Package de.jungblut.classification.regression

Class LogisticRegression

java.lang.Object

de.jungblut.classification.AbstractPredictor

de.jungblut.classification.AbstractClassifier

de.jungblut.classification.regression.LogisticRegression

All Implemented Interfaces:

Classifier, Predictor

public final class LogisticRegression
extends AbstractClassifier

Constructor Summary

Constructors

Constructor	Description
LogisticRegression(double lambda, Minimizer minimizer, int numIterations, boolean verbose)	Creates a new logistic regression.
LogisticRegression(de.jungblut.math.DoubleVector theta)	Creates a new logistic regression by already existing parameters.

Method Summary

Modifier and Type	Method	Description
de.jungblut.math.DoubleVector	getTheta()
de.jungblut.math.DoubleVector	predict(de.jungblut.math.DoubleVector features)	Classifies the given features.
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

LogisticRegression

public LogisticRegression(double lambda,
                          Minimizer minimizer,
                          int numIterations,
                          boolean verbose)

Creates a new logistic regression.

Parameters:

lambda - the regularization parameter.

minimizer - the minimizer to use to train this model.

numIterations - the number of iterations to make.

verbose - output the progress to STDOUT if true.

LogisticRegression

public LogisticRegression(de.jungblut.math.DoubleVector theta)

Creates a new logistic regression by already existing parameters.

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.DoubleVector getTheta()

Returns:

the learned weights.FSO