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## org.encog.neural.networks.training.pnn
Class TrainBasicPNN

    java.lang.Object
      org.encog.ml.train.BasicTraining
          org.encog.neural.networks.training.pnn.TrainBasicPNN

All Implemented Interfaces:
  MLTrain, CalculationCriteria

    public class TrainBasicPNN

extends BasicTraining
implements CalculationCriteria

Train a PNN.

Field Summary
`static double` | `DEFAULT_MAX_ERROR` The default max error.
`static double` | `DEFAULT_MIN_IMPROVEMENT` The default minimum improvement before stop.
`static int` | `DEFAULT_NUM_SIGMAS` The default number of sigmas to evaluate between the low and high.
`static double` | `DEFAULT_SIGMA_HIGH` The default sigma high value.
`static double` | `DEFAULT_SIGMA_LOW` THe default sigma low value.

Constructor Summary
`TrainBasicPNN(BasicPNN network, MLDataSet training)` Train a BasicPNN.

Method Summary
`double` | `calcErrorWithMultipleSigma(double[] x, double[] der1, double[] der2, boolean der)` Calculate the error with multiple sigmas.
`double` | `calcErrorWithSingleSigma(double sig)` Calculate the error using a common sigma.
`double` | `calculateError(MLDataSet training, boolean deriv)` Calculate the error for the entire training set.
`boolean` | `canContinue()`
`MLData` | `computeDeriv(MLData input, MLData target)` Compute the derivative for target data.
`double` | `getMaxError()`
`MLMethod` | `getMethod()` Get the current best machine learning method from the training.
`double` | `getMinImprovement()`
`int` | `getNumSigmas()`
`double` | `getSigmaHigh()`
`double` | `getSigmaLow()`
`void` | `iteration()` Perform one iteration of training.
`TrainingContinuation` | `pause()` Pause the training to continue later.
`void` | `resume(TrainingContinuation state)` Resume training.
`void` | `setMaxError(double maxError)`
`void` | `setMinImprovement(double minImprovement)`
`void` | `setNumSigmas(int numSigmas)`
`void` | `setSigmaHigh(double sigmaHigh)`
`void` | `setSigmaLow(double sigmaLow)`

Methods inherited from class org.encog.ml.train.BasicTraining
`addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, iteration, postIteration, preIteration, setError, setIteration, setTraining`

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

Field Detail

### DEFAULT_MAX_ERROR

    public static final double DEFAULT_MAX_ERROR

  The default max error.

  See Also:
    Constant Field Values

### DEFAULT_MIN_IMPROVEMENT

    public static final double DEFAULT_MIN_IMPROVEMENT

  The default minimum improvement before stop.

  See Also:
    Constant Field Values

### DEFAULT_SIGMA_LOW

    public static final double DEFAULT_SIGMA_LOW

  THe default sigma low value.

  See Also:
    Constant Field Values

### DEFAULT_SIGMA_HIGH

    public static final double DEFAULT_SIGMA_HIGH

  The default sigma high value.

  See Also:
    Constant Field Values

### DEFAULT_NUM_SIGMAS

    public static final int DEFAULT_NUM_SIGMAS

  The default number of sigmas to evaluate between the low and high.

  See Also:
    Constant Field Values

Constructor Detail

### TrainBasicPNN

    public TrainBasicPNN(BasicPNN network,
                         MLDataSet training)

  Train a BasicPNN.

  Parameters:
    `network` - The network to train.
    `training` - The training data.

Method Detail

### calcErrorWithMultipleSigma

    public final double calcErrorWithMultipleSigma(double[] x,
                                                   double[] der1,
                                                   double[] der2,
                                                   boolean der)

  Calculate the error with multiple sigmas.

  Specified by:
    `calcErrorWithMultipleSigma` in interface `CalculationCriteria`
  Parameters:
    `x` - The data.
    `der1` - The first derivative.
    `der2` - The 2nd derivatives.
    `der` - Calculate the derivative.
  Returns:
    The error.

### calcErrorWithSingleSigma

    public final double calcErrorWithSingleSigma(double sig)

  Calculate the error using a common sigma.

  Specified by:
    `calcErrorWithSingleSigma` in interface `CalculationCriteria`
  Parameters:
    `sig` - The sigma to use.
  Returns:
    The training error.

### calculateError

    public final double calculateError(MLDataSet training,
                                       boolean deriv)

  Calculate the error for the entire training set.

  Parameters:
    `training` - Training set to use.
    `deriv` - Should we find the derivative.
  Returns:
    The error.

### canContinue

    public final boolean canContinue()

  Specified by:
    `canContinue` in interface `MLTrain`
  Returns:
    True if the training can be paused, and later continued.

### computeDeriv

    public final MLData computeDeriv(MLData input,
                                     MLData target)

  Compute the derivative for target data.

  Parameters:
    `input` - The input.
    `target` - The target data.
  Returns:
    The output.

### getMaxError

    public final double getMaxError()

  Returns:
    the maxError

### getMethod

    public final MLMethod getMethod()

  Get the current best machine learning method from the training.

  Specified by:
    `getMethod` in interface `MLTrain`
  Returns:
    The best machine learningm method.

### getMinImprovement

    public final double getMinImprovement()

  Returns:
    the minImprovement

### getNumSigmas

    public final int getNumSigmas()

  Returns:
    the numSigmas

### getSigmaHigh

    public final double getSigmaHigh()

  Returns:
    the sigmaHigh

### getSigmaLow

    public final double getSigmaLow()

  Returns:
    the sigmaLow

### iteration

    public final void iteration()

  Perform one iteration of training.

  Specified by:
    `iteration` in interface `MLTrain`

### pause

    public final TrainingContinuation pause()

  Pause the training to continue later.

  Specified by:
    `pause` in interface `MLTrain`
  Returns:
    A training continuation object.

### resume

    public void resume(TrainingContinuation state)

  Resume training.

  Specified by:
    `resume` in interface `MLTrain`
  Parameters:
    `state` - The training continuation object to use to continue.

### setMaxError

    public final void setMaxError(double maxError)

  Parameters:
    `maxError` - the maxError to set

### setMinImprovement

    public final void setMinImprovement(double minImprovement)

  Parameters:
    `minImprovement` - the minImprovement to set

### setNumSigmas

    public final void setNumSigmas(int numSigmas)

  Parameters:
    `numSigmas` - the numSigmas to set

### setSigmaHigh

    public final void setSigmaHigh(double sigmaHigh)

  Parameters:
    `sigmaHigh` - the sigmaHigh to set

### setSigmaLow

    public final void setSigmaLow(double sigmaLow)

  Parameters:
    `sigmaLow` - the sigmaLow to set

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