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<title>The SVM class</title>
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<h1 class="title">The SVM class</h1>
<div class="partintro"><p class="verinfo">(PECL svm >= 0.1.0)</p>
<div class="section" id="svm.intro">
<h2 class="title">Introduction</h2>
<p class="para">
</p>
</div>
<div class="section" id="svm.synopsis">
<h2 class="title">Class synopsis</h2>
<div class="classsynopsis">
<div class="ooclass"></div>
<div class="classsynopsisinfo">
<span class="ooclass">
<strong class="classname">SVM</strong>
</span>
{</div>
<div class="classsynopsisinfo classsynopsisinfo_comment">/* Constants */</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.c-svc"><var class="varname">C_SVC</var></a></var>
<span class="initializer"> = 0</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.nu-svc"><var class="varname">NU_SVC</var></a></var>
<span class="initializer"> = 1</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.one-class"><var class="varname">ONE_CLASS</var></a></var>
<span class="initializer"> = 2</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.epsilon-svr"><var class="varname">EPSILON_SVR</var></a></var>
<span class="initializer"> = 3</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.nu-svr"><var class="varname">NU_SVR</var></a></var>
<span class="initializer"> = 4</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.kernel-linear"><var class="varname">KERNEL_LINEAR</var></a></var>
<span class="initializer"> = 0</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.kernel-poly"><var class="varname">KERNEL_POLY</var></a></var>
<span class="initializer"> = 1</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.kernel-rbf"><var class="varname">KERNEL_RBF</var></a></var>
<span class="initializer"> = 2</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.kernel-sigmoid"><var class="varname">KERNEL_SIGMOID</var></a></var>
<span class="initializer"> = 3</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.kernel-precomputed"><var class="varname">KERNEL_PRECOMPUTED</var></a></var>
<span class="initializer"> = 4</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-type"><var class="varname">OPT_TYPE</var></a></var>
<span class="initializer"> = 101</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-kernel-type"><var class="varname">OPT_KERNEL_TYPE</var></a></var>
<span class="initializer"> = 102</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-degree"><var class="varname">OPT_DEGREE</var></a></var>
<span class="initializer"> = 103</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-shrinking"><var class="varname">OPT_SHRINKING</var></a></var>
<span class="initializer"> = 104</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-propability"><var class="varname">OPT_PROPABILITY</var></a></var>
<span class="initializer"> = 105</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-gamma"><var class="varname">OPT_GAMMA</var></a></var>
<span class="initializer"> = 201</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-nu"><var class="varname">OPT_NU</var></a></var>
<span class="initializer"> = 202</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-eps"><var class="varname">OPT_EPS</var></a></var>
<span class="initializer"> = 203</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-p"><var class="varname">OPT_P</var></a></var>
<span class="initializer"> = 204</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-coef-zero"><var class="varname">OPT_COEF_ZERO</var></a></var>
<span class="initializer"> = 205</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-c"><var class="varname">OPT_C</var></a></var>
<span class="initializer"> = 206</span>
;</div>
<div class="fieldsynopsis">
<span class="modifier">const</span>
<span class="type">integer</span>
<var class="fieldsynopsis_varname"><a href="class.svm.html#svm.constants.opt-cache-size"><var class="varname">OPT_CACHE_SIZE</var></a></var>
<span class="initializer"> = 207</span>
;</div>
<div class="classsynopsisinfo classsynopsisinfo_comment">/* Methods */</div>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="methodname"><a href="svm.construct.html" class="methodname">__construct</a></span>
( <span class="methodparam">void</span>
)</div>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="type">float</span> <span class="methodname"><a href="svm.crossvalidate.html" class="methodname">svm::crossvalidate</a></span>
( <span class="methodparam"><span class="type">array</span> <code class="parameter">$problem</code></span>
, <span class="methodparam"><span class="type">int</span> <code class="parameter">$number_of_folds</code></span>
)</div>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="type">array</span> <span class="methodname"><a href="svm.getoptions.html" class="methodname">getOptions</a></span>
( <span class="methodparam">void</span>
)</div>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="type">bool</span> <span class="methodname"><a href="svm.setoptions.html" class="methodname">setOptions</a></span>
( <span class="methodparam"><span class="type">array</span> <code class="parameter">$params</code></span>
)</div>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="type">SVMModel</span> <span class="methodname"><a href="svm.train.html" class="methodname">svm::train</a></span>
( <span class="methodparam"><span class="type">array</span> <code class="parameter">$problem</code></span>
