798 lines
28 KiB
C#
Executable File
798 lines
28 KiB
C#
Executable File
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using OpenCVForUnity.CoreModule;
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using OpenCVForUnity.UtilsModule;
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using System;
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using System.Collections.Generic;
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using System.Runtime.InteropServices;
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namespace OpenCVForUnity.MlModule
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{
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// C++: class EM
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//javadoc: EM
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public class EM : StatModel
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{
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protected override void Dispose (bool disposing)
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{
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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try {
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if (disposing) {
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}
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if (IsEnabledDispose) {
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if (nativeObj != IntPtr.Zero)
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ml_EM_delete (nativeObj);
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nativeObj = IntPtr.Zero;
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}
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} finally {
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base.Dispose (disposing);
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}
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#else
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return;
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#endif
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}
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protected internal EM (IntPtr addr)
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: base (addr)
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{
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}
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// internal usage only
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public static new EM __fromPtr__ (IntPtr addr)
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{
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return new EM (addr);
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}
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// C++: enum Types
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public const int COV_MAT_SPHERICAL = 0;
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public const int COV_MAT_DIAGONAL = 1;
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public const int COV_MAT_GENERIC = 2;
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public const int COV_MAT_DEFAULT = COV_MAT_DIAGONAL;
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// C++: enum <unnamed>
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public const int DEFAULT_NCLUSTERS = 5;
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public const int DEFAULT_MAX_ITERS = 100;
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public const int START_E_STEP = 1;
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public const int START_M_STEP = 2;
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public const int START_AUTO_STEP = 0;
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//
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// C++: Mat cv::ml::EM::getMeans()
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//
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//javadoc: EM::getMeans()
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public Mat getMeans ()
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{
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ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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Mat retVal = new Mat (ml_EM_getMeans_10 (nativeObj));
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return retVal;
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#else
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return null;
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#endif
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}
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//
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// C++: Mat cv::ml::EM::getWeights()
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//
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//javadoc: EM::getWeights()
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public Mat getWeights ()
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{
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ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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Mat retVal = new Mat (ml_EM_getWeights_10 (nativeObj));
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return retVal;
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#else
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return null;
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#endif
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}
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//
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// C++: static Ptr_EM cv::ml::EM::create()
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//
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//javadoc: EM::create()
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public static EM create ()
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{
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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EM retVal = EM.__fromPtr__ (ml_EM_create_10 ());
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return retVal;
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#else
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return null;
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#endif
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}
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//
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// C++: static Ptr_EM cv::ml::EM::load(String filepath, String nodeName = String())
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//
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//javadoc: EM::load(filepath, nodeName)
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public static EM load (string filepath, string nodeName)
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{
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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EM retVal = EM.__fromPtr__ (ml_EM_load_10 (filepath, nodeName));
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return retVal;
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#else
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return null;
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#endif
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}
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//javadoc: EM::load(filepath)
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public static EM load (string filepath)
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{
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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EM retVal = EM.__fromPtr__ (ml_EM_load_11 (filepath));
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return retVal;
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#else
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return null;
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#endif
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}
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//
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// C++: TermCriteria cv::ml::EM::getTermCriteria()
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//
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//javadoc: EM::getTermCriteria()
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public TermCriteria getTermCriteria ()
