forked from scikit-learn/scikit-learn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsklearn.covariance.graph_lasso.html
349 lines (313 loc) · 15.4 KB
/
sklearn.covariance.graph_lasso.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /><meta content="noindex" name="robots" />
<title>sklearn.covariance.graph_lasso — scikit-learn 0.20.dev0 documentation</title>
<!-- htmltitle is before nature.css - we use this hack to load bootstrap first -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="stylesheet" href="../../_static/css/bootstrap.min.css" media="screen" />
<link rel="stylesheet" href="../../_static/css/bootstrap-responsive.css"/>
<link rel="stylesheet" href="../../_static/nature.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
VERSION: '0.20.dev0',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/js/copybutton.js"></script>
<script type="text/javascript" src="../../_static/js/extra.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_SVG"></script>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../../about.html" />
<link rel="search" title="Search" href="../../search.html" />
<link rel="next" title="sklearn.linear_model.RandomizedLasso" href="sklearn.linear_model.RandomizedLasso.html" />
<link rel="prev" title="sklearn.preprocessing.Imputer" href="sklearn.preprocessing.Imputer.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<script>
VERSION_SUBDIR = (function(groups) {
return groups ? groups[1] : null;
})(location.href.match(/^https?:\/\/scikit-learn.org\/([^\/]+)/));
</script>
<link rel="canonical" href="http://scikit-learn.org/stable/modules/generated/sklearn.covariance.graph_lasso.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
function showMenu() {
var topNav = document.getElementById("scikit-navbar");
if (topNav.className === "navbar") {
topNav.className += " responsive";
} else {
topNav.className = "navbar";
}
};
</script>
</head>
<body>
<div class="header-wrapper">
<div class="header">
<p class="logo"><a href="../../index.html">
<img src="../../_static/scikit-learn-logo-small.png" alt="Logo"/>
</a>
</p><div class="navbar" id="scikit-navbar">
<ul>
<li><a href="../../index.html">Home</a></li>
<li><a href="../../install.html">Installation</a></li>
<li class="btn-li"><div class="btn-group">
<a href="../../documentation.html">Documentation</a>
<a class="btn dropdown-toggle" data-toggle="dropdown">
<span class="caret"></span>
</a>
<ul class="dropdown-menu">
<li class="link-title">Scikit-learn <script>document.write(DOCUMENTATION_OPTIONS.VERSION + (VERSION_SUBDIR ? " (" + VERSION_SUBDIR + ")" : ""));</script></li>
<li><a href="../../tutorial/index.html">Tutorials</a></li>
<li><a href="../../user_guide.html">User guide</a></li>
<li><a href="../classes.html">API</a></li>
<li><a href="../../glossary.html">Glossary</a></li>
<li><a href="../../faq.html">FAQ</a></li>
<li><a href="../../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<script>if (VERSION_SUBDIR != "stable") document.write('<li><a href="http://scikit-learn.org/stable/documentation.html">Stable version</a></li>')</script>
<script>if (VERSION_SUBDIR != "dev") document.write('<li><a href="http://scikit-learn.org/dev/documentation.html">Development version</a></li>')</script>
<li><a href="http://scikit-learn.org/dev/versions.html">All available versions</a></li>
<li><a href="../../_downloads/scikit-learn-docs.pdf">PDF documentation</a></li>
</ul>
</div>
</li>
<li><a href="../../auto_examples/index.html">Examples</a></li>
</ul>
<a href="javascript:void(0);" onclick="showMenu()">
<div class="nav-icon">
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
</div>
</a>
<div class="search_form">
<div class="gcse-search" id="cse" style="width: 100%;"></div>
</div>
</div> <!-- end navbar --></div>
</div>
<!-- GitHub "fork me" ribbon -->
<a href="https://github.com/scikit-learn/scikit-learn">
<img class="fork-me"
style="position: absolute; top: 0; right: 0; border: 0;"
src="../../_static/img/forkme.png"
alt="Fork me on GitHub" />
</a>
<div class="content-wrapper">
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<div class="rel">
<div class="rellink">
<a href="sklearn.preprocessing.Imputer.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
sklearn.prepr...
</span>
<span class="hiddenrellink">
sklearn.preprocessing.Imputer
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="sklearn.linear_model.RandomizedLasso.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
sklearn.linea...
