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<h1 class="refname">SVMModel::predict</h1>
<p class="verinfo">(PECL svm >= 0.1.0)</p><p class="refpurpose"><span class="refname">SVMModel::predict</span> — <span class="dc-title">Predict a value for previously unseen data</span></p>
</div>
<div class="refsect1 description" id="refsect1-svmmodel.predict-description">
<h3 class="title">Description</h3>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="type">float</span> <span class="methodname"><strong>SVMModel::predict</strong></span>
( <span class="methodparam"><span class="type">array</span> <code class="parameter">$data</code></span>
)</div>
<p class="para rdfs-comment">
This function accepts an array of data and attempts to predict the class
or regression value based on the model extracted from previously trained data.
</p>
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<div class="refsect1 parameters" id="refsect1-svmmodel.predict-parameters">
<h3 class="title">Parameters</h3>
<p class="para">
<dl>
<dt>
<span class="term"><em><code class="parameter">data</code></em></span>
<dd>
<p class="para">
The array to be classified. This should be a series of key => value
pairs in increasing key order, but not necessarily continuous.
</p>
</dd>
</dt>
</dl>
</p>
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<div class="refsect1 returnvalues" id="refsect1-svmmodel.predict-returnvalues">
<h3 class="title">Return Values</h3>
<p class="para">
Float the predicted value. This will be a class label in the case of
classification, a real value in the case of regression.
Throws SVMException on error
</p>
</div>
<div class="refsect1 seealso" id="refsect1-svmmodel.predict-seealso">
<h3 class="title">See Also</h3>
<p class="para">
<ul class="simplelist">
<li class="member"> <span class="methodname"><a href="svm.train.html" class="methodname" rel="rdfs-seeAlso">SVM::train()</a> - Create a SVMModel based on training data</span></li>
</ul>
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