  2 | #
  3 | 
  4 | # You can set these variables from the command line.
  5 | SPHINXOPTS    =
  6 | SPHINXBUILD   = sphinx-build
  7 | PAPER         =
  8 | BUILDDIR      = _build
  9 | 
 10 | # User-friendly check for sphinx-build
 11 | ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
 12 | $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
 13 | endif
 14 | 
 15 | # Internal variables.
 16 | PAPEROPT_a4     = -D latex_paper_size=a4
 17 | PAPEROPT_letter = -D latex_paper_size=letter
 18 | ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
 19 | # the i18n builder cannot share the environment and doctrees with the others
 20 | I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
 21 | 
 22 | .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
 23 | 
 24 | help:
 25 | 	@echo "Please use \`make <target>' where <target> is one of"
 26 | 	@echo "  html       to make standalone HTML files"
 27 | 	@echo "  dirhtml    to make HTML files named index.html in directories"
 28 | 	@echo "  singlehtml to make a single large HTML file"
 29 | 	@echo "  pickle     to make pickle files"
 30 | 	@echo "  json       to make JSON files"
 31 | 	@echo "  htmlhelp   to make HTML files and a HTML help project"
 32 | 	@echo "  qthelp     to make HTML files and a qthelp project"
 33 | 	@echo "  devhelp    to make HTML files and a Devhelp project"
 34 | 	@echo "  epub       to make an epub"
 35 | 	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
 36 | 	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
 37 | 	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
 38 | 	@echo "  text       to make text files"
 39 | 	@echo "  man        to make manual pages"
 40 | 	@echo "  texinfo    to make Texinfo files"
 41 | 	@echo "  info       to make Texinfo files and run them through makeinfo"
 42 | 	@echo "  gettext    to make PO message catalogs"
 43 | 	@echo "  changes    to make an overview of all changed/added/deprecated items"
 44 | 	@echo "  xml        to make Docutils-native XML files"
 45 | 	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
 46 | 	@echo "  linkcheck  to check all external links for integrity"
 47 | 	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
 48 | 
 49 | clean:
 50 | 	-rm -rf $(BUILDDIR)/*
 51 | 	-rm -rf auto_examples/
 52 | 	-rm -rf generated/*
 53 | 	-rm -rf modules/generated/*
 54 | 
 55 | html:
 56 | 	# These two lines make the build a bit more lengthy, and the
 57 | 	# the embedding of images more robust
 58 | 	rm -rf $(BUILDDIR)/html/_images
 59 | 	#rm -rf _build/doctrees/
 60 | 	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
 61 | 	@echo
 62 | 	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
 63 | 
 64 | dirhtml:
 65 | 	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
 66 | 	@echo
 67 | 	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
 68 | 
 69 | singlehtml:
 70 | 	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
 71 | 	@echo
 72 | 	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
 73 | 
 74 | pickle:
 75 | 	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
 76 | 	@echo
 77 | 	@echo "Build finished; now you can process the pickle files."
 78 | 
 79 | json:
 80 | 	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
 81 | 	@echo
 82 | 	@echo "Build finished; now you can process the JSON files."
 83 | 
 84 | htmlhelp:
 85 | 	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
 86 | 	@echo
 87 | 	@echo "Build finished; now you can run HTML Help Workshop with the" \
 88 | 	      ".hhp project file in $(BUILDDIR)/htmlhelp."
 89 | 
 90 | qthelp:
 91 | 	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
 92 | 	@echo
 93 | 	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
 94 | 	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
 95 | 	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/sklearn-rvm.qhcp"
 96 | 	@echo "To view the help file:"
 97 | 	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/sklearn-rvm.qhc"
 98 | 
 99 | devhelp:
100 | 	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
101 | 	@echo
102 | 	@echo "Build finished."
103 | 	@echo "To view the help file:"
104 | 	@echo "# mkdir -p $$HOME/.local/share/devhelp/sklearn-rvm"
105 | 	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/sklearn-rvm"
106 | 	@echo "# devhelp"
107 | 
108 | epub:
109 | 	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
110 | 	@echo
111 | 	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
112 | 
113 | latex:
114 | 	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
115 | 	@echo
116 | 	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
117 | 	@echo "Run \`make' in that directory to run these through (pdf)latex" \
118 | 	      "(use \`make latexpdf' here to do that automatically)."
119 | 
120 | latexpdf:
121 | 	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
122 | 	@echo "Running LaTeX files through pdflatex..."
123 | 	$(MAKE) -C $(BUILDDIR)/latex all-pdf
124 | 	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
125 | 
126 | latexpdfja:
127 | 	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
128 | 	@echo "Running LaTeX files through platex and dvipdfmx..."
129 | 	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
130 | 	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
131 | 
132 | text:
133 | 	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
134 | 	@echo
135 | 	@echo "Build finished. The text files are in $(BUILDDIR)/text."
136 | 
137 | man:
138 | 	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
139 | 	@echo
140 | 	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
141 | 
142 | texinfo:
143 | 	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
144 | 	@echo
145 | 	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
146 | 	@echo "Run \`make' in that directory to run these through makeinfo" \
147 | 	      "(use \`make info' here to do that automatically)."
148 | 
149 | info:
150 | 	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
151 | 	@echo "Running Texinfo files through makeinfo..."
152 | 	make -C $(BUILDDIR)/texinfo info
153 | 	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
154 | 
155 | gettext:
156 | 	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
157 | 	@echo
158 | 	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
159 | 
160 | changes:
161 | 	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
162 | 	@echo
163 | 	@echo "The overview file is in $(BUILDDIR)/changes."
164 | 
165 | linkcheck:
166 | 	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
167 | 	@echo
168 | 	@echo "Link check complete; look for any errors in the above output " \
169 | 	      "or in $(BUILDDIR)/linkcheck/output.txt."
170 | 
171 | doctest:
172 | 	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
173 | 	@echo "Testing of doctests in the sources finished, look at the " \
174 | 	      "results in $(BUILDDIR)/doctest/output.txt."
175 | 
176 | xml:
177 | 	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
178 | 	@echo
179 | 	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
180 | 
181 | pseudoxml:
182 | 	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
183 | 	@echo
184 | 	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
185 | 


--------------------------------------------------------------------------------
/doc/conf.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: utf-8 -*-
  2 | #
  3 | # project-template documentation build configuration file, created by
  4 | # sphinx-quickstart on Mon Jan 18 14:44:12 2016.
  5 | #
  6 | # This file is execfile()d with the current directory set to its
  7 | # containing dir.
  8 | #
  9 | # Note that not all possible configuration values are present in this
 10 | # autogenerated file.
 11 | #
 12 | # All configuration values have a default; values that are commented out
 13 | # serve to show the default.
 14 | 
 15 | import sys
 16 | import os
 17 | 
 18 | import sphinx_rtd_theme
 19 | 
 20 | # If extensions (or modules to document with autodoc) are in another
 21 | # directory, add these directories to sys.path here. If the directory
 22 | # is relative to the documentation root, use os.path.abspath to make it
 23 | # absolute, like shown here.
 24 | sys.path.insert(0, os.path.abspath('sphinxext'))
 25 | 
 26 | from github_link import make_linkcode_resolve
 27 | import sphinx_gallery
 28 | 
 29 | 
 30 | # -- General configuration ------------------------------------------------
 31 | 
 32 | # If your documentation needs a minimal Sphinx version, state it here.
 33 | #needs_sphinx = '1.0'
 34 | 
 35 | # Add any Sphinx extension module names here, as strings. They can be
 36 | # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 37 | # ones.
 38 | extensions = [
 39 |     'sphinx.ext.autodoc', 'sphinx.ext.autosummary',
 40 |     'numpydoc',
 41 |     'sphinx.ext.linkcode',
 42 |     'sphinxcontrib.fulltoc',
 43 |     'sphinx.ext.doctest',
 44 |     'sphinx.ext.intersphinx',
 45 |     'sphinx.ext.imgconverter',
 46 |     'sphinx_gallery.gen_gallery',
 47 | ]
 48 | 
 49 | # For maths, use mathjax by default and svg if NO_MATHJAX env variable is set
 50 | # (useful for viewing the doc offline)
 51 | if os.environ.get('NO_MATHJAX'):
 52 |     extensions.append('sphinx.ext.imgmath')
 53 |     imgmath_image_format = 'svg'
 54 |     mathjax_path = ''
 55 | else:
 56 |     extensions.append('sphinx.ext.mathjax')
 57 |     mathjax_path = ('https://cdn.jsdelivr.net/npm/mathjax@3/es5/'
 58 |                     'tex-chtml.js')
 59 | 
 60 | autodoc_default_options = {
 61 |     'members': True,
 62 |     'inherited-members': True
 63 | }
 64 | 
 65 | # Add any paths that contain templates here, relative to this directory.
 66 | templates_path = ['templates']
 67 | 
 68 | # generate autosummary even if no references
 69 | autosummary_generate = True
 70 | 
 71 | # The suffix of source filenames.
 72 | source_suffix = '.rst'
 73 | 
 74 | # The encoding of source files.
 75 | #source_encoding = 'utf-8-sig'
 76 | 
 77 | # Generate the plots for the gallery
 78 | # plot_gallery = True
 79 | 
 80 | # The master toctree document.
 81 | master_doc = 'index'
 82 | 
 83 | # General information about the project.
 84 | project = u'sklearn-rvm'
 85 | copyright = u'2019 - 2020, Mind the Pineapple'
 86 | 
 87 | # The version info for the project you're documenting, acts as replacement for
 88 | # |version| and |release|, also used in various other places throughout the
 89 | # built documents.
 90 | #
 91 | # The short X.Y version.
 92 | from sklearn_rvm import __version__
 93 | version = __version__
 94 | # # The full version, including alpha/beta/rc tags.
 95 | release = __version__
 96 | 
 97 | # The language for content autogenerated by Sphinx. Refer to documentation
 98 | # for a list of supported languages.
 99 | #language = None
100 | 
101 | # There are two options for replacing |today|: either, you set today to some
102 | # non-false value, then it is used:
103 | #today = ''
104 | # Else, today_fmt is used as the format for a strftime call.
105 | #today_fmt = '%B %d, %Y'
106 | 
107 | # List of patterns, relative to source directory, that match files and
108 | # directories to ignore when looking for source files.
109 | exclude_patterns = ['_build', 'templates', 'includes', 'themes']
110 | 
111 | # The reST default role (used for this markup: `text`) to use for all
112 | # documents.
113 | default_role = 'literal'
114 | 
115 | # If true, '()' will be appended to :func: etc. cross-reference text.
116 | add_function_parentheses = False
117 | 
118 | # If true, the current module name will be prepended to all description
119 | # unit titles (such as .. function::).
120 | #add_module_names = True
121 | 
122 | # If true, sectionauthor and moduleauthor directives will be shown in the
123 | # output. They are ignored by default.
124 | #show_authors = False
125 | 
126 | # The name of the Pygments (syntax highlighting) style to use.
127 | pygments_style = 'sphinx'
128 | 
129 | # Custom style
130 | # html_style = 'css/sklearn-rvm.css'
131 | 
132 | # A list of ignored prefixes for module index sorting.
133 | #modindex_common_prefix = []
134 | 
135 | # If true, keep warnings as "system message" paragraphs in the built documents.
136 | #keep_warnings = False
137 | 
138 | 
139 | # -- Options for HTML output ----------------------------------------------
140 | 
141 | # The theme to use for HTML and HTML Help pages.  See the documentation for
142 | # a list of builtin themes.
143 | # html_theme = 'sphinx_rtd_theme'
144 | html_theme = 'scikit-learn-modern'
145 | 
146 | # Theme options are theme-specific and customize the look and feel of a theme
147 | # further.  For a list of options available for each theme, see the
148 | # documentation.
149 | html_theme_options = {'google_analytics': True,
150 |                       'mathjax_path': mathjax_path}
151 | 
152 | # Add any paths that contain custom themes here, relative to this directory.
153 | html_theme_path = ['themes']
154 | 
155 | # The name for this set of Sphinx documents.  If None, it defaults to
156 | # "<project> v<release> documentation".
157 | #html_title = None
158 | 
159 | # A shorter title for the navigation bar.  Default is the same as html_title.
160 | html_short_title = 'scikit-rvm'
161 | 
162 | # The name of an image file (relative to this directory) to place at the top
163 | # of the sidebar.
164 | html_logo = 'logos/sklearn-rvm-logo-small.png'
165 | 
166 | # The name of an image file (within the static path) to use as favicon of the
167 | # docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
168 | # pixels large.
169 | html_favicon = 'logos/favicon.ico'
170 | 
171 | # Add any paths that contain custom static files (such as style sheets) here,
172 | # relative to this directory. They are copied after the builtin static files,
173 | # so a file named "default.css" will overwrite the builtin "default.css".
174 | # html_static_path = ['_static']
175 | 
176 | # Add any extra paths that contain custom files (such as robots.txt or
177 | # .htaccess) here, relative to this directory. These files are copied
178 | # directly to the root of the documentation.
179 | #html_extra_path = []
180 | 
181 | # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
182 | # using the given strftime format.
183 | #html_last_updated_fmt = '%b %d, %Y'
184 | 
185 | # If true, SmartyPants will be used to convert quotes and dashes to
186 | # typographically correct entities.
187 | #html_use_smartypants = True
188 | 
189 | # Custom sidebar templates, maps document names to template names.
190 | #html_sidebars = {}
191 | 
192 | # Additional templates that should be rendered to pages, maps page names to
193 | # template names.
194 | #html_additional_pages = {}
195 | 
196 | # If false, no module index is generated.
197 | #html_domain_indices = True
198 | 
199 | # If false, no index is generated.
200 | #html_use_index = True
201 | 
202 | # If true, the index is split into individual pages for each letter.
203 | #html_split_index = False
204 | 
205 | # If true, links to the reST sources are added to the pages.
