mulan.classifier.neural
Class MMPRandomizedUpdateRule

java.lang.Object
  mulan.classifier.neural.MMPUpdateRuleBase
      mulan.classifier.neural.MMPRandomizedUpdateRule

All Implemented Interfaces:

ModelUpdateRule

public class MMPRandomizedUpdateRule
extends MMPUpdateRuleBase

Implementation of randomized update rule for MMPLearner. It is a randomized variation of MMPUniformUpdateRule. A opposed to uniformed update, the randomized version will penalize each wrongly order pair of labels by random value from interval <0,1>. Afterwards, the penalty weights are normalized, so their sum is equal to 1.

The model is represented as a list of perceptrons (one for each label), each represented by Neuron. Perceptrons are expected to be in the same order as labels in training data set.

Version:

2012.02.27

Author:

Jozef Vilcek

See Also:

MMPUpdateRuleBase

Constructor Summary

MMPRandomizedUpdateRule(List<Neuron> perceptrons, RankingLossFunction lossMeasure)
Creates a new instance of MMPRandomizedUpdateRule.

Method Summary

protected double[]

computeUpdateParameters(DataPair example, double[] confidences, double loss) Computes update parameters for each perceptron which will be subsequently used for updating the weights.

Methods inherited from class mulan.classifier.neural.MMPUpdateRuleBase

process

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MMPRandomizedUpdateRule

public MMPRandomizedUpdateRule(List<Neuron> perceptrons,
                               RankingLossFunction lossMeasure)

Creates a new instance of MMPRandomizedUpdateRule.

Parameters:

perceptrons - the list of perceptrons, representing the model, which will receive updates.

lossMeasure - the loss measure used to decide when the model should be updated by the rule

Method Detail

computeUpdateParameters

protected double[] computeUpdateParameters(DataPair example,
                                           double[] confidences,
                                           double loss)

Description copied from class: MMPUpdateRuleBase

Computes update parameters for each perceptron which will be subsequently used for updating the weights. The function is called internally from MMPUpdateRuleBase.process(DataPair, Map) function, when update of model for given input example is needed.

Specified by:

computeUpdateParameters in class MMPUpdateRuleBase

Parameters:

example - the input example

confidences - the confidences outputed by the model the input example

loss - the lossFunction measure of the model for given input example

Returns:

the parameters for updating preceptrons