</span>
<span class="hiddenrellink">
sklearn.linear_model.RandomizedLasso
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../classes.html">
Up
<br/>
<span class="smallrellink">
API Reference
</span>
<span class="hiddenrellink">
API Reference
</span>
</a>
</div>
</div>
<p class="doc-version"><b>scikit-learn v0.20.dev0</b><br/>
<a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">Please <b><a href="../../about.html#citing-scikit-learn" style="font-size: 110%;">cite us </a></b>if you use the software.</p>
<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.covariance</span></code>.graph_lasso</a></li>
</ul>
</div>
</div>
<input type="checkbox" id="nav-trigger" class="nav-trigger" checked />
<label for="nav-trigger"></label>
<div class="content">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="sklearn-covariance-graph-lasso">
<h1><a class="reference internal" href="../classes.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.covariance</span></code></a>.graph_lasso<a class="headerlink" href="#sklearn-covariance-graph-lasso" title="Permalink to this headline">¶</a></h1>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><strong>DEPRECATED</strong></p>
</div>
<dl class="function">
<dt id="sklearn.covariance.graph_lasso">
<code class="descclassname">sklearn.covariance.</code><code class="descname">graph_lasso</code><span class="sig-paren">(</span><em>emp_cov</em>, <em>alpha</em>, <em>cov_init=None</em>, <em>mode='cd'</em>, <em>tol=0.0001</em>, <em>enet_tol=0.0001</em>, <em>max_iter=100</em>, <em>verbose=False</em>, <em>return_costs=False</em>, <em>eps=2.220446049250313e-16</em>, <em>return_n_iter=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/utils/deprecation.py#L715"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.graph_lasso" title="Permalink to this definition">¶</a></dt>
<dd><p>DEPRECATED: The ‘graph_lasso’ was renamed to ‘graphical_lasso’ in version 0.20 and will be removed in 0.22.</p>
<p>l1-penalized covariance estimator</p>
<blockquote>
<div>Read more in the <a class="reference internal" href="../covariance.html#sparse-inverse-covariance"><span class="std std-ref">User Guide</span></a>.</div></blockquote>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>emp_cov</strong> : 2D ndarray, shape (n_features, n_features)</p>
<blockquote>
<div><blockquote>
<div><p>Empirical covariance from which to compute the covariance estimate.</p>
</div></blockquote>
<dl class="docutils">
<dt>alpha <span class="classifier-delimiter">:</span> <span class="classifier">positive float</span></dt>
<dd><p class="first last">The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.</p>
</dd>
<dt>cov_init <span class="classifier-delimiter">:</span> <span class="classifier">2D array (n_features, n_features), optional</span></dt>
<dd><p class="first last">The initial guess for the covariance.</p>
</dd>
<dt>mode <span class="classifier-delimiter">:</span> <span class="classifier">{‘cd’, ‘lars’}</span></dt>
<dd><p class="first last">The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.</p>
</dd>
<dt>tol <span class="classifier-delimiter">:</span> <span class="classifier">positive float, optional</span></dt>
<dd><p class="first last">The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.</p>
</dd>
<dt>enet_tol <span class="classifier-delimiter">:</span> <span class="classifier">positive float, optional</span></dt>
<dd><p class="first last">The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode=’cd’.</p>
</dd>
<dt>max_iter <span class="classifier-delimiter">:</span> <span class="classifier">integer, optional</span></dt>
<dd><p class="first last">The maximum number of iterations.</p>
</dd>
<dt>verbose <span class="classifier-delimiter">:</span> <span class="classifier">boolean, optional</span></dt>
<dd><p class="first last">If verbose is True, the objective function and dual gap are
printed at each iteration.</p>
</dd>
<dt>return_costs <span class="classifier-delimiter">:</span> <span class="classifier">boolean, optional</span></dt>
<dd><p class="first last">If return_costs is True, the objective function and dual gap
at each iteration are returned.</p>
</dd>
<dt>eps <span class="classifier-delimiter">:</span> <span class="classifier">float, optional</span></dt>
<dd><p class="first last">The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.</p>
</dd>
<dt>return_n_iter <span class="classifier-delimiter">:</span> <span class="classifier">bool, optional</span></dt>
<dd><p class="first last">Whether or not to return the number of iterations.</p>
</dd>
</dl>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>covariance</strong> : 2D ndarray, shape (n_features, n_features)</p>
<blockquote class="last">
<div><blockquote>
<div><p>The estimated covariance matrix.</p>
</div></blockquote>
<dl class="docutils">
<dt>precision <span class="classifier-delimiter">:</span> <span class="classifier">2D ndarray, shape (n_features, n_features)</span></dt>
<dd><p class="first last">The estimated (sparse) precision matrix.</p>
</dd>
<dt>costs <span class="classifier-delimiter">:</span> <span class="classifier">list of (objective, dual_gap) pairs</span></dt>
<dd><p class="first last">The list of values of the objective function and the dual gap at
each iteration. Returned only if return_costs is True.</p>
</dd>
<dt>n_iter <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of iterations. Returned only if <cite>return_n_iter</cite> is set to True.</p>
</dd>
</dl>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>The algorithm employed to solve this problem is the GLasso algorithm,
from the Friedman 2008 Biostatistics paper. It is the same algorithm
as in the R <cite>glasso</cite> package.</p>
<p>One possible difference with the <cite>glasso</cite> R package is that the
diagonal coefficients are not penalized.</p>
</dd></dl>
<div class="clearer"></div></div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2007 - 2018, scikit-learn developers (BSD License).
<a href="../../_sources/modules/generated/sklearn.covariance.graph_lasso.rst.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="sklearn.preprocessing.Imputer.html">Previous
</a>
</div>
<div class="buttonNext">
<a href="sklearn.linear_model.RandomizedLasso.html">Next
</a>
</div>
</div>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-22606712-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
<script>
(function() {
var cx = '016639176250731907682:tjtqbvtvij0';
var gcse = document.createElement('script'); gcse.type = 'text/javascript'; gcse.async = true;
gcse.src = 'https://cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(gcse, s);
})();
</script>
</body>
</html>