edu.cmu.minorthird.classify.algorithms.trees
Class RandomForests

java.lang.Object
  edu.cmu.minorthird.classify.BatchClassifierLearner
      edu.cmu.minorthird.classify.BatchBinaryClassifierLearner
          edu.cmu.minorthird.classify.algorithms.trees.RandomForests

All Implemented Interfaces:

BinaryClassifierLearner, ClassifierLearner, java.lang.Cloneable

public class RandomForests
extends BatchBinaryClassifierLearner

Random Forests implementation. See http://www.stat.berkeley.edu/~breiman/RandomForests/ For algorith details.

Author:

Alexander Friedman

Nested Class Summary

static class

RandomForests.VotingClassifier A set of RandomTree Classifiers FIXME!! All of this stuff is copied directly for AdaBoost with (very) minor modifications

Field Summary

Fields inherited from class edu.cmu.minorthird.classify.BatchClassifierLearner

classifier, dataset

Constructor Summary

RandomForests()

RandomForests(FastRandomTreeLearner baseLearner, int numComponents)

RandomForests(int numComponents)

Method Summary

Classifier	batchTrain(Dataset dataset) subclasses should use this method to implement a batch supervised learning algorithm.
static java.util.Vector<Feature>	getDatasetFeatures(Dataset dataset) Return a vector of all features in the dataset.
static RandomForests	RepeatableForest()
RandomForests	setCollectStats(boolean b)
RandomForests	setScaleWeights(boolean b)
RandomForests	setSelectionSize(int c)
RandomForests	setSelectionSizeLog()
RandomForests	setThreadCount(int c)
RandomForests	setThreaded(boolean b)

Methods inherited from class edu.cmu.minorthird.classify.BatchBinaryClassifierLearner

batchTrainBinary, getBinaryClassifier, getSchema, setSchema

Methods inherited from class edu.cmu.minorthird.classify.BatchClassifierLearner

addExample, completeTraining, copy, getClassifier, hasNextQuery, nextQuery, reset, setInstancePool

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface edu.cmu.minorthird.classify.ClassifierLearner

addExample, completeTraining, copy, getClassifier, hasNextQuery, nextQuery, reset, setInstancePool

Constructor Detail

RandomForests

public RandomForests()

RandomForests

public RandomForests(int numComponents)

RandomForests

public RandomForests(FastRandomTreeLearner baseLearner,
                     int numComponents)

Method Detail

setThreaded

public RandomForests setThreaded(boolean b)

setThreadCount

public RandomForests setThreadCount(int c)

setCollectStats

public RandomForests setCollectStats(boolean b)

setScaleWeights

public RandomForests setScaleWeights(boolean b)

setSelectionSizeLog

public RandomForests setSelectionSizeLog()

setSelectionSize

public RandomForests setSelectionSize(int c)

RepeatableForest

public static RandomForests RepeatableForest()

batchTrain

public Classifier batchTrain(Dataset dataset)

Description copied from class: BatchClassifierLearner

subclasses should use this method to implement a batch supervised learning algorithm.

Specified by:

batchTrain in class BatchClassifierLearner

getDatasetFeatures

public static java.util.Vector<Feature> getDatasetFeatures(Dataset dataset)

Return a vector of all features in the dataset. This should really be in the 'Dataset' interface

Parameters:

dataset -

Returns:

a vector of all features in the dataset