BasicPNN
org.encog.neural.pnn

Class BasicPNN

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


    public class BasicPNNextends AbstractPNNimplements 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 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 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 int[] getCountPer()
        Returns:
        the countPer
      • getPriors

        public double[] getPriors()
        Returns:
        the priors
      • getSigma

        public double[] getSigma()
        Returns:
        the sigma
      • setSamples

        public void setSamples(BasicMLDataSet samples)
        Parameters:
        samples - the samples to set
      • 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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