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<div class="subTitle">org.opencv.ml</div>
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<h2 title="Class EM" class="title">Class EM</h2>
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<ul class="inheritance">
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<li>java.lang.Object</li>
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<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>
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extends <a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></pre>
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<div class="block">The class implements the Expectation Maximization algorithm.
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SEE: REF: ml_intro_em</div>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<th class="colLast" scope="col">Field and Description</th>
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_DEFAULT">COV_MAT_DEFAULT</a></span></code> </td>
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</tr>
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<tr class="rowColor">
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_DIAGONAL">COV_MAT_DIAGONAL</a></span></code> </td>
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</tr>
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<tr class="altColor">
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_GENERIC">COV_MAT_GENERIC</a></span></code> </td>
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</tr>
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<tr class="rowColor">
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_SPHERICAL">COV_MAT_SPHERICAL</a></span></code> </td>
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</tr>
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<tr class="altColor">
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<td class="colFirst"><code>static int</code></td>
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<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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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#DEFAULT_NCLUSTERS">DEFAULT_NCLUSTERS</a></span></code> </td>
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</tr>
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<tr class="altColor">
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#START_AUTO_STEP">START_AUTO_STEP</a></span></code> </td>
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</tr>
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<tr class="rowColor">
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#START_E_STEP">START_E_STEP</a></span></code> </td>
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</tr>
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<tr class="altColor">
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<td class="colFirst"><code>static int</code></td>
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<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>
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<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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<table class="memberSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
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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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<tr id="i0" class="altColor">
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<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
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<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>
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</tr>
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<tr id="i1" class="rowColor">
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<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#create--">create</a></span>()</code>
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<div class="block">Creates empty %EM model.</div>
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</td>
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</tr>
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<tr id="i2" class="altColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getClustersNumber--">getClustersNumber</a></span>()</code>
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<div class="block">SEE: setClustersNumber</div>
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</td>
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</tr>
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<tr id="i3" class="rowColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getCovarianceMatrixType--">getCovarianceMatrixType</a></span>()</code>
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<div class="block">SEE: setCovarianceMatrixType</div>
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</td>
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</tr>
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<tr id="i4" class="altColor">
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<td class="colFirst"><code>void</code></td>
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<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>
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<div class="block">Returns covariation matrices
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Returns vector of covariation matrices.</div>
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</td>
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</tr>
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<tr id="i5" class="rowColor">
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<td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getMeans--">getMeans</a></span>()</code>
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<div class="block">Returns the cluster centers (means of the Gaussian mixture)
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Returns matrix with the number of rows equal to the number of mixtures and number of columns
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equal to the space dimensionality.</div>
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</td>
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</tr>
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<tr id="i6" class="altColor">
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<td class="colFirst"><code><a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a></code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getTermCriteria--">getTermCriteria</a></span>()</code>
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<div class="block">SEE: setTermCriteria</div>
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</td>
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</tr>
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<tr id="i7" class="rowColor">
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<td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getWeights--">getWeights</a></span>()</code>
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<div class="block">Returns weights of the mixtures
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Returns vector with the number of elements equal to the number of mixtures.</div>
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</td>
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</tr>
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<tr id="i8" class="altColor">
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<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
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<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>
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<div class="block">Loads and creates a serialized EM from a file
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Use EM::save to serialize and store an EM to disk.</div>
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</td>
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</tr>
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<tr id="i9" class="rowColor">
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<td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
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<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,
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java.lang.String nodeName)</code>
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<div class="block">Loads and creates a serialized EM from a file
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Use EM::save to serialize and store an EM to disk.</div>
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</td>
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</tr>
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<tr id="i10" class="altColor">
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<td class="colFirst"><code>float</code></td>
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<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>
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<div class="block">Returns posterior probabilities for the provided samples</div>
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</td>
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</tr>
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<tr id="i11" class="rowColor">
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<td class="colFirst"><code>float</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> results)</code>
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<div class="block">Returns posterior probabilities for the provided samples</div>
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</td>
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</tr>
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<tr id="i12" class="altColor">
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<td class="colFirst"><code>float</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> results,
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int flags)</code>
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<div class="block">Returns posterior probabilities for the provided samples</div>
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</td>
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</tr>
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<tr id="i13" class="rowColor">
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<td class="colFirst"><code>double[]</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</code>
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<div class="block">Returns a likelihood logarithm value and an index of the most probable mixture component
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for the given sample.</div>
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</td>
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</tr>
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<tr id="i14" class="altColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setClustersNumber-int-">setClustersNumber</a></span>(int val)</code>
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<div class="block">getClustersNumber SEE: getClustersNumber</div>
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</td>
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</tr>
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<tr id="i15" class="rowColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setCovarianceMatrixType-int-">setCovarianceMatrixType</a></span>(int val)</code>
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<div class="block">getCovarianceMatrixType SEE: getCovarianceMatrixType</div>
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</td>
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</tr>
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<tr id="i16" class="altColor">
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<td class="colFirst"><code>void</code></td>
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<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>
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<div class="block">getTermCriteria SEE: getTermCriteria</div>
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</td>
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</tr>
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<tr id="i17" class="rowColor">
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<td class="colFirst"><code>boolean</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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</td>
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</tr>
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<tr id="i18" class="altColor">
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<td class="colFirst"><code>boolean</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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</td>