206 | #html_show_sourcelink = True
207 | 
208 | # If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
209 | #html_show_sphinx = True
210 | 
211 | # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
212 | #html_show_copyright = True
213 | 
214 | # If true, an OpenSearch description file will be output, and all pages will
215 | # contain a <link> tag referring to it.  The value of this option must be the
216 | # base URL from which the finished HTML is served.
217 | #html_use_opensearch = ''
218 | 
219 | # This is the file name suffix for HTML files (e.g. ".xhtml").
220 | #html_file_suffix = None
221 | 
222 | # Output file base name for HTML help builder.
223 | htmlhelp_basename = 'sklearn-rvmdoc'
224 | 
225 | 
226 | # -- Options for LaTeX output ---------------------------------------------
227 | 
228 | latex_elements = {
229 | # The paper size ('letterpaper' or 'a4paper').
230 | #'papersize': 'letterpaper',
231 | 
232 | # The font size ('10pt', '11pt' or '12pt').
233 | #'pointsize': '10pt',
234 | 
235 | # Additional stuff for the LaTeX preamble.
236 | #'preamble': '',
237 | }
238 | 
239 | # Grouping the document tree into LaTeX files. List of tuples
240 | # (source start file, target name, title,
241 | #  author, documentclass [howto, manual, or own class]).
242 | latex_documents = [
243 |   ('index', 'sklearn-rvm.tex', u'sklear-rvm Documentation',
244 |    u'Mind the Pineapple', 'manual'),
245 | ]
246 | 
247 | # The name of an image file (relative to this directory) to place at the top of
248 | # the title page.
249 | #latex_logo = None
250 | 
251 | # For "manual" documents, if this is true, then toplevel headings are parts,
252 | # not chapters.
253 | #latex_use_parts = False
254 | 
255 | # If true, show page references after internal links.
256 | #latex_show_pagerefs = False
257 | 
258 | # If true, show URL addresses after external links.
259 | #latex_show_urls = False
260 | 
261 | # Documents to append as an appendix to all manuals.
262 | #latex_appendices = []
263 | 
264 | # If false, no module index is generated.
265 | #latex_domain_indices = True
266 | 
267 | 
268 | # -- Options for manual page output ---------------------------------------
269 | 
270 | # One entry per manual page. List of tuples
271 | # (source start file, name, description, authors, manual section).
272 | man_pages = [
273 |     ('index', 'sklearn-rvm', u'sklearn-rvm Documentation',
274 |      [u'Mind the Pineapple'], 1)
275 | ]
276 | 
277 | # If true, show URL addresses after external links.
278 | #man_show_urls = False
279 | 
280 | 
281 | # -- Options for Texinfo output -------------------------------------------
282 | 
283 | # Grouping the document tree into Texinfo files. List of tuples
284 | # (source start file, target name, title, author,
285 | #  dir menu entry, description, category)
286 | texinfo_documents = [
287 |   ('index', 'sklearn-rvm', u'sklearn-rvm Documentation',
288 |    u'Mind the Pineapple', 'sklearn-rvm', 'One line description of project.',
289 |    'Miscellaneous'),
290 | ]
291 | 
292 | # Documents to append as an appendix to all manuals.
293 | #texinfo_appendices = []
294 | 
295 | # If false, no module index is generated.
296 | #texinfo_domain_indices = True
297 | 
298 | # How to display URL addresses: 'footnote', 'no', or 'inline'.
299 | #texinfo_show_urls = 'footnote'
300 | 
301 | # If true, do not generate a @detailmenu in the "Top" node's menu.
302 | #texinfo_no_detailmenu = False
303 | 
304 | 
305 | # Example configuration for intersphinx: refer to the Python standard library.
306 | # intersphinx configuration
307 | intersphinx_mapping = {
308 |     'python': ('https://docs.python.org/{.major}'.format(
309 |         sys.version_info), None),
310 |     'numpy': ('https://docs.scipy.org/doc/numpy/', None),
311 |     'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
312 |     'matplotlib': ('https://matplotlib.org/', None),
313 |     'sklearn': ('http://scikit-learn.org/stable', None)
314 | }
315 | 
316 | # sphinx-gallery configuration
317 | sphinx_gallery_conf = {
318 |     'doc_module': 'sklearn_rvm',
319 |     'backreferences_dir': os.path.join('generated'),
320 |     'reference_url': {
321 |         'sklearn_rvm': None}
322 | }
323 | 
324 | def setup(app):
325 |     # a copy button to copy snippet of code from the documentation
326 |     app.add_javascript('js/copybutton.js')
327 | 
328 | # The following is used by sphinx.ext.linkcode to provide links to github
329 | linkcode_resolve = make_linkcode_resolve('sklearn',
330 |                                          'https://github.com/Mind-the-Pineapple/'
331 |                                          'sklearn-rvm/blob/{revision}/'
332 |                                          '{package}/{path}#L{lineno}')


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/static/js/searchtools.js:
--------------------------------------------------------------------------------
  1 | /*
  2 |  * searchtools.js
  3 |  * ~~~~~~~~~~~~~~~~
  4 |  *
  5 |  * Sphinx JavaScript utilities for the full-text search.
  6 |  *
  7 |  * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
  8 |  * :license: BSD, see LICENSE for details.
  9 |  *
 10 |  * CHANGELOG:
 11 |  * - Removes ajax call to get context for each result
 12 |  * - Adjusts Search.query to remove duplicates in search results.
 13 |  * - Adjusts Scorer to rank objects higher.
 14 |  * - Adds Search._total_non_object_results to limit the number of search non
 15 |  * object results. Object results do not perform another GET resquest, so they
 16 |  * are cheap to display.
 17 |  */
 18 | 
 19 | if (!Scorer) {
 20 |     /**
 21 |      * Simple result scoring code.
 22 |      */
 23 |     var Scorer = {
 24 |         // Implement the following function to further tweak the score for each result
 25 |         // The function takes a result array [filename, title, anchor, descr, score]
 26 |         // and returns the new score.
 27 |         /*
 28 |               score: function(result) {
 29 |                 return result[4];
 30 |               },
 31 |         */
 32 | 
 33 |         // query matches the full name of an object
 34 |         objNameMatch: 15,
 35 |         // or matches in the last dotted part of the object name
 36 |         objPartialMatch: 15,
 37 |         // Additive scores depending on the priority of the object
 38 |         objPrio: {
 39 |             0: 15, // used to be importantResults
 40 |             1: 5, // used to be objectResults
 41 |             2: -5
 42 |         }, // used to be unimportantResults
 43 |         //  Used when the priority is not in the mapping.
 44 |         objPrioDefault: 0,
 45 | 
 46 |         // query found in title
 47 |         title: 15,
 48 |         partialTitle: 7,
 49 |         // query found in terms
 50 |         term: 10,
 51 |         partialTerm: 2
 52 |     };
 53 | }
 54 | 
 55 | if (!splitQuery) {
 56 |     function splitQuery(query) {
 57 |         return query.split(/\s+/);
 58 |     }
 59 | }
 60 | 
 61 | /**
 62 |  * Search Module
 63 |  */
 64 | var Search = {
 65 |     _index: null,
 66 |     _queued_query: null,
 67 |     _pulse_status: -1,
 68 |     _total_non_object_results: 10,
 69 | 
 70 |     htmlToText: function (htmlString) {
 71 |         var htmlString = htmlString.replace(/<img[\s\S]+?>/g, "");
 72 |         var htmlElement = document.createElement("span");
 73 |         htmlElement.innerHTML = htmlString;
 74 |         $(htmlElement)
 75 |             .find(".headerlink")
 76 |             .remove();
 77 |         docContent = $(htmlElement).find("[role=main]")[0];
 78 |         return docContent.textContent || docContent.innerText;
 79 |     },
 80 | 
 81 |     init: function () {
 82 |         var params = $.getQueryParameters();
 83 |         if (params.q) {
 84 |             var query = params.q[0];
 85 |             $('input[name="q"]')[0].value = query;
 86 |             this.performSearch(query);
 87 |         }
 88 |     },
 89 | 
 90 |     loadIndex: function (url) {
 91 |         $.ajax({
 92 |             type: "GET",
 93 |             url: url,
 94 |             data: null,
 95 |             dataType: "script",
 96 |             cache: true,
 97 |             complete: function (jqxhr, textstatus) {
 98 |                 if (textstatus != "success") {
 99 |                     document.getElementById("searchindexloader").src = url;
100 |                 }
101 |             }
102 |         });
103 |     },
104 | 
105 |     setIndex: function (index) {
106 |         var q;
107 |         this._index = index;
108 |         if ((q = this._queued_query) !== null) {
109 |             this._queued_query = null;
110 |             Search.query(q);
111 |         }
112 |     },
113 | 
114 |     hasIndex: function () {
115 |         return this._index !== null;
116 |     },
117 | 
118 |     deferQuery: function (query) {
119 |         this._queued_query = query;
120 |     },
121 | 
122 |     stopPulse: function () {
123 |         this._pulse_status = 0;
124 |     },
125 | 
126 |     startPulse: function () {
127 |         if (this._pulse_status >= 0) return;
128 |         function pulse() {
129 |             var i;
130 |             Search._pulse_status = (Search._pulse_status + 1) % 4;
131 |             var dotString = "";
132 |             for (i = 0; i < Search._pulse_status; i++) dotString += ".";
133 |             Search.dots.text(dotString);
134 |             if (Search._pulse_status > -1) window.setTimeout(pulse, 500);
135 |         }
136 |         pulse();
137 |     },
138 | 
139 |     /**
140 |      * perform a search for something (or wait until index is loaded)
141 |      */
142 |     performSearch: function (query) {
143 |         // create the required interface elements
144 |         this.out = $("#search-results");
145 |         this.title = $("<h2>" + _("Searching") + "</h2>").appendTo(this.out);
146 |         this.dots = $("<span></span>").appendTo(this.title);
147 |         this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
148 |         this.output = $('<ul class="search"/>').appendTo(this.out);
149 | 
150 |         $("#search-progress").text(_("Preparing search..."));
151 |         this.startPulse();
152 | 
153 |         // index already loaded, the browser was quick!
154 |         if (this.hasIndex()) this.query(query);
155 |         else this.deferQuery(query);
156 |     },
157 | 
158 |     /**
159 |      * execute search (requires search index to be loaded)
160 |      */
161 |     query: function (query) {
162 |         var i;
163 | 
164 |         // stem the searchterms and add them to the correct list
165 |         var stemmer = new Stemmer();
166 |         var searchterms = [];
167 |         var excluded = [];
168 |         var hlterms = [];
169 |         var tmp = splitQuery(query);
170 |         var objectterms = [];
171 |         for (i = 0; i < tmp.length; i++) {
172 |             if (tmp[i] !== "") {
173 |                 objectterms.push(tmp[i].toLowerCase());
174 |             }
175 | 
176 |             if (
177 |                 $u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 ||
178 |                 tmp[i].match(/^\d+$/) ||
179 |                 tmp[i] === ""
180 |             ) {
181 |                 // skip this "word"
182 |                 continue;
183 |             }
184 |             // stem the word
185 |             var word = stemmer.stemWord(tmp[i].toLowerCase());
186 |             // prevent stemmer from cutting word smaller than two chars
187 |             if (word.length < 3 && tmp[i].length >= 3) {
188 |                 word = tmp[i];
189 |             }
190 |             var toAppend;
191 |             // select the correct list
192 |             if (word[0] == "-") {
193 |                 toAppend = excluded;
194 |                 word = word.substr(1);
195 |             } else {
196 |                 toAppend = searchterms;
197 |                 hlterms.push(tmp[i].toLowerCase());
198 |             }
199 |             // only add if not already in the list
200 |             if (!$u.contains(toAppend, word)) toAppend.push(word);
201 |         }
202 |         var highlightstring = "?highlight=" + $.urlencode(hlterms.join(" "));
203 | 
204 |         // console.debug('SEARCH: searching for:');
205 |         // console.info('required: ', searchterms);
206 |         // console.info('excluded: ', excluded);
207 | 
208 |         // prepare search
209 |         var terms = this._index.terms;
210 |         var titleterms = this._index.titleterms;
211 | 
212 |         // array of [filename, title, anchor, descr, score]
213 |         var results = [];
214 |         $("#search-progress").empty();
215 | 
216 |         // lookup as object
217 |         for (i = 0; i < objectterms.length; i++) {
218 |             var others = [].concat(
219 |                 objectterms.slice(0, i),
220 |                 objectterms.slice(i + 1, objectterms.length)
221 |             );
222 | 
223 |             results = $u.uniq(results.concat(
224 |                 this.performObjectSearch(objectterms[i], others)
225 |             ), false, function (item) {return item[1]});
226 |         }
227 | 
228 |         var total_object_results = results.length;
229 | 
230 |         // lookup as search terms in fulltext
231 |         results = results.concat(
232 |             this.performTermsSearch(searchterms, excluded, terms, titleterms)
233 |         );
234 | 
235 |         // Only have _total_non_object_results results above the number of
236 |         // total number of object results
237 |         var results_limit = total_object_results + this._total_non_object_results
238 |         if (results.length > results_limit) {
239 |             results = results.slice(0, results_limit);
240 |         }
241 | 
242 |         // let the scorer override scores with a custom scoring function
243 |         if (Scorer.score) {
244 |             for (i = 0; i < results.length; i++)
245 |                 results[i][4] = Scorer.score(results[i]);
246 |         }
247 | 
248 |         // now sort the results by score (in opposite order of appearance, since the
249 |         // display function below uses pop() to retrieve items) and then
250 |         // alphabetically
251 |         results.sort(function (a, b) {
252 |             var left = a[4];
253 |             var right = b[4];
254 |             if (left > right) {
255 |                 return 1;
256 |             } else if (left < right) {
257 |                 return -1;
258 |             } else {
259 |                 // same score: sort alphabetically
260 |                 left = a[1].toLowerCase();
261 |                 right = b[1].toLowerCase();
262 |                 return left > right ? -1 : left < right ? 1 : 0;
263 |             }
264 |         });
265 | 
266 |         // for debugging
267 |         //Search.lastresults = results.slice();  // a copy
268 |         //console.info('search results:', Search.lastresults);
269 | 
270 |         // print the results
271 |         var resultCount = results.length;
272 |         function displayNextItem() {
273 |             // results left, load the summary and display it
274 |             if (results.length) {
275 |                 var item = results.pop();
276 |                 var listItem = $('<li style="display:none"></li>');
277 |                 if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === "") {
278 |                     // dirhtml builder
279 |                     var dirname = item[0] + "/";
280 |                     if (dirname.match(/\/index\/$/)) {
281 |                         dirname = dirname.substring(0, dirname.length - 6);
282 |                     } else if (dirname == "index/") {
283 |                         dirname = "";
284 |                     }
285 |                     listItem.append(
286 |                         $("<a/>")
287 |                             .attr(
288 |                                 "href",
289 |                                 DOCUMENTATION_OPTIONS.URL_ROOT +
290 |                                 dirname +
291 |                                 highlightstring +
292 |                                 item[2]
293 |                             )
294 |                             .html(item[1])
295 |                     );
296 |                 } else {
297 |                     // normal html builders
298 |                     listItem.append(
299 |                         $("<a/>")
300 |                             .attr(
301 |                                 "href",
302 |                                 item[0] +
303 |                                 DOCUMENTATION_OPTIONS.FILE_SUFFIX +
304 |                                 highlightstring +
305 |                                 item[2]
306 |                             )
307 |                             .html(item[1])
308 |                     );
309 |                 }
310 |                 if (item[3]) {
311 |                     // listItem.append($("<span> (" + item[3] + ")</span>"));
312 |                     Search.output.append(listItem);
313 |                     listItem.slideDown(5, function () {
314 |                         displayNextItem();
315 |                     });
316 |                 } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
317 |                     $.ajax({
318 |                         url:
319 |                             DOCUMENTATION_OPTIONS.URL_ROOT +
320 |                             item[0] +
321 |                             DOCUMENTATION_OPTIONS.FILE_SUFFIX,
322 |                         dataType: "text",
323 |                         complete: function (jqxhr, textstatus) {
324 |                             var data = jqxhr.responseText;
325 |                             if (data !== "" && data !== undefined) {
326 |                                 listItem.append(
327 |                                     Search.makeSearchSummary(data, searchterms, hlterms)
328 |                                 );
329 |                             }
330 |                             Search.output.append(listItem);
331 |                             listItem.slideDown(5, function () {
332 |                                 displayNextItem();
333 |                             });
334 |                         }
335 |                     });
336 |                 } else {
337 |                     // no source available, just display title
338 |                     Search.output.append(listItem);
339 |                     listItem.slideDown(5, function () {
340 |                         displayNextItem();
341 |                     });
342 |                 }
343 |             }
344 |             // search finished, update title and status message
345 |             else {
346 |                 Search.stopPulse();
347 |                 Search.title.text(_("Search Results"));
348 |                 if (!resultCount)
349 |                     Search.status.text(
350 |                         _(
351 |                             "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
352 |                         )
353 |                     );
354 |                 else
355 |                     Search.status.text(
356 |                         _(
357 |                             "Search finished, found %s page(s) matching the search query."
