edu.stanford.nlp.classify
Class BiasedLogConditionalObjectiveFunction

java.lang.Object
  extended by edu.stanford.nlp.optimization.AbstractCachingDiffFunction
      extended by 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
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
BiasedLogConditionalObjectiveFunction(GeneralDataset<?,?> dataset, double[][] confusionMatrix)
           
BiasedLogConditionalObjectiveFunction(GeneralDataset<?,?> dataset, double[][] confusionMatrix, LogPrior prior)
           
BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix)
           
BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix, LogPrior prior)
           
 
Method Summary
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, 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


Stanford NLP Group