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java.lang.Objectorg.encog.mathutil.matrices.hessian.BasicHessian
org.encog.mathutil.matrices.hessian.HessianFD
public class HessianFD
Calculate the Hessian matrix using the finite difference method. This is a very simple method of calculating the Hessian. The algorithm does not vary greatly by number layers. This makes it very useful as a tool to check the accuracy of other methods of determining the Hessian. For more information on the Finite Difference Method see the following article. http://en.wikipedia.org/wiki/Finite_difference_method
Field Summary | |
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double |
INITIAL_STEP
The initial step size for dStep. |
Fields inherited from class org.encog.mathutil.matrices.hessian.BasicHessian |
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flat, gradients, hessian, hessianMatrix, network, sse, training |
Constructor Summary | |
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HessianFD()
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Method Summary | |
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void |
compute()
Compute the Hessian. |
double[] |
createCoefficients()
Compute finite difference coefficients according to the method provided here: http://en.wikipedia.org/wiki/Finite_difference_coefficients |
int |
getPointsPerSide()
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void |
init(BasicNetwork theNetwork,
MLDataSet theTraining)
Init the class. |
void |
setPointsPerSide(int pointsPerSide)
This specifies the number of points per side, default is 5. |
Methods inherited from class org.encog.mathutil.matrices.hessian.BasicHessian |
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clear, getGradients, getHessian, getHessianMatrix, getSSE, updateHessian |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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public final double INITIAL_STEP
Constructor Detail |
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public HessianFD()
Method Detail |
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public void init(BasicNetwork theNetwork, MLDataSet theTraining)
init
in interface ComputeHessian
init
in class BasicHessian
theNetwork
- The neural network to train.theTraining
- The training set to train with.public void compute()
public double[] createCoefficients()
public int getPointsPerSide()
public void setPointsPerSide(int pointsPerSide)
pointsPerSide
- The number of points per side.
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