cc.mallet.topics
Class DMROptimizable
java.lang.Object
cc.mallet.topics.DMROptimizable
- All Implemented Interfaces:
- Optimizable, Optimizable.ByGradientValue
public class DMROptimizable
- extends java.lang.Object
- implements Optimizable.ByGradientValue
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
DMROptimizable
public DMROptimizable()
DMROptimizable
public DMROptimizable(InstanceList instances,
MaxEnt initialClassifier)
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