de.jungblut.classification.tree

Class RandomForest

java.lang.Object

de.jungblut.classification.AbstractPredictor

de.jungblut.classification.AbstractClassifier

de.jungblut.classification.tree.RandomForest

All Implemented Interfaces:

Classifier, Predictor

public final class RandomForest
extends AbstractClassifier

A decision tree forest, using bagging. The decision trees inside are compiled directly into byte code for fast performance. The training can be done multithreaded.

Author:

thomasjungblut

Method Summary

Modifier and Type	Method and Description
RandomForest	compile()
static RandomForest	create(int numTrees) Creates a new random forest, trains on one thread with the number of trees supplied.
static RandomForest	create(int numTrees, FeatureType[] types) Creates a new random forest, trains on one thread with the number of trees supplied.
static RandomForest	deserialize(DataInput in) Reads a new forest from the given stream.
RandomForest	numThreads(int numThreads)
de.jungblut.math.DoubleVector	predict(de.jungblut.math.DoubleVector features) Classifies the given features.
de.jungblut.math.DoubleVector	predictProbability(de.jungblut.math.DoubleVector features) Classifies the given features.
static void	serialize(RandomForest tree, DataOutput out) Writes the given forest to the output stream.
RandomForest	setFeatureTypes(FeatureType[] types)
RandomForest	setMaxHeight(int max) Sets the maximum height of this random forest.
RandomForest	setNumRandomFeaturesToChoose(int numRandomFeaturesToChoose)
void	train(de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome) Trains this classifier with the given features and the outcome.
RandomForest	verbose()
RandomForest	verbose(boolean verb)

Methods inherited from class de.jungblut.classification.AbstractClassifier

train

Methods inherited from class de.jungblut.classification.AbstractPredictor

extractPredictedClass, extractPredictedClass, predictedClass, predictedClass

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jungblut.classification.Predictor

asClassifier, extractPredictedClass, extractPredictedClass, predictedClass, predictedClass

Method Detail

train

public void train(de.jungblut.math.DoubleVector[] features,
                  de.jungblut.math.DoubleVector[] outcome)

Description copied from interface: Classifier

Trains this classifier with the given features and the outcome.

Specified by:

train in interface Classifier

Overrides:

train in class AbstractClassifier

outcome - the outcome must have classes labeled as doubles. E.G. in the binary case you have a single element and decide between 0d and 1d. In higher dimensional cases you have each of these single elements mapped to a dimension.

predict

public de.jungblut.math.DoubleVector predict(de.jungblut.math.DoubleVector features)

Description copied from interface: Predictor

Classifies the given features.

Returns:

the vector that contains an indicator at the index of the class. Usually zero or 1, in some cases it is a probability or activation value.

predictProbability

public de.jungblut.math.DoubleVector predictProbability(de.jungblut.math.DoubleVector features)

Description copied from interface: Predictor

Classifies the given features.

Specified by:

predictProbability in interface Predictor

Overrides:

predictProbability in class AbstractPredictor

Returns:

a vector that returns the probability of all outcomes. The output vector should sum to one.

compile

public RandomForest compile()

Returns:

sets this instance to compile and returns it.

verbose

public RandomForest verbose()

Returns:

sets this instance to verbose and returns it.

verbose

public RandomForest verbose(boolean verb)

Returns:

sets this instance to verbose and returns it.

setMaxHeight

public RandomForest setMaxHeight(int max)

Sets the maximum height of this random forest.

Returns:

this instance.

numThreads

public RandomForest numThreads(int numThreads)

Returns:

this instance, set the number of threads for training the forest.

setNumRandomFeaturesToChoose

public RandomForest setNumRandomFeaturesToChoose(int numRandomFeaturesToChoose)

Returns:

this instance, set to the number of random features to choose at every decision tree level.

setFeatureTypes

public RandomForest setFeatureTypes(FeatureType[] types)

Returns:

sets the feature types of the decision tree.

create

public static RandomForest create(int numTrees)

Creates a new random forest, trains on one thread with the number of trees supplied. It chooses sqrt(#features) random features at each tree level. In addition, it treats all features as categorical values.

create

public static RandomForest create(int numTrees,
                                  FeatureType[] types)

Creates a new random forest, trains on one thread with the number of trees supplied. It chooses log(#features) random features at each tree level.

serialize

public static void serialize(RandomForest tree,
                             DataOutput out)
                      throws IOException

Writes the given forest to the output stream. Note that the stream isn't closed here.

Throws:

IOException

deserialize

public static RandomForest deserialize(DataInput in)
                                throws IOException

Reads a new forest from the given stream. Note that the stream isn't closed here.

Throws:

IOException