diff options
| author | Calvin <calvin@EESI> | 2013-04-05 13:51:26 -0400 |
|---|---|---|
| committer | Calvin <calvin@EESI> | 2013-04-05 13:51:26 -0400 |
| commit | 1e857f0420c6423fb7453ed3cbc6a1d062e97bf3 (patch) | |
| tree | dc928668b49a5d47085136719b44d081b11546f2 /feast-module.html | |
added basic docs generated with epydocs, and stripped downgh-pages
Diffstat (limited to 'feast-module.html')
| -rw-r--r-- | feast-module.html | 864 |
1 files changed, 864 insertions, 0 deletions
diff --git a/feast-module.html b/feast-module.html new file mode 100644 index 0000000..557d352 --- /dev/null +++ b/feast-module.html @@ -0,0 +1,864 @@ +<?xml version="1.0" encoding="ascii"?> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" + "DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> +<head> + <title>feast</title> + <link rel="stylesheet" href="epydoc.css" type="text/css" /> + <script type="text/javascript" src="epydoc.js"></script> +</head> + +<body bgcolor="white" text="black" link="blue" vlink="#204080" + alink="#204080"> +<!-- ==================== NAVIGATION BAR ==================== --> +<table class="navbar" border="0" width="100%" cellpadding="0" + bgcolor="#a0c0ff" cellspacing="0"> + <tr valign="middle"> + <!-- Home link --> + <th bgcolor="#70b0f0" class="navbar-select" + > Home </th> + + <!-- Tree link --> + <th> <a + href="module-tree.html">Trees</a> </th> + + <!-- Index link --> + <th> <a + href="identifier-index.html">Indices</a> </th> + + <!-- Help link --> + <th> <a + href="help.html">Help</a> </th> + + <!-- Project homepage --> + <th class="navbar" align="right" width="100%"> + <table border="0" cellpadding="0" cellspacing="0"> + <tr><th class="navbar" align="center" + >PyFeast</th> + </tr></table></th> + </tr> +</table> +<table width="100%" cellpadding="0" cellspacing="0"> + <tr valign="top"> + <td width="100%"> + <span class="breadcrumbs"> + Module feast + </span> + </td> + <td> + <table cellpadding="0" cellspacing="0"> + <!-- hide/show private --> + </table> + </td> + </tr> +</table> +<!-- ==================== MODULE DESCRIPTION ==================== --> +<h1 class="epydoc">Module feast</h1><p class="nomargin-top"><span class="codelink"><a href="feast-pysrc.html">source code</a></span></p> +<pre class="literalblock"> + +The FEAST module provides an interface between the C-library +for feature selection to Python. + +References: +1) G. Brown, A. Pocock, M.-J. Zhao, and M. Lujan, "Conditional + likelihood maximization: A unifying framework for information + theoretic feature selection," Journal of Machine Learning + Research, vol. 13, pp. 27-66, 2012. + +</pre> + +<hr /> +<div class="fields"> <p><strong>Version:</strong> + 0.2.0 + </p> + <p><strong>Author:</strong> + Calvin Morrison + </p> + <p><strong>Copyright:</strong> + Copyright 2013, EESI Laboratory + </p> + <p><strong>License:</strong> + GPL + </p> +</div><!-- ==================== FUNCTIONS ==================== --> +<a name="section-Functions"></a> +<table class="summary" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr bgcolor="#70b0f0" class="table-header"> + <td align="left" colspan="2" class="table-header"> + <span class="table-header">Functions</span></td> +</tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#BetaGamma" class="summary-sig-name">BetaGamma</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>, + <span class="summary-sig-arg">beta</span>=<span class="summary-sig-default">1.0</span>, + <span class="summary-sig-arg">gamma</span>=<span class="summary-sig-default">1.0</span>)</span><br /> + This algorithm implements conditional mutual information feature + select, such that beta and gamma control the weight attached to the + redundant mutual and conditional mutual information, respectively.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#BetaGamma">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#CIFE" class="summary-sig-name">CIFE</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the Condred feature selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#CIFE">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#CMIM" class="summary-sig-name">CMIM</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the conditional mutual information + maximization feature selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#CMIM">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#CondMI" class="summary-sig-name">CondMI</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the conditional mutual information + maximization feature selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#CondMI">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#Condred" class="summary-sig-name">Condred</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the Condred feature selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#Condred">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#DISR" class="summary-sig-name">DISR</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the double input symmetrical relevance + feature selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#DISR">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#ICAP" class="summary-sig-name">ICAP</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the interaction capping feature selection + algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#ICAP">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#JMI" class="summary-sig-name">JMI</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the joint mutual information feature + selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#JMI">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#MIFS" class="summary-sig-name">MIFS</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the MIFS algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#MIFS">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#MIM" class="summary-sig-name">MIM</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This function implements the MIM algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#MIM">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">list</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#mRMR" class="summary-sig-name">mRMR</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>, + <span class="summary-sig-arg">n_select</span>)</span><br /> + This funciton implements the max-relevance min-redundancy feature + selection algorithm.