org.encog.neural.networks.training.pnn
Class DeriveMinimum
java.lang.Object
org.encog.neural.networks.training.pnn.DeriveMinimum
public class DeriveMinimum
- extends Object
This class determines optimal values for multiple sigmas in a PNN kernel.
This is done using a CJ (conjugate gradient) method.
Some of the algorithms in this class are based on C++ code from:
Advanced Algorithms for Neural Networks: A C++ Sourcebook by Timothy Masters
John Wiley & Sons Inc (Computers); April 3, 1995 ISBN: 0471105880
Method Summary |
double |
calculate(int maxIterations,
double maxError,
double eps,
double tol,
CalculationCriteria network,
int n,
double[] x,
double ystart,
double[] base,
double[] direc,
double[] g,
double[] h,
double[] deriv2)
Derive the minimum, using a conjugate gradient method. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
DeriveMinimum
public DeriveMinimum()
calculate
public double calculate(int maxIterations,
double maxError,
double eps,
double tol,
CalculationCriteria network,
int n,
double[] x,
double ystart,
double[] base,
double[] direc,
double[] g,
double[] h,
double[] deriv2)
- Derive the minimum, using a conjugate gradient method.
- Parameters:
maxIterations
- The max iterations.maxError
- Stop at this error rate.eps
- The machine's precision.tol
- The convergence tolerance.network
- The network to get the error from.n
- The number of variables.x
- The independent variable.ystart
- The start for y.base
- Work vector, must have n elements.direc
- Work vector, must have n elements.g
- Work vector, must have n elements.h
- Work vector, must have n elements.deriv2
- Work vector, must have n elements.
- Returns:
- The best error.
Copyright © 2014. All Rights Reserved.