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ACS v3 1차 최종 버전.
2025-05-20 14:21:24 +09:00
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<h2 title="Class SVM" class="title">Class SVM</h2>
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<li>java.lang.Object</li>
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<li>org.opencv.ml.SVM</li>
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<pre>public class <span class="typeNameLabel">SVM</span>
extends <a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></pre>
<div class="block">Support Vector Machines.
SEE: REF: ml_intro_svm</div>
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<caption><span>Fields</span><span class="tabEnd">&nbsp;</span></caption>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Field and Description</th>
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<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#C">C</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#C_SVC">C_SVC</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#CHI2">CHI2</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#COEF">COEF</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#CUSTOM">CUSTOM</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#DEGREE">DEGREE</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#EPS_SVR">EPS_SVR</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#GAMMA">GAMMA</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#INTER">INTER</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#LINEAR">LINEAR</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#NU">NU</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#NU_SVC">NU_SVC</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#NU_SVR">NU_SVR</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#ONE_CLASS">ONE_CLASS</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#P">P</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#POLY">POLY</a></span></code>&nbsp;</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#RBF">RBF</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#SIGMOID">SIGMOID</a></span></code>&nbsp;</td>
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<h3>Fields inherited from class&nbsp;org.opencv.ml.<a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></h3>
<code><a href="../../../org/opencv/ml/StatModel.html#COMPRESSED_INPUT">COMPRESSED_INPUT</a>, <a href="../../../org/opencv/ml/StatModel.html#PREPROCESSED_INPUT">PREPROCESSED_INPUT</a>, <a href="../../../org/opencv/ml/StatModel.html#RAW_OUTPUT">RAW_OUTPUT</a>, <a href="../../../org/opencv/ml/StatModel.html#UPDATE_MODEL">UPDATE_MODEL</a></code></li>
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<h3>Method Summary</h3>
<table class="memberSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
<caption><span id="t0" class="activeTableTab"><span>All Methods</span><span class="tabEnd">&nbsp;</span></span><span id="t1" class="tableTab"><span><a href="javascript:show(1);">Static Methods</a></span><span class="tabEnd">&nbsp;</span></span><span id="t2" class="tableTab"><span><a href="javascript:show(2);">Instance Methods</a></span><span class="tabEnd">&nbsp;</span></span><span id="t4" class="tableTab"><span><a href="javascript:show(8);">Concrete Methods</a></span><span class="tabEnd">&nbsp;</span></span></caption>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Method and Description</th>
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<tr id="i0" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/SVM.html" title="class in org.opencv.ml">SVM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#Z:Z__fromPtr__-long-">__fromPtr__</a></span>(long&nbsp;addr)</code>&nbsp;</td>
</tr>
<tr id="i1" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/SVM.html" title="class in org.opencv.ml">SVM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#create--">create</a></span>()</code>
<div class="block">Creates empty model.</div>
</td>
</tr>
<tr id="i2" class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getC--">getC</a></span>()</code>
<div class="block">SEE: setC</div>
</td>
</tr>
<tr id="i3" class="rowColor">
<td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getClassWeights--">getClassWeights</a></span>()</code>
<div class="block">SEE: setClassWeights</div>
</td>
</tr>
<tr id="i4" class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getCoef0--">getCoef0</a></span>()</code>
<div class="block">SEE: setCoef0</div>
</td>
</tr>
<tr id="i5" class="rowColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getDecisionFunction-int-org.opencv.core.Mat-org.opencv.core.Mat-">getDecisionFunction</a></span>(int&nbsp;i,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;alpha,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;svidx)</code>
<div class="block">Retrieves the decision function</div>
</td>
</tr>
<tr id="i6" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getDefaultGridPtr-int-">getDefaultGridPtr</a></span>(int&nbsp;param_id)</code>
<div class="block">Generates a grid for %SVM parameters.</div>
</td>
</tr>
<tr id="i7" class="rowColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getDegree--">getDegree</a></span>()</code>
<div class="block">SEE: setDegree</div>
</td>
</tr>
<tr id="i8" class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getGamma--">getGamma</a></span>()</code>
<div class="block">SEE: setGamma</div>
</td>
</tr>
<tr id="i9" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getKernelType--">getKernelType</a></span>()</code>
<div class="block">Type of a %SVM kernel.</div>
</td>
</tr>
<tr id="i10" class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getNu--">getNu</a></span>()</code>
<div class="block">SEE: setNu</div>
</td>
</tr>
<tr id="i11" class="rowColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getP--">getP</a></span>()</code>
<div class="block">SEE: setP</div>
</td>
</tr>
<tr id="i12" class="altColor">
<td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getSupportVectors--">getSupportVectors</a></span>()</code>
<div class="block">Retrieves all the support vectors