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{
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ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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double[] tmpArray = new double[3];
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ml_EM_getTermCriteria_10 (nativeObj, tmpArray);
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TermCriteria retVal = new TermCriteria (tmpArray);
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return retVal;
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#else
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return null;
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#endif
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}
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//
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// C++: Vec2d cv::ml::EM::predict2(Mat sample, Mat& probs)
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//
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//javadoc: EM::predict2(sample, probs)
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public double[] predict2 (Mat sample, Mat probs)
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{
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ThrowIfDisposed ();
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if (sample != null)
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sample.ThrowIfDisposed ();
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if (probs != null)
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probs.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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double[] retVal = new double[2];
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ml_EM_predict2_10 (nativeObj, sample.nativeObj, probs.nativeObj, retVal);
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return retVal;
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#else
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return null;
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#endif
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}
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//
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// C++: bool cv::ml::EM::trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
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//
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//javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods, labels, probs)
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public bool trainE (Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (means0 != null)
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means0.ThrowIfDisposed ();
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if (covs0 != null)
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covs0.ThrowIfDisposed ();
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if (weights0 != null)
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weights0.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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if (labels != null)
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labels.ThrowIfDisposed ();
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if (probs != null)
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probs.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainE_10 (nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods, labels)
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public bool trainE (Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (means0 != null)
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means0.ThrowIfDisposed ();
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if (covs0 != null)
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covs0.ThrowIfDisposed ();
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if (weights0 != null)
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weights0.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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if (labels != null)
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labels.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainE_11 (nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods)
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public bool trainE (Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (means0 != null)
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means0.ThrowIfDisposed ();
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if (covs0 != null)
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covs0.ThrowIfDisposed ();
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if (weights0 != null)
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weights0.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainE_12 (nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainE(samples, means0, covs0, weights0)
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public bool trainE (Mat samples, Mat means0, Mat covs0, Mat weights0)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (means0 != null)
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means0.ThrowIfDisposed ();
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if (covs0 != null)
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covs0.ThrowIfDisposed ();
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if (weights0 != null)
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weights0.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainE_13 (nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainE(samples, means0, covs0)
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public bool trainE (Mat samples, Mat means0, Mat covs0)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (means0 != null)
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means0.ThrowIfDisposed ();
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if (covs0 != null)
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covs0.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainE_14 (nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainE(samples, means0)
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public bool trainE (Mat samples, Mat means0)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (means0 != null)
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means0.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainE_15 (nativeObj, samples.nativeObj, means0.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//
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// C++: bool cv::ml::EM::trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
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//
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//javadoc: EM::trainEM(samples, logLikelihoods, labels, probs)
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public bool trainEM (Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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if (labels != null)
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labels.ThrowIfDisposed ();
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if (probs != null)
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probs.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainEM_10 (nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainEM(samples, logLikelihoods, labels)
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public bool trainEM (Mat samples, Mat logLikelihoods, Mat labels)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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if (labels != null)
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labels.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainEM_11 (nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainEM(samples, logLikelihoods)
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public bool trainEM (Mat samples, Mat logLikelihoods)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainEM_12 (nativeObj, samples.nativeObj, logLikelihoods.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainEM(samples)
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public bool trainEM (Mat samples)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainEM_13 (nativeObj, samples.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//
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// C++: bool cv::ml::EM::trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
