calhoun.analysis.crf
Interface CRFObjectiveFunctionGradient

All Known Implementing Classes:

CleanLocalScoreSemiMarkovGradient, CleanMaximumLikelihoodSemiMarkovGradient, MaximumExpectedAccuracySemiMarkovGradient, MaximumLikelihoodGradient, MaximumLikelihoodSemiMarkovGradient

public interface CRFObjectiveFunctionGradient

an interface to algorithms that compute an objective function and its gradient for CRFs. The objective function is used to determine the optimal weights during the CRF training process. Usually the objective function value and gradient will be computed many times for different values of feature weights during a training. A numerical optimizer which implements the CRFTraining interface is responsible for choosing the weights and calling this class. Different implementations are avialable to handle the Markov and semi-Markov CRFs and to handle different speed/memory tradeoffs.

Method Summary

double	apply(double[] weights, double[] grad) computes the objective function value and the gradient.
void	clean() Frees resources allocated by setTrainingData
void	setTrainingData(ModelManager fm, java.util.List<? extends TrainingSequence<?>> data) sets the training data that will be used for evaluation of the objective function.

Method Detail

setTrainingData

void setTrainingData(ModelManager fm,
                     java.util.List<? extends TrainingSequence<?>> data)

sets the training data that will be used for evaluation of the objective function. This function will be called before apply is called to set up the training data. Since it is expected that apply will be called many times, this funtion is the place to do one time setup and caching.

Parameters:

fm - the model to use. Defines the hidden states, transitions, and features.

data - the training sequences on which to calculate the objective function.

apply

double apply(double[] weights,
             double[] grad)

computes the objective function value and the gradient. This function will be called for each iteration of the numerical solver during training. In each iteration it takes a new set of feature weights and computes a value for the objective function along with its gradient.

Parameters:

weights - an array of feature weights to use. The length will equal the number of features in the model, and the values will change for each call to apply.

grad - an array which must be filled with the gradient vector when the function returns. For each feature index, the array should contain an entry with the partial derivative with respect to that feature.

Returns:

the value of the objective function. This is what the numerical optimizer will attempt to maximize.

clean

void clean()

Frees resources allocated by setTrainingData