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#mRMR">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type">tuple</span> + </td><td class="summary"> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr> + <td><span class="summary-sig"><a href="feast-module.html#check_data" class="summary-sig-name">check_data</a>(<span class="summary-sig-arg">data</span>, + <span class="summary-sig-arg">labels</span>)</span><br /> + Check dimensions of the data and the labels.</td> + <td align="right" valign="top"> + <span class="codelink"><a href="feast-pysrc.html#check_data">source code</a></span> + + </td> + </tr> + </table> + + </td> + </tr> +</table> +<!-- ==================== VARIABLES ==================== --> +<a name="section-Variables"></a> +<table class="summary" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr bgcolor="#70b0f0" class="table-header"> + <td align="left" colspan="2" class="table-header"> + <span class="table-header">Variables</span></td> +</tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <a name="__credits__"></a><span class="summary-name">__credits__</span> = <code title="['Calvin Morrison', 'Gregory Ditzler']"><code class="variable-group">[</code><code class="variable-quote">'</code><code class="variable-string">Calvin Morrison</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-quote">'</code><code class="variable-string">Gregory Ditzler</code><code class="variable-quote">'</code><code class="variable-group">]</code></code> + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <a name="__maintainer__"></a><span class="summary-name">__maintainer__</span> = <code title="'Calvin Morrison'"><code class="variable-quote">'</code><code class="variable-string">Calvin Morrison</code><code class="variable-quote">'</code></code> + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <a name="__email__"></a><span class="summary-name">__email__</span> = <code title="'mutantturkey@gmail.com'"><code class="variable-quote">'</code><code class="variable-string">mutantturkey@gmail.com</code><code class="variable-quote">'</code></code> + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <a name="__status__"></a><span class="summary-name">__status__</span> = <code title="'Release'"><code class="variable-quote">'</code><code class="variable-string">Release</code><code class="variable-quote">'</code></code> + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <a href="feast-module.html#libFSToolbox" class="summary-name">libFSToolbox</a> = <code title="<CDLL 'libFSToolbox.so', handle 2be1240 at 2b4bc10>"><CDLL 'libFSToolbox.so', handle 2be1240 at 2b4b<code class="variable-ellipsis">...</code></code> + </td> + </tr> +<tr> + <td width="15%" align="right" valign="top" class="summary"> + <span class="summary-type"> </span> + </td><td class="summary"> + <a name="__package__"></a><span class="summary-name">__package__</span> = <code title="None">None</code> + </td> + </tr> +</table> +<!-- ==================== FUNCTION DETAILS ==================== --> +<a name="section-FunctionDetails"></a> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr bgcolor="#70b0f0" class="table-header"> + <td align="left" colspan="2" class="table-header"> + <span class="table-header">Function Details</span></td> +</tr> +</table> +<a name="BetaGamma"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">BetaGamma</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>, + <span class="sig-arg">beta</span>=<span class="sig-default">1.0</span>, + <span class="sig-arg">gamma</span>=<span class="sig-default">1.0</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#BetaGamma">source code</a></span> + </td> + </tr></table> + + <p>This algorithm implements conditional mutual information feature + select, such that beta and gamma control the weight attached to the + redundant mutual and conditional mutual information, respectively.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features (REQUIRED)</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations. (REQUIRED)</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + <li><strong class="pname"><code>beta</code></strong> (float between 0 and 1.0) - penalty attacted to I(X_j;X_k)</li> + <li><strong class="pname"><code>gamma</code></strong> (float between 0 and 1.0) - positive weight attached to the conditional redundancy term + I(X_k;X_j|Y)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="CIFE"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">CIFE</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#CIFE">source code</a></span> + </td> + </tr></table> + + <p>This function implements the Condred feature selection algorithm. beta + = 1; gamma = 1;</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - A Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select.</li> + </ul></dd> + <dt>Returns: list</dt> + </dl> +</td></tr></table> +</div> +<a name="CMIM"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">CMIM</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#CMIM">source code</a></span> + </td> + </tr></table> + + <p>This function implements the conditional mutual information + maximization feature selection algorithm. Note that this implementation + does not allow for the weighting of the redundancy terms that BetaGamma + will allow you to do.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - A Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy array with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select.</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>features in the order that they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="CondMI"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">CondMI</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#CondMI">source code</a></span> + </td> + </tr></table> + + <p>This function implements the conditional mutual information + maximization feature selection algorithm.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - represented in a numpy list with n_observations as the number of + elements. That is len(labels) = len(data) = n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select.