Documentation of 'jhplot.HNeuralNet' Java class.
HNeuralNet
jhplot

Class HNeuralNet



  • public class HNeuralNet
    extends java.lang.Object
    Neural Netwrork calculations. Based on Backpropagation.
    • Constructor Summary

      Constructors 
      Constructor and Description
      HNeuralNet()
      Create a network net and set name for the network
    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method and Description
      void addFeedForwardLayer(int neuronCount)
      Construct this layer with a sigmoid threshold function.
      void addFeedForwardLayerWithBias(int neuronCount)
      Construct this layer with a sigmoid threshold function.
      AnalyzeNetwork analyzeNetwork()
      Analyse the current neural network.
      void doc()
      Show online documentation.
      BasicNetwork editNetwork()
      Edit a neural net in a frame
      BasicNeuralDataSet getData()
      Get data
      java.util.ArrayList<java.lang.Double> getEpochError()
      Returns errors for each epoch.
      BasicNetwork getNetwork()
      Return neural net back.
      MLData predict(MLData input)
      Evaluate data using current NN
      P0D predict(P0D input)
      Generate prediction for input data
      PND predict(PND input)
      Generate predictions for all input data.
      int read(java.lang.String file)
      Read a neural net from a file.
      void reset()
      Reset the weight matrix and the thresholds.
      java.lang.String save(java.lang.String file)
      Save current status of neural net.
      void setData(double[][] input)
      Construct a data set from an input
      void setData(double[][] input, double[][] ideal)
      Construct a data set from an input and idea array.
      void setData(PND input)
      Set data
      void setData(PND input, PND ideal)
      Set data for training.
      void show()
      Show Net in EncodeDocument.
      void showNetwork()
      Show a neural net in a frame.
      void showWeights()
      Show a neural net weights in a separate frame.
      PND standardize(PND input)
      Standardize each column.
      int trainBackpropagation(boolean isShow, int maxEpoch, double learnRate, double momentum, double errorMinEpoch)
      Training neural network.Construct a backpropagation trainer.
      • Methods inherited from class java.lang.Object

        equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    • Constructor Detail

      • HNeuralNet

        public HNeuralNet()
        Create a network net and set name for the network
        Parameters:
        name - name for the network
    • Method Detail

      • reset

        public void reset()
        Reset the weight matrix and the thresholds.
      • addFeedForwardLayer

        public void addFeedForwardLayer(int neuronCount)
        Construct this layer with a sigmoid threshold function. Use sigmoid for activation.
        Parameters:
        neuronCount - How many neurons in this layer
      • addFeedForwardLayerWithBias

        public void addFeedForwardLayerWithBias(int neuronCount)
        Construct this layer with a sigmoid threshold function. Use sigmoid for activation.
        Parameters:
        neuronCount - How many neurons in this layer
      • setData

        public void setData(double[][] input,
                            double[][] ideal)
        Construct a data set from an input and idea array. Used for supervized training.
        Parameters:
        input - The input into the neural network for training.
        ideal - The ideal output for training.
      • setData

        public void setData(double[][] input)
        Construct a data set from an input
        Parameters:
        input - The input into the neural network for training.
      • setData

        public void setData(PND input,
                            PND ideal)
        Set data for training.
        Parameters:
        input - input data set
        ideal - expected resul.
      • setData

        public void setData(PND input)
        Set data
        Parameters:
        input - input data set
      • standardize

        public PND standardize(PND input)
        Standardize each column. This means S(i)= (X(i) - mean) / std fot each column in PND;
        Parameters:
        input - PND
        Returns:
        new PND after standardize
      • predict

        public MLData predict(MLData input)
        Evaluate data using current NN
        Returns:
        data
      • predict

        public P0D predict(P0D input)
        Generate prediction for input data
        Parameters:
        input - input data for predictions
      • predict

        public PND predict(PND input)
        Generate predictions for all input data. Assumes that the predicted array has less then 3 dimensions.
        Parameters:
        input - input data for prediction
        Returns:
        data with predictions
      • trainBackpropagation

        public int trainBackpropagation(boolean isShow,
                                        int maxEpoch,
                                        double learnRate,
                                        double momentum,
                                        double errorMinEpoch)
        Training neural network.Construct a backpropagation trainer. Typical example: train(5000, 0.1, 0.25, 0.001);
        Parameters:
        isShow - Show learning on a pop-up plot
        maxEpoch - maximum number of epochs
        learnRate - The rate at which the weight matrix will be adjusted based on learning.
        momentum - The influence that previous iteration's training deltas will have on the current iteration.
        errorMinEpoch - min error for epoch.
        Returns:
        returns the epoch at which training was stopped.
      • save

        public java.lang.String save(java.lang.String file)
        Save current status of neural net.
        Parameters:
        file - File name
        Returns:
        what is done
      • read

        public int read(java.lang.String file)
        Read a neural net from a file.
        Parameters:
        file - File name
        Returns:
        0 if it is OK. -1 if file not found; -2: if NN not found.
      • getNetwork

        public BasicNetwork getNetwork()
        Return neural net back.
        Returns:
        network
      • showNetwork

        public void showNetwork()
        Show a neural net in a frame.
      • showWeights

        public void showWeights()
        Show a neural net weights in a separate frame.
      • analyzeNetwork

        public AnalyzeNetwork analyzeNetwork()
        Analyse the current neural network.
        Returns:
        analyzer
      • editNetwork

        public BasicNetwork editNetwork()
        Edit a neural net in a frame
      • show

        public void show()
        Show Net in EncodeDocument.
      • getEpochError

        public java.util.ArrayList<java.lang.Double> getEpochError()
        Returns errors for each epoch. If the max epoch number was set in the train() method. The array may have less entries if learning has reached the minimum error.
        Returns:
        arrays of errors for each epoch
      • doc

        public void doc()
        Show online documentation.

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