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

Class TrainBasicPNN

    • 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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