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## org.encog.neural.pnn
Class AbstractPNN

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

All Implemented Interfaces:
  Serializable, MLMethod, MLProperties

Direct Known Subclasses:
  BasicPNN

    public abstract class AbstractPNN

extends BasicML

Abstract class to build PNN networks upon.

See Also:
  Serialized Form

Constructor Summary
`AbstractPNN(PNNKernelType kernel, PNNOutputMode outputMode, int inputCount, int outputCount)` Constructor.

Method Summary
`abstract MLData` | `compute(MLData input)` Compute the output from the network.
`double[]` | `getDeriv()`
`double[]` | `getDeriv2()`
`double` | `getError()`
`int` | `getExclude()`
`int` | `getInputCount()`
`PNNKernelType` | `getKernel()`
`int` | `getOutputCount()`
`PNNOutputMode` | `getOutputMode()`
`boolean` | `isSeparateClass()`
`boolean` | `isTrained()`
`void` | `resetConfusion()` Reset the confusion.
`void` | `setError(double error)`
`void` | `setExclude(int exclude)`
`void` | `setSeparateClass(boolean separateClass)`
`void` | `setTrained(boolean trained)`

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

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

Constructor Detail

### AbstractPNN

    public AbstractPNN(PNNKernelType kernel,
                       PNNOutputMode outputMode,
                       int inputCount,
                       int outputCount)

  Constructor.

  Parameters:
    `kernel` - The kernel type to use.
    `outputMode` - The output mode to use.
    `inputCount` - The input count.
    `outputCount` - The output count.

Method Detail

### compute

    public abstract MLData compute(MLData input)

  Compute the output from the network.

  Parameters:
    `input` - The input to the network.
  Returns:
    The output from the network.

### getDeriv

    public double[] getDeriv()

  Returns:
    the deriv

### getDeriv2

    public double[] getDeriv2()

  Returns:
    the deriv2

### getError

    public double getError()

  Returns:
    the error

### getExclude

    public int getExclude()

  Returns:
    the exclude

### getInputCount

    public int getInputCount()

  Returns:
    the inputCount

### getKernel

    public PNNKernelType getKernel()

  Returns:
    the kernel

### getOutputCount

    public int getOutputCount()

  Returns:
    the outputCount

### getOutputMode

    public PNNOutputMode getOutputMode()

  Returns:
    the outputMode

### isTrained

    public boolean isTrained()

  Returns:
    the trained

### resetConfusion

    public void resetConfusion()

  Reset the confusion.

### setError

    public void setError(double error)

  Parameters:
    `error` - the error to set

### setExclude

    public void setExclude(int exclude)

  Parameters:
    `exclude` - the exclude to set

### setTrained

    public void setTrained(boolean trained)

  Parameters:
    `trained` - the trained to set

### isSeparateClass

    public boolean isSeparateClass()

  Returns:
    the separateClass

### setSeparateClass

    public void setSeparateClass(boolean separateClass)

  Parameters:
    `separateClass` - the separateClass to set

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