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<div class="subTitle">org.opencv.ml</div>
<h2 title="Class EM" class="title">Class EM</h2>
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<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li><a href="../../../org/opencv/core/Algorithm.html" title="class in org.opencv.core">org.opencv.core.Algorithm</a></li>
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<li><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">org.opencv.ml.StatModel</a></li>
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<li>org.opencv.ml.EM</li>
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<pre>public class <span class="typeNameLabel">EM</span>
extends <a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></pre>
<div class="block">The class implements the Expectation Maximization algorithm.
SEE: REF: ml_intro_em</div>
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<caption><span>Fields</span><span class="tabEnd"> </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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<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_DEFAULT">COV_MAT_DEFAULT</a></span></code> </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/EM.html#COV_MAT_DIAGONAL">COV_MAT_DIAGONAL</a></span></code> </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/EM.html#COV_MAT_GENERIC">COV_MAT_GENERIC</a></span></code> </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/EM.html#COV_MAT_SPHERICAL">COV_MAT_SPHERICAL</a></span></code> </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/EM.html#DEFAULT_MAX_ITERS">DEFAULT_MAX_ITERS</a></span></code> </td>
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<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#DEFAULT_NCLUSTERS">DEFAULT_NCLUSTERS</a></span></code> </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/EM.html#START_AUTO_STEP">START_AUTO_STEP</a></span></code> </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/EM.html#START_E_STEP">START_E_STEP</a></span></code> </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/EM.html#START_M_STEP">START_M_STEP</a></span></code> </td>
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<h3>Fields inherited from class 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>
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<caption><span id="t0" class="activeTableTab"><span>All Methods</span><span class="tabEnd"> </span></span><span id="t1" class="tableTab"><span><a href="javascript:show(1);">Static Methods</a></span><span class="tabEnd"> </span></span><span id="t2" class="tableTab"><span><a href="javascript:show(2);">Instance Methods</a></span><span class="tabEnd"> </span></span><span id="t4" class="tableTab"><span><a href="javascript:show(8);">Concrete Methods</a></span><span class="tabEnd"> </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/EM.html" title="class in org.opencv.ml">EM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#Z:Z__fromPtr__-long-">__fromPtr__</a></span>(long addr)</code> </td>
</tr>
<tr id="i1" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#create--">create</a></span>()</code>
<div class="block">Creates empty %EM model.</div>
</td>
</tr>
<tr id="i2" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getClustersNumber--">getClustersNumber</a></span>()</code>
<div class="block">SEE: setClustersNumber</div>
</td>
</tr>
<tr id="i3" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getCovarianceMatrixType--">getCovarianceMatrixType</a></span>()</code>
<div class="block">SEE: setCovarianceMatrixType</div>
</td>
</tr>
<tr id="i4" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getCovs-java.util.List-">getCovs</a></span>(java.util.List<<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>> covs)</code>
<div class="block">Returns covariation matrices
Returns vector of covariation matrices.</div>
</td>
</tr>
<tr id="i5" 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/EM.html#getMeans--">getMeans</a></span>()</code>
<div class="block">Returns the cluster centers (means of the Gaussian mixture)
Returns matrix with the number of rows equal to the number of mixtures and number of columns
equal to the space dimensionality.</div>
</td>
</tr>
<tr id="i6" class="altColor">
<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/EM.html#getTermCriteria--">getTermCriteria</a></span>()</code>
<div class="block">SEE: setTermCriteria</div>
</td>
</tr>
<tr id="i7" 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/EM.html#getWeights--">getWeights</a></span>()</code>
<div class="block">Returns weights of the mixtures
Returns vector with the number of elements equal to the number of mixtures.</div>
</td>
</tr>
<tr id="i8" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#load-java.lang.String-">load</a></span>(java.lang.String filepath)</code>
<div class="block">Loads and creates a serialized EM from a file
Use EM::save to serialize and store an EM to disk.</div>
</td>
</tr>
<tr id="i9" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#load-java.lang.String-java.lang.String-">load</a></span>(java.lang.String filepath,
java.lang.String nodeName)</code>
<div class="block">Loads and creates a serialized EM from a file
Use EM::save to serialize and store an EM to disk.</div>
</td>
</tr>
<tr id="i10" class="altColor">
<td class="colFirst"><code>float</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict-org.opencv.core.Mat-">predict</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples)</code>
<div class="block">Returns posterior probabilities for the provided samples</div>
</td>
</tr>
<tr id="i11" class="rowColor">
<td class="colFirst"><code>float</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-">predict</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> results)</code>
<div class="block">Returns posterior probabilities for the provided samples</div>
</td>
</tr>
<tr id="i12" class="altColor">
<td class="colFirst"><code>float</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-int-">predict</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> results,
int flags)</code>
<div class="block">Returns posterior probabilities for the provided samples</div>
</td>
</tr>
<tr id="i13" class="rowColor">
<td class="colFirst"><code>double[]</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict2-org.opencv.core.Mat-org.opencv.core.Mat-">predict2</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> sample,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</code>
<div class="block">Returns a likelihood logarithm value and an index of the most probable mixture component
for the given sample.</div>
</td>
</tr>
<tr id="i14" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setClustersNumber-int-">setClustersNumber</a></span>(int val)</code>
<div class="block">getClustersNumber SEE: getClustersNumber</div>
</td>
</tr>
<tr id="i15" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setCovarianceMatrixType-int-">setCovarianceMatrixType</a></span>(int val)</code>
<div class="block">getCovarianceMatrixType SEE: getCovarianceMatrixType</div>
</td>
</tr>
<tr id="i16" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setTermCriteria-org.opencv.core.TermCriteria-">setTermCriteria</a></span>(<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a> val)</code>
<div class="block">getTermCriteria SEE: getTermCriteria</div>
</td>
</tr>