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//
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//javadoc: EM::trainM(samples, probs0, logLikelihoods, labels, probs)
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public bool trainM (Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (probs0 != null)
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probs0.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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if (labels != null)
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labels.ThrowIfDisposed ();
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if (probs != null)
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probs.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainM_10 (nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainM(samples, probs0, logLikelihoods, labels)
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public bool trainM (Mat samples, Mat probs0, Mat logLikelihoods, Mat labels)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (probs0 != null)
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probs0.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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if (labels != null)
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labels.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainM_11 (nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainM(samples, probs0, logLikelihoods)
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public bool trainM (Mat samples, Mat probs0, Mat logLikelihoods)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (probs0 != null)
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probs0.ThrowIfDisposed ();
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if (logLikelihoods != null)
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logLikelihoods.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainM_12 (nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//javadoc: EM::trainM(samples, probs0)
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public bool trainM (Mat samples, Mat probs0)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (probs0 != null)
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probs0.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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bool retVal = ml_EM_trainM_13 (nativeObj, samples.nativeObj, probs0.nativeObj);
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return retVal;
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#else
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return false;
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#endif
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}
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//
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// C++: float cv::ml::EM::predict(Mat samples, Mat& results = Mat(), int flags = 0)
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//
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//javadoc: EM::predict(samples, results, flags)
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public override float predict (Mat samples, Mat results, int flags)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (results != null)
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results.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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float retVal = ml_EM_predict_10 (nativeObj, samples.nativeObj, results.nativeObj, flags);
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return retVal;
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#else
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return -1;
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#endif
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}
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//javadoc: EM::predict(samples, results)
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public override float predict (Mat samples, Mat results)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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if (results != null)
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results.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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float retVal = ml_EM_predict_11 (nativeObj, samples.nativeObj, results.nativeObj);
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return retVal;
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#else
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return -1;
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#endif
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}
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//javadoc: EM::predict(samples)
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public override float predict (Mat samples)
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{
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ThrowIfDisposed ();
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if (samples != null)
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samples.ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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float retVal = ml_EM_predict_12 (nativeObj, samples.nativeObj);
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return retVal;
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#else
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return -1;
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#endif
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}
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//
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// C++: int cv::ml::EM::getClustersNumber()
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//
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//javadoc: EM::getClustersNumber()
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public int getClustersNumber ()
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{
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ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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int retVal = ml_EM_getClustersNumber_10 (nativeObj);
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return retVal;
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#else
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return -1;
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#endif
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}
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//
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// C++: int cv::ml::EM::getCovarianceMatrixType()
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//
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//javadoc: EM::getCovarianceMatrixType()
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public int getCovarianceMatrixType ()
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{
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ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
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int retVal = ml_EM_getCovarianceMatrixType_10 (nativeObj);
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return retVal;
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#else
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return -1;
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#endif
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}
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//
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// C++: void cv::ml::EM::getCovs(vector_Mat& covs)
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//
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//javadoc: EM::getCovs(covs)
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public void getCovs (List<Mat> covs)
|
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{
|
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ThrowIfDisposed ();
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#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
|
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Mat covs_mat = new Mat ();
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ml_EM_getCovs_10 (nativeObj, covs_mat.nativeObj);
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Converters.Mat_to_vector_Mat (covs_mat, covs);
