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java.lang.Objectorg.knime.base.data.neural.methods.RProp
public class RProp
Implementation of the RProp Algorithm, as proposed by M. Riedmiller, H.Braun: 'A Direct Adaptive Method for Faster backpropagation Learning: The RPROP Algorithm', Proc. of the IEEE Intl. Conf. on Neural Networks 1993.
Constructor Summary | |
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RProp()
Constructor, uses default learning rate of 0.1, increase parameter 1.2 and decrease parameter 0.5 as proposed in the paper. |
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RProp(double etaPlus,
double etaMinus,
double etaNull)
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Method Summary | |
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double[] |
evaluate(double[] in)
Evaluates input and returns output of output neurons. |
double |
getEtaMinus()
Get negative learning rate. |
double |
getEtaNull()
Get starting value for eta. |
double |
getEtaPlus()
Get positive learning rate. |
void |
setEtaMinus(double etaMinus)
Set negative learning rate. |
void |
setEtaNull(double etaNull)
set starting value for eta. |
void |
setEtaPlus(double etaPlus)
Set positive learning rate. |
static double |
sgn(double d)
Method computes the sign of a double number. |
void |
train(MultiLayerPerceptron nn,
Double[][] samples,
Double[][] outputs)
Train the neural network once. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public RProp()
public RProp(double etaPlus, double etaMinus, double etaNull)
etaPlus
- increase parameteretaMinus
- decrease parameteretaNull
- initial learning rateMethod Detail |
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public void train(MultiLayerPerceptron nn, Double[][] samples, Double[][] outputs)
nn
- neural net to trainsamples
- the samplesoutputs
- the desired outputs for these samplespublic static double sgn(double d)
d
- the number
public double getEtaMinus()
public double getEtaPlus()
public void setEtaMinus(double etaMinus)
etaMinus
- new negative learning ratepublic void setEtaPlus(double etaPlus)
etaPlus
- new positive learning ratepublic double getEtaNull()
public void setEtaNull(double etaNull)
etaNull
- new starting valuepublic double[] evaluate(double[] in)
in
- input for the net
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