<tr id="i17" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i18" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i19" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i20" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i21" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i22" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i23" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-org.opencv.core.Mat-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples)</code>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
</td>
</tr>
<tr id="i24" class="altColor">
<td class="colFirst"><code>boolean</code></td>
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<td class="colFirst"><code>boolean</code></td>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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<tr id="i27" class="rowColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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<tr id="i28" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</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/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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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/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<pre>public static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a> __fromPtr__(long addr)</pre>
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<h4>create</h4>
<pre>public static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a> create()</pre>
<div class="block">Creates empty %EM model.
The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
can use one of the EM::train\* methods or load it from file using Algorithm::load<EM>(filename).</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<h4>getClustersNumber</h4>
<pre>public int getClustersNumber()</pre>
<div class="block">SEE: setClustersNumber</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<pre>public int getCovarianceMatrixType()</pre>
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<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<pre>public void getCovs(java.util.List<<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>> covs)</pre>
<div class="block">Returns covariation matrices
Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
each matrix is a square floating-point matrix NxN, where N is the space dimensionality.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>covs</code> - automatically generated</dd>
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<h4>getMeans</h4>
<pre>public <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> getMeans()</pre>
<div class="block">Returns the cluster centers (means of the Gaussian mixture)
Returns matrix with the number of rows equal to the number of mixtures and number of columns
equal to the space dimensionality.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<h4>getTermCriteria</h4>
<pre>public <a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a> getTermCriteria()</pre>
<div class="block">SEE: setTermCriteria</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<pre>public <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> getWeights()</pre>
<div class="block">Returns weights of the mixtures
Returns vector with the number of elements equal to the number of mixtures.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>load</h4>
<pre>public static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a> load(java.lang.String filepath)</pre>
<div class="block">Loads and creates a serialized EM from a file
Use EM::save to serialize and store an EM to disk.
Load the EM from this file again, by calling this function with the path to the file.
Optionally specify the node for the file containing the classifier</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>filepath</code> - path to serialized EM</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<pre>public static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a> load(java.lang.String filepath,
java.lang.String nodeName)</pre>
<div class="block">Loads and creates a serialized EM from a file
Use EM::save to serialize and store an EM to disk.
Load the EM from this file again, by calling this function with the path to the file.
Optionally specify the node for the file containing the classifier</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>filepath</code> - path to serialized EM</dd>
<dd><code>nodeName</code> - name of node containing the classifier</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>predict</h4>
<pre>public float predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples)</pre>
<div class="block">Returns posterior probabilities for the provided samples</div>
<dl>
<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
<dd><code><a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-">predict</a></code> in class <code><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></code></dd>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - The input samples, floating-point matrix
posterior probabilities for each sample from the input</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<h4>predict</h4>
<pre>public float predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<div class="block">Returns posterior probabilities for the provided samples</div>
<dl>
<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
<dd><code><a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-">predict</a></code> in class <code><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></code></dd>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - The input samples, floating-point matrix</dd>
<dd><code>results</code> - The optional output \( nSamples \times nClusters\) matrix of results. It contains
posterior probabilities for each sample from the input</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<h4>predict</h4>
<pre>public float predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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int flags)</pre>
<div class="block">Returns posterior probabilities for the provided samples</div>
<dl>
<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
<dd><code><a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-int-">predict</a></code> in class <code><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></code></dd>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - The input samples, floating-point matrix</dd>
<dd><code>results</code> - The optional output \( nSamples \times nClusters\) matrix of results. It contains
posterior probabilities for each sample from the input</dd>
<dd><code>flags</code> - This parameter will be ignored</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<h4>predict2</h4>
<pre>public double[] predict2(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> sample,
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<div class="block">Returns a likelihood logarithm value and an index of the most probable mixture component
for the given sample.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>sample</code> - A sample for classification. It should be a one-channel matrix of
\(1 \times dims\) or \(dims \times 1\) size.</dd>
<dd><code>probs</code> - Optional output matrix that contains posterior probabilities of each component
given the sample. It has \(1 \times nclusters\) size and CV_64FC1 type.