358 |                         ).replace("%s", resultCount)
359 |                     );
360 |                 Search.status.fadeIn(500);
361 |             }
362 |         }
363 |         displayNextItem();
364 |     },
365 | 
366 |     /**
367 |      * search for object names
368 |      */
369 |     performObjectSearch: function (object, otherterms) {
370 |         var filenames = this._index.filenames;
371 |         var docnames = this._index.docnames;
372 |         var objects = this._index.objects;
373 |         var objnames = this._index.objnames;
374 |         var titles = this._index.titles;
375 | 
376 |         var i;
377 |         var results = [];
378 | 
379 |         for (var prefix in objects) {
380 |             for (var name in objects[prefix]) {
381 |                 var fullname = (prefix ? prefix + "." : "") + name;
382 |                 var fullnameLower = fullname.toLowerCase();
383 |                 if (fullnameLower.indexOf(object) > -1) {
384 |                     var score = 0;
385 |                     var parts = fullnameLower.split(".");
386 |                     // check for different match types: exact matches of full name or
387 |                     // "last name" (i.e. last dotted part)
388 |                     if (fullnameLower == object || parts[parts.length - 1] == object) {
389 |                         score += Scorer.objNameMatch;
390 |                         // matches in last name
391 |                     } else if (parts[parts.length - 1].indexOf(object) > -1) {
392 |                         score += Scorer.objPartialMatch;
393 |                     }
394 |                     var match = objects[prefix][name];
395 |                     var objname = objnames[match[1]][2];
396 |                     var title = titles[match[0]];
397 |                     // If more than one term searched for, we require other words to be
398 |                     // found in the name/title/description
399 |                     if (otherterms.length > 0) {
400 |                         var haystack = (
401 |                             prefix +
402 |                             " " +
403 |                             name +
404 |                             " " +
405 |                             objname +
406 |                             " " +
407 |                             title
408 |                         ).toLowerCase();
409 |                         var allfound = true;
410 |                         for (i = 0; i < otherterms.length; i++) {
411 |                             if (haystack.indexOf(otherterms[i]) == -1) {
412 |                                 allfound = false;
413 |                                 break;
414 |                             }
415 |                         }
416 |                         if (!allfound) {
417 |                             continue;
418 |                         }
419 |                     }
420 |                     var descr = objname + _(", in ") + title;
421 | 
422 |                     var anchor = match[3];
423 |                     if (anchor === "") anchor = fullname;
424 |                     else if (anchor == "-")
425 |                         anchor = objnames[match[1]][1] + "-" + fullname;
426 |                     // add custom score for some objects according to scorer
427 |                     if (Scorer.objPrio.hasOwnProperty(match[2])) {
428 |                         score += Scorer.objPrio[match[2]];
429 |                     } else {
430 |                         score += Scorer.objPrioDefault;
431 |                     }
432 | 
433 |                     results.push([
434 |                         docnames[match[0]],
435 |                         fullname,
436 |                         "#" + anchor,
437 |                         descr,
438 |                         score,
439 |                         filenames[match[0]]
440 |                     ]);
441 |                 }
442 |             }
443 |         }
444 | 
445 |         return results;
446 |     },
447 | 
448 |     /**
449 |      * search for full-text terms in the index
450 |      */
451 |     performTermsSearch: function (searchterms, excluded, terms, titleterms) {
452 |         var docnames = this._index.docnames;
453 |         var filenames = this._index.filenames;
454 |         var titles = this._index.titles;
455 | 
456 |         var i, j, file;
457 |         var fileMap = {};
458 |         var scoreMap = {};
459 |         var results = [];
460 | 
461 |         // perform the search on the required terms
462 |         for (i = 0; i < searchterms.length; i++) {
463 |             var word = searchterms[i];
464 |             var files = [];
465 |             var _o = [
466 |                 { files: terms[word], score: Scorer.term },
467 |                 { files: titleterms[word], score: Scorer.title }
468 |             ];
469 |             // add support for partial matches
470 |             if (word.length > 2) {
471 |                 for (var w in terms) {
472 |                     if (w.match(word) && !terms[word]) {
473 |                         _o.push({ files: terms[w], score: Scorer.partialTerm });
474 |                     }
475 |                 }
476 |                 for (var w in titleterms) {
477 |                     if (w.match(word) && !titleterms[word]) {
478 |                         _o.push({ files: titleterms[w], score: Scorer.partialTitle });
479 |                     }
480 |                 }
481 |             }
482 | 
483 |             // no match but word was a required one
484 |             if (
485 |                 $u.every(_o, function (o) {
486 |                     return o.files === undefined;
487 |                 })
488 |             ) {
489 |                 break;
490 |             }
491 |             // found search word in contents
492 |             $u.each(_o, function (o) {
493 |                 var _files = o.files;
494 |                 if (_files === undefined) return;
495 | 
496 |                 if (_files.length === undefined) _files = [_files];
497 |                 files = files.concat(_files);
498 | 
499 |                 // set score for the word in each file to Scorer.term
500 |                 for (j = 0; j < _files.length; j++) {
501 |                     file = _files[j];
502 |                     if (!(file in scoreMap)) scoreMap[file] = {};
503 |                     scoreMap[file][word] = o.score;
504 |                 }
505 |             });
506 | 
507 |             // create the mapping
508 |             for (j = 0; j < files.length; j++) {
509 |                 file = files[j];
510 |                 if (file in fileMap) fileMap[file].push(word);
511 |                 else fileMap[file] = [word];
512 |             }
513 |         }
514 | 
515 |         // now check if the files don't contain excluded terms
516 |         for (file in fileMap) {
517 |             var valid = true;
518 | 
519 |             // check if all requirements are matched
520 |             var filteredTermCount = searchterms.filter(function (term) {
521 |                 // as search terms with length < 3 are discarded: ignore
522 |                 return term.length > 2;
523 |             }).length;
524 |             if (
525 |                 fileMap[file].length != searchterms.length &&
526 |                 fileMap[file].length != filteredTermCount
527 |             )
528 |                 continue;
529 | 
530 |             // ensure that none of the excluded terms is in the search result
531 |             for (i = 0; i < excluded.length; i++) {
532 |                 if (
533 |                     terms[excluded[i]] == file ||
534 |                     titleterms[excluded[i]] == file ||
535 |                     $u.contains(terms[excluded[i]] || [], file) ||
536 |                     $u.contains(titleterms[excluded[i]] || [], file)
537 |                 ) {
538 |                     valid = false;
539 |                     break;
540 |                 }
541 |             }
542 | 
543 |             // if we have still a valid result we can add it to the result list
544 |             if (valid) {
545 |                 // select one (max) score for the file.
546 |                 // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
547 |                 var score = $u.max(
548 |                     $u.map(fileMap[file], function (w) {
549 |                         return scoreMap[file][w];
550 |                     })
551 |                 );
552 |                 results.push([
553 |                     docnames[file],
554 |                     titles[file],
555 |                     "",
556 |                     null,
557 |                     score,
558 |                     filenames[file]
559 |                 ]);
560 |             }
561 |         }
562 |         return results;
563 |     },
564 | 
565 |     /**
566 |      * helper function to return a node containing the
567 |      * search summary for a given text. keywords is a list
568 |      * of stemmed words, hlwords is the list of normal, unstemmed
569 |      * words. the first one is used to find the occurrence, the
570 |      * latter for highlighting it.
