de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
Enum LearningPrinciple

java.lang.Object
  java.lang.Enum<LearningPrinciple>
      de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.LearningPrinciple

All Implemented Interfaces:

Serializable, Comparable<LearningPrinciple>

public enum LearningPrinciple
extends Enum<LearningPrinciple>

This enum can be used to obtain the weights for well-known optimization tasks.

Author:

Jens Keilwagen

See Also:

LogGenDisMixFunction

Enum Constant Summary

MAP
Maximum a posteriori.

MCL
Maximum conditional likelihood.

ML
Maximum Likelihood.

MSP
Maximum supervised posterior.

Field Summary

static int	CONDITIONAL_LIKELIHOOD_INDEX This constant is the array index of the weighting factor for the conditional likelihood.
static int	LIKELIHOOD_INDEX This constant is the array index of the weighting factor for the likelihood.
static int	PRIOR_INDEX This constant is the array index of the weighting factor for the prior.

Method Summary

static double[]	checkWeights(double[] weights) This method checks the values of the weights array.
static double[]	getBeta(LearningPrinciple key) This method returns the standard weights for a predefined key.
static LearningPrinciple	valueOf(String name) Returns the enum constant of this type with the specified name.
static LearningPrinciple[]	values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods inherited from class java.lang.Enum

clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Enum Constant Detail

ML

public static final LearningPrinciple ML

Maximum Likelihood.

MAP

public static final LearningPrinciple MAP

Maximum a posteriori.

MCL

public static final LearningPrinciple MCL

Maximum conditional likelihood.

MSP

public static final LearningPrinciple MSP

Maximum supervised posterior.

Field Detail

CONDITIONAL_LIKELIHOOD_INDEX

public static final int CONDITIONAL_LIKELIHOOD_INDEX

This constant is the array index of the weighting factor for the conditional likelihood.

See Also:

getBeta(LearningPrinciple), Constant Field Values

LIKELIHOOD_INDEX

public static final int LIKELIHOOD_INDEX

This constant is the array index of the weighting factor for the likelihood.

See Also:

getBeta(LearningPrinciple), Constant Field Values

PRIOR_INDEX

public static final int PRIOR_INDEX

This constant is the array index of the weighting factor for the prior.

See Also:

getBeta(LearningPrinciple), Constant Field Values

Method Detail

values

public static LearningPrinciple[] values()

Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:

for (LearningPrinciple c : LearningPrinciple.values())
    System.out.println(c);

Returns:

an array containing the constants of this enum type, in the order they are declared

valueOf

public static LearningPrinciple valueOf(String name)

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Parameters:

name - the name of the enum constant to be returned.

Returns:

the enum constant with the specified name

Throws:

IllegalArgumentException - if this enum type has no constant with the specified name

NullPointerException - if the argument is null

getBeta

public static double[] getBeta(LearningPrinciple key)

This method returns the standard weights for a predefined key.

Parameters:

key - the key

Returns:

the weights array

See Also:

ML, MAP, MCL, MSP

checkWeights

public static double[] checkWeights(double[] weights)
                             throws IllegalArgumentException

This method checks the values of the weights array. If everything is okay it returns a deep copy of the array, otherwise it throws an exception

Parameters:

weights - and array of length 3 with non-negative entries that sum to 1

Returns:

a deep copy of the array

Throws:

IllegalArgumentException - if the weights array is not correct