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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    RandomTree.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.ContingencyTables;
import weka.core.Drawable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.PartitionGenerator;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.gui.ProgrammaticProperty;

import java.io.Serializable;
import java.util.Collections;
import java.util.Enumeration;
import java.util.LinkedList;
import java.util.Queue;
import java.util.Random;
import java.util.Vector;

/**
 *  Class for constructing a tree that considers K
 * randomly chosen attributes at each node. Performs no pruning. Also has an
 * option to allow estimation of class probabilities (or target mean in the
 * regression case) based on a hold-out set (backfitting). 

 * 

 * 
 * 
 *  Valid options are:
 * 
 * 
 * 
 * -K <number of attributes>
 *  Number of attributes to randomly investigate. (default 0)
 *  (<1 = int(log_2(#predictors)+1)).
 * 
 * 
 *  * -M <minimum number of instances>
 *  Set minimum number of instances per leaf.
 *  (default 1)
 * 
 * 
 *  * -V <minimum variance for split>
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).
 * 
 * 
 *  * -S <num>
 *  Seed for random number generator.
 *  (default 1)
 * 
 * 
 *  * -depth <num>
 *  The maximum depth of the tree, 0 for unlimited.
 *  (default 0)
 * 
 * 
 *  * -N <num>
 *  Number of folds for backfitting (default 0, no backfitting).
 * 
 * 
 *  * -U
 *  Allow unclassified instances.
 * 
 * 
 *  * -B
 *  Break ties randomly when several attributes look equally good.
 * 
 * 
 *  * -output-debug-info
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * 
 * 
 *  * -do-not-check-capabilities
 *  If set, classifier capabilities are not checked before classifier is built
 *  (use with caution).
 * 
 * 
 *  * -num-decimal-places
 *  The number of decimal places for the output of numbers in the model (default 2).
 * 
 * 
 * 
 * 
 * @author Eibe Frank ([email protected])
 * @author Richard Kirkby ([email protected])
 * @version $Revision: 13864 $
 */
public class RandomTree extends AbstractClassifier implements OptionHandler,
  WeightedInstancesHandler, Randomizable, Drawable, PartitionGenerator {

  /** for serialization */
  private static final long serialVersionUID = -9051119597407396024L;

  /** The Tree object */
  protected Tree m_Tree = null;

  /** The header information. */
  protected Instances m_Info = null;

  /** Minimum number of instances for leaf. */
  protected double m_MinNum = 1.0;

  /** The number of attributes considered for a split. */
  protected int m_KValue = 0;

  /** The random seed to use. */
  protected int m_randomSeed = 1;

  /** The maximum depth of the tree (0 = unlimited) */
  protected int m_MaxDepth = 0;

  /** Determines how much data is used for backfitting */
  protected int m_NumFolds = 0;

  /** Whether unclassified instances are allowed */
  protected boolean m_AllowUnclassifiedInstances = false;

  /** Whether to break ties randomly. */
  protected boolean m_BreakTiesRandomly = false;

  /** a ZeroR model in case no model can be built from the data */
  protected Classifier m_zeroR;

  /**
   * The minimum proportion of the total variance (over all the data) required
   * for split.
   */
  protected double m_MinVarianceProp = 1e-3;

  /** Whether to store the impurity decrease/gain sum */
  protected boolean m_computeImpurityDecreases;

  /**
   * Indexed by attribute, each two element array contains impurity
   * decrease/gain sum in first element and count in the second
   */
  protected double[][] m_impurityDecreasees;

  /**
   * Returns a string describing classifier
   * 
   * @return a description suitable for displaying in the explorer/experimenter
   *         gui
   */
  public String globalInfo() {

    return "Class for constructing a tree that considers K randomly "
      + " chosen attributes at each node. Performs no pruning. Also has"
      + " an option to allow estimation of class probabilities (or target mean "
      + "in the regression case) based on a hold-out set (backfitting).";
  }

  /**
   * Get the array of impurity decrease/gain sums
   *
   * @return the array of impurity decrease/gain sums
   */
  public double[][] getImpurityDecreases() {
    return m_impurityDecreasees;
  }

  /**
   * Set whether to compute/store impurity decreases for variable importance
   * in RandomForest
   *
   * @param computeImpurityDecreases true to compute and store impurity decrease
   *                                 values for splitting attributes
   */
  @ProgrammaticProperty
  public void setComputeImpurityDecreases(boolean computeImpurityDecreases) {
    m_computeImpurityDecreases = computeImpurityDecreases;
  }