571 |      */
572 |     makeSearchSummary: function (htmlText, keywords, hlwords) {
573 |         var text = Search.htmlToText(htmlText);
574 |         var textLower = text.toLowerCase();
575 |         var start = 0;
576 |         $.each(keywords, function () {
577 |             var i = textLower.indexOf(this.toLowerCase());
578 |             if (i > -1) start = i;
579 |         });
580 |         start = Math.max(start - 120, 0);
581 |         var excerpt =
582 |             (start > 0 ? "..." : "") +
583 |             $.trim(text.substr(start, 240)) +
584 |             (start + 240 - text.length ? "..." : "");
585 |         var rv = $('<div class="context"></div>').text(excerpt);
586 |         $.each(hlwords, function () {
587 |             rv = rv.highlightText(this, "highlighted");
588 |         });
589 |         return rv;
590 |     }
591 | };
592 | 
593 | $(document).ready(function () {
594 |     Search.init();
595 | });
596 | 


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/static/css/theme.css:
--------------------------------------------------------------------------------
   1 | /* Elements */
   2 | a {
   3 |   color: #2878A2;
   4 |   word-wrap: break-word;
   5 | }
   6 | 
   7 | a:focus {
   8 |   outline: none;
   9 | }
  10 | 
  11 | /* Anchor links */
  12 | 
  13 | a.headerlink {
  14 |   color: #c60f0f;
  15 |   font-size: 0.8em;
  16 |   padding: 0 4px 0 4px;
  17 |   text-decoration: none;
  18 |   visibility: hidden;
  19 | }
  20 | 
  21 | a.headerlink:hover {
  22 |   background-color: #c60f0f;
  23 |   color: white;
  24 | }
  25 | 
  26 | p {
  27 |   word-break: break-word;
  28 |   hyphens: auto;
  29 | }
  30 | 
  31 | input:focus {
  32 |   outline: none;
  33 | }
  34 | 
  35 | code {
  36 |   color: #222;
  37 |   background-color: #ecf0f3;
  38 |   border-radius: 0.2rem;
  39 |   white-space: nowrap;
  40 |   padding: 0.15rem;
  41 | }
  42 | 
  43 | nav {
  44 |   z-index: 3;
  45 | }
  46 | 
  47 | h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
  48 |   background-color: transparent;
  49 | }
  50 | 
  51 | h1:hover a.headerlink,
  52 | h2:hover a.headerlink,
  53 | h3:hover a.headerlink,
  54 | h4:hover a.headerlink,
  55 | h5:hover a.headerlink,
  56 | h6:hover a.headerlink,
  57 | dt:hover a.headerlink {
  58 |   visibility: visible;
  59 | }
  60 | 
  61 | strong {
  62 |   font-weight: bold;
  63 | }
  64 | 
  65 | a code {
  66 |   color: inherit;
  67 | }
  68 | 
  69 | a code {
  70 |   background-color: transparent;
  71 |   font-weight: bold;
  72 |   color: #2878A2;
  73 |   border-radius: 0;
  74 |   padding: 0;
  75 | }
  76 | 
  77 | img {
  78 |    max-width: 100%;
  79 | }
  80 | 
  81 | span.highlighted {
  82 |     background-color: #fbe54e;
  83 | }
  84 | 
  85 | div.highlight {
  86 |   padding: 0.2rem 0.5rem;
  87 |   border: 1px solid #ddd;
  88 |   margin-bottom: 1rem;
  89 | }
  90 | 
  91 | div.highlight pre {
  92 |   margin-bottom: 0;
  93 |   line-height: 1rem;
  94 | }
  95 | 
  96 | div.highlight a {
  97 |   text-decoration: underline;
  98 | }
  99 | 
 100 | .versionmodified {
 101 |   font-style: italic;
 102 | }
 103 | 
 104 | a.sk-landing-btn {
 105 |   background-color: #ff9c34;
 106 |   color: black;
 107 |   cursor: pointer;
 108 |   font-size: 1.1rem;
 109 |   font-weight: 500;
 110 | }
 111 | 
 112 | a.sk-landing-btn:hover {
 113 |   background-color: #ffb05f;
 114 | }
 115 | 
 116 | .sk-donate-btn {
 117 |   cursor: pointer;
 118 | }
 119 | 
 120 | .sk-page-content div.logo {
 121 |   float: left;
 122 |   width: 200px;
 123 | }
 124 | 
 125 | @media screen and (min-width: 992px) {
 126 |   .sk-page-content {
 127 |     padding-left: 2rem!important;
 128 |     padding-right: 2rem!important;
 129 |   }
 130 | }
 131 | 
 132 | @media screen and (min-width: 1200px) {
 133 |   .sk-px-xl-4 {
 134 |     padding-left: 1.3rem!important;
 135 |     padding-right: 1.3rem!important;
 136 |   }
 137 | }
 138 | 
 139 | /* clearfix */
 140 | 
 141 | div.clearer {
 142 |   clear: both;
 143 | }
 144 | 
 145 | /* Button */
 146 | 
 147 | .sk-btn-primary {
 148 |   background-color: #30799C;
 149 |   border-color: #30799C;
 150 |   color: white;
 151 | }
 152 | 
 153 | .sk-btn-primary:hover,
 154 | .sk-btn-primary:active {
 155 |   background-color: #3499cd;
 156 |   border-color: #3499cd;
 157 | }
 158 | 
 159 | /* Quote */
 160 | 
 161 | .quote {
 162 |   text-align: right;
 163 |   line-height: 1.5em;
 164 |   font-style: italic;
 165 |   margin: 2em 3em 1em 3em;
 166 | }
 167 | 
 168 | .line-block {
 169 |   display: block;
 170 |   margin-top: 1em;
 171 |   margin-bottom: 1em;
 172 | }
 173 | 
 174 | /* Search */
 175 | 
 176 | #search-results {
 177 |   margin-top: 1rem;
 178 | }
 179 | 
 180 | #searchbox {
 181 |   padding-top: 0.1rem;
 182 | }
 183 | 
 184 | .sk-search-text-input {
 185 |   width: 12rem;
 186 | }
 187 | 
 188 | .sk-search-text-btn {
 189 |   padding-left: 0.2rem;
 190 |   padding-right: 0.2rem;
 191 | }
 192 | 
 193 | ul.search li div.context {
 194 |   color: #888;
 195 |   margin: 0.1rem 0 0 0;
 196 |   text-align: left;
 197 | }
 198 | 
 199 | @media screen and (min-width: 768px) {
 200 |   ul.search li div.context {
 201 |     margin-left: 1rem;
 202 |   }
 203 | }
 204 | 
 205 | ul.search li a {
 206 |   font-weight: bold;
 207 | }
 208 | /* navbar */
 209 | 
 210 | img.sk-brand-img {
 211 |   height: 48px;
 212 | }
 213 | 
 214 | .navbar-light .navbar-nav a.nav-link, a.sk-dropdown-item  {
 215 |   color: rgba(77, 77, 77, 1);
 216 |   font-weight: 500;
 217 | }
 218 | 
 219 | .navbar-light .navbar-nav a.nav-link:hover, a.sk-dropdown-item:hover {
 220 |   color: rgba(246, 126, 0, 1);
 221 | }
 222 | 
 223 | a.sk-nav-dropdown-item:active {
 224 |   color: white;
 225 |   background-color: rgba(246, 126, 0, 1);
 226 | }
 227 | 
 228 | .nav-more-item-mobile-items {
 229 |   display: inherit;
 230 | }
 231 | 
 232 | .nav-more-item-dropdown {
 233 |   display: none;
 234 | }
 235 | 
 236 | @media screen and (min-width: 768px) {
 237 |   .nav-more-item-dropdown {
 238 |     display: inherit;
 239 |   }
 240 | 
 241 |   .nav-more-item-mobile-items {
 242 |     display: none;
 243 |   }
 244 | }
 245 | /* LANDING PAGE STYLE */
 246 | 
 247 | div.sk-landing-container {
 248 |   max-width: 1400px;
 249 | }
 250 | 
 251 | div.sk-landing-container .text-white {
 252 |     text-shadow: 0px 0px 8px rgb(42, 98, 128);
 253 | }
 254 | 
 255 | ul.sk-landing-header-body {
 256 |   margin-top: auto;
 257 |   margin-bottom: auto;
 258 |   font-size: 1.2rem;
 259 |   font-weight: 500;
 260 | }
 261 | 
 262 | div.sk-landing-bg-more-info dd {
 263 |   padding-left: 0;
 264 | }
 265 | 
 266 | div.sk-landing-bg {
 267 |   background-image: linear-gradient(160deg, rgba(42,98,128,1) 0%, rgba(52,153,205,1) 17%, rgba(255,243,211,1) 59%, rgba(255,178,96,1) 100%);
 268 | }
 269 | 
 270 | div.sk-landing-bg-more-info {
 271 |   background-color: #f8f8f8;
 272 |   font-size: 0.96rem;
 273 | }
 274 | 
 275 | .sk-card-title {
 276 |   font-weight: 700;
 277 | }
 278 | 
 279 | .sk-landing-header {
 280 |   font-size: 3.2rem;
 281 | }
 282 | 
 283 | .sk-landing-subheader {
 284 |   letter-spacing: 0.17rem;
 285 | }
 286 | 
 287 | .sk-landing-call-header {
 288 |   color: #E07200;
 289 |   font-weight: 700;
 290 | }
 291 | 
 292 | img.sk-index-img {
 293 |   max-height: 240px;
 294 |   margin: auto;
 295 |   margin-bottom: 1em;
 296 |   width: auto;
 297 | }
 298 | 
 299 | @media screen and (min-width: 768px) {
 300 |   img.sk-index-img {
 301 |     width: 100%
 302 |   }
 303 | }
 304 | 
 305 | img.sk-who-uses-carousel-img {
 306 |   max-height: 100px;
 307 |   max-width: 50%;
 308 | }
 309 | 
 310 | div#carouselExampleSlidesOnly {
 311 |   min-height: 200px;
 312 | }
 313 | 
 314 | ul.sk-landing-call-list li {
 315 |   margin-bottom: 0.25rem;
 316 | }
 317 | 
 318 | img.sk-footer-funding-logo {
 319 |   max-height: 36px;
 320 |   max-width: 80px;
 321 |   margin: 0 8px;
 322 |   margin-bottom: 8px;
 323 | }
 324 | 
 325 | a.sk-footer-funding-link:hover {
 326 |   text-decoration: none;
 327 | }
 328 | /* DOCS STYLE */
 329 | 
 330 | .navbar > .sk-docs-container {
 331 |   max-width: 1400px;
 332 |   margin: 0 auto;
 333 | }
 334 | 
 335 | #sk-sidebar-wrapper {
 336 |   height: 100%;
 337 |   overflow-y: hidden;
 338 |   overflow-x: hidden;
 339 |   position: fixed;
 340 |   margin-left: -240px;
 341 |   width: 240px;
 342 |   -webkit-transition: margin 0.25s ease-out, opacity 0.25s ease-out;
 343 |   -moz-transition: margin 0.25s ease-out, opacity 0.25s ease-out;
 344 |   -o-transition: margin 0.25s ease-out, opacity 0.25s ease-out;
 345 |   transition: margin 0.25s ease-out, opacity 0.25s ease-out;
 346 |   background-color: white;
 347 |   opacity: 0;
 348 |   top: 0;
 349 |   padding: 0 0.5rem 0.5rem 0.5rem;
 350 |   z-index: 2;
 351 | }
 352 | 
 353 | #sk-toggle-checkbox {
 354 |   display: none;
 355 | }
 356 | 
 357 | #sk-toggle-checkbox:checked ~ #sk-sidebar-wrapper {
 358 |   margin-left: 0;
 359 |   opacity: 1;
 360 | }
 361 | 
 362 | #sk-doc-wrapper {
 363 |   max-width: 1400px;
 364 |   margin: 0 auto;
 365 | }
 366 | 
 367 | #sk-page-content-wrapper {
 368 |   width: 100%;
 369 | }
 370 | 
 371 | div.sk-page-content {
 372 |   background-color: white;
 373 |   position: relative;
 374 |   margin-top: 0.5rem;
 375 | }
 376 | 
 377 | div.sk-page-content {
 378 |   table-layout: fixed;
 379 |   max-width: 100%;
 380 | }
 381 | 
 382 | div.section h2,
 383 | div.section h3,
 384 | div.section h4,
 385 | div.section h5,
 386 | div.section h6 {
 387 |   margin-top: 1rem;
 388 | }
 389 | 
 390 | .sk-btn-toggle-toc {
 391 |   position: fixed;
 392 |   bottom: 0;
 393 |   margin: 0;
 394 |   border-radius: 0;
 395 |   border-top-right-radius: 0.5rem;
 396 |   z-index: 3;
 397 |   cursor: pointer;
 398 | }
 399 | 
 400 | div.sk-page-content {
 401 |   margin-top: 52px;
 402 | }
 403 | 
 404 | @media screen and (min-width: 1400px) {
 405 |   .sk-btn-toggle-toc {
 406 |     border-top-left-radius: 0.5rem;
 407 |   }
 408 | }
 409 | 
 410 | .sk-btn-toggle-toc:hover {
 411 |   color: white;
 412 |   background-color: #297ca7;
 413 | }
 414 | 
 415 | footer.sk-content-footer {
 416 |   padding: 1rem 0;
 417 |   color: #999;
 418 |   text-align: right;
 419 | }
 420 | 
 421 | nav.sk-docs-navbar {
 422 |   width: 100%;
 423 |   z-index: 3;
 424 |   -webkit-transition: top .2s ease-in-out;
 425 |   -moz-transition: top .2s ease-in-out .05s;
 426 |   -o-transition: top .2s ease-in-out .05s;
 427 |   transition: top .2s ease-in-out .05s;
 428 |   position: fixed;
 429 |   max-height: 100vh;
 430 |   overflow-y: auto;
 431 |   align-items: initial;
 432 | }
 433 | 
 434 | div.sk-navbar-collapse {
 435 |   padding-bottom: 4rem;
 436 | }
 437 | 
 438 | @media screen and (min-width: 768px) {
 439 | 
 440 |   nav.sk-docs-navbar {
 441 |     overflow-y: visible;
 442 |     max-height: none;
 443 |   }
 444 | 
 445 |   div.sk-navbar-collapse {
 446 |     padding-bottom: 0;
 447 |   }
 448 | 
 449 |   #sk-page-content-wrapper {
 450 |     padding-left: 240px;
 451 |     max-width: 1240px;
 452 |     margin-left: auto;
 453 |     margin-right: auto;
 454 |   }
 455 | 
 456 |   #sk-sidebar-wrapper {
 457 |     margin-left: 0;
 458 |     opacity: 1;
 459 |   }
 460 | 
 461 |   #sk-toggle-checkbox:checked ~ #sk-sidebar-wrapper {
 462 |     margin-left: -240px;
 463 |     opacity: 0;
 464 |   }
 465 | 
 466 |   #sk-toggle-checkbox:checked ~ #sk-page-content-wrapper {
 467 |     padding-left: 0;
 468 |     margin-left: auto;
 469 |     margin-right: auto;
 470 |   }
 471 | }
 472 | 
 473 | .centered {
 474 |   text-align: center;
 475 | }
 476 | 
 477 | dl.citation > dd > ol > li {
 478 |   display: inline;
 479 | }
 480 | 
 481 | dl.citation > dd > ol {
 482 |   margin-bottom: 0;
 483 | }
 484 | 
 485 | /* docs index */
 486 | 
 487 | div.sk-documentation-index-card {
 488 |   border-left: 0.15rem solid #ff9c34;
 489 | }
 490 | div.sk-documentation-index-card:hover {
 491 |   box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);
 492 | }
 493 | 
 494 | a.sk-documentation-index-anchor:hover {
 495 |   text-decoration: none;
 496 |   color: #2878A2;
 497 | }
 498 | 
 499 | .sk-documentation-index-header {
 500 |   background-color: #cde8ef;
 501 |   padding: 0.5rem;
 502 |   border-radius: 0 1rem;
 503 |   text-align: center;
 504 |   font-size: 2rem;
 505 |   font-weight: 500;
 506 | }
 507 | 
 508 | /* toc  */
 509 | 
 510 | div.sk-sidebar-toc-logo {
 511 |   height: 52px;
 512 | }
 513 | 
 514 | div.sk-sidebar-toc-wrapper {