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covs_mat.release ();
|
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return;
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#else
|
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return;
|
|
#endif
|
|
}
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|
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//
|
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// C++: void cv::ml::EM::setClustersNumber(int val)
|
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//
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|
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//javadoc: EM::setClustersNumber(val)
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public void setClustersNumber (int val)
|
|
{
|
|
ThrowIfDisposed ();
|
|
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
|
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|
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ml_EM_setClustersNumber_10 (nativeObj, val);
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|
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return;
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#else
|
|
return;
|
|
#endif
|
|
}
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|
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//
|
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// C++: void cv::ml::EM::setCovarianceMatrixType(int val)
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//
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|
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//javadoc: EM::setCovarianceMatrixType(val)
|
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public void setCovarianceMatrixType (int val)
|
|
{
|
|
ThrowIfDisposed ();
|
|
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
|
|
|
|
ml_EM_setCovarianceMatrixType_10 (nativeObj, val);
|
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|
|
return;
|
|
#else
|
|
return;
|
|
#endif
|
|
}
|
|
|
|
|
|
//
|
|
// C++: void cv::ml::EM::setTermCriteria(TermCriteria val)
|
|
//
|
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|
|
//javadoc: EM::setTermCriteria(val)
|
|
public void setTermCriteria (TermCriteria val)
|
|
{
|
|
ThrowIfDisposed ();
|
|
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
|
|
|
|
ml_EM_setTermCriteria_10 (nativeObj, val.type, val.maxCount, val.epsilon);
|
|
|
|
return;
|
|
#else
|
|
return;
|
|
#endif
|
|
}
|
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|
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
|
|
const string LIBNAME = "__Internal";
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|
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#else
|
|
const string LIBNAME = "opencvforunity";
|
|
#endif
|
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// C++: Mat cv::ml::EM::getMeans()
|
|
[DllImport (LIBNAME)]
|
|
private static extern IntPtr ml_EM_getMeans_10 (IntPtr nativeObj);
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|
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// C++: Mat cv::ml::EM::getWeights()
|
|
[DllImport (LIBNAME)]
|
|
private static extern IntPtr ml_EM_getWeights_10 (IntPtr nativeObj);
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// C++: static Ptr_EM cv::ml::EM::create()
|
|
[DllImport (LIBNAME)]
|
|
private static extern IntPtr ml_EM_create_10 ();
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// C++: static Ptr_EM cv::ml::EM::load(String filepath, String nodeName = String())
|
|
[DllImport (LIBNAME)]
|
|
private static extern IntPtr ml_EM_load_10 (string filepath, string nodeName);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern IntPtr ml_EM_load_11 (string filepath);
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|
|
|
// C++: TermCriteria cv::ml::EM::getTermCriteria()
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_getTermCriteria_10 (IntPtr nativeObj, double[] retVal);
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|
|
// C++: Vec2d cv::ml::EM::predict2(Mat sample, Mat& probs)
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_predict2_10 (IntPtr nativeObj, IntPtr sample_nativeObj, IntPtr probs_nativeObj, double[] retVal);
|
|
|
|
// C++: bool cv::ml::EM::trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainE_10 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr means0_nativeObj, IntPtr covs0_nativeObj, IntPtr weights0_nativeObj, IntPtr logLikelihoods_nativeObj, IntPtr labels_nativeObj, IntPtr probs_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainE_11 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr means0_nativeObj, IntPtr covs0_nativeObj, IntPtr weights0_nativeObj, IntPtr logLikelihoods_nativeObj, IntPtr labels_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainE_12 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr means0_nativeObj, IntPtr covs0_nativeObj, IntPtr weights0_nativeObj, IntPtr logLikelihoods_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainE_13 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr means0_nativeObj, IntPtr covs0_nativeObj, IntPtr weights0_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainE_14 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr means0_nativeObj, IntPtr covs0_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainE_15 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr means0_nativeObj);
|
|
|
|
// C++: bool cv::ml::EM::trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainEM_10 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr logLikelihoods_nativeObj, IntPtr labels_nativeObj, IntPtr probs_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainEM_11 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr logLikelihoods_nativeObj, IntPtr labels_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainEM_12 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr logLikelihoods_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainEM_13 (IntPtr nativeObj, IntPtr samples_nativeObj);
|
|
|
|
// C++: bool cv::ml::EM::trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainM_10 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr probs0_nativeObj, IntPtr logLikelihoods_nativeObj, IntPtr labels_nativeObj, IntPtr probs_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainM_11 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr probs0_nativeObj, IntPtr logLikelihoods_nativeObj, IntPtr labels_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainM_12 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr probs0_nativeObj, IntPtr logLikelihoods_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern bool ml_EM_trainM_13 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr probs0_nativeObj);
|
|
|
|
// C++: float cv::ml::EM::predict(Mat samples, Mat& results = Mat(), int flags = 0)
|
|
[DllImport (LIBNAME)]
|
|
private static extern float ml_EM_predict_10 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr results_nativeObj, int flags);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern float ml_EM_predict_11 (IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr results_nativeObj);
|
|
|
|
[DllImport (LIBNAME)]
|
|
private static extern float ml_EM_predict_12 (IntPtr nativeObj, IntPtr samples_nativeObj);
|
|
|
|
// C++: int cv::ml::EM::getClustersNumber()
|
|
[DllImport (LIBNAME)]
|
|
private static extern int ml_EM_getClustersNumber_10 (IntPtr nativeObj);
|
|
|
|
// C++: int cv::ml::EM::getCovarianceMatrixType()
|
|
[DllImport (LIBNAME)]
|
|
private static extern int ml_EM_getCovarianceMatrixType_10 (IntPtr nativeObj);
|
|
|
|
// C++: void cv::ml::EM::getCovs(vector_Mat& covs)
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_getCovs_10 (IntPtr nativeObj, IntPtr covs_mat_nativeObj);
|
|
|
|
// C++: void cv::ml::EM::setClustersNumber(int val)
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_setClustersNumber_10 (IntPtr nativeObj, int val);
|
|
|
|
// C++: void cv::ml::EM::setCovarianceMatrixType(int val)
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_setCovarianceMatrixType_10 (IntPtr nativeObj, int val);
|
|
|
|
// C++: void cv::ml::EM::setTermCriteria(TermCriteria val)
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_setTermCriteria_10 (IntPtr nativeObj, int val_type, int val_maxCount, double val_epsilon);
|
|
|
|
// native support for java finalize()
|
|
[DllImport (LIBNAME)]
|
|
private static extern void ml_EM_delete (IntPtr nativeObj);
|
|
|
|
}
|
|
}
|