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</tr>
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<tr id="i19" class="rowColor">
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<td class="colFirst"><code>boolean</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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</td>
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</tr>
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<tr id="i20" class="altColor">
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<td class="colFirst"><code>boolean</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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</td>
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</tr>
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<tr id="i21" class="rowColor">
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<td class="colFirst"><code>boolean</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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</td>
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<tr id="i22" class="altColor">
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<td class="colFirst"><code>boolean</code></td>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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<tr id="i23" class="rowColor">
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<td class="colFirst"><code>boolean</code></td>
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<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>
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-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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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</code>
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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>
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<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-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
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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>
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<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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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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<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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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0)</code>
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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>
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<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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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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</td>
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<tr id="i29" class="rowColor">
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<td class="colFirst"><code>boolean</code></td>
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<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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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</code>
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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">
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<td class="colFirst"><code>boolean</code></td>
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<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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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</code>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
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<code><a href="../../../org/opencv/ml/StatModel.html#calcError-org.opencv.ml.TrainData-boolean-org.opencv.core.Mat-">calcError</a>, <a href="../../../org/opencv/ml/StatModel.html#empty--">empty</a>, <a href="../../../org/opencv/ml/StatModel.html#getVarCount--">getVarCount</a>, <a href="../../../org/opencv/ml/StatModel.html#isClassifier--">isClassifier</a>, <a href="../../../org/opencv/ml/StatModel.html#isTrained--">isTrained</a>, <a href="../../../org/opencv/ml/StatModel.html#train-org.opencv.core.Mat-int-org.opencv.core.Mat-">train</a>, <a href="../../../org/opencv/ml/StatModel.html#train-org.opencv.ml.TrainData-">train</a>, <a href="../../../org/opencv/ml/StatModel.html#train-org.opencv.ml.TrainData-int-">train</a></code></li>
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<code><a href="../../../org/opencv/core/Algorithm.html#clear--">clear</a>, <a href="../../../org/opencv/core/Algorithm.html#getDefaultName--">getDefaultName</a>, <a href="../../../org/opencv/core/Algorithm.html#getNativeObjAddr--">getNativeObjAddr</a>, <a href="../../../org/opencv/core/Algorithm.html#save-java.lang.String-">save</a></code></li>
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<pre>public static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a> create()</pre>
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<div class="block">Creates empty %EM model.
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can use one of the EM::train\* methods or load it from file using Algorithm::load<EM>(filename).</div>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public int getClustersNumber()</pre>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public int getCovarianceMatrixType()</pre>
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<div class="block">SEE: setCovarianceMatrixType</div>
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<dt><span class="returnLabel">Returns:</span></dt>
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<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>
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<div class="block">Returns covariation matrices
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Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
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each matrix is a square floating-point matrix NxN, where N is the space dimensionality.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>covs</code> - automatically generated</dd>
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<pre>public <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> getMeans()</pre>
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<div class="block">Returns the cluster centers (means of the Gaussian mixture)
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Returns matrix with the number of rows equal to the number of mixtures and number of columns
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equal to the space dimensionality.</div>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public <a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a> getTermCriteria()</pre>
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<div class="block">SEE: setTermCriteria</div>
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<dl>
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<dt><span class="returnLabel">Returns:</span></dt>
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<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>
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<div class="block">Returns weights of the mixtures
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Returns vector with the number of elements equal to the number of mixtures.</div>
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<dt><span class="returnLabel">Returns:</span></dt>
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<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)</pre>
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<div class="block">Loads and creates a serialized EM from a file
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Use EM::save to serialize and store an EM to disk.
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Load the EM from this file again, by calling this function with the path to the file.
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Optionally specify the node for the file containing the classifier</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>filepath</code> - path to serialized EM</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<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,
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java.lang.String nodeName)</pre>
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<div class="block">Loads and creates a serialized EM from a file
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Use EM::save to serialize and store an EM to disk.
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Load the EM from this file again, by calling this function with the path to the file.
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Optionally specify the node for the file containing the classifier</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>filepath</code> - path to serialized EM</dd>
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<dd><code>nodeName</code> - name of node containing the classifier</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public float predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples)</pre>
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<div class="block">Returns posterior probabilities for the provided samples</div>
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<dl>
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<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
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<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>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>samples</code> - The input samples, floating-point matrix
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posterior probabilities for each sample from the input</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public float predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> results)</pre>
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<div class="block">Returns posterior probabilities for the provided samples</div>
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<dl>
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<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
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<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>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>samples</code> - The input samples, floating-point matrix</dd>
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<dd><code>results</code> - The optional output \( nSamples \times nClusters\) matrix of results. It contains
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posterior probabilities for each sample from the input</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<pre>public float predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> results,
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int flags)</pre>
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<div class="block">Returns posterior probabilities for the provided samples</div>
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<dl>
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<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
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<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>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>samples</code> - The input samples, floating-point matrix</dd>
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<dd><code>results</code> - The optional output \( nSamples \times nClusters\) matrix of results. It contains
|
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posterior probabilities for each sample from the input</dd>
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<dd><code>flags</code> - This parameter will be ignored</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public double[] predict2(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> sample,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs)</pre>
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<div class="block">Returns a likelihood logarithm value and an index of the most probable mixture component
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for the given sample.</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>sample</code> - A sample for classification. It should be a one-channel matrix of
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\(1 \times dims\) or \(dims \times 1\) size.</dd>
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<dd><code>probs</code> - Optional output matrix that contains posterior probabilities of each component
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given the sample. It has \(1 \times nclusters\) size and CV_64FC1 type.