 515 |   font-size: 0.9rem;
 516 |   width: 252px;
 517 |   overflow-x: hidden;
 518 |   overflow-y: scroll;
 519 |   height: 100vh;
 520 |   padding-right: 1.75rem;
 521 | 
 522 |   /* Hide scrollbar for IE and Edge */
 523 |   -ms-overflow-style: none;
 524 | 
 525 |   /* Hide scrollbar for Firefox */
 526 |   scrollbar-width: none;
 527 | }
 528 | 
 529 | div.sk-sidebar-toc-wrapper::-webkit-scrollbar {
 530 |   display: none;
 531 | }
 532 | 
 533 | div.sk-sidebar-toc-wrapper::after {
 534 |   display: block;
 535 |   content: "";
 536 |   height: 3rem;
 537 |   visibility: hidden;
 538 | }
 539 | 
 540 | div.sk-sidebar-toc > ul > li > a{
 541 |   font-weight: bold;
 542 | }
 543 | 
 544 | div.sk-sidebar-toc > ul,
 545 | div.sk-sidebar-toc ul ul {
 546 |   list-style: none;
 547 |   margin-left: 0;
 548 |   padding-left: 0;
 549 | }
 550 | 
 551 | div.sk-sidebar-toc ul ul ul {
 552 |   list-style: square;
 553 |   margin-left: 1rem;
 554 | }
 555 | 
 556 | 
 557 | div.sk-sidebar-toc ul li ul li ul{
 558 |   display: none;
 559 | }
 560 | 
 561 | div.sk-sidebar-toc span {
 562 |   white-space: pre;
 563 | }
 564 | 
 565 | div.sk-sidebar-global-toc ul ul {
 566 |   padding-left: 0.75rem;
 567 | }
 568 | /* content styling element style */
 569 | 
 570 | div.sk-page-content h1 {
 571 |   background-color: #cde8ef;
 572 |   padding: 0.5rem;
 573 |   border-radius: 0 1rem;
 574 |   text-align: center;
 575 |   font-size: 2rem;
 576 |   word-wrap: break-word;
 577 | }
 578 | 
 579 | div.sk-page-content h2 {
 580 |   padding: 0.5rem;
 581 |   background-color: #BED4EB;
 582 |   border-radius: 0.3rem;
 583 |   font-size: 1.5rem;
 584 |   margin-bottom: 1rem;
 585 |   word-wrap: break-word;
 586 | }
 587 | 
 588 | div.sk-page-content h3 {
 589 |   padding: 0.3rem;
 590 |   background-color: #eee;
 591 |   border-radius: 0.3rem;
 592 |   font-size: 1.2rem;
 593 |   word-wrap: break-word;
 594 | }
 595 | 
 596 | div.sk-page-content h4 {
 597 |   padding: 0.2rem;
 598 |   background-color: #F4F4F4;
 599 |   border-radius: 0.3rem;
 600 |   font-size: 1.2rem;
 601 |   word-wrap: break-word;
 602 | }
 603 | 
 604 | div.sk-page-content h1 code,
 605 | div.sk-page-content h2 code,
 606 | div.sk-page-content h3 code,
 607 | div.sk-page-content h4 code {
 608 |   white-space: normal;
 609 | }
 610 | 
 611 | /* longtables */
 612 | 
 613 | table.longtable p {
 614 |     -moz-hyphens: none;
 615 |     -ms-hyphens: none;
 616 |     -webkit-hyphens: none;
 617 |     hyphens: none;
 618 |     line-height: 1.1em;
 619 |     margin-bottom: 0;
 620 | }
 621 | 
 622 | table.longtable td, table.longtable th {
 623 |   border-top: 1px solid #ddd;
 624 |   border-bottom: 1px solid #ddd;
 625 |   padding-right: 0.5rem;
 626 |   white-space:nowrap;
 627 | }
 628 | 
 629 | table.longtable tr.row-odd {
 630 |   background-color: #F0F7FA;
 631 | }
 632 | 
 633 | /* api docs */
 634 | 
 635 | .class > dt, .function > dt, .method > dt {
 636 |   padding: 0.5rem;
 637 |   background-color: #f8f8f8;
 638 |   font-weight: normal;
 639 |   border: 1px solid rgba(0, 0, 0, 0.125);
 640 |   border-left: 2px solid #ff9c34;
 641 |   overflow: auto;
 642 |   margin-bottom: 1rem;
 643 | }
 644 | 
 645 | .class > dt::after, .function > dt::after, .method > dt::after {
 646 |   overflow: auto;
 647 | }
 648 | 
 649 | code.descname {
 650 |   font-weight: bold;
 651 |   background-color: transparent;
 652 |   padding: 0;
 653 | }
 654 | 
 655 | code.descclassname {
 656 |   background-color: transparent;
 657 | }
 658 | 
 659 | .viewcode-link {
 660 |   float: right;
 661 | }
 662 | 
 663 | dl.field-list {
 664 |   display: flex;
 665 |   flex-wrap: wrap;
 666 | }
 667 | 
 668 | dl.field-list > dt {
 669 |   flex-basis: 100%;
 670 |   font-weight: bold;
 671 |   word-break: break-word;
 672 | }
 673 | 
 674 | dl.field-list > dd {
 675 |   flex-basis: 100%;
 676 |   margin-bottom: 0;
 677 | }
 678 | 
 679 | @media screen and (min-width: 768px) {
 680 |   dl.field-list > dt {
 681 |     flex-basis: 110px;
 682 |   }
 683 |   dl.field-list > dd {
 684 |     flex: 1 0 calc(100% - 110px);
 685 |     max-width: calc(100% - 110px);
 686 |   }
 687 | 
 688 | }
 689 | 
 690 | dt.field-odd, dt.field-even {
 691 |   background-color: #F0F7FA;
 692 |   padding-left: 0.25rem;
 693 | }
 694 | 
 695 | .field-odd, .field-even {
 696 |   margin-top: 0;
 697 |   border-bottom: 1px solid #ddd;
 698 |   border-top: 1px solid #ddd;
 699 |   box-sizing: border-box;
 700 | }
 701 | 
 702 | dl.field-list > dt:after {
 703 |   content: ":";
 704 | }
 705 | 
 706 | .classifier {
 707 |   font-style: italic;
 708 | }
 709 | 
 710 | .classifier::before {
 711 |   font-style: normal;
 712 |   margin: 0.3em;
 713 |   content: ":";
 714 | }
 715 | 
 716 | dd {
 717 |   padding-left: 1rem;
 718 | }
 719 | 
 720 | .rubric {
 721 |   font-weight: bold;
 722 |   margin-top: 1rem;
 723 | }
 724 | 
 725 | ul.simple li p {
 726 |   margin-bottom: 0;
 727 | }
 728 | 
 729 | ul.simple {
 730 |   padding-left: 1.5rem;
 731 | }
 732 | 
 733 | /* info boxes */
 734 | 
 735 | div.topic {
 736 |   padding: 0.5rem;
 737 |   background-color: #eee;
 738 |   margin-bottom: 1rem;
 739 |   border-radius: 0.25rem;
 740 |   border: 1px solid #CCC;
 741 | }
 742 | 
 743 | div.topic p {
 744 |   margin-bottom: 0.25rem;
 745 | }
 746 | 
 747 | div.topic dd {
 748 |   margin-bottom: 0.25rem;
 749 | }
 750 | 
 751 | p.topic-title {
 752 |   font-weight: bold;
 753 |   margin-bottom: 0.5rem;
 754 | }
 755 | 
 756 | div.topic > ul.simple {
 757 |   margin-bottom: 0.25rem;
 758 | }
 759 | 
 760 | p.admonition-title {
 761 |   margin-right: 0.5rem;
 762 |   font-weight: bold;
 763 |   display: inline;
 764 | }
 765 | 
 766 | p.admonition-title:after {
 767 |   content: ":";
 768 | }
 769 | 
 770 | div.admonition p.admonition-title + p, div.deprecated p {
 771 |   display: inline;
 772 | }
 773 | 
 774 | div.admonition, div.deprecated {
 775 |   padding: 0.5rem;
 776 |   border-radius: 0.5rem;
 777 |   border: 1px solid #ddd;
 778 |   margin-bottom: 1rem;
 779 | }
 780 | 
 781 | div.admonition {
 782 |   background-color: #eee;
 783 | }
 784 | 
 785 | div.admonition p, div.admonition dl, div.admonition dd {
 786 |   margin-bottom: 0
 787 | }
 788 | 
 789 | div.deprecated {
 790 |   color: #b94a48;
 791 |   background-color: #F3E5E5;
 792 |   border: 1px solid #eed3d7;
 793 | }
 794 | 
 795 | div.seealso {
 796 |   background-color: #FFFBE8;
 797 |   border: 1px solid #fbeed5;
 798 |   color: #AF8A4B;
 799 | }
 800 | 
 801 | div.versionchanged {
 802 |   margin-top: 0.5rem;
 803 |   padding: 0.5rem;
 804 |   background-color: #FFFBE8;
 805 |   border: 1px solid #fbeed5;
 806 |   border-radius: 0.5rem;
 807 | }
 808 | 
 809 | div.versionchanged p {
 810 |   margin-bottom: 0;
 811 | }
 812 | 
 813 | dt.label {
 814 |   float: left;
 815 |   padding-right: 0.5rem;
 816 | }
 817 | 
 818 | /* copy buttonn */
 819 | div.highlight:hover span.copybutton {
 820 |   background-color: #3F556B;
 821 |   color: white;
 822 | }
 823 | 
 824 | div.highlight:hover span.copybutton:hover {
 825 |     background-color: #20252B;
 826 | }
 827 | 
 828 | div.body img.align-center {
 829 |   max-width: 800px;
 830 | }
 831 | 
 832 | div.body img {
 833 |     max-width: 100%;
 834 |     height: unset!important; /* Needed because sphinx sets the height */
 835 | }
 836 | 
 837 | div.body dd > p {
 838 |     hyphens: none;
 839 | }
 840 | 
 841 | img.align-center, .figure.align-center, object.align-center {
 842 |   display: block;
 843 |   margin-left: auto;
 844 |   margin-right: auto;
 845 |   margin-bottom: 1rem;
 846 |   text-align: center;
 847 | }
 848 | 
 849 | img.align-right, .figure.align-right, object.align-right {
 850 |   clear: right;
 851 |   float: right;
 852 |   margin-left: 1em;
 853 | }
 854 | 
 855 | a.brackets::after, span.brackets > a::after {
 856 |   content: "]";
 857 | }
 858 | 
 859 | a.brackets::before, span.brackets > a::before {
 860 |     content: "[";
 861 | }
 862 | 
 863 | /* copybutton */
 864 | 
 865 | .copybutton {
 866 |   cursor: pointer;
 867 |   position: absolute;
 868 |   top: 0px;
 869 |   right: 0px;
 870 |   border: 1px solid rgb(221, 221, 221);
 871 |   color: rgb(221, 221, 221);
 872 |   font-family: monospace;
 873 |   padding-left: 0.2rem;
 874 |   padding-right: 0.2rem;
 875 | }
 876 | 
 877 | div.highlight:hover span.copybutton::after {
 878 |   background: #3F556B;
 879 |   border-radius: 0.25rem;
 880 |   color: white;
 881 |   content: attr(title);
 882 |   padding: 0.25rem;
 883 |   position: absolute;
 884 |   z-index: 98;
 885 |   width: 100px;
 886 |   font-size: 0.7rem;
 887 |   top: 0;
 888 |   right: 0;
 889 | }
 890 | 
 891 | /* world */
 892 | 
 893 | img.avatar {
 894 |   width: 100%;
 895 | }
 896 | 
 897 | /* table */
 898 | table.align-default {
 899 |   margin-left: auto;
 900 |   margin-right: auto;
 901 | }
 902 | 
 903 | table.docutils tr:nth-child(odd) {
 904 |   background-color: #F0F7FA;
 905 | }
 906 | 
 907 | table.docutils tr {
 908 |   border-style: solid none solid none;
 909 |   border-width: 1px 0;
 910 |   border-color: #ddd;
 911 | }
 912 | 
 913 | table.docutils td, table.docutils th {
 914 |   padding: 0.125rem 0.5rem 0.125rem 0.25rem;
 915 | }
 916 | 
 917 | table.docutils {
 918 |   margin-bottom: 1rem;
 919 |   line-height: 1rem;
 920 |   max-width: 100%;
 921 | }
 922 | 
 923 | table.docutils p {
 924 |   margin-bottom: 0;
 925 | }
 926 | 
 927 | table.docutils p {
 928 |   white-space: pre-wrap;
 929 |   word-wrap: break-word;
 930 |   word-break: initial;
 931 | }
 932 | 
 933 | /* gallery */
 934 | 
 935 | div.sphx-glr-thumbcontainer {
 936 |   min-height: 250px;
 937 |   font-size: 0.9rem;
 938 | }
 939 | 
 940 | .sphx-glr-example-title > :target::before {
 941 |   display: block;
 942 |   content: "";
 943 |   margin-top: -150px;
 944 |   height: 150px;
 945 |   visibility: hidden;
 946 | }
 947 | 
 948 | .sphx-glr-script-out .highlight pre {
 949 |   padding: 1ex;
 950 | }
 951 | 
 952 | .sphx-glr-script-out div.highlight {
 953 |   padding: 0;
 954 | }
 955 | 
 956 | 
 957 | @media screen and (min-width: 1540px) {
 958 |   .sphx-glr-download-link-note {
 959 |     position: absolute;
 960 |     position: absolute;
 961 |     left: 98%;
 962 |     width: 20ex;
 963 |   }
 964 | }
 965 | 
 966 | /* rellinks */
 967 | 
 968 | .sk-btn-rellink {
 969 |   background-color: #ff9c34;
 970 |   border-color: #ff9c34;
 971 |   color: white;
 972 |   cursor: pointer;
 973 |   font-size: 0.8rem;
 974 |   font-weight: bold;
 975 | }
 976 | 
 977 | .sk-btn-rellink:hover {
 978 |   color: black;
 979 |   border: 1px solid black;
 980 | }
 981 | 
 982 | [sk-rellink-tooltip] {
 983 |   position: relative;
 984 |   cursor: pointer;
 985 | }
 986 | 
 987 | [sk-rellink-tooltip]::before {
 988 |   visibility: hidden;
 989 |   position: absolute;
 990 |   padding: 0.5rem;
 991 |   overflow: hidden;
 992 |   background-color: #ff9c34;
 993 |   border: 1px solid #ff9c34;
 994 |   white-space: pre;
 995 |   content: attr(sk-rellink-tooltip);
 996 |   text-align: left;
 997 |   width: 222px;
 998 |   top: 100%;
 999 |   left: -78px;
1000 |   border: 1px solid black;
1001 | }
1002 | 
1003 | [sk-rellink-tooltip]:first-child::before {
1004 |   left: 0;
1005 | }
1006 | 
1007 | [sk-rellink-tooltip]:last-child::before {
1008 |   left: -144px;
1009 | }
1010 | 
1011 | [sk-rellink-tooltip]:hover::before {
1012 |   visibility: visible;
1013 |   white-space: pre-wrap;
1014 |   word-wrap: break-word;
1015 | }
1016 | 
1017 | /* authors */
1018 | .sk-authors-container {
1019 |   display: flex;
1020 |   flex-wrap: wrap;
1021 |   justify-content: center;
1022 | }
1023 | 
1024 | .sk-authors-container > div {
1025 |   width: 100px;
1026 |   margin: 5px;
1027 |   font-size: 0.9rem;
1028 | }
1029 | 
1030 | 
1031 | /* testimonial */
1032 | 
1033 | div.testimonial h2 {
1034 |   background-color: transparent;
1035 |   color: #008EB2;
1036 |   padding: 0;
1037 |   height: 26px;
1038 |   line-height: 1.1em;
1039 |   font-size: 22px;
1040 |   font-weight: bold;
1041 |   text-align: left;
1042 | }
1043 | 
1044 | div.testimonial p {
1045 |   color: #1c1c1c;
1046 | }
1047 | 
1048 | div.testimonial span.testimonial-author p {
1049 |   font-size: 0.8em;
1050 |   font-style: italic;
1051 |   color: #808080;
1052 | }
1053 | 
1054 | div.testimonial p {
1055 |   color: #1c1c1c;
1056 | }
1057 | 
1058 | /* Installation quickstart */
1059 | /* This quickstart installation is a hack of the awesome
1060 |    https://spacy.io/usage/#quickstart page.
