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<div class="subTitle">org.opencv.ml</div>
<h2 title="Class KNearest" class="title">Class KNearest</h2>
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<div class="contentContainer">
<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>
<li>
<ul class="inheritance">
<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.KNearest</li>
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<pre>public class <span class="typeNameLabel">KNearest</span>
extends <a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></pre>
<div class="block">The class implements K-Nearest Neighbors model
SEE: REF: ml_intro_knn</div>
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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/KNearest.html#BRUTE_FORCE">BRUTE_FORCE</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#KDTREE">KDTREE</a></span></code>&nbsp;</td>
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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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<td class="colFirst"><code>static <a href="../../../org/opencv/ml/KNearest.html" title="class in org.opencv.ml">KNearest</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#Z:Z__fromPtr__-long-">__fromPtr__</a></span>(long&nbsp;addr)</code>&nbsp;</td>
</tr>
<tr id="i1" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/KNearest.html" title="class in org.opencv.ml">KNearest</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#create--">create</a></span>()</code>
<div class="block">Creates the empty model
The static method creates empty %KNearest classifier.</div>
</td>
</tr>
<tr id="i2" class="altColor">
<td class="colFirst"><code>float</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#findNearest-org.opencv.core.Mat-int-org.opencv.core.Mat-">findNearest</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;k,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results)</code>
<div class="block">Finds the neighbors and predicts responses for input vectors.</div>
</td>
</tr>
<tr id="i3" class="rowColor">
<td class="colFirst"><code>float</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#findNearest-org.opencv.core.Mat-int-org.opencv.core.Mat-org.opencv.core.Mat-">findNearest</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;k,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;neighborResponses)</code>
<div class="block">Finds the neighbors and predicts responses for input vectors.</div>
</td>
</tr>
<tr id="i4" class="altColor">
<td class="colFirst"><code>float</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#findNearest-org.opencv.core.Mat-int-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">findNearest</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;k,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;neighborResponses,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;dist)</code>
<div class="block">Finds the neighbors and predicts responses for input vectors.</div>
</td>
</tr>
<tr id="i5" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#getAlgorithmType--">getAlgorithmType</a></span>()</code>
<div class="block">SEE: setAlgorithmType</div>
</td>
</tr>
<tr id="i6" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#getDefaultK--">getDefaultK</a></span>()</code>
<div class="block">SEE: setDefaultK</div>
</td>
</tr>
<tr id="i7" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#getEmax--">getEmax</a></span>()</code>
<div class="block">SEE: setEmax</div>
</td>
</tr>
<tr id="i8" class="altColor">
<td class="colFirst"><code>boolean</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#getIsClassifier--">getIsClassifier</a></span>()</code>
<div class="block">SEE: setIsClassifier</div>
</td>
</tr>
<tr id="i9" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/ml/KNearest.html" title="class in org.opencv.ml">KNearest</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#load-java.lang.String-">load</a></span>(java.lang.String&nbsp;filepath)</code>
<div class="block">Loads and creates a serialized knearest from a file
Use KNearest::save to serialize and store an KNearest to disk.</div>
</td>
</tr>
<tr id="i10" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#setAlgorithmType-int-">setAlgorithmType</a></span>(int&nbsp;val)</code>
<div class="block">getAlgorithmType SEE: getAlgorithmType</div>
</td>
</tr>
<tr id="i11" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#setDefaultK-int-">setDefaultK</a></span>(int&nbsp;val)</code>
<div class="block">getDefaultK SEE: getDefaultK</div>
</td>
</tr>
<tr id="i12" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#setEmax-int-">setEmax</a></span>(int&nbsp;val)</code>
<div class="block">getEmax SEE: getEmax</div>
</td>
</tr>
<tr id="i13" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/KNearest.html#setIsClassifier-boolean-">setIsClassifier</a></span>(boolean&nbsp;val)</code>
