org.encog.neural.pnn
public class BasicPNN extends AbstractPNN implements MLRegression, MLError, MLClassification
Constructor and Description |
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BasicPNN(PNNKernelType kernel,
PNNOutputMode outmodel,
int inputCount,
int outputCount)
Construct a BasicPNN network.
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Modifier and Type | Method and Description |
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double |
calculateError(MLDataSet data)
Calculate the error of the ML method, given a dataset.
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int |
classify(MLData input)
Classify the input into a group.
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MLData |
compute(MLData input)
Compute the output from this network.
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int[] |
getCountPer() |
double[] |
getPriors() |
BasicMLDataSet |
getSamples() |
double[] |
getSigma() |
void |
setSamples(BasicMLDataSet samples) |
void |
updateProperties()
Update any objeccts when a property changes.
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getDeriv, getDeriv2, getError, getExclude, getInputCount, getKernel, getOutputCount, getOutputMode, isSeparateClass, isTrained, resetConfusion, setError, setExclude, setSeparateClass, setTrained
getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getInputCount
getOutputCount
public BasicPNN(PNNKernelType kernel, PNNOutputMode outmodel, int inputCount, int outputCount)
kernel
- The kernel to use.outmodel
- The output model for this network.inputCount
- The number of inputs in this network.outputCount
- The number of outputs in this network.public MLData compute(MLData input)
compute
in interface MLRegression
compute
in class AbstractPNN
input
- The input to the network.public int[] getCountPer()
public double[] getPriors()
public BasicMLDataSet getSamples()
public double[] getSigma()
public void setSamples(BasicMLDataSet samples)
samples
- the samples to setpublic void updateProperties()
updateProperties
in interface MLProperties
updateProperties
in class BasicML
public double calculateError(MLDataSet data)
calculateError
in interface MLError
data
- The dataset.public int classify(MLData input)
classify
in interface MLClassification
input
- The input data to classify.Copyright © 2014. All Rights Reserved.