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The method returns a two-element double vector. Zero element is a likelihood logarithm value for
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the sample. First element is an index of the most probable mixture component for the given
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sample.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<pre>public void setClustersNumber(int val)</pre>
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<div class="block">getClustersNumber SEE: getClustersNumber</div>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>val</code> - automatically generated</dd>
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<pre>public void setCovarianceMatrixType(int val)</pre>
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<div class="block">getCovarianceMatrixType SEE: getCovarianceMatrixType</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<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>
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<div class="block">getTermCriteria SEE: getTermCriteria</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<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,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0)</pre>
|
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.
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This variation starts with Expectation step. You need to provide initial means \(a_k\) of
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mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
|
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\(S_k\) of mixture components.</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
|
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one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
|
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it will be converted to the inner matrix of such type for the further computing.</dd>
|
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<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
|
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\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
|
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converted to the inner matrix of such type for the further computing.
|
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covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
|
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do not have CV_64F type they will be converted to the inner matrices of such type for the
|
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further computing.
|
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floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
|
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each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
|
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\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
|
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mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
|
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mixture component given the each sample. It has \(nsamples \times nclusters\) size and
|
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CV_64FC1 type.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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</dl>
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<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0)</pre>
|
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.
|
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|
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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
|
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\(S_k\) of mixture components.</div>
|
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<dl>
|
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<dt><span class="paramLabel">Parameters:</span></dt>
|
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<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
|
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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>
|
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<dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
|
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\(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
|
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converted to the inner matrix of such type for the further computing.</dd>
|
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<dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
|
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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>
|
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<dt><span class="returnLabel">Returns:</span></dt>
|
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<dd>automatically generated</dd>
|
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</dl>
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</li>
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</ul>
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<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0)</pre>
|
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<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>
|
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<dt><span class="returnLabel">Returns:</span></dt>
|
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<dd>automatically generated</dd>
|
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</dl>
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</li>
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</ul>
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<pre>public boolean trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> means0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</pre>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.
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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>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<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>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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</dl>
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</li>
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</ul>
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<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>
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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,
|
|
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> weights0,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
|
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<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>
|
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<dt><span class="returnLabel">Returns:</span></dt>
|
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<dd>automatically generated</dd>
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</dl>
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</li>
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</ul>
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<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>
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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,
|
|
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> covs0,
|
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<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,
|
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<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>
|
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</dl>
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</li>
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</ul>
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<h4>trainEM</h4>
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<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>
|
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<dd>automatically generated</dd>
|
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</dl>
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</li>
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</ul>
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<h4>trainEM</h4>
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<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>
|
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</li>
|
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</ul>
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<h4>trainEM</h4>
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<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>
|
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</dl>
|
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</li>
|
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</ul>
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<a name="trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainEM</h4>
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<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>
|
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</li>
|
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</ul>
|
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<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-">
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<h4>trainM</h4>
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<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>
|
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<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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</a>
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<h4>trainM</h4>
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<pre>public boolean trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods)</pre>
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<div class="block">Estimate the Gaussian mixture parameters from a samples set.
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This variation starts with Maximization step. You need to provide initial probabilities
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\(p_{i,k}\) to use this option.</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
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one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
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it will be converted to the inner matrix of such type for the further computing.</dd>
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<dd><code>probs0</code> - the probabilities</dd>
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<dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
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each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
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\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
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mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
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mixture component given the each sample. It has \(nsamples \times nclusters\) size and
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CV_64FC1 type.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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</dl>
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</li>
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</ul>
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<a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
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</a>
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<h4>trainM</h4>
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<pre>public boolean trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> samples,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> probs0,
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> logLikelihoods,
|
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<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a> labels)</pre>
|
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<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>
|
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<dd><code>probs0</code> - the probabilities</dd>
|
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<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>
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<dd><code>labels</code> - The optional output "class label" for each sample:
|
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\(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (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
|
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CV_64FC1 type.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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</dl>
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</li>
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</ul>
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<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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</a>
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<ul class="blockListLast">
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<li class="blockList">
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<h4>trainM</h4>
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<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,
|
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<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>
|
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<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
|
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CV_64FC1 type.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
|
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<dd>automatically generated</dd>
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</dl>
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</li>
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</ul>
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</li>
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</ul>
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<p class="legalCopy"><small>Generated on 2021-12-25 08:13:27 / OpenCV 4.5.5</small></p>
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