  /**
   * Get whether to compute/store impurity decreases for variable importance
   * in RandomForest
   *
   * @return true to compute and store impurity decrease
   *                                 values for splitting attributes
   */
  public boolean getComputeImpurityDecreases() {
    return m_computeImpurityDecreases;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String minNumTipText() {
    return "The minimum total weight of the instances in a leaf.";
  }

  /**
   * Get the value of MinNum.
   * 
   * @return Value of MinNum.
   */
  public double getMinNum() {

    return m_MinNum;
  }

  /**
   * Set the value of MinNum.
   * 
   * @param newMinNum Value to assign to MinNum.
   */
  public void setMinNum(double newMinNum) {

    m_MinNum = newMinNum;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String minVariancePropTipText() {
    return "The minimum proportion of the variance on all the data "
      + "that needs to be present at a node in order for splitting to "
      + "be performed in regression trees.";
  }

  /**
   * Get the value of MinVarianceProp.
   * 
   * @return Value of MinVarianceProp.
   */
  public double getMinVarianceProp() {

    return m_MinVarianceProp;
  }

  /**
   * Set the value of MinVarianceProp.
   * 
   * @param newMinVarianceProp Value to assign to MinVarianceProp.
   */
  public void setMinVarianceProp(double newMinVarianceProp) {

    m_MinVarianceProp = newMinVarianceProp;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String KValueTipText() {
    return "Sets the number of randomly chosen attributes. If 0, int(log_2(#predictors) + 1) is used.";
  }

  /**
   * Get the value of K.
   * 
   * @return Value of K.
   */
  public int getKValue() {

    return m_KValue;
  }

  /**
   * Set the value of K.
   * 
   * @param k Value to assign to K.
   */
  public void setKValue(int k) {

    m_KValue = k;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String seedTipText() {
    return "The random number seed used for selecting attributes.";
  }

  /**
   * Set the seed for random number generation.
   * 
   * @param seed the seed
   */
  @Override
  public void setSeed(int seed) {

    m_randomSeed = seed;
  }

  /**
   * Gets the seed for the random number generations
   * 
   * @return the seed for the random number generation
   */
  @Override
  public int getSeed() {

    return m_randomSeed;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String maxDepthTipText() {
    return "The maximum depth of the tree, 0 for unlimited.";
  }

  /**
   * Get the maximum depth of trh tree, 0 for unlimited.
   * 
   * @return the maximum depth.
   */
  public int getMaxDepth() {
    return m_MaxDepth;
  }

  /**
   * Set the maximum depth of the tree, 0 for unlimited.
   *
   * @param value the maximum depth.
   */
  public void setMaxDepth(int value) {
    m_MaxDepth = value;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numFoldsTipText() {
    return "Determines the amount of data used for backfitting. One fold is used for "
      + "backfitting, the rest for growing the tree. (Default: 0, no backfitting)";
  }

  /**
   * Get the value of NumFolds.
   * 
   * @return Value of NumFolds.
   */
  public int getNumFolds() {

    return m_NumFolds;
  }

  /**
   * Set the value of NumFolds.
   * 
   * @param newNumFolds Value to assign to NumFolds.
   */
  public void setNumFolds(int newNumFolds) {

    m_NumFolds = newNumFolds;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String allowUnclassifiedInstancesTipText() {
    return "Whether to allow unclassified instances.";
  }

  /**
   * Gets whether tree is allowed to abstain from making a prediction.
   * 
   * @return true if tree is allowed to abstain from making a prediction.
   */
  public boolean getAllowUnclassifiedInstances() {

    return m_AllowUnclassifiedInstances;
  }

  /**
   * Set the value of AllowUnclassifiedInstances.
   * 
   * @param newAllowUnclassifiedInstances true if tree is allowed to abstain
   *          from making a prediction
   */
  public void setAllowUnclassifiedInstances(
    boolean newAllowUnclassifiedInstances) {

    m_AllowUnclassifiedInstances = newAllowUnclassifiedInstances;
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String breakTiesRandomlyTipText() {
    return "Break ties randomly when several attributes look equally good.";
  }

  /**
   * Get whether to break ties randomly.
   *
   * @return true if ties are to be broken randomly.
   */
  public boolean getBreakTiesRandomly() {

    return m_BreakTiesRandomly;
  }

  /**
   * Set whether to break ties randomly.
   *
   * @param newBreakTiesRandomly true if ties are to be broken randomly
   */
  public void setBreakTiesRandomly(boolean newBreakTiesRandomly) {

    m_BreakTiesRandomly = newBreakTiesRandomly;
  }

  /**
   * Lists the command-line options for this classifier.
   * 
   * @return an enumeration over all possible options
   */
  @Override
  public Enumeration