edu.stanford.nlp.classify
Class LogPrior
java.lang.Object
edu.stanford.nlp.classify.LogPrior
- All Implemented Interfaces:
- java.io.Serializable
public class LogPrior
- extends java.lang.Object
- implements java.io.Serializable
A Prior for functions. Immutable.
- Author:
- Galen Andrew
- See Also:
- Serialized Form
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
type
public final LogPrior.LogPriorType type
LogPrior
public LogPrior()
LogPrior
public LogPrior(int intPrior)
LogPrior
public LogPrior(LogPrior.LogPriorType type)
LogPrior
public LogPrior(int intPrior,
double sigma,
double epsilon)
LogPrior
public LogPrior(LogPrior.LogPriorType type,
double sigma,
double epsilon)
LogPrior
public LogPrior(double[] C)
- IMPORTANT NOTE: This constructor allows non-uniform regularization, but it
transforms the inputs C (like the machine learning people like) to sigma
(like we NLP folks like). C = 1/\sigma^2
getType
public static LogPrior.LogPriorType getType(java.lang.String name)
getAdaptationPrior
public static LogPrior getAdaptationPrior(double[] means,
LogPrior otherPrior)
getType
public LogPrior.LogPriorType getType()
getSigma
public double getSigma()
getSigmaSquared
public double getSigmaSquared()
getSigmaSquaredM
public double[] getSigmaSquaredM()
getEpsilon
public double getEpsilon()
setSigma
public void setSigma(double sigma)
setSigmaSquared
public void setSigmaSquared(double sigmaSq)
setSigmaSquaredM
public void setSigmaSquaredM(double[] sigmaSq)
setEpsilon
public void setEpsilon(double epsilon)
computeStochastic
public double computeStochastic(double[] x,
double[] grad,
double fractionOfData)
compute
public double compute(double[] x,
double[] grad)
- Adjust the given grad array by adding the prior's gradient component
and return the value of the logPrior
- Parameters:
x
- the input pointgrad
- the gradient array
- Returns:
- the value
Stanford NLP Group