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## org.encog.neural.pnn
Class BasicPNN

    java.lang.Object
      org.encog.ml.BasicML
          org.encog.neural.pnn.AbstractPNN
              org.encog.neural.pnn.BasicPNN

All Implemented Interfaces:
  Serializable, MLClassification, MLError, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLRegression

    public class BasicPNN

extends AbstractPNN
implements MLRegression, MLError, MLClassification

This class implements either a: Probabilistic Neural Network (PNN) General Regression Neural Network (GRNN) To use a PNN specify an output mode of classification, to make use of a GRNN specify either an output mode of regression or un-supervised autoassociation. The PNN/GRNN networks are potentially very useful. They share some similarities with RBF-neural networks and also the Support Vector Machine (SVM). These network types directly support the use of classification. The following book was very helpful in implementing PNN/GRNN's in Encog. Advanced Algorithms for Neural Networks: A C++ Sourcebook by Timothy Masters, PhD (http://www.timothymasters.info/) John Wiley & Sons Inc (Computers); April 3, 1995, ISBN: 0471105880

See Also:
  Serialized Form

Constructor Summary
`BasicPNN(PNNKernelType kernel, PNNOutputMode outmodel, int inputCount, int outputCount)` Construct a BasicPNN network.

Method Summary
`double` | `calculateError(MLDataSet data)` Calculate the error of the ML method, given a dataset.
`int` | `classify(MLData input)` Classify the input into a group.
`MLData` | `compute(MLData input)` Compute the output from this network.
`int[]` | `getCountPer()`
`double[]` | `getPriors()`
`BasicMLDataSet` | `getSamples()`
`double[]` | `getSigma()`
`void` | `setSamples(BasicMLDataSet samples)`
`void` | `updateProperties()` Update any objeccts when a property changes.

Methods inherited from class org.encog.neural.pnn.AbstractPNN
`getDeriv, getDeriv2, getError, getExclude, getInputCount, getKernel, getOutputCount, getOutputMode, isSeparateClass, isTrained, resetConfusion, setError, setExclude, setSeparateClass, setTrained`

Methods inherited from class org.encog.ml.BasicML
`getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Methods inherited from interface org.encog.ml.MLInput
`getInputCount`

Methods inherited from interface org.encog.ml.MLOutput
`getOutputCount`

Constructor Detail

### BasicPNN

    public BasicPNN(PNNKernelType kernel,
                    PNNOutputMode outmodel,
                    int inputCount,
                    int outputCount)

  Construct a BasicPNN network.

  Parameters:
    `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.

Method Detail

### compute

    public final MLData compute(MLData input)

  Compute the output from this network.

  Specified by:
    `compute` in interface `MLRegression`
  Specified by:
    `compute` in class `AbstractPNN`
  Parameters:
    `input` - The input to the network.
  Returns:
    The output from the network.

### getCountPer

    public final int[] getCountPer()

  Returns:
    the countPer

### getPriors

    public final double[] getPriors()

  Returns:
    the priors

### getSamples

    public final BasicMLDataSet getSamples()

  Returns:
    the samples

### getSigma

    public final double[] getSigma()

  Returns:
    the sigma

### setSamples

    public final void setSamples(BasicMLDataSet samples)

  Parameters:
    `samples` - the samples to set

### updateProperties

    public void updateProperties()

  Update any objeccts when a property changes.

  Specified by:
    `updateProperties` in interface `MLProperties`
  Specified by:
    `updateProperties` in class `BasicML`

### calculateError

    public double calculateError(MLDataSet data)

  Calculate the error of the ML method, given a dataset.

  Specified by:
    `calculateError` in interface `MLError`
  Parameters:
    `data` - The dataset.
  Returns:
    The error.

### classify

    public int classify(MLData input)

  Classify the input into a group.

  Specified by:
    `classify` in interface `MLClassification`
  Parameters:
    `input` - The input data to classify.
  Returns:
    The group that the data was classified into.

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