[, <span class="methodparam"><span class="type">array</span> <code class="parameter">$weights</code></span>
] )</div>
}</div>
</div>
<div class="section" id="svm.constants">
<h2 class="title">Predefined Constants</h2>
<div class="section" id="svm.constants.types">
<h2 class="title">SVM Constants</h2>
<dl>
<dt id="svm.constants.c-svc">
<span class="term"><strong><code>SVM::C_SVC</code></strong></span>
<dd>
<p class="para">The basic C_SVC SVM type. The default, and a good starting point</p>
</dd>
</dt>
<dt id="svm.constants.nu-svc">
<span class="term"><strong><code>SVM::NU_SVC</code></strong></span>
<dd>
<p class="para">The NU_SVC type uses a different, more flexible, error weighting</p>
</dd>
</dt>
<dt id="svm.constants.one-class">
<span class="term"><strong><code>SVM::ONE_CLASS</code></strong></span>
<dd>
<p class="para">One class SVM type. Train just on a single class, using outliers as negative examples</p>
</dd>
</dt>
<dt id="svm.constants.epsilon-svr">
<span class="term"><strong><code>SVM::EPSILON_SVR</code></strong></span>
<dd>
<p class="para">A SVM type for regression (predicting a value rather than just a class)</p>
</dd>
</dt>
<dt id="svm.constants.nu-svr">
<span class="term"><strong><code>SVM::NU_SVR</code></strong></span>
<dd>
<p class="para">A NU style SVM regression type</p>
</dd>
</dt>
<dt id="svm.constants.kernel-linear">
<span class="term"><strong><code>SVM::KERNEL_LINEAR</code></strong></span>
<dd>
<p class="para">A very simple kernel, can work well on large document classification problems</p>
</dd>
</dt>
<dt id="svm.constants.kernel-poly">
<span class="term"><strong><code>SVM::KERNEL_POLY</code></strong></span>
<dd>
<p class="para">A polynomial kernel</p>
</dd>
</dt>
<dt id="svm.constants.kernel-rbf">
<span class="term"><strong><code>SVM::KERNEL_RBF</code></strong></span>
<dd>
<p class="para">The common Gaussian RBD kernel. Handles non-linear problems well and is a good default for classification</p>
</dd>
</dt>
<dt id="svm.constants.kernel-sigmoid">
<span class="term"><strong><code>SVM::KERNEL_SIGMOID</code></strong></span>
<dd>
<p class="para">A kernel based on the sigmoid function. Using this makes the SVM very similar to a two layer sigmoid based neural network</p>
</dd>
</dt>
<dt id="svm.constants.kernel-precomputed">
<span class="term"><strong><code>SVM::KERNEL_PRECOMPUTED</code></strong></span>
<dd>
<p class="para">A precomputed kernel - currently unsupported.</p>
</dd>
</dt>
<dt id="svm.constants.opt-type">
<span class="term"><strong><code>SVM::OPT_TYPE</code></strong></span>
<dd>
<p class="para">The options key for the SVM type</p>
</dd>
</dt>
<dt id="svm.constants.opt-kernel-type">
<span class="term"><strong><code>SVM::OPT_KERNEL_TYPE</code></strong></span>
<dd>
<p class="para">The options key for the kernel type</p>
</dd>
</dt>
<dt id="svm.constants.opt-degree">
<span class="term"><strong><code>SVM::OPT_DEGREE</code></strong></span>
<dd>
<p class="para"/>
</dd>
</dt>
<dt id="svm.constants.opt-shrinking">
<span class="term"><strong><code>SVM::OPT_SHRINKING</code></strong></span>
<dd>
<p class="para">Training parameter, boolean, for whether to use the shrinking heuristics</p>
</dd>
</dt>
<dt id="svm.constants.opt-propability">
<span class="term"><strong><code>SVM::OPT_PROBABILITY</code></strong></span>
<dd>
<p class="para">Training parameter, boolean, for whether to collect and use probability estimates</p>
</dd>
</dt>
<dt id="svm.constants.opt-gamma">
<span class="term"><strong><code>SVM::OPT_GAMMA</code></strong></span>
<dd>
<p class="para">Algorithm parameter for Poly, RBF and Sigmoid kernel types.</p>
</dd>
</dt>
<dt id="svm.constants.opt-nu">
<span class="term"><strong><code>SVM::OPT_NU</code></strong></span>
<dd>
<p class="para">The option key for the nu parameter, only used in the NU_ SVM types</p>
</dd>
</dt>
<dt id="svm.constants.opt-eps">
<span class="term"><strong><code>SVM::OPT_EPS</code></strong></span>
<dd>
<p class="para">The option key for the Epsilon parameter, used in epsilon regression</p>
</dd>
</dt>
<dt id="svm.constants.opt-p">
<span class="term"><strong><code>SVM::OPT_P</code></strong></span>
<dd>
<p class="para">Training parameter used by Episilon SVR regression</p>
</dd>
</dt>
<dt id="svm.constants.opt-coef-zero">
<span class="term"><strong><code>SVM::OPT_COEF_ZERO</code></strong></span>
<dd>
<p class="para">Algorithm parameter for poly and sigmoid kernels</p>
</dd>
</dt>
<dt id="svm.constants.opt-c">
<span class="term"><strong><code>SVM::OPT_C</code></strong></span>
<dd>
<p class="para">The option for the cost parameter that controls tradeoff between errors and generality - effectively the penalty for misclassifying training examples. </p>
</dd>
</dt>
<dt id="svm.constants.opt-cache-size">
<span class="term"><strong><code>SVM::OPT_CACHE_SIZE</code></strong></span>
<dd>
<p class="para">Memory cache size, in MB</p>
</dd>
</dt>
</dl>
</div>
</div>
</div>
<h2>Table of Contents</h2><ul class="chunklist chunklist_reference"><li><a href="svm.construct.html">SVM::__construct</a> — Construct a new SVM object</li><li><a href="svm.crossvalidate.html">SVM::crossvalidate</a> — Test training params on subsets of the training data.</li><li><a href="svm.getoptions.html">SVM::getOptions</a> — Return the current training parameters</li><li><a href="svm.setoptions.html">SVM::setOptions</a> — Set training parameters</li><li><a href="svm.train.html">SVM::train</a> — Create a SVMModel based on training data</li></ul>
</div>
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