The method returns a two-element double vector. Zero element is a likelihood logarithm value for
the sample. First element is an index of the most probable mixture component for the given
sample.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
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<pre>public void setClustersNumber(int val)</pre>
<div class="block">getClustersNumber SEE: getClustersNumber</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
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<pre>public void setCovarianceMatrixType(int val)</pre>
<div class="block">getCovarianceMatrixType SEE: getCovarianceMatrixType</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
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<pre>public void setTermCriteria(<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a> val)</pre>
<div class="block">getTermCriteria SEE: getTermCriteria</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
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<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \(a_k\) of
mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
\(S_k\) of mixture components.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.
covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.
floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>trainE</h4>
<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \(a_k\) of
mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
\(S_k\) of mixture components.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.</dd>
<dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.
floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>trainE</h4>
<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \(a_k\) of
mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
\(S_k\) of mixture components.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.</dd>
<dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.</dd>
<dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>trainE</h4>
<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \(a_k\) of
mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
\(S_k\) of mixture components.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.</dd>
<dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.</dd>
<dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainE</h4>
<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \(a_k\) of
mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
\(S_k\) of mixture components.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.</dd>
<dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.</dd>
<dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
<dd><code>labels</code> - The optional output "class label" for each sample:
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainE</h4>
<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \(a_k\) of
mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
\(S_k\) of mixture components.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.</dd>
<dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.</dd>
<dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
<dd><code>labels</code> - The optional output "class label" for each sample:
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.</dd>
<dd><code>probs</code> - The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>trainEM</h4>
<pre>public boolean trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be
estimated by the k-means algorithm.
Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
responses (class labels or function values) as input. Instead, it computes the *Maximum
Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The
trained model is similar to the NormalBayesClassifier.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainEM-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainEM</h4>
<pre>public boolean trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be
estimated by the k-means algorithm.
Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
responses (class labels or function values) as input. Instead, it computes the *Maximum
Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The
trained model is similar to the NormalBayesClassifier.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainEM</h4>
<pre>public boolean trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be
estimated by the k-means algorithm.
Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
responses (class labels or function values) as input. Instead, it computes the *Maximum
Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The
trained model is similar to the NormalBayesClassifier.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
<dd><code>labels</code> - The optional output "class label" for each sample:
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>trainEM</h4>
<pre>public boolean trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be
estimated by the k-means algorithm.
Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
responses (class labels or function values) as input. Instead, it computes the *Maximum
Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The
trained model is similar to the NormalBayesClassifier.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
<dd><code>labels</code> - The optional output "class label" for each sample:
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.</dd>
<dd><code>probs</code> - The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainM</h4>
<pre>public boolean trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Maximization step. You need to provide initial probabilities
\(p_{i,k}\) to use this option.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>probs0</code> - the probabilities
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainM</h4>
<pre>public boolean trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Maximization step. You need to provide initial probabilities
\(p_{i,k}\) to use this option.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>probs0</code> - the probabilities</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<ul class="blockList">
<li class="blockList">
<h4>trainM</h4>
<pre>public boolean trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Maximization step. You need to provide initial probabilities
\(p_{i,k}\) to use this option.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>probs0</code> - the probabilities</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
<dd><code>labels</code> - The optional output "class label" for each sample:
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<ul class="blockListLast">
<li class="blockList">
<h4>trainM</h4>
<pre>public boolean trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</pre>
<div class="block">Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Maximization step. You need to provide initial probabilities
\(p_{i,k}\) to use this option.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.</dd>
<dd><code>probs0</code> - the probabilities</dd>
<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
<dd><code>labels</code> - The optional output "class label" for each sample:
\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.</dd>
<dd><code>probs</code> - The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \(nsamples \times nclusters\) size and
CV_64FC1 type.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
</li>
</ul>
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<p class="legalCopy"><small>Generated on 2024-06-02 16:58:23 / OpenCV 4.10.0</small></p>
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