</li> + </ul></dd> + <dt>Returns:</dt> + <dd>features in the order they were selected. @rtype list</dd> + </dl> +</td></tr></table> +</div> +<a name="Condred"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">Condred</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#Condred">source code</a></span> + </td> + </tr></table> + + <p>This function implements the Condred feature selection algorithm. beta + = 0; gamma = 1;</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select.</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="DISR"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">DISR</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#DISR">source code</a></span> + </td> + </tr></table> + + <p>This function implements the double input symmetrical relevance + feature selection algorithm.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="ICAP"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">ICAP</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#ICAP">source code</a></span> + </td> + </tr></table> + + <p>This function implements the interaction capping feature selection + algorithm.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="JMI"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">JMI</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#JMI">source code</a></span> + </td> + </tr></table> + + <p>This function implements the joint mutual information feature + selection algorithm.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="MIFS"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">MIFS</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#MIFS">source code</a></span> + </td> + </tr></table> + + <p>This function implements the MIFS algorithm. beta = 1; gamma = 0;</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="MIM"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">MIM</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#MIM">source code</a></span> + </td> + </tr></table> + + <p>This function implements the MIM algorithm. beta = 0; gamma = 0;</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="mRMR"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">mRMR</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>, + <span class="sig-arg">n_select</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#mRMR">source code</a></span> + </td> + </tr></table> + + <p>This funciton implements the max-relevance min-redundancy feature + selection algorithm.</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> (ndarray) - data in a Numpy array such that len(data) = n_observations, and + len(data.transpose()) = n_features</li> + <li><strong class="pname"><code>labels</code></strong> (ndarray) - labels represented in a numpy list with n_observations as the + number of elements. That is len(labels) = len(data) = + n_observations.</li> + <li><strong class="pname"><code>n_select</code></strong> (integer) - number of features to select. (REQUIRED)</li> + </ul></dd> + <dt>Returns: list</dt> + <dd>the features in the order they were selected.</dd> + </dl> +</td></tr></table> +</div> +<a name="check_data"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <table width="100%" cellpadding="0" cellspacing="0" border="0"> + <tr valign="top"><td> + <h3 class="epydoc"><span class="sig"><span class="sig-name">check_data</span>(<span class="sig-arg">data</span>, + <span class="sig-arg">labels</span>)</span> + </h3> + </td><td align="right" valign="top" + ><span class="codelink"><a href="feast-pysrc.html#check_data">source code</a></span> + </td> + </tr></table> + + <p>Check dimensions of the data and the labels. Raise and exception if + there is a problem.</p> + <p>Data and Labels are automatically cast as doubles before calling the + feature selection functions</p> + <dl class="fields"> + <dt>Parameters:</dt> + <dd><ul class="nomargin-top"> + <li><strong class="pname"><code>data</code></strong> - the data</li> + <li><strong class="pname"><code>labels</code></strong> - the labels</li> + </ul></dd> + <dt>Returns: tuple</dt> + </dl> +</td></tr></table> +</div> +<br /> +<!-- ==================== VARIABLES DETAILS ==================== --> +<a name="section-VariablesDetails"></a> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr bgcolor="#70b0f0" class="table-header"> + <td align="left" colspan="2" class="table-header"> + <span class="table-header">Variables Details</span></td> +</tr> +</table> +<a name="libFSToolbox"></a> +<div> +<table class="details" border="1" cellpadding="3" + cellspacing="0" width="100%" bgcolor="white"> +<tr><td> + <h3 class="epydoc">libFSToolbox</h3> + + <dl class="fields"> + </dl> + <dl class="fields"> + <dt>Value:</dt> + <dd><table><tr><td><pre class="variable"> +<CDLL 'libFSToolbox.so', handle 2be1240 at 2b4bc10> +</pre></td></tr></table> +</dd> + </dl> +</td></tr></table> +</div> +<br /> +<!-- ==================== NAVIGATION BAR ==================== --> +<table class="navbar" border="0" width="100%" cellpadding="0" + bgcolor="#a0c0ff" cellspacing="0"> + <tr valign="middle"> + <!-- Home link --> + <th bgcolor="#70b0f0" class="navbar-select" + > Home </th> + + <!-- Tree link --> + <th> <a + href="module-tree.html">Trees</a> </th> + + <!-- Index link --> + <th> <a + href="identifier-index.html">Indices</a> </th> + + <!-- Help link --> + <th> <a + href="help.html">Help</a> </th> + + <!-- Project homepage --> + <th class="navbar" align="right" width="100%"> + <table border="0" cellpadding="0" cellspacing="0"> + <tr><th class="navbar" align="center" + >PyFeast</th> + </tr></table></th> + </tr> +</table> +<table border="0" cellpadding="0" cellspacing="0" width="100%%"> + <tr> + <td align="left" class="footer"> + Generated by Epydoc 3.0.1 on Fri Apr 5 13:44:32 2013 + </td> + <td align="right" class="footer"> + <a target="mainFrame" href="http://epydoc.sourceforge.net" + >http://epydoc.sourceforge.net</a> + </td> + </tr> +</table> + +<script type="text/javascript"> + <!-- + // Private objects are initially displayed (because if + // javascript is turned off then we want them to be + // visible); but by default, we want to hide them. So hide + // them unless we have a cookie that says to show them. + checkCookie(); + // --> +</script> +</body> +</html> |