cc.mallet.topics
Class DMROptimizable

java.lang.Object
  extended by cc.mallet.topics.DMROptimizable
All Implemented Interfaces:
Optimizable, Optimizable.ByGradientValue

public class DMROptimizable
extends java.lang.Object
implements Optimizable.ByGradientValue


Nested Class Summary
 
Nested classes/interfaces inherited from interface cc.mallet.optimize.Optimizable
Optimizable.ByBatchGradient, Optimizable.ByCombiningBatchGradient, Optimizable.ByGISUpdate, Optimizable.ByGradient, Optimizable.ByGradientValue, Optimizable.ByHessian, Optimizable.ByValue, Optimizable.ByVotedPerceptron
 
Constructor Summary
DMROptimizable()
           
DMROptimizable(InstanceList instances, MaxEnt initialClassifier)
           
 
Method Summary
 MaxEnt getClassifier()
           
 int getNumParameters()
           
 double getParameter(int index)
           
 void getParameters(double[] buff)
           
 double getValue()
          The log probability of the observed count vectors given the features.
 void getValueGradient(double[] buffer)
           
 void setInterceptGaussianPriorVariance(double sigmaSquared)
          Set the variance for the default features (aka intercept terms), generally larger than the variance for the regular features.
 void setParameter(int index, double v)
           
 void setParameters(double[] buff)
           
 void setRegularGaussianPriorVariance(double sigmaSquared)
          Set the variance for regular (non default) features, generally smaller than the variance for the default features.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

DMROptimizable

public DMROptimizable()

DMROptimizable

public DMROptimizable(InstanceList instances,
                      MaxEnt initialClassifier)
Method Detail

setInterceptGaussianPriorVariance

public void setInterceptGaussianPriorVariance(double sigmaSquared)
Set the variance for the default features (aka intercept terms), generally larger than the variance for the regular features.


setRegularGaussianPriorVariance

public void setRegularGaussianPriorVariance(double sigmaSquared)
Set the variance for regular (non default) features, generally smaller than the variance for the default features.


getClassifier

public MaxEnt getClassifier()

getParameter

public double getParameter(int index)
Specified by:
getParameter in interface Optimizable

setParameter

public void setParameter(int index,
                         double v)
Specified by:
setParameter in interface Optimizable

getNumParameters

public int getNumParameters()
Specified by:
getNumParameters in interface Optimizable

getParameters

public void getParameters(double[] buff)
Specified by:
getParameters in interface Optimizable

setParameters

public void setParameters(double[] buff)
Specified by:
setParameters in interface Optimizable

getValue

public double getValue()
The log probability of the observed count vectors given the features.

Specified by:
getValue in interface Optimizable.ByGradientValue

getValueGradient

public void getValueGradient(double[] buffer)
Specified by:
getValueGradient in interface Optimizable.ByGradientValue