edu.stanford.nlp.classify
Class AdaptedGaussianPriorObjectiveFunction

java.lang.Object
  extended by edu.stanford.nlp.optimization.AbstractCachingDiffFunction
      extended by edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
          extended by edu.stanford.nlp.classify.LogConditionalObjectiveFunction
              extended by edu.stanford.nlp.classify.AdaptedGaussianPriorObjectiveFunction
All Implemented Interfaces:
DiffFunction, Function, HasInitial

public class AdaptedGaussianPriorObjectiveFunction
extends LogConditionalObjectiveFunction

Adapt the mean of the Gaussian Prior by shifting the mean to the previously trained weights

Author:
Pi-Chuan Chang

Nested Class Summary
 
Nested classes/interfaces inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
AbstractStochasticCachingDiffFunction.SamplingMethod
 
Field Summary
 
Fields inherited from class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
data, dataweights, derivativeAD, derivativeNumerator, labels, numClasses, numFeatures, prior, priorDerivative, probs, sums, useSummedConditionalLikelihood, values, xAD
 
Fields inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
allIndices, curElement, extFiniteDiffDerivative, finiteDifferenceStepSize, gradPerturbed, hasNewVals, HdotV, lastBatch, lastBatchSize, lastElement, lastVBatch, lastXBatch, method, randGenerator, recalculatePrevBatch, returnPreviousValues, sampleMethod, xPerturbed
 
Fields inherited from class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
derivative, value
 
Constructor Summary
AdaptedGaussianPriorObjectiveFunction(GeneralDataset dataset, LogPrior prior, double[][] weights)
           
 
Method Summary
protected  void calculate(double[] x)
          Calculate the conditional likelihood.
protected  void rvfcalculate(double[] x)
          Calculate conditional likelihood for datasets with real-valued features.
 double[] to1D(double[][] x2)
           
 
Methods inherited from class edu.stanford.nlp.classify.LogConditionalObjectiveFunction
calculateStochastic, calculateStochasticAlgorithmicDifferentiation, calculateStochasticFiniteDifference, calculateStochasticGradientOnly, dataDimension, domainDimension, indexOf, setPrior, setUseSumCondObjFun, to2D
 
Methods inherited from class edu.stanford.nlp.optimization.AbstractStochasticCachingDiffFunction
clearCache, copy, decrementBatch, derivativeAt, derivativeAt, HdotVAt, HdotVAt, HdotVAt, incrementBatch, initial, lastValue, setValue, valueAt, valueAt
 
Methods inherited from class edu.stanford.nlp.optimization.AbstractCachingDiffFunction
derivativeAt, valueAt
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

AdaptedGaussianPriorObjectiveFunction

public AdaptedGaussianPriorObjectiveFunction(GeneralDataset dataset,
                                             LogPrior prior,
                                             double[][] weights)
Method Detail

calculate

protected void calculate(double[] x)
Calculate the conditional likelihood.

Overrides:
calculate in class LogConditionalObjectiveFunction

rvfcalculate

protected void rvfcalculate(double[] x)
Description copied from class: LogConditionalObjectiveFunction
Calculate conditional likelihood for datasets with real-valued features. Currently this can calculate CL only (no support for SCL). TODO: sum-conditional obj. fun. with RVFs.

Overrides:
rvfcalculate in class LogConditionalObjectiveFunction
Parameters:
x -

to1D

public double[] to1D(double[][] x2)


Stanford NLP Group