de.jungblut.ner

Class MaxEntMarkovModel

java.lang.Object

de.jungblut.classification.AbstractPredictor

de.jungblut.classification.AbstractClassifier

de.jungblut.ner.MaxEntMarkovModel

All Implemented Interfaces:

Classifier, Predictor

public final class MaxEntMarkovModel
extends AbstractClassifier

Maximum entropy markov model for named entity recognition (classifying labels in sequence learning).

Author:

thomas.jungblut

Constructor Summary

Constructors

Constructor and Description
MaxEntMarkovModel(de.jungblut.math.dense.DenseDoubleMatrix theta, int classes)
MaxEntMarkovModel(Minimizer minimizer, int numIterations, boolean verbose)

Method Summary

Modifier and Type	Method and Description
de.jungblut.math.DoubleMatrix	getTheta()
de.jungblut.math.DoubleMatrix	predict(de.jungblut.math.DoubleMatrix features, de.jungblut.math.DoubleMatrix featuresPerState)
de.jungblut.math.DoubleVector	predict(de.jungblut.math.DoubleVector features) Classifies the given features.
de.jungblut.math.DoubleVector	predict(de.jungblut.math.DoubleVector feature, de.jungblut.math.DoubleVector[] featuresPerState)
void	train(de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome) Trains this classifier with the given features and the outcome.

Methods inherited from class de.jungblut.classification.AbstractClassifier

train

Methods inherited from class de.jungblut.classification.AbstractPredictor

extractPredictedClass, extractPredictedClass, predictedClass, predictedClass, predictProbability

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, predictProbability

Constructor Detail

MaxEntMarkovModel

public MaxEntMarkovModel(Minimizer minimizer,
                         int numIterations,
                         boolean verbose)

MaxEntMarkovModel

public MaxEntMarkovModel(de.jungblut.math.dense.DenseDoubleMatrix theta,
                         int classes)

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.

getTheta

public de.jungblut.math.DoubleMatrix getTheta()

Returns:

the learned parameters.

predict

public de.jungblut.math.DoubleVector predict(de.jungblut.math.DoubleVector feature,
                                             de.jungblut.math.DoubleVector[] featuresPerState)

predict

public de.jungblut.math.DoubleMatrix predict(de.jungblut.math.DoubleMatrix features,
                                             de.jungblut.math.DoubleMatrix featuresPerState)