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//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
// C++: class EM
//javadoc: EM
public class EM extends StatModel {
protected EM(long addr) { super(addr); }
public static final int
COV_MAT_SPHERICAL = 0,
COV_MAT_DIAGONAL = 1,
COV_MAT_GENERIC = 2,
COV_MAT_DEFAULT = COV_MAT_DIAGONAL,
DEFAULT_NCLUSTERS = 5,
DEFAULT_MAX_ITERS = 100,
START_E_STEP = 1,
START_M_STEP = 2,
START_AUTO_STEP = 0;
//
// C++: int getClustersNumber()
//
//javadoc: EM::getClustersNumber()
public int getClustersNumber()
{
int retVal = getClustersNumber_0(nativeObj);
return retVal;
}
//
// C++: void setClustersNumber(int val)
//
//javadoc: EM::setClustersNumber(val)
public void setClustersNumber(int val)
{
setClustersNumber_0(nativeObj, val);
return;
}
//
// C++: int getCovarianceMatrixType()
//
//javadoc: EM::getCovarianceMatrixType()
public int getCovarianceMatrixType()
{
int retVal = getCovarianceMatrixType_0(nativeObj);
return retVal;
}
//
// C++: void setCovarianceMatrixType(int val)
//
//javadoc: EM::setCovarianceMatrixType(val)
public void setCovarianceMatrixType(int val)
{
setCovarianceMatrixType_0(nativeObj, val);
return;
}
//
// C++: TermCriteria getTermCriteria()
//
//javadoc: EM::getTermCriteria()
public TermCriteria getTermCriteria()
{
TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
return retVal;
}
//
// C++: void setTermCriteria(TermCriteria val)
//
//javadoc: EM::setTermCriteria(val)
public void setTermCriteria(TermCriteria val)
{
setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
return;
}
//
// C++: Mat getWeights()
//
//javadoc: EM::getWeights()
public Mat getWeights()
{
Mat retVal = new Mat(getWeights_0(nativeObj));
return retVal;
}
//
// C++: Mat getMeans()
//
//javadoc: EM::getMeans()
public Mat getMeans()
{
Mat retVal = new Mat(getMeans_0(nativeObj));
return retVal;
}
//
// C++: Vec2d predict2(Mat sample, Mat& probs)
//
//javadoc: EM::predict2(sample, probs)
public double[] predict2(Mat sample, Mat probs)
{
double[] retVal = predict2_0(nativeObj, sample.nativeObj, probs.nativeObj);
return retVal;
}
//
// C++: bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
//
//javadoc: EM::trainEM(samples, logLikelihoods, labels, probs)
public boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
{
boolean retVal = trainEM_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
return retVal;
}
//javadoc: EM::trainEM(samples)
public boolean trainEM(Mat samples)
{
boolean retVal = trainEM_1(nativeObj, samples.nativeObj);
return retVal;
}
//
// C++: bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
//
//javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods, labels, probs)
public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
{
boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
return retVal;
}
//javadoc: EM::trainE(samples, means0)
public boolean trainE(Mat samples, Mat means0)
{
boolean retVal = trainE_1(nativeObj, samples.nativeObj, means0.nativeObj);
return retVal;
}
//
// C++: bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
//
//javadoc: EM::trainM(samples, probs0, logLikelihoods, labels, probs)
public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
{
boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
return retVal;
}
//javadoc: EM::trainM(samples, probs0)
public boolean trainM(Mat samples, Mat probs0)
{
boolean retVal = trainM_1(nativeObj, samples.nativeObj, probs0.nativeObj);
return retVal;
}
//
// C++: static Ptr_EM create()
//
//javadoc: EM::create()
public static EM create()
{
EM retVal = new EM(create_0());
return retVal;
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: int getClustersNumber()
private static native int getClustersNumber_0(long nativeObj);
// C++: void setClustersNumber(int val)
private static native void setClustersNumber_0(long nativeObj, int val);
// C++: int getCovarianceMatrixType()
private static native int getCovarianceMatrixType_0(long nativeObj);
// C++: void setCovarianceMatrixType(int val)
private static native void setCovarianceMatrixType_0(long nativeObj, int val);
// C++: TermCriteria getTermCriteria()
private static native double[] getTermCriteria_0(long nativeObj);
// C++: void setTermCriteria(TermCriteria val)
private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
// C++: Mat getWeights()
private static native long getWeights_0(long nativeObj);
// C++: Mat getMeans()
private static native long getMeans_0(long nativeObj);
// C++: Vec2d predict2(Mat sample, Mat& probs)
private static native double[] predict2_0(long nativeObj, long sample_nativeObj, long probs_nativeObj);
// C++: bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
private static native boolean trainEM_0(long nativeObj, long samples_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
private static native boolean trainEM_1(long nativeObj, long samples_nativeObj);
// C++: bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
private static native boolean trainE_0(long nativeObj, long samples_nativeObj, long means0_nativeObj, long covs0_nativeObj, long weights0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
private static native boolean trainE_1(long nativeObj, long samples_nativeObj, long means0_nativeObj);
// C++: bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
private static native boolean trainM_0(long nativeObj, long samples_nativeObj, long probs0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
private static native boolean trainM_1(long nativeObj, long samples_nativeObj, long probs0_nativeObj);
// C++: static Ptr_EM create()
private static native long create_0();
// native support for java finalize()
private static native void delete(long nativeObj);
}
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