<div class="block">getIsClassifier SEE: getIsClassifier</div>
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<h3>Methods inherited from class&nbsp;org.opencv.ml.<a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></h3>
<code><a href="../../../org/opencv/ml/StatModel.html#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#predict-org.opencv.core.Mat-">predict</a>, <a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-">predict</a>, <a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-int-">predict</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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<h3>Methods inherited from class&nbsp;org.opencv.core.<a href="../../../org/opencv/core/Algorithm.html" title="class in org.opencv.core">Algorithm</a></h3>
<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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<h3>Methods inherited from class&nbsp;java.lang.Object</h3>
<code>equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
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<a name="BRUTE_FORCE">
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<h4>BRUTE_FORCE</h4>
<pre>public static final&nbsp;int BRUTE_FORCE</pre>
<dl>
<dt><span class="seeLabel">See Also:</span></dt>
<dd><a href="../../../constant-values.html#org.opencv.ml.KNearest.BRUTE_FORCE">Constant Field Values</a></dd>
</dl>
</li>
</ul>
<a name="KDTREE">
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<h4>KDTREE</h4>
<pre>public static final&nbsp;int KDTREE</pre>
<dl>
<dt><span class="seeLabel">See Also:</span></dt>
<dd><a href="../../../constant-values.html#org.opencv.ml.KNearest.KDTREE">Constant Field Values</a></dd>
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<h4>__fromPtr__</h4>
<pre>public static&nbsp;<a href="../../../org/opencv/ml/KNearest.html" title="class in org.opencv.ml">KNearest</a>&nbsp;__fromPtr__(long&nbsp;addr)</pre>
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<h4>create</h4>
<pre>public static&nbsp;<a href="../../../org/opencv/ml/KNearest.html" title="class in org.opencv.ml">KNearest</a>&nbsp;create()</pre>
<div class="block">Creates the empty model
The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<h4>findNearest</h4>
<pre>public&nbsp;float&nbsp;findNearest(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;k,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results)</pre>
<div class="block">Finds the neighbors and predicts responses for input vectors.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Input samples stored by rows. It is a single-precision floating-point matrix of
<code>&amp;lt;number_of_samples&amp;gt; * k</code> size.</dd>
<dd><code>k</code> - Number of used nearest neighbors. Should be greater than 1.</dd>
<dd><code>results</code> - Vector with results of prediction (regression or classification) for each input
sample. It is a single-precision floating-point vector with <code>&amp;lt;number_of_samples&amp;gt;</code> elements.
precision floating-point matrix of <code>&amp;lt;number_of_samples&amp;gt; * k</code> size.
is a single-precision floating-point matrix of <code>&amp;lt;number_of_samples&amp;gt; * k</code> size.
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
In case of regression, the predicted result is a mean value of the particular vector's neighbor
responses. In case of classification, the class is determined by voting.
For each input vector, the neighbors are sorted by their distances to the vector.
In case of C++ interface you can use output pointers to empty matrices and the function will
allocate memory itself.
If only a single input vector is passed, all output matrices are optional and the predicted
value is returned by the method.
The function is parallelized with the TBB library.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>findNearest</h4>
<pre>public&nbsp;float&nbsp;findNearest(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;k,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;neighborResponses)</pre>
<div class="block">Finds the neighbors and predicts responses for input vectors.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Input samples stored by rows. It is a single-precision floating-point matrix of
<code>&amp;lt;number_of_samples&amp;gt; * k</code> size.</dd>
<dd><code>k</code> - Number of used nearest neighbors. Should be greater than 1.</dd>
<dd><code>results</code> - Vector with results of prediction (regression or classification) for each input
sample. It is a single-precision floating-point vector with <code>&amp;lt;number_of_samples&amp;gt;</code> elements.</dd>
<dd><code>neighborResponses</code> - Optional output values for corresponding neighbors. It is a single-
precision floating-point matrix of <code>&amp;lt;number_of_samples&amp;gt; * k</code> size.
is a single-precision floating-point matrix of <code>&amp;lt;number_of_samples&amp;gt; * k</code> size.
