org.opencv.ml.CvRTrees Maven / Gradle / Ivy
//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
// C++: class CvRTrees
/**
* The class implements the random forest predictor as described in the
* beginning of this section.
*
* @see org.opencv.ml.CvRTrees : public CvStatModel
*/
public class CvRTrees extends CvStatModel {
protected CvRTrees(long addr) { super(addr); }
//
// C++: CvRTrees::CvRTrees()
//
public CvRTrees()
{
super( CvRTrees_0() );
return;
}
//
// C++: void CvRTrees::clear()
//
public void clear()
{
clear_0(nativeObj);
return;
}
//
// C++: Mat CvRTrees::getVarImportance()
//
/**
* Returns the variable importance array.
*
* The method returns the variable importance vector, computed at the training
* stage when CvRTParams.calc_var_importance is set to true. If
* this flag was set to false, the NULL pointer is returned. This
* differs from the decision trees where variable importance can be computed
* anytime after the training.
*
* @see org.opencv.ml.CvRTrees.getVarImportance
*/
public Mat getVarImportance()
{
Mat retVal = new Mat(getVarImportance_0(nativeObj));
return retVal;
}
//
// C++: float CvRTrees::predict(Mat sample, Mat missing = cv::Mat())
//
/**
* Predicts the output for an input sample.
*
* The input parameters of the prediction method are the same as in
* "CvDTree.predict" but the return value type is different. This method
* returns the cumulative result from all the trees in the forest (the class
* that receives the majority of voices, or the mean of the regression function
* estimates).
*
* @param sample Sample for classification.
* @param missing Optional missing measurement mask of the sample.
*
* @see org.opencv.ml.CvRTrees.predict
*/
public float predict(Mat sample, Mat missing)
{
float retVal = predict_0(nativeObj, sample.nativeObj, missing.nativeObj);
return retVal;
}
/**
* Predicts the output for an input sample.
*
* The input parameters of the prediction method are the same as in
* "CvDTree.predict" but the return value type is different. This method
* returns the cumulative result from all the trees in the forest (the class
* that receives the majority of voices, or the mean of the regression function
* estimates).
*
* @param sample Sample for classification.
*
* @see org.opencv.ml.CvRTrees.predict
*/
public float predict(Mat sample)
{
float retVal = predict_1(nativeObj, sample.nativeObj);
return retVal;
}
//
// C++: float CvRTrees::predict_prob(Mat sample, Mat missing = cv::Mat())
//
/**
* Returns a fuzzy-predicted class label.
*
* The function works for binary classification problems only. It returns the
* number between 0 and 1. This number represents probability or confidence of
* the sample belonging to the second class. It is calculated as the proportion
* of decision trees that classified the sample to the second class.
*
* @param sample Sample for classification.
* @param missing Optional missing measurement mask of the sample.
*
* @see org.opencv.ml.CvRTrees.predict_prob
*/
public float predict_prob(Mat sample, Mat missing)
{
float retVal = predict_prob_0(nativeObj, sample.nativeObj, missing.nativeObj);
return retVal;
}
/**
* Returns a fuzzy-predicted class label.
*
* The function works for binary classification problems only. It returns the
* number between 0 and 1. This number represents probability or confidence of
* the sample belonging to the second class. It is calculated as the proportion
* of decision trees that classified the sample to the second class.
*
* @param sample Sample for classification.
*
* @see org.opencv.ml.CvRTrees.predict_prob
*/
public float predict_prob(Mat sample)
{
float retVal = predict_prob_1(nativeObj, sample.nativeObj);
return retVal;
}
//
// C++: bool CvRTrees::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvRTParams params = CvRTParams())
//
/**
* Trains the Random Trees model.
*
* The method "CvRTrees.train" is very similar to the method "CvDTree.train"
* and follows the generic method "CvStatModel.train" conventions. All the
* parameters specific to the algorithm training are passed as a "CvRTParams"
* instance. The estimate of the training error (oob-error) is
* stored in the protected class member oob_error.
*
* The function is parallelized with the TBB library.
*
* @param trainData a trainData
* @param tflag a tflag
* @param responses a responses
* @param varIdx a varIdx
* @param sampleIdx a sampleIdx
* @param varType a varType
* @param missingDataMask a missingDataMask
* @param params a params
*
* @see org.opencv.ml.CvRTrees.train
*/
public boolean train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvRTParams params)
{
boolean retVal = train_0(nativeObj, trainData.nativeObj, tflag, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, varType.nativeObj, missingDataMask.nativeObj, params.nativeObj);
return retVal;
}
/**
* Trains the Random Trees model.
*
* The method "CvRTrees.train" is very similar to the method "CvDTree.train"
* and follows the generic method "CvStatModel.train" conventions. All the
* parameters specific to the algorithm training are passed as a "CvRTParams"
* instance. The estimate of the training error (oob-error) is
* stored in the protected class member oob_error.
*
* The function is parallelized with the TBB library.
*
* @param trainData a trainData
* @param tflag a tflag
* @param responses a responses
*
* @see org.opencv.ml.CvRTrees.train
*/
public boolean train(Mat trainData, int tflag, Mat responses)
{
boolean retVal = train_1(nativeObj, trainData.nativeObj, tflag, responses.nativeObj);
return retVal;
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: CvRTrees::CvRTrees()
private static native long CvRTrees_0();
// C++: void CvRTrees::clear()
private static native void clear_0(long nativeObj);
// C++: Mat CvRTrees::getVarImportance()
private static native long getVarImportance_0(long nativeObj);
// C++: float CvRTrees::predict(Mat sample, Mat missing = cv::Mat())
private static native float predict_0(long nativeObj, long sample_nativeObj, long missing_nativeObj);
private static native float predict_1(long nativeObj, long sample_nativeObj);
// C++: float CvRTrees::predict_prob(Mat sample, Mat missing = cv::Mat())
private static native float predict_prob_0(long nativeObj, long sample_nativeObj, long missing_nativeObj);
private static native float predict_prob_1(long nativeObj, long sample_nativeObj);
// C++: bool CvRTrees::train(Mat trainData, int tflag, Mat responses, Mat varIdx = cv::Mat(), Mat sampleIdx = cv::Mat(), Mat varType = cv::Mat(), Mat missingDataMask = cv::Mat(), CvRTParams params = CvRTParams())
private static native boolean train_0(long nativeObj, long trainData_nativeObj, int tflag, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long varType_nativeObj, long missingDataMask_nativeObj, long params_nativeObj);
private static native boolean train_1(long nativeObj, long trainData_nativeObj, int tflag, long responses_nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}