1061 |    See the original javascript implementation
1062 |    https://github.com/ines/quickstart */
1063 | 
1064 | /* style input radio and checkbox */
1065 | 
1066 | div.install > input {
1067 |   -moz-appearance: none;
1068 |   -webkit-appearance: none;
1069 |   appearance: none;
1070 |   opacity: 0;
1071 | }
1072 | 
1073 | /* Style the button */
1074 | div.install > label {
1075 |   display: inline-block;
1076 |   margin-top: 12px;
1077 |   padding: 5px 11px;
1078 |   background-color: #fff3cd;
1079 |   border: none;
1080 |   border-radius: 3px;
1081 |   color: black;
1082 | }
1083 | 
1084 | div.install > label:hover {
1085 |   cursor: pointer;
1086 | }
1087 | 
1088 | /* Style the button when the checkbox is checked */
1089 | div.install > input:checked + label {
1090 |   background-color: #ff9c34;
1091 |   color: white;
1092 | }
1093 | 
1094 | /* Hide expandable content by default */
1095 | .sk-expandable {
1096 |   display: none;
1097 | }
1098 | 
1099 | div.highlight span.sk-expandable:before {
1100 |   content: "$ ";
1101 | }
1102 | 
1103 | /* Show hidden content when the checkbox is checked */
1104 | /* for conda */
1105 | #quickstart-conda:checked  ~* [data-packager="conda"] {
1106 |   display: block;
1107 | }
1108 | 
1109 | #quickstart-conda:checked ~ label[for="quickstart-venv"]:before  {
1110 |   content: "Use conda environment";
1111 | }
1112 | 
1113 | /* for pip */
1114 | #quickstart-pip:checked ~* [data-packager="pip"] {
1115 |   display: block;
1116 | }
1117 | 
1118 | #quickstart-pip:checked ~ label[for="quickstart-venv"]:before  {
1119 |   content: "Use pip virtualenv";
1120 | }
1121 | 
1122 | #quickstart-win:not(:checked) ~* [data-os="windows"] {
1123 |   display: none;
1124 | }
1125 | #quickstart-lin:not(:checked) ~* [data-os="linux"] {
1126 |   display: none;
1127 | }
1128 | #quickstart-mac:not(:checked) ~* [data-os="mac"] {
1129 |   display: none;
1130 | }
1131 | 
1132 | #quickstart-venv:not(:checked) ~* [data-venv=""] {
1133 |   display: none;
1134 | }
1135 | 
1136 | #quickstart-venv:checked ~* [data-venv="no"] {
1137 |   display: none;
1138 | }
1139 | 
1140 | /* Algorithm cheet-sheet */
1141 | 
1142 | div.sk-page-content img.map {
1143 |   position: absolute;
1144 |   max-width: none;
1145 |   transform-origin: left top;
1146 |   -webkit-transform: scale(0.5);
1147 |       -ms-transform: scale(0.5);
1148 |           transform: scale(0.5);
1149 | }
1150 | 
1151 | /* sponsors and testimonials */
1152 | 
1153 | div.sk-sponsor-div, div.sk-testimonial-div {
1154 |   display: flex;
1155 |   flex-wrap: wrap;
1156 |   -webkit-flex-align: center;
1157 |   -ms-flex-align: center;
1158 |   -webkit-align-items: center;
1159 |   align-items: center;
1160 | }
1161 | 
1162 | div.sk-sponsor-div-box, div.sk-testimonial-div-box {
1163 |   width: 100%;
1164 | }
1165 | 
1166 | @media screen and (min-width: 500px) {
1167 |   div.sk-sponsor-div-box, div.sk-testimonial-div-box {
1168 |     width: 50%;
1169 |   }
1170 | }
1171 | 
1172 | table.sk-sponsor-table tr, table.sk-sponsor-table tr:nth-child(odd) {
1173 |   border-style: none;
1174 |   background-color: white;
1175 |   vertical-align: middle;
1176 |   text-align: center;
1177 | }
1178 | 
1179 | table.sk-sponsor-table td {
1180 |   padding: 0.30rem;
1181 | }
1182 | 
1183 | /* pygments - highlightning */
1184 | 
1185 | .highlight .hll { background-color: #ffffcc }
1186 | .highlight  { background: #f8f8f8; }
1187 | .highlight .c { color: #408090; font-style: italic } /* Comment */
1188 | .highlight .err { border: 1px solid #FF0000 } /* Error */
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1199 | .highlight .gr { color: #FF0000 } /* Generic.Error */
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1254 | 


--------------------------------------------------------------------------------
/sklearn_rvm/em_rvm.py:
--------------------------------------------------------------------------------
  1 | """Relevance vector machine using expectation maximization like algorithm.
  2 | 
  3 | Based on
  4 | --------
  5 |     https://github.com/JamesRitchie/scikit-rvm
  6 |     https://github.com/ctgk/PRML/blob/master/prml/kernel/relevance_vector_regressor.py
  7 | 
  8 | """
  9 | # Author: Pedro Ferreira da Costa
 10 | #         Walter Hugo Lopez Pinaya
 11 | # License: BSD 3 clause
 12 | import warnings
 13 | from abc import ABCMeta, abstractmethod
 14 | 
 15 | import numpy as np
 16 | import scipy.linalg
 17 | from numpy import linalg
 18 | from scipy.optimize import minimize
 19 | from scipy.special import expit
 20 | from sklearn.base import RegressorMixin, BaseEstimator, ClassifierMixin
 21 | from sklearn.metrics.pairwise import pairwise_kernels
 22 | from sklearn.multiclass import OneVsRestClassifier
 23 | from sklearn.utils.validation import check_X_y, check_is_fitted, check_array
 24 | 
 25 | 
 26 | class BaseRVM(BaseEstimator, metaclass=ABCMeta):
 27 |     """Basic class for Relevance Vector Machine."""
 28 | 
 29 |     @abstractmethod
 30 |     def __init__(self, kernel, degree, gamma, coef0, tol, threshold_alpha,
 31 |                  beta_fixed, alpha_max, init_alpha, bias_used,
 32 |                  max_iter, compute_score, epsilon, verbose):
 33 | 
 34 |         if gamma == 0:
 35 |             msg = ("The gamma value of 0.0 is invalid. Use 'auto' to set"
 36 |                    " gamma to a value of 1 / n_features.")
 37 |             raise ValueError(msg)
 38 | 
 39 |         self.kernel = kernel
 40 |         self.degree = degree
 41 |         self.gamma = gamma
 42 |         self.coef0 = coef0
 43 |         self.tol = tol
 44 |         self.threshold_alpha = threshold_alpha
 45 |         self.beta_fixed = beta_fixed
 46 |         self.alpha_max = alpha_max
 47 |         self.init_alpha = init_alpha
 48 |         self.bias_used = bias_used
 49 |         self.max_iter = max_iter
 50 |         self.compute_score = compute_score
 51 |         self.epsilon = epsilon
 52 |         self.verbose = verbose
 53 | 
 54 |     def _get_kernel(self, X, Y=None):
 55 |         """Calculate kernelised features."""
 56 |         if callable(self.kernel):
 57 |             params = self.kernel_params or {}
 58 |         else:
 59 |             params = {"gamma": self._gamma,
 60 |                       "degree": self.degree,
 61 |                       "coef0": self.coef0}
 62 |             return pairwise_kernels(X, Y, metric=self.kernel,
 63 |                                     filter_params=True, **params)
 64 | 
 65 |     def _prune(self):
 66 |         """Remove basis functions based on alpha values."""
 67 |         keep_alpha = self.alpha_ < self.threshold_alpha
 68 | 
 69 |         if not np.any(keep_alpha):
 70 |             keep_alpha[0] = True
 71 | 
 72 |         if self.bias_used:
 73 |             if not keep_alpha[0]:
 74 |                 self.bias_used = False
 75 |             if self.kernel != "precomputed":
 76 |                 self.relevance_vectors_ = self.relevance_vectors_[
 77 |                     keep_alpha[1:]]
 78 |             self.relevance_ = self.relevance_[keep_alpha[1:]]
 79 |         else:
 80 |             if self.kernel != "precomputed":
 81 |                 self.relevance_vectors_ = self.relevance_vectors_[keep_alpha]
 82 |             self.relevance_ = self.relevance_[keep_alpha]
 83 | 
 84 |         self.alpha_ = self.alpha_[keep_alpha]
 85 |         self._alpha_old = self._alpha_old[keep_alpha]
 86 |         self.gamma_ = self.gamma_[keep_alpha]
 87 |         self.Phi_ = self.Phi_[:, keep_alpha]
 88 |         self.Sigma_ = self.Sigma_[np.ix_(keep_alpha, keep_alpha)]
 89 |         self.mu_ = self.mu_[keep_alpha]
 90 | 
 91 |     @property
 92 |     def _pairwise(self):
 93 |         return self.kernel == "precomputed"
 94 | 
 95 |     @property
 96 |     def coef_(self):
 97 |         if self.kernel != "linear":
 98 |             raise AttributeError(
 99 |                 "coef_ is only available when using a linear kernel")
100 | 
101 |         coef = self._get_coef()
102 |         return coef
103 | 
104 |     def _get_coef(self):
105 |         "Calculate coefficients."
106 |         return np.dot(self.mu_, self.relevance_vectors_)
107 | 
108 | 
109 | class EMRVR(RegressorMixin, BaseRVM):
110 |     """Relevance Vector Regressor.
111 | 
112 |     Implementation of the relevance vector regressor using the algorithm
113 |     based on expectation maximization.
114 | 
115 |     Parameters
116 |     ----------
117 |     kernel : string, optional (default="rbf")
118 |         Specifies the kernel type to be used in the algorithm.
119 |         It must be one of "linear", "poly", "rbf", "sigmoid" or "precomputed".
120 |         If none is given, "rbf" will be used.
121 | 
122 |     degree : int, optional (default=3)
123 |         Degree of the polynomial kernel function ("poly"). Ignored by all other
124 |         kernels.
125 | 
126 |     gamma : {"auto", "scale"} or float, optional (default="auto")
127 |         Kernel coefficient for "rbf", "poly" and "sigmoid".
128 | 
129 |         Current default is "auto" which uses 1 / n_features,
130 |         if ``gamma="scale"`` is passed then it uses 1 / (n_features * X.var())
131 |         as value of gamma.
132 | 
133 |     coef0 : float, optional (default=0.0)
134 |         Independent term in kernel function. It is only significant in "poly"
135 |         and "sigmoid".
136 | 
137 |     tol : float, optional (default=1e-6)
138 |         Tolerance for stopping criterion.
139 | 
140 |     threshold_alpha : float, optional (default=1e5)
141 |         Threshold for alpha selection criterion.
142 | 
143 |     beta_fixed : {"not_fixed"} or float, optional (default="not_fixed")
144 |         Fixed value for beta. If "not_fixed" selected, the beta is updated at
145 |         each iteration.
146 | 
147 |     alpha_max : int, optional (default=1e9)
148 |         Basis functions associated with alpha value beyond this limit will be
149 |         purged. Must be a positive and big number.
150 | 
151 |     init_alpha : array-like of shape (n_sample) or None, optional (default=None)
152 |         Initial value for alpha. If None is selected, the initial value of
153 |         alpha is defined by init_alpha = 1 / M ** 2.
154 | 
155 |     bias_used : boolean, optional (default=False)
156 |         Specifies if a constant (a.k.a. bias) should be added to the decision
157 |         function.
158 | 
159 |     max_iter : int, optional (default=5000)
160 |         Hard limit on iterations within solver.
161 | 
162 |     compute_score : boolean, optional (default=False)
163 |         Specifies if the objective function is computed at each step of the model.
164 | 
165 |     verbose : boolean, optional (default=False)
166 |         Enable verbose output.
167 | 
168 |     Attributes
169 |     ----------
170 |     relevance_ : array-like, shape (n_relevance)
171 |         Indices of relevance vectors.
172 | 
173 |     relevance_vectors_ : array-like, shape (n_relevance, n_features)
174 |         Relevance vectors (equivalent to X[relevance_]).
175 | 
176 |     alpha_ : array-like, shape (n_samples)
177 |         Estimated alpha values.
178 | 
179 |     gamma_ : array-like, shape (n_samples)
180 |         Estimated gamma values.
181 | 
182 |     Phi_ : array-like, shape (n_samples, n_features)
183 |         Estimated phi values.
