259 lines
9.3 KiB
ReStructuredText
259 lines
9.3 KiB
ReStructuredText
|
|
..
|
|
We are putting the title as a raw HTML so that it doesn't appear in
|
|
the contents
|
|
|
|
.. raw:: html
|
|
|
|
<h1>scikit-learn: machine learning in Python</h1>
|
|
<style type="text/css">
|
|
p {
|
|
margin: 7px 0 7px 0 ;
|
|
}
|
|
span.linkdescr a {
|
|
color: #3E4349 ;
|
|
}
|
|
</style>
|
|
|
|
..
|
|
Here we are building a banner: a javascript selects randomly 4 images in
|
|
the list
|
|
|
|
.. only:: html
|
|
|
|
.. |banner1| image:: auto_examples/svm/images/plot_oneclass_1.png
|
|
:height: 139
|
|
:target: auto_examples/svm/plot_oneclass.html
|
|
|
|
.. |banner2| image:: auto_examples/cluster/images/plot_ward_structured_vs_unstructured_2.png
|
|
:height: 139
|
|
:target: auto_examples/cluster/plot_ward_structured_vs_unstructured.html
|
|
|
|
.. |banner3| image:: auto_examples/gaussian_process/images/plot_gp_regression_1.png
|
|
:height: 139
|
|
:target: auto_examples/gaussian_process/plot_gp_regression.html
|
|
|
|
.. |banner4| image:: auto_examples/cluster/images/plot_lena_ward_segmentation_1.png
|
|
:height: 139
|
|
:target: auto_examples/cluster/plot_lena_ward_segmentation.html
|
|
|
|
.. |banner5| image:: auto_examples/svm/images/plot_svm_nonlinear_1.png
|
|
:height: 139
|
|
:target: auto_examples/svm/plot_svm_nonlinear.html
|
|
|
|
.. |banner6| image:: auto_examples/applications/images/plot_species_distribution_modeling_1.png
|
|
:height: 139
|
|
:target: auto_examples/applications/plot_species_distribution_modeling.html
|
|
|
|
.. |banner7| image:: auto_examples/gaussian_process/images/plot_gp_probabilistic_classification_after_regression_1.png
|
|
:height: 139
|
|
:target: auto_examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.html
|
|
|
|
.. |banner8| image:: auto_examples/ensemble/images/plot_forest_importances_faces_1.png
|
|
:height: 139
|
|
:target: auto_examples/ensemble/plot_forest_importances_faces.html
|
|
|
|
.. |banner9| image:: auto_examples/svm/images/plot_weighted_samples_1.png
|
|
:height: 139
|
|
:target: auto_examples/svm/plot_weighted_samples.html
|
|
|
|
.. |banner10| image:: auto_examples/linear_model/images/plot_sgd_weighted_samples_1.png
|
|
:height: 139
|
|
:target: auto_examples/linear_model/plot_sgd_weighted_samples.html
|
|
|
|
.. |banner11| image:: auto_examples/cluster/images/plot_kmeans_digits_1.png
|
|
:height: 139
|
|
:target: auto_examples/cluster/plot_kmeans_digits.html
|
|
|
|
.. |banner12| image:: auto_examples/decomposition/images/plot_faces_decomposition_2.png
|
|
:height: 139
|
|
:target: auto_examples/decomposition/plot_faces_decomposition.html
|
|
|
|
.. |banner13| image:: auto_examples/decomposition/images/plot_faces_decomposition_3.png
|
|
:height: 139
|
|
:target: auto_examples/decomposition/plot_faces_decomposition.html
|
|
|
|
.. |banner14| image:: auto_examples/images/plot_lda_qda_1.png
|
|
:height: 139
|
|
:target: auto_examples/plot_lda_vs_qda.html
|
|
|
|
.. |banner15| image:: auto_examples/cluster/images/plot_cluster_comparison_1.png
|
|
:height: 139
|
|
:target: auto_examples/cluster/plot_cluster_comparison.html
|
|
|
|
.. |center-div| raw:: html
|
|
|
|
<div style="text-align: center; margin: -7px 0 -10px 0;" id="banner">
|
|
|
|
.. |end-div| raw:: html
|
|
|
|
</div>
|
|
|
|
<SCRIPT>
|
|
// Function to select 4 imgs in random order from a div
|
|
function shuffle(e) { // pass the divs to the function
|
|
var replace = $('<div>');
|
|
var size = 4;
|
|
var num_choices = e.size();
|
|
|
|
while (size >= 1) {
|
|
var rand = Math.floor(Math.random() * num_choices);
|
|
var temp = e.get(rand); // grab a random div from our set
|
|
replace.append(temp); // add the selected div to our new set
|
|
e = e.not(temp); // remove our selected div from the main set
|
|
size--;
|
|
num_choices--;
|
|
}
|
|
$('#banner').html(replace.html() ); // update our container div
|
|
// with the new, randomized divs
|
|
}
|
|
shuffle ($('#banner a.external'));
|
|
|
|
function preload_images() {
|
|
var img = new Image();
|
|
img.src="_static/plusBox.png";
|
|
img.src="_static/minBox.png";
|
|
img.src="_static/plusBoxHighlight.png";
|
|
img.src="_static/minBoxHighlight.png";
|
|
img.src="_static/noneBox.png";
|
|
}
|
|
preload_images();
|
|
|
|
//Function to make the index toctree collapsible
|
|
$(function () {
|
|
$('.toctree-l2')
|
|
.click(function(event){
|
|
if (event.target.tagName.toLowerCase() != "a") {
|
|
if ($(this).children('ul').length > 0) {
