Package org.opencv.ml

Class RTrees


  • public class RTrees
    extends DTrees
    The class implements the random forest predictor. SEE: REF: ml_intro_rtrees
    • Constructor Detail

      • RTrees

        protected RTrees​(long addr)
    • Method Detail

      • __fromPtr__

        public static RTrees __fromPtr__​(long addr)
      • getCalculateVarImportance

        public boolean getCalculateVarImportance()
        SEE: setCalculateVarImportance
        Returns:
        automatically generated
      • setCalculateVarImportance

        public void setCalculateVarImportance​(boolean val)
        getCalculateVarImportance SEE: getCalculateVarImportance
        Parameters:
        val - automatically generated
      • getActiveVarCount

        public int getActiveVarCount()
        SEE: setActiveVarCount
        Returns:
        automatically generated
      • setActiveVarCount

        public void setActiveVarCount​(int val)
        getActiveVarCount SEE: getActiveVarCount
        Parameters:
        val - automatically generated
      • getTermCriteria

        public TermCriteria getTermCriteria()
        SEE: setTermCriteria
        Returns:
        automatically generated
      • setTermCriteria

        public void setTermCriteria​(TermCriteria val)
        getTermCriteria SEE: getTermCriteria
        Parameters:
        val - automatically generated
      • getVarImportance

        public Mat getVarImportance()
        Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
        Returns:
        automatically generated
      • getVotes

        public void getVotes​(Mat samples,
                             Mat results,
                             int flags)
        Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.
        Parameters:
        samples - Array containing the samples for which votes will be calculated.
        results - Array where the result of the calculation will be written.
        flags - Flags for defining the type of RTrees.
      • getOOBError

        public double getOOBError()
        Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
        Returns:
        automatically generated
      • create

        public static RTrees create()
        Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.
        Returns:
        automatically generated
      • load

        public static RTrees load​(String filepath,
                                  String nodeName)
        Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
        Parameters:
        filepath - path to serialized RTree
        nodeName - name of node containing the classifier
        Returns:
        automatically generated
      • load

        public static RTrees load​(String filepath)
        Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
        Parameters:
        filepath - path to serialized RTree
        Returns:
        automatically generated