184 | 
185 |     Sigma_ : array-like, shape (n_samples, n_features)
186 |         Estimated covariance matrix of the weights.
187 | 
188 |     mu_ : array-like, shape (n_relevance, n_features)
189 |         Coefficients of the regression model (mean of posterior distribution)
190 | 
191 |     coef_ : array, shape (n_class * (n_class-1) / 2, n_features)
192 |         Coefficients of the regression model (mean of posterior distribution).
193 |         Weights assigned to the features. This is only available in the case
194 |         of a linear kernel. `coef_` is a readonly property derived from `mu`
195 |         and `relevance_vectors_`.
196 | 
197 |     See Also
198 |     --------
199 |     EMRVC
200 |         Relevant Vector Machine for Classification.
201 | 
202 |     Notes
203 |     -----
204 |     **References:**
205 |     `The relevance vector machine.
206 |     <http://www.miketipping.com/sparsebayes.htm>`__
207 |     """
208 | 
209 |     def __init__(self, kernel="rbf", degree=3, gamma="auto_deprecated",
210 |                  coef0=0.0, tol=1e-3, threshold_alpha=1e9,
211 |                  beta_fixed="not_fixed", alpha_max=1e10, init_alpha=None,
212 |                  bias_used=True, max_iter=5000, compute_score=False,
213 |                  epsilon=1e-08, verbose=False):
214 | 
215 |         super().__init__(
216 |             kernel=kernel, degree=degree, gamma=gamma, coef0=coef0, tol=tol,
217 |             threshold_alpha=threshold_alpha, beta_fixed=beta_fixed,
218 |             alpha_max=alpha_max, init_alpha=init_alpha, bias_used=bias_used,
219 |             max_iter=max_iter, compute_score=compute_score, epsilon=epsilon,
220 |             verbose=verbose)
221 | 
222 |     def compute_marginal_likelihood(self, upper_inv, ed, n_samples, y):
223 |         """Calculate marginal likelihood."""
224 |         dataLikely = (n_samples * np.log(self.beta_) - self.beta_ * ed) / 2
225 |         logdetH = -2 * np.sum(np.log(np.diag(upper_inv)))
226 |         marginal = dataLikely - 0.5 * (
227 |                 logdetH - np.sum(np.log(self.alpha_)) + (
228 |                 self.mu_ ** 2).T @ self.alpha_)
229 |         return marginal
230 | 
231 |     def fit(self, X, y):
232 |         """Fit the RVR model according to the given training data.
233 | 
234 |         Parameters
235 |         ----------
236 |         X : array-like, shape (n_samples, n_features)
237 |             Training vectors.
238 | 
239 |         y : array-like, shape (n_samples,)
240 |             Target values.
241 | 
242 |         Returns
243 |         -------
244 |         self : object
245 |         """
246 |         X, y = check_X_y(X, y, y_numeric=True, ensure_min_samples=2,
247 |                          dtype="float64")
248 | 
249 |         if self.kernel == "precomputed" and X.shape[0] != X.shape[1]:
250 |             raise ValueError("X.shape[0] should be equal to X.shape[1]")
251 | 
252 |         if self.gamma in ("scale", "auto_deprecated"):
253 |             X_var = X.var()
254 |             if self.gamma == "scale":
255 |                 if X_var != 0:
256 |                     self._gamma = 1.0 / (X.shape[1] * X_var)
257 |                 else:
258 |                     self._gamma = 1.0
259 |             else:
260 |                 kernel_uses_gamma = (not callable(self.kernel) and self.kernel
261 |                                      not in ("linear", "precomputed"))
262 |                 if kernel_uses_gamma and not np.isclose(X_var, 1.0):
263 |                     # NOTE: when deprecation ends we need to remove explicitly
264 |                     # setting `gamma` in examples (also in tests). See
265 |                     # https://github.com/scikit-learn/scikit-learn/pull/10331
266 |                     # for the examples/tests that need to be reverted.
267 |                     warnings.warn("The default value of gamma will change "
268 |                                   "from 'auto' to 'scale' in version 0.22 to "
269 |                                   "account better for unscaled features. Set "
270 |                                   "gamma explicitly to 'auto' or 'scale' to "
271 |                                   "avoid this warning.", FutureWarning)
272 |                 self._gamma = 1.0 / X.shape[1]
273 |         elif self.gamma == "auto":
274 |             self._gamma = 1.0 / X.shape[1]
275 |         else:
276 |             self._gamma = self.gamma
277 | 
278 |         self.scores_ = list()
279 | 
280 |         n_samples = X.shape[0]
281 |         self.Phi_ = self._get_kernel(X)
282 | 
283 |         # Scale Phi based on PRoNTO implementation
284 |         # http://www.mlnl.cs.ucl.ac.uk/pronto/
285 |         self._scale = np.sqrt(np.sum(self.Phi_) / n_samples ** 2)
286 |         self.Phi_ = self.Phi_ / self._scale
287 | 
288 |         if self.bias_used:
289 |             self.Phi_ = np.hstack((np.ones((n_samples, 1)), self.Phi_))
290 | 
291 |         M = self.Phi_.shape[1]
292 | 
293 |         if self.init_alpha == None:
294 |             self.init_alpha = 1 / M ** 2
295 | 
296 |         self.relevance_ = np.array(range(n_samples))
297 |         if self.kernel != "precomputed":
298 |             self.relevance_vectors_ = X
299 |         else:
300 |             self.relevance_vectors_ = None
301 | 
302 |         # Initialize beta (1 / sigma squared)
303 |         if self.beta_fixed == "not_fixed":
304 |             sigma_squared = (max(self.epsilon, np.std(y) * 0.1) ** 2)
305 |             self.beta_ = 1 / sigma_squared
306 |         else:
307 |             self.beta_ = self.beta_fixed
308 | 
309 |         self.alpha_ = self.init_alpha * np.ones(M)
310 | 
311 |         self._alpha_old = self.alpha_.copy()
312 | 
313 |         for i in range(self.max_iter):
314 |             A = np.diag(self.alpha_)
315 |             hessian = self.beta_ * self.Phi_.T @ self.Phi_ + A
316 | 
317 |             # Calculate Sigma and mu
318 |             # Use Cholesky decomposition for efficiency
319 |             # Ref: https://arxiv.org/abs/1111.4144
320 |             chol_fail = False
321 |             try:
322 |                 upper = scipy.linalg.cholesky(hessian)
323 |             except linalg.LinAlgError:
324 |                 warnings.warn("Hessian not positive definite")
325 |                 chol_fail = True
326 | 
327 |             if chol_fail:
328 |                 try:
329 |                     self.Sigma_ = np.linalg.inv(hessian)
330 |                 except linalg.LinAlgError:
331 |                     warnings.warn("Using Pseudo-Inverse")
332 |                     self.Sigma_ = np.linalg.pinv(hessian)
333 | 
334 |                 self.mu_ = self.beta_ * (self.Sigma_ @ self.Phi_.T @ y)
335 |                 sigma_diag = np.diag(self.Sigma_)
336 | 
337 |             else:
338 |                 try:
339 |                     upper_inv = np.linalg.inv(upper)
340 |                 except linalg.LinAlgError:
341 |                     warnings.warn("Using Pseudo-Inverse")
342 |                     upper_inv = np.linalg.pinv(upper)
343 | 
344 |                 self.Sigma_ = np.dot(upper_inv, upper_inv.conj().T)
345 | 
346 |                 self.mu_ = (upper_inv @ (
347 |                         upper_inv.conj().T @ self.Phi_.T @ y)) * self.beta_
348 | 
349 |                 # Equivalent sigma_diag = np.diag(self.Sigma_)
350 |                 sigma_diag = np.sum(upper_inv ** 2, axis=1)
351 | 
352 |             # Well-determinedness parameters (gamma)
353 |             self.gamma_ = 1 - self.alpha_ * sigma_diag
354 | 
355 |             # Alpha re-estimation
356 |             # MacKay-style update for alpha given in original NIPS paper
357 |             self.alpha_ = np.maximum(self.gamma_, self.epsilon) / (
358 |                     self.mu_ ** 2) + self.epsilon
359 | 
360 |             if self.beta_fixed == "not_fixed":
361 |                 # Prediction error
362 |                 ed = np.sum((y - self.Phi_ @ self.mu_) ** 2)
363 |                 self.beta_ = max((n_samples - np.sum(self.gamma_)),
364 |                                  self.epsilon) / ed + self.epsilon
365 | 
366 |             # Compute marginal likelihood
367 |             if not chol_fail:
368 |                 if self.compute_score:
369 |                     ll = self.compute_marginal_likelihood(upper_inv, ed,
370 |                                                           n_samples, y)
371 |                     self.scores_.append(ll)
372 | 
373 |             # Passes on variable information on each iteration
374 |             if self.verbose:
375 |                 print("Iteration: {}".format(i))
376 |                 print("Alpha: {}".format(self.alpha_))
377 |                 print("Beta: {}".format(self.beta_))
378 |                 print("Gamma: {}".format(self.gamma_))
379 |                 print("mu: {}".format(self.mu_))
380 |                 print("Relevance Vectors: {}".format(self.relevance_.shape[0]))
381 |                 if self.compute_score:
382 |                     print("Marginal Likelihood: {}".format(ll))
383 |                 print()
384 | 
385 |             # Prune based on large values of alpha
386 |             self._prune()
387 | 
388 |             # Terminate if the largest alpha change is smaller than threshold
389 |             delta = np.amax(
390 |                 np.absolute(np.log(self.alpha_ + self.epsilon) - np.log(
391 |                     self._alpha_old + self.epsilon)))
392 |             if delta < self.tol and i > 1:
393 |                 break
394 | 
395 |             self._alpha_old = self.alpha_.copy()
396 | 
397 |     def predict(self, X, return_std=False):
398 |         """Predict using the RVR model.
399 | 
400 |         In addition to the mean of the predictive distribution, its
401 |         standard deviation can also be returned.
402 | 
403 |         Parameters
404 |         ----------
405 |         X : array-like, shape (n_samples, n_features)
406 |             Query points to be evaluate.
407 | 
408 |         return_std : bool, optional (default=False)
409 |             If True, the standard-deviation of the predictive distribution at
410 |             the query points is returned along with the mean.
411 | 
412 |         Returns
413 |         -------
414 |         y_mean : array, shape (n_samples, n_output_dims)
415 |             Mean of predictive distribution at query points
416 | 
417 |         y_std : array, shape (n_samples,), optional
418 |             Standard deviation of predictive distribution at query points.
419 |             Only returned when return_std is True.
420 |         """
421 |         check_is_fitted(self, ["relevance_", "mu_", "Sigma_"])
422 | 
423 |         X = check_array(X)
424 | 
425 |         n_samples = X.shape[0]
426 |         if self.kernel != "precomputed":
427 |             K = self._get_kernel(X, self.relevance_vectors_)
428 |         else:
429 |             K = X[:, self.relevance_]
430 |         K = K / self._scale
431 | 
432 |         if self.bias_used:
433 |             K = np.hstack((np.ones((n_samples, 1)), K))
434 | 
435 |         y_mean = K @ self.mu_
436 |         if return_std is False:
437 |             return y_mean
438 |         else:
439 |             err_var = (1 / self.beta_) + K @ self.Sigma_ @ K.T
440 |             y_std = np.sqrt(np.diag(err_var))
441 |             return y_mean, y_std
442 | 
443 | 
444 | class EMRVC(BaseRVM, ClassifierMixin):
445 |     """Relevance Vector Classifier.
446 | 
447 |     Implementation of Mike Tipping"s Relevance Vector Machine for
448 |     classification using the scikit-learn API.
449 | 
450 |     The multiclass support is handled according to a one-vs-rest scheme.
451 | 
452 |     For details on the precise mathematical formulation of the provided
453 |     kernel functions and how `gamma`, `coef0` and `degree` affect each
454 |     other, see the corresponding section in the narrative documentation:
455 |     :ref:`svm_kernels`.
456 | 
457 |     Parameters
458 |     ----------
459 |     n_iter_posterior : int, optional (default=50)
460 |         Number of iterations to calculate posterior.
461 | 
462 |     kernel : string, optional (default="rbf")
463 |         Specifies the kernel type to be used in the algorithm.
464 |         It must be one of "linear", "poly", "rbf", "sigmoid" or ‘precomputed’.
465 |         If none is given, "rbf" will be used.
466 | 
467 |     degree : int, optional (default=3)
468 |         Degree of the polynomial kernel function ("poly"). Ignored by all other
469 |         kernels.
470 | 
471 |     gamma : float, optional (default="auto")
472 |         Kernel coefficient for "rbf", "poly" and "sigmoid".
473 | 
474 |         Current default is "auto" which uses 1 / n_features,
475 |         if ``gamma="scale"`` is passed then it uses 1 / (n_features * X.var())
476 |         as value of gamma. The current default of gamma, "auto", will change
477 |         to "scale" in version 0.22. "auto_deprecated", a deprecated version of
478 |         "auto" is used as a default indicating that no explicit value of gamma
479 |         was passed.
480 | 
481 |     coef0 : float, optional (default=0.0)
482 |         Independent term in kernel function. It is only significant in "poly"
483 |         and "sigmoid".
484 | 
485 |     tol : float, optional (default=1e-6)
486 |         Tolerance for stopping criterion.
487 | 
488 |     threshold_alpha : float, optional (default=1e5)
489 |         Threshold for alpha selection criterion.
490 | 
491 |     beta_fixed : {"not_fixed"} or float, optional (default="not_fixed")
492 |         Fixed value for beta. If "not_fixed" selected, the beta is updated at
493 |         each iteration.
494 | 
495 |     alpha_max : int, optional (default=1e9)
496 |         Basis functions associated with alpha value beyond this limit will be
497 |         purged. Must be a positive and big number.
