com.imsl.datamining.neural

Class QuasiNewtonTrainer.BlockGradObjective

java.lang.Object

com.imsl.datamining.neural.QuasiNewtonTrainer.Objective

com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective

com.imsl.datamining.neural.QuasiNewtonTrainer.BlockGradObjective

All Implemented Interfaces:

MinUnconMultiVar.Function, MinUnconMultiVar.Gradient

Enclosing class:

QuasiNewtonTrainer

protected class QuasiNewtonTrainer.BlockGradObjective
extends QuasiNewtonTrainer.GradObjective

Field Summary

Fields inherited from class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective

nFunctionEvaluations, nObs, nY

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	BlockGradObjective()

Method Summary

Modifier and Type	Method and Description
double	f(double[] weights) Public interface for the multivariate function to be minimized.
void	gradient(double[] weights, double[] gradient) Public interface for the gradient of the multivariate function to be minimized.

Methods inherited from class java.lang.Object

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

Constructor Detail

BlockGradObjective

protected BlockGradObjective()

Method Detail

f

public double f(double[] weights)

Description copied from interface: MinUnconMultiVar.Function

Public interface for the multivariate function to be minimized.

Specified by:

f in interface MinUnconMultiVar.Function

Overrides:

f in class QuasiNewtonTrainer.Objective

Parameters:

weights - a double array, the point at which the function is to be evaluated

Returns:

a double, the value of the function at x

gradient

public void gradient(double[] weights,
                     double[] gradient)

Description copied from interface: MinUnconMultiVar.Gradient

Public interface for the gradient of the multivariate function to be minimized.

Specified by:

gradient in interface MinUnconMultiVar.Gradient

Overrides:

gradient in class QuasiNewtonTrainer.GradObjective

Parameters:

weights - a double array, the point at which the gradient of the function is to be evaluated

gradient - a double array, the value of the gradient of the function at x