Package org.opencv.ml

Class DTrees

  • Direct Known Subclasses:
    Boost, RTrees
    public class DTrees
extends StatModel
    The class represents a single decision tree or a collection of decision trees. The current public interface of the class allows user to train only a single decision tree, however the class is capable of storing multiple decision trees and using them for prediction (by summing responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) use this capability to implement decision tree ensembles. SEE: REF: ml_intro_trees

    • Constructor Detail

      • DTrees

        protected DTrees​(long addr)

    • Method Detail

      • __fromPtr__

        public static DTrees __fromPtr__​(long addr)

      • getMaxCategories

        public int getMaxCategories()
        SEE: setMaxCategories
        Returns:
        automatically generated

      • setMaxCategories

        public void setMaxCategories​(int val)
        getMaxCategories SEE: getMaxCategories
        Parameters:
        val - automatically generated

      • getMaxDepth

        public int getMaxDepth()
        SEE: setMaxDepth
        Returns:
        automatically generated

      • setMaxDepth

        public void setMaxDepth​(int val)
        getMaxDepth SEE: getMaxDepth
        Parameters:
        val - automatically generated

      • getMinSampleCount

        public int getMinSampleCount()
        SEE: setMinSampleCount
        Returns:
        automatically generated

      • setMinSampleCount

        public void setMinSampleCount​(int val)
        getMinSampleCount SEE: getMinSampleCount
        Parameters:
        val - automatically generated

      • getCVFolds

        public int getCVFolds()
        SEE: setCVFolds
        Returns:
        automatically generated

      • setCVFolds

        public void setCVFolds​(int val)
        getCVFolds SEE: getCVFolds
        Parameters:
        val - automatically generated

      • getUseSurrogates

        public boolean getUseSurrogates()
        SEE: setUseSurrogates
        Returns:
        automatically generated

      • setUseSurrogates

        public void setUseSurrogates​(boolean val)
        getUseSurrogates SEE: getUseSurrogates
        Parameters:
        val - automatically generated

      • getUse1SERule

        public boolean getUse1SERule()
        SEE: setUse1SERule
        Returns:
        automatically generated

      • setUse1SERule

        public void setUse1SERule​(boolean val)
        getUse1SERule SEE: getUse1SERule
        Parameters:
        val - automatically generated

      • getTruncatePrunedTree

        public boolean getTruncatePrunedTree()
        SEE: setTruncatePrunedTree
        Returns:
        automatically generated

      • setTruncatePrunedTree

        public void setTruncatePrunedTree​(boolean val)
        getTruncatePrunedTree SEE: getTruncatePrunedTree
        Parameters:
        val - automatically generated

      • getRegressionAccuracy

        public float getRegressionAccuracy()
        SEE: setRegressionAccuracy
        Returns:
        automatically generated

      • setRegressionAccuracy

        public void setRegressionAccuracy​(float val)
        getRegressionAccuracy SEE: getRegressionAccuracy
        Parameters:
        val - automatically generated

      • getPriors

        public Mat getPriors()
        SEE: setPriors
        Returns:
        automatically generated

      • setPriors

        public void setPriors​(Mat val)
        getPriors SEE: getPriors
        Parameters:
        val - automatically generated

      • create

        public static DTrees create()
        Creates the empty model The static method creates empty decision tree with the specified parameters. It should be then trained using train method (see StatModel::train). Alternatively, you can load the model from file using Algorithm::load<DTrees>(filename).
        Returns:
        automatically generated

      • load

        public static DTrees load​(java.lang.String filepath,
                          java.lang.String nodeName)
        Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk. Load the DTree 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 DTree
        nodeName - name of node containing the classifier
        Returns:
        automatically generated

      • load

        public static DTrees load​(java.lang.String filepath)
        Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk. Load the DTree 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 DTree
        Returns:
        automatically generated

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class StatModel
        Throws:
        java.lang.Throwable