The method returns all the support vectors as a floating-point matrix, where support vectors are
stored as matrix rows.</div>
</td>
</tr>
<tr id="i13" class="rowColor">
<td class="colFirst"><code><a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getTermCriteria--">getTermCriteria</a></span>()</code>
<div class="block">SEE: setTermCriteria</div>
</td>
</tr>
<tr id="i14" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getType--">getType</a></span>()</code>
<div class="block">SEE: setType</div>
</td>
</tr>
<tr id="i15" class="rowColor">
<td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#getUncompressedSupportVectors--">getUncompressedSupportVectors</a></span>()</code>
<div class="block">Retrieves all the uncompressed support vectors of a linear %SVM
The method returns all the uncompressed support vectors of a linear %SVM that the compressed
support vector, used for prediction, was derived from.</div>
</td>
</tr>
<tr id="i16" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/SVM.html" title="class in org.opencv.ml">SVM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#load-java.lang.String-">load</a></span>(java.lang.String&nbsp;filepath)</code>
<div class="block">Loads and creates a serialized svm from a file
Use SVM::save to serialize and store an SVM to disk.</div>
</td>
</tr>
<tr id="i17" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setC-double-">setC</a></span>(double&nbsp;val)</code>
<div class="block">getC SEE: getC</div>
</td>
</tr>
<tr id="i18" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setClassWeights-org.opencv.core.Mat-">setClassWeights</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;val)</code>
<div class="block">getClassWeights SEE: getClassWeights</div>
</td>
</tr>
<tr id="i19" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setCoef0-double-">setCoef0</a></span>(double&nbsp;val)</code>
<div class="block">getCoef0 SEE: getCoef0</div>
</td>
</tr>
<tr id="i20" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setDegree-double-">setDegree</a></span>(double&nbsp;val)</code>
<div class="block">getDegree SEE: getDegree</div>
</td>
</tr>
<tr id="i21" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setGamma-double-">setGamma</a></span>(double&nbsp;val)</code>
<div class="block">getGamma SEE: getGamma</div>
</td>
</tr>
<tr id="i22" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setKernel-int-">setKernel</a></span>(int&nbsp;kernelType)</code>
<div class="block">Initialize with one of predefined kernels.</div>
</td>
</tr>
<tr id="i23" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setNu-double-">setNu</a></span>(double&nbsp;val)</code>
<div class="block">getNu SEE: getNu</div>
</td>
</tr>
<tr id="i24" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setP-double-">setP</a></span>(double&nbsp;val)</code>
<div class="block">getP SEE: getP</div>
</td>
</tr>
<tr id="i25" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setTermCriteria-org.opencv.core.TermCriteria-">setTermCriteria</a></span>(<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a>&nbsp;val)</code>
<div class="block">getTermCriteria SEE: getTermCriteria</div>
</td>
</tr>
<tr id="i26" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#setType-int-">setType</a></span>(int&nbsp;val)</code>
<div class="block">getType SEE: getType</div>
</td>
</tr>
<tr id="i27" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-">trainAuto</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses)</code>
<div class="block">Trains an %SVM with optimal parameters</div>
</td>
</tr>
<tr id="i28" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-">trainAuto</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
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int&nbsp;kFold)</code>
<div class="block">Trains an %SVM with optimal parameters</div>
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<tr id="i29" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-">trainAuto</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
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<div class="block">Trains an %SVM with optimal parameters</div>
</td>
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<tr id="i30" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">trainAuto</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
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<div class="block">Trains an %SVM with optimal parameters</div>
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<tr id="i31" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">trainAuto</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
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<div class="block">Trains an %SVM with optimal parameters</div>
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<tr id="i32" class="altColor">
<td class="colFirst"><code>boolean</code></td>
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<div class="block">Trains an %SVM with optimal parameters</div>
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<tr id="i33" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/SVM.html#trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">trainAuto</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
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<div class="block">Trains an %SVM with optimal parameters</div>
</td>
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<tr id="i34" class="altColor">
<td class="colFirst"><code>boolean</code></td>
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<div class="block">Trains an %SVM with optimal parameters</div>
</td>
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<tr id="i35" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
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<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
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<div class="block">Trains an %SVM with optimal parameters</div>
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<pre>public static&nbsp;<a href="../../../org/opencv/ml/SVM.html" title="class in org.opencv.ml">SVM</a>&nbsp;create()</pre>
<div class="block">Creates empty model.
Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
find the best parameters for your problem, it can be done with SVM::trainAuto.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public&nbsp;double&nbsp;getC()</pre>
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<div class="block">SEE: setClassWeights</div>
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<pre>public&nbsp;double&nbsp;getDecisionFunction(int&nbsp;i,
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<div class="block">Retrieves the decision function</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>i</code> - the index of the decision function. If the problem solved is regression, 1-class or
2-class classification, then there will be just one decision function and the index should
always be 0. Otherwise, in the case of N-class classification, there will be \(N(N-1)/2\)
decision functions.</dd>
<dd><code>alpha</code> - the optional output vector for weights, corresponding to different support vectors.
In the case of linear %SVM all the alpha's will be 1's.</dd>
<dd><code>svidx</code> - the optional output vector of indices of support vectors within the matrix of
support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
%SVM each decision function consists of a single "compressed" support vector.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted
sum of kernel responses.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
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<div class="block">Generates a grid for %SVM parameters.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>param_id</code> - %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
generated for the parameter with this ID.
The function generates a grid pointer for the specified parameter of the %SVM algorithm.
The grid may be passed to the function SVM::trainAuto.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<h4>getDegree</h4>
<pre>public&nbsp;double&nbsp;getDegree()</pre>
<div class="block">SEE: setDegree</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getGamma</h4>
<pre>public&nbsp;double&nbsp;getGamma()</pre>
<div class="block">SEE: setGamma</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>getKernelType</h4>
<pre>public&nbsp;int&nbsp;getKernelType()</pre>
<div class="block">Type of a %SVM kernel.
See SVM::KernelTypes. Default value is SVM::RBF.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getNu</h4>
<pre>public&nbsp;double&nbsp;getNu()</pre>
<div class="block">SEE: setNu</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getP</h4>
<pre>public&nbsp;double&nbsp;getP()</pre>
<div class="block">SEE: setP</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getSupportVectors</h4>
<pre>public&nbsp;<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;getSupportVectors()</pre>
<div class="block">Retrieves all the support vectors
The method returns all the support vectors as a floating-point matrix, where support vectors are
stored as matrix rows.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<h4>getTermCriteria</h4>
<pre>public&nbsp;<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a>&nbsp;getTermCriteria()</pre>
<div class="block">SEE: setTermCriteria</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getType</h4>
<pre>public&nbsp;int&nbsp;getType()</pre>
<div class="block">SEE: setType</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getUncompressedSupportVectors</h4>
<pre>public&nbsp;<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;getUncompressedSupportVectors()</pre>
<div class="block">Retrieves all the uncompressed support vectors of a linear %SVM
The method returns all the uncompressed support vectors of a linear %SVM that the compressed
support vector, used for prediction, was derived from. They are returned in a floating-point
matrix, where the support vectors are stored as matrix rows.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>load</h4>
<pre>public static&nbsp;<a href="../../../org/opencv/ml/SVM.html" title="class in org.opencv.ml">SVM</a>&nbsp;load(java.lang.String&nbsp;filepath)</pre>
<div class="block">Loads and creates a serialized svm from a file
Use SVM::save to serialize and store an SVM to disk.