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
In case of regression, the predicted result is a mean value of the particular vector's neighbor
responses. In case of classification, the class is determined by voting.
For each input vector, the neighbors are sorted by their distances to the vector.
In case of C++ interface you can use output pointers to empty matrices and the function will
allocate memory itself.
If only a single input vector is passed, all output matrices are optional and the predicted
value is returned by the method.
The function is parallelized with the TBB library.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>findNearest</h4>
<pre>public&nbsp;float&nbsp;findNearest(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
int&nbsp;k,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;neighborResponses,
<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;dist)</pre>
<div class="block">Finds the neighbors and predicts responses for input vectors.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>samples</code> - Input samples stored by rows. It is a single-precision floating-point matrix of
<code>&amp;lt;number_of_samples&amp;gt; * k</code> size.</dd>
<dd><code>k</code> - Number of used nearest neighbors. Should be greater than 1.</dd>
<dd><code>results</code> - Vector with results of prediction (regression or classification) for each input
sample. It is a single-precision floating-point vector with <code>&amp;lt;number_of_samples&amp;gt;</code> elements.</dd>
<dd><code>neighborResponses</code> - Optional output values for corresponding neighbors. It is a single-
precision floating-point matrix of <code>&amp;lt;number_of_samples&amp;gt; * k</code> size.</dd>
<dd><code>dist</code> - Optional output distances from the input vectors to the corresponding neighbors. It
is a single-precision floating-point matrix of <code>&amp;lt;number_of_samples&amp;gt; * k</code> size.
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
In case of regression, the predicted result is a mean value of the particular vector's neighbor
responses. In case of classification, the class is determined by voting.
For each input vector, the neighbors are sorted by their distances to the vector.
In case of C++ interface you can use output pointers to empty matrices and the function will
allocate memory itself.
If only a single input vector is passed, all output matrices are optional and the predicted
value is returned by the method.
The function is parallelized with the TBB library.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getAlgorithmType</h4>
<pre>public&nbsp;int&nbsp;getAlgorithmType()</pre>
<div class="block">SEE: setAlgorithmType</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<h4>getDefaultK</h4>
<pre>public&nbsp;int&nbsp;getDefaultK()</pre>
<div class="block">SEE: setDefaultK</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>getEmax</h4>
<pre>public&nbsp;int&nbsp;getEmax()</pre>
<div class="block">SEE: setEmax</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>getIsClassifier</h4>
<pre>public&nbsp;boolean&nbsp;getIsClassifier()</pre>
<div class="block">SEE: setIsClassifier</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>load</h4>
<pre>public static&nbsp;<a href="../../../org/opencv/ml/KNearest.html" title="class in org.opencv.ml">KNearest</a>&nbsp;load(java.lang.String&nbsp;filepath)</pre>
<div class="block">Loads and creates a serialized knearest from a file
Use KNearest::save to serialize and store an KNearest to disk.
Load the KNearest from this file again, by calling this function with the path to the file.</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>filepath</code> - path to serialized KNearest</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<h4>setAlgorithmType</h4>
<pre>public&nbsp;void&nbsp;setAlgorithmType(int&nbsp;val)</pre>
<div class="block">getAlgorithmType SEE: getAlgorithmType</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
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<pre>public&nbsp;void&nbsp;setDefaultK(int&nbsp;val)</pre>
<div class="block">getDefaultK SEE: getDefaultK</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
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<h4>setEmax</h4>
<pre>public&nbsp;void&nbsp;setEmax(int&nbsp;val)</pre>
<div class="block">getEmax SEE: getEmax</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
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<h4>setIsClassifier</h4>
<pre>public&nbsp;void&nbsp;setIsClassifier(boolean&nbsp;val)</pre>
<div class="block">getIsClassifier SEE: getIsClassifier</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>val</code> - automatically generated</dd>
</dl>
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