|
|
$(this).css('list-style-image',
|
|
(!$(this).children('ul').is(':hidden')) ? 'url(_static/plusBoxHighlight.png)' : 'url(_static/minBoxHighlight.png)');
|
|
$(this).children('ul').toggle();
|
|
}
|
|
return true; //Makes links clickable
|
|
}
|
|
})
|
|
.mousedown(function(event){ return false; }) //Firefox highlighting fix
|
|
.css({cursor:'pointer', 'list-style-image':'url(_static/plusBox.png)'})
|
|
.children('ul').hide();
|
|
$('ul li ul li:not(:has(ul))').css({cursor:'default', 'list-style-image':'url(_static/noneBox.png)'});
|
|
$('.toctree-l3').css({cursor:'default', 'list-style-image':'url(_static/noneBox.png)'});
|
|
var sidebarbutton = $('#sidebarbutton');
|
|
sidebarbutton.css({
|
|
'display': 'none'
|
|
});
|
|
|
|
$('.toctree-l2').hover(
|
|
function () {
|
|
if ($(this).children('ul').length > 0) {
|
|
$(this).css('background-color', '#D0D0D0').children('ul').css('background-color', '#F0F0F0');
|
|
$(this).css('list-style-image',
|
|
(!$(this).children('ul').is(':hidden')) ? 'url(_static/minBoxHighlight.png)' : 'url(_static/plusBoxHighlight.png)');
|
|
}
|
|
else {
|
|
$(this).css('background-color', '#F9F9F9');
|
|
}
|
|
},
|
|
function () {
|
|
$(this).css('background-color', 'white').children('ul').css('background-color', 'white');
|
|
if ($(this).children('ul').length > 0) {
|
|
$(this).css('list-style-image',
|
|
(!$(this).children('ul').is(':hidden')) ? 'url(_static/minBox.png)' : 'url(_static/plusBox.png)');
|
|
}
|
|
}
|
|
);
|
|
});
|
|
|
|
</SCRIPT>
|
|
|
|
|center-div| |banner1| |banner2| |banner3| |banner4| |banner5| |banner6| |banner7| |banner8| |banner9| |banner10| |banner11| |banner12| |banner13| |banner14| |end-div|
|
|
|
|
|
|
.. topic:: Easy-to-use and general-purpose machine learning in Python
|
|
|
|
``scikit-learn`` is a Python module integrating classic machine
|
|
learning algorithms in the tightly-knit scientific Python
|
|
world (`numpy <http://numpy.scipy.org>`_, `scipy
|
|
<http://www.scipy.org>`_, `matplotlib
|
|
<http://matplotlib.sourceforge.net/>`_).
|
|
It aims to provide simple and efficient solutions to learning
|
|
problems, accessible to everybody and reusable in various
|
|
contexts: **machine-learning as a versatile tool for science and
|
|
engineering**.
|
|
|
|
|
|
.. raw:: html
|
|
|
|
<table class="contentstable" style="width: 100% ; margin-top: -8px">
|
|
<tr valign="top"><td width="28%">
|
|
<p class="biglink"><a class="biglink" href="supervised_learning.html">
|
|
Supervised learning</a><br/>
|
|
<span class="linkdescr">
|
|
<a href="modules/svm.html">Support vector machines</a>,
|
|
<a href="modules/linear_model.html">linear models</a>,
|
|
<a href="modules/naive_bayes.html">naive Bayes</a>,
|
|
<a href="modules/gaussian_process.html">Gaussian processes</a>...
|
|
</span></p>
|
|
</td><td align="center" width="32%">
|
|
<p class="biglink"><a class="biglink" href="unsupervised_learning.html">
|
|
Unsupervised learning</a><br/>
|
|
<span class="linkdescr">
|
|
<a href="modules/clustering.html">Clustering</a>,
|
|
<a href="modules/mixture.html">Gaussian mixture models</a>,
|
|
<a href="modules/manifold.html">manifold learning</a>,
|
|
<a href="modules/decomposition.html">matrix factorization</a>,
|
|
<a href="modules/covariance.html">covariance</a>...
|
|
</span></p>
|
|
</td><td align="right" width="30%">
|
|
<p class="biglink"><a class="biglink" href="index.html#user-guide">
|
|
And much more</a><br/>
|
|
<span class="linkdescr">
|
|
<a href="model_selection.html">Model selection</a>,
|
|
<a href="datasets/index.html">datasets</a>,
|
|
<a href="modules/feature_extraction.html">feature extraction...</a>
|
|
<strong>See below</strong>.</span></p>
|
|
</td></tr>
|
|
</table>
|
|
|
|
**License:** Open source, commercially usable: **BSD license** (3 clause)
|
|
|
|
.. include:: includes/big_toc_css.rst
|
|
|
|
Documentation for scikit-learn **version** |release|. For other versions and
|
|
printable format, see :ref:`documentation_resources`.
|
|
|
|
User Guide
|
|
==========
|
|
|
|
.. toctree::
|
|
:numbered:
|
|
|
|
user_guide.rst
|
|
|
|
Example Gallery
|
|
===============
|
|
|
|
.. toctree::
|
|
:maxdepth: 2
|
|
|
|
auto_examples/index
|
|
|
|
|
|
Development
|
|
===========
|
|
.. toctree::
|
|
:numbered:
|
|
|
|
developers/index
|
|
developers/performance
|
|
developers/utilities
|
|
developers/debugging
|
|
developers/maintainer
|
|
about
|
|
|
|
.. toctree::
|
|
:hidden:
|
|
|
|
support
|
|
whats_new
|
|
presentations
|