JMSLTM Numerical Library 6.1

com.imsl.datamining.neural
Class QuasiNewtonTrainer.BlockGradObjective

java.lang.Object
  extended by com.imsl.datamining.neural.QuasiNewtonTrainer.Objective
      extended by com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
          extended by 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
protected QuasiNewtonTrainer.BlockGradObjective()
           
 
Method Summary
 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

QuasiNewtonTrainer.BlockGradObjective

protected QuasiNewtonTrainer.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

JMSLTM Numerical Library 6.1

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Built July 30 2010.