Package edu.stanford.nlp.classify

Class BiasedLogConditionalObjectiveFunction

java.lang.Object

edu.stanford.nlp.optimization.AbstractCachingDiffFunction

edu.stanford.nlp.classify.BiasedLogConditionalObjectiveFunction

All Implemented Interfaces:

DiffFunction, Function, HasInitial

public class BiasedLogConditionalObjectiveFunction
extends AbstractCachingDiffFunction

Maximizes the conditional likelihood with a given prior.

Author:

Jenny Finkel

Field Summary

Fields

Modifier and Type	Field	Description
protected int[][]	data
protected int[]	labels
protected int	numClasses
protected int	numFeatures
protected LogPrior	prior

Fields inherited from class edu.stanford.nlp.optimization.AbstractCachingDiffFunction

derivative, value

Constructor Summary

Constructors

Constructor	Description
BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix)
BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix, LogPrior prior)
BiasedLogConditionalObjectiveFunction(GeneralDataset<?,?> dataset, double[][] confusionMatrix)
BiasedLogConditionalObjectiveFunction(GeneralDataset<?,?> dataset, double[][] confusionMatrix, LogPrior prior)

Method Summary

Modifier and Type	Method	Description
protected void	calculate(double[] x)	Calculate the value at x and the derivative and save them in the respective fields.
int	domainDimension()	Returns the number of dimensions in the function's domain
protected int	indexOf(int f, int c)
void	setPrior(LogPrior prior)
double[][]	to2D(double[] x)

Methods inherited from class edu.stanford.nlp.optimization.AbstractCachingDiffFunction

clearCache, copy, derivativeAt, ensure, getDerivative, gradientCheck, gradientCheck, initial, lastValue, randomInitial, valueAt

Methods inherited from class java.lang.Object

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

Field Detail

prior

protected LogPrior prior

numFeatures

protected int numFeatures

numClasses

protected int numClasses

data

protected int[][] data

labels

protected int[] labels

Constructor Detail

BiasedLogConditionalObjectiveFunction

public BiasedLogConditionalObjectiveFunction(GeneralDataset<?,?> dataset,
                                             double[][] confusionMatrix)

BiasedLogConditionalObjectiveFunction

public BiasedLogConditionalObjectiveFunction(GeneralDataset<?,?> dataset,
                                             double[][] confusionMatrix,
                                             LogPrior prior)

BiasedLogConditionalObjectiveFunction

public BiasedLogConditionalObjectiveFunction(int numFeatures,
                                             int numClasses,
                                             int[][] data,
                                             int[] labels,
                                             double[][] confusionMatrix)

BiasedLogConditionalObjectiveFunction

public BiasedLogConditionalObjectiveFunction(int numFeatures,
                                             int numClasses,
                                             int[][] data,
                                             int[] labels,
                                             double[][] confusionMatrix,
                                             LogPrior prior)

Method Detail

setPrior

public void setPrior(LogPrior prior)

domainDimension

public int domainDimension()

Description copied from interface: Function

Returns the number of dimensions in the function's domain

Returns:

the number of domain dimensions

indexOf

protected int indexOf(int f,
                      int c)

to2D

public double[][] to2D(double[] x)

calculate

protected void calculate(double[] x)

Description copied from class: AbstractCachingDiffFunction

Calculate the value at x and the derivative and save them in the respective fields.

Specified by:

calculate in class AbstractCachingDiffFunction

Parameters:

x - The point at which to calculate the function