498 | 
499 |     init_alpha : array-like of shape (n_sample) or None, optional (default=None)
500 |         Initial value for alpha. If None is selected, the initial value of
501 |         alpha is defined by init_alpha = 1 / M ** 2.
502 | 
503 |     bias_used : boolean, optional (default=False)
504 |         Specifies if a constant (a.k.a. bias) should be added to the decision
505 |         function.
506 | 
507 |     max_iter : int, optional (default=5000)
508 |         Hard limit on iterations within solver.
509 | 
510 |     compute_score : boolean, optional (default=False)
511 |         Specifies if the objective function is computed at each step of the model.
512 | 
513 |     verbose : boolean, optional (default=False)
514 |         Enable verbose output.
515 | 
516 |     Attributes
517 |     ----------
518 |     relevance_ : array-like, shape (n_relevance)
519 |         Indices of relevance vectors.
520 | 
521 |     relevance_vectors_ : array-like, shape (n_relevance, n_features)
522 |         Relevance vectors (equivalent to X[relevance_]).
523 | 
524 |     alpha_ : array-like, shape (n_samples)
525 |         Estimated alpha values.
526 | 
527 |     gamma_ : array-like, shape (n_samples)
528 |         Estimated gamma values.
529 | 
530 |     Phi_ : array-like, shape (n_samples, n_features)
531 |         Estimated phi values.
532 | 
533 |     Sigma_ : array-like, shape (n_samples, n_features)
534 |         Estimated covariance matrix of the weights.
535 | 
536 |     mu_ : array-like, shape (n_relevance, n_features)
537 |         Coefficients of the classifier (mean of posterior distribution)
538 | 
539 |     coef_ : array, shape (n_class * (n_class-1) / 2, n_features)
540 |         Coefficients of the classfier (mean of posterior distribution).
541 |         Weights assigned to the features. This is only available in the case
542 |         of a linear kernel. `coef_` is a readonly property derived from `mu`
543 |         and `relevance_vectors_`.
544 | 
545 |     See Also
546 |     --------
547 |     EMRVR
548 |         Relevant Vector Machine for Regression.
549 | 
550 |     Notes
551 |     -----
552 |     **References:**
553 |     `The relevance vector machine.
554 |     <http://www.miketipping.com/sparsebayes.htm>`__
555 |     """
556 | 
557 |     def __init__(self, n_iter_posterior=50, kernel="rbf", degree=3,
558 |                  gamma="auto_deprecated", coef0=0.0, tol=1e-3,
559 |                  threshold_alpha=1e9, beta_fixed="not_fixed", alpha_max=1e10,
560 |                  init_alpha=None, bias_used=True, max_iter=5000,
561 |                  compute_score=False, epsilon=1e-08, verbose=False):
562 | 
563 |         self.n_iter_posterior = n_iter_posterior
564 | 
565 |         super().__init__(
566 |             kernel=kernel, degree=degree, gamma=gamma, coef0=coef0, tol=tol,
567 |             threshold_alpha=threshold_alpha, beta_fixed=beta_fixed,
568 |             alpha_max=alpha_max, init_alpha=init_alpha, bias_used=bias_used,
569 |             max_iter=max_iter, compute_score=compute_score, epsilon=epsilon,
570 |             verbose=verbose)
571 | 
572 |     def _classify(self, mu, Phi_):
573 |         """ Perform Sigmoid Classification."""
574 |         return expit(np.dot(Phi_, mu))
575 | 
576 |     def _log_posterior(self, mu, alpha, Phi_, t):
577 |         """ Calculate log posterior."""
578 |         y = self._classify(mu, Phi_)
579 | 
580 |         log_p = -1 * (np.sum(np.log(y[t == 1]), 0) +
581 |                       np.sum(np.log(1 - y[t == 0]), 0))
582 |         log_p = log_p + 0.5 * np.dot(mu.T, np.dot(np.diag(alpha), mu))
583 | 
584 |         jacobian = np.dot(np.diag(alpha), mu) - np.dot(Phi_.T, (t - y))
585 | 
586 |         return log_p, jacobian
587 | 
588 |     def _compute_hessian(self, mu, alpha, Phi_, t):
589 |         """ Perform the Inverse of Covariance."""
590 |         y = self._classify(mu, Phi_)
591 |         B = np.diag(y * (1 - y))
592 |         return np.diag(alpha) + np.dot(Phi_.T, np.dot(B, Phi_))
593 | 
594 |     def _posterior(self):
595 |         """ Calculate the posterior likelihood."""
596 |         result = minimize(
597 |             fun=self._log_posterior,
598 |             hess=self._compute_hessian,
599 |             x0=self.mu_,
600 |             args=(self.alpha_, self.Phi_, self.t),
601 |             method="Newton-CG",
602 |             jac=True,
603 |             options={
604 |                 "maxiter": self.n_iter_posterior
605 |             }
606 |         )
607 | 
608 |         self.mu_ = result.x
609 | 
610 |         hessian = self._compute_hessian(self.mu_, self.alpha_, self.Phi_,
611 |                                         self.t)
612 | 
613 |         # Calculate Sigma
614 |         # Use Cholesky decomposition for efficiency
615 |         # Ref: https://arxiv.org/abs/1111.4144
616 |         chol_fail = False
617 |         try:
618 |             upper = scipy.linalg.cholesky(hessian)
619 |         except linalg.LinAlgError:
620 |             warnings.warn("Hessian not positive definite")
621 |             chol_fail = True
622 | 
623 |         if chol_fail:
624 |             try:
625 |                 self.Sigma_ = np.linalg.inv(hessian)
626 |             except linalg.LinAlgError:
627 |                 warnings.warn("Using Pseudo-Inverse")
628 |                 self.Sigma_ = np.linalg.pinv(hessian)
629 | 
630 |         else:
631 |             try:
632 |                 upper_inv = np.linalg.inv(upper)
633 |             except linalg.LinAlgError:
634 |                 warnings.warn("Using Pseudo-Inverse")
635 |                 upper_inv = np.linalg.pinv(upper)
636 | 
637 |             self.Sigma_ = np.dot(upper_inv, upper_inv.conj().T)
638 | 
639 |     def fit(self, X, y):
640 |         """Fit the RVC model according to the given training data.
641 | 
642 |         Parameters
643 |         ----------
644 |         X : array-like, shape (n_samples, n_features)
645 |             Training vectors.
646 | 
647 |         y : array-like, shape (n_samples,)
648 |             Target values.
649 | 
650 |         Returns
651 |         -------
652 |         self : object
653 |         """
654 |         X, y = check_X_y(X, y, y_numeric=True, ensure_min_samples=2,
655 |                          dtype="float64")
656 | 
657 |         if self.kernel == "precomputed" and X.shape[0] != X.shape[1]:
658 |             raise ValueError("X.shape[0] should be equal to X.shape[1]")
659 | 
660 |         if self.gamma in ("scale", "auto_deprecated"):
661 |             X_var = X.var()
662 |             if self.gamma == "scale":
663 |                 if X_var != 0:
664 |                     self._gamma = 1.0 / (X.shape[1] * X_var)
665 |                 else:
666 |                     self._gamma = 1.0
667 |             else:
668 |                 kernel_uses_gamma = (not callable(self.kernel) and self.kernel
669 |                                      not in ("linear", "precomputed"))
670 |                 if kernel_uses_gamma and not np.isclose(X_var, 1.0):
671 |                     # NOTE: when deprecation ends we need to remove explicitly
672 |                     # setting `gamma` in examples (also in tests). See
673 |                     # https://github.com/scikit-learn/scikit-learn/pull/10331
674 |                     # for the examples/tests that need to be reverted.
675 |                     warnings.warn("The default value of gamma will change "
676 |                                   "from 'auto' to 'scale' in version 0.22 to "
677 |                                   "account better for unscaled features. Set "
678 |                                   "gamma explicitly to 'auto' or 'scale' to "
679 |                                   "avoid this warning.", FutureWarning)
680 |                 self._gamma = 1.0 / X.shape[1]
681 |         elif self.gamma == 'auto':
682 |             self._gamma = 1.0 / X.shape[1]
683 |         else:
684 |             self._gamma = self.gamma
685 | 
686 |         self.classes_ = np.unique(y)
687 | 
688 |         n_classes = len(self.classes_)
689 | 
690 |         self.scores_ = list()
691 | 
692 |         if n_classes < 2:
693 |             raise ValueError("Need 2 or more classes.")
694 | 
695 |         elif n_classes == 2:
696 |             
697 |             self.t = np.zeros(y.shape)
698 |             self.t[y == self.classes_[1]] = 1
699 | 
700 |             n_samples = X.shape[0]
701 |             self.Phi_ = self._get_kernel(X)
702 | 
703 |             # Scale Phi based on PRoNTO implementation
704 |             # http://www.mlnl.cs.ucl.ac.uk/pronto/
705 |             self._scale = np.sqrt(np.sum(self.Phi_) / n_samples ** 2)
706 |             self.Phi_ = self.Phi_ / self._scale
707 | 
708 |             if self.bias_used:
709 |                 self.Phi_ = np.hstack((np.ones((n_samples, 1)), self.Phi_))
710 | 
711 |             M = self.Phi_.shape[1]
712 | 
713 |             self.y = y
714 | 
715 |             if self.init_alpha == None:
716 |                 self.init_alpha = 1 / M ** 2
717 | 
718 |             self.relevance_ = np.array(range(n_samples))
719 |             if self.kernel != "precomputed":
720 |                 self.relevance_vectors_ = X
721 |             else:
722 |                 self.relevance_vectors_ = None
723 | 
724 |             if self.beta_fixed == "not_fixed":
725 |                 # Suggested in the paper [1].
726 |                 self.beta_ = 1e-6
727 |             else:
728 |                 self.beta_ = self.beta_fixed
729 | 
730 |             self.mu_ = np.zeros(M)
731 | 
732 |             self.alpha_ = self.init_alpha * np.ones(M)
733 | 
734 |             self._alpha_old = self.alpha_.copy()
735 | 
736 |             for i in range(self.max_iter):
737 |                 self._posterior()
738 | 
739 |                 # Well-determinedness parameters (gamma)
740 |                 self.gamma_ = 1 - self.alpha_ * np.diag(self.Sigma_)
741 |                 self.alpha_ = np.maximum(self.gamma_, self.epsilon) / (
742 |                         self.mu_ ** 2) + self.epsilon
743 |                 self.alpha_ = np.clip(self.alpha_, 0, self.alpha_max)
744 | 
745 |                 if not self.beta_fixed:
746 |                     ed = np.sum((y - self.Phi_ @ self.mu_) ** 2)
747 |                     self.beta_ = np.maximum((n_samples - np.sum(self.gamma_)),
748 |                                             self.epsilon) / ed + self.epsilon
749 | 
750 |                 if self.compute_score:
751 |                     raise ("Score not yet implemented.")
752 | 
753 |                 self._prune()
754 | 
755 |                 if self.verbose:
756 |                     print("Iteration: {}".format(i))
757 |                     print("Alpha: {}".format(self.alpha_))
758 |                     print("Beta: {}".format(self.beta_))
759 |                     print("Gamma: {}".format(self.gamma_))
760 |                     print("m: {}".format(self.mu_))
761 |                     print("Relevance Vectors: {}".format(
762 |                         self.relevance_.shape[0]))
763 |                     if self.compute_score:
764 |                         pass
765 |                     print()
766 | 
767 |                 delta = np.amax(
768 |                     np.absolute(np.log(self.alpha_ + self.epsilon) - np.log(
769 |                         self._alpha_old + self.epsilon)))
770 | 
771 |                 if delta < self.tol and i > 1:
772 |                     break
773 | 
774 |                 self._alpha_old = self.alpha_.copy()
775 | 
776 |             return self
777 | 
778 |         else:
779 |             self.multi_ = None
780 |             self.multi_ = OneVsRestClassifier(self)
781 |             self.multi_.fit(X, y)
782 |             return self
783 | 
784 |     def predict_proba(self, X):
785 |         """Return an array of class probabilities."""
786 |         #check_is_fitted(self, ["relevance_", "mu_", "Sigma_"])
787 | 
788 |         if len(self.classes_) == 2:
789 |             X = check_array(X)
790 |             n_samples = X.shape[0]
791 | 
792 |             K = self._get_kernel(X, self.relevance_vectors_)
793 |             K = K / self._scale
794 | 
795 |             if self.bias_used:
796 |                 K = np.hstack((np.ones((n_samples, 1)), K))
797 | 
798 |             y = self._classify(self.mu_, K)
799 |             return np.column_stack((1 - y, y))
800 |         else:
801 |             return self.multi_.predict_proba(X)
802 | 
803 |     def predict(self, X):
804 |         """Predict using the RVC model.
805 | 
806 |         In addition to the mean of the predictive distribution, its
807 |         standard deviation can also be returned.
808 | 
809 |         Parameters
810 |         ----------
811 |         X : array-like, shape (n_samples, n_features)
812 |             Query points to be evaluate.
813 | 
814 |         Returns
815 |         -------
816 |         results : array, shape = (n_samples, [n_output_dims])
817 |             Mean of predictive distribution at query points
818 |         """
819 |         # Check is fit had been called
820 |         #check_is_fitted(self, ["relevance_", "mu_", "Sigma_"])
821 | 
822 |         if len(self.classes_) == 2:
823 |             y = self.predict_proba(X)
824 | 
825 |             results = np.empty(y.shape[0], dtype=self.classes_.dtype)
826 |             results[y[:, 1] <= 0.5] = self.classes_[0]
827 |             results[y[:, 1] >= 0.5] = self.classes_[1]
828 |             return results
829 | 
830 |         else:
831 |             return self.multi_.predict(X)
832 | 


--------------------------------------------------------------------------------
/doc/themes/scikit-learn-modern/static/js/vendor/bootstrap.min.js:
--------------------------------------------------------------------------------
1 | /*!
2 |   * Bootstrap v4.3.1 (https://getbootstrap.com/)
3 |   * Copyright 2011-2019 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
4 |   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
5 |   */
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