Load the SVM from this file again, by calling this function with the path to the file.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>filepath</code> - path to serialized svm</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<h4>setC</h4>
<pre>public&nbsp;void&nbsp;setC(double&nbsp;val)</pre>
<div class="block">getC SEE: getC</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
</ul>
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<pre>public&nbsp;void&nbsp;setClassWeights(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;val)</pre>
<div class="block">getClassWeights SEE: getClassWeights</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
</ul>
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<h4>setCoef0</h4>
<pre>public&nbsp;void&nbsp;setCoef0(double&nbsp;val)</pre>
<div class="block">getCoef0 SEE: getCoef0</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
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<h4>setDegree</h4>
<pre>public&nbsp;void&nbsp;setDegree(double&nbsp;val)</pre>
<div class="block">getDegree SEE: getDegree</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
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<pre>public&nbsp;void&nbsp;setGamma(double&nbsp;val)</pre>
<div class="block">getGamma SEE: getGamma</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
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<pre>public&nbsp;void&nbsp;setKernel(int&nbsp;kernelType)</pre>
<div class="block">Initialize with one of predefined kernels.
See SVM::KernelTypes.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>kernelType</code> - automatically generated</dd>
</dl>
</li>
</ul>
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<pre>public&nbsp;void&nbsp;setNu(double&nbsp;val)</pre>
<div class="block">getNu SEE: getNu</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
</ul>
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<pre>public&nbsp;void&nbsp;setP(double&nbsp;val)</pre>
<div class="block">getP SEE: getP</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
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<pre>public&nbsp;void&nbsp;setTermCriteria(<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a>&nbsp;val)</pre>
<div class="block">getTermCriteria SEE: getTermCriteria</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
</ul>
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<pre>public&nbsp;void&nbsp;setType(int&nbsp;val)</pre>
<div class="block">getType SEE: getType</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
</li>
</ul>
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<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-">
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;gammaGrid)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C</dd>
<dd><code>gammaGrid</code> - grid for gamma
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;gammaGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;pGrid)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C</dd>
<dd><code>gammaGrid</code> - grid for gamma</dd>
<dd><code>pGrid</code> - grid for p
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;gammaGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;pGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;nuGrid)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C</dd>
<dd><code>gammaGrid</code> - grid for gamma</dd>
<dd><code>pGrid</code> - grid for p</dd>
<dd><code>nuGrid</code> - grid for nu
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;gammaGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;pGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;nuGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;coeffGrid)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C</dd>
<dd><code>gammaGrid</code> - grid for gamma</dd>
<dd><code>pGrid</code> - grid for p</dd>
<dd><code>nuGrid</code> - grid for nu</dd>
<dd><code>coeffGrid</code> - grid for coeff
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-">
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<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;gammaGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;pGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;nuGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;coeffGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;degreeGrid)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C</dd>
<dd><code>gammaGrid</code> - grid for gamma</dd>
<dd><code>pGrid</code> - grid for p</dd>
<dd><code>nuGrid</code> - grid for nu</dd>
<dd><code>coeffGrid</code> - grid for coeff</dd>
<dd><code>degreeGrid</code> - grid for degree
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainAuto-org.opencv.core.Mat-int-org.opencv.core.Mat-int-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-org.opencv.ml.ParamGrid-boolean-">
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<ul class="blockListLast">
<li class="blockList">
<h4>trainAuto</h4>
<pre>public&nbsp;boolean&nbsp;trainAuto(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;layout,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;responses,
int&nbsp;kFold,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;Cgrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;gammaGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;pGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;nuGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;coeffGrid,
<a href="../../../org/opencv/ml/ParamGrid.html" title="class in org.opencv.ml">ParamGrid</a>&nbsp;degreeGrid,
boolean&nbsp;balanced)</pre>
<div class="block">Trains an %SVM with optimal parameters</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - training samples</dd>
<dd><code>layout</code> - See ml::SampleTypes.</dd>
<dd><code>responses</code> - vector of responses associated with the training samples.</dd>
<dd><code>kFold</code> - Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is</dd>
<dd><code>Cgrid</code> - grid for C</dd>
<dd><code>gammaGrid</code> - grid for gamma</dd>
<dd><code>pGrid</code> - grid for p</dd>
<dd><code>nuGrid</code> - grid for nu</dd>
<dd><code>coeffGrid</code> - grid for coeff</dd>
<dd><code>degreeGrid</code> - grid for degree</dd>
<dd><code>balanced</code> - If true and the problem is 2-class classification then the method creates more
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
</li>
</ul>
</li>
</ul>
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