EncogUtility
org.encog.util.simple

Class EncogUtility



  • public final class EncogUtilityextends Object
    General utility class for Encog. Provides for some common Encog procedures.
    • Method Detail

      • convertCSV2Binary

        public static void convertCSV2Binary(File csvFile,                     File binFile,                     int inputCount,                     int outputCount,                     boolean headers)
        Convert a CSV file to a binary training file.
        Parameters:
        csvFile - The CSV file.
        binFile - The binary file.
        inputCount - The number of input values.
        outputCount - The number of output values.
        headers - True, if there are headers on the3 CSV.
      • loadCSV2Memory

        public static MLDataSet loadCSV2Memory(String filename,                       int input,                       int ideal,                       boolean headers,                       CSVFormat format,                       boolean significance)
        Load CSV to memory.
        Parameters:
        filename - The CSV file to load.
        input - The input count.
        ideal - The ideal count.
        headers - True, if headers are present.
        format - The loaded dataset.
        significance - True, if there is a significance column.
        Returns:
        The loaded dataset.
      • evaluate

        public static void evaluate(MLRegression network,            MLDataSet training)
        Evaluate the network and display (to the console) the output for every value in the training set. Displays ideal and actual.
        Parameters:
        network - The network to evaluate.
        training - The training set to evaluate.
      • formatNeuralData

        public static String formatNeuralData(MLData data)
        Format neural data as a list of numbers.
        Parameters:
        data - The neural data to format.
        Returns:
        The formatted neural data.
      • simpleFeedForward

        public static BasicNetwork simpleFeedForward(int input,                             int hidden1,                             int hidden2,                             int output,                             boolean tanh)
        Create a simple feedforward neural network.
        Parameters:
        input - The number of input neurons.
        hidden1 - The number of hidden layer 1 neurons.
        hidden2 - The number of hidden layer 2 neurons.
        output - The number of output neurons.
        tanh - True to use hyperbolic tangent activation function, false to use the sigmoid activation function.
        Returns:
        The neural network.
      • trainConsole

        public static void trainConsole(BasicNetwork network,                MLDataSet trainingSet,                int minutes)
        Train the neural network, using SCG training, and output status to the console.
        Parameters:
        network - The network to train.
        trainingSet - The training set.
        minutes - The number of minutes to train for.
      • trainConsole

        public static void trainConsole(MLTrain train,                BasicNetwork network,                MLDataSet trainingSet,                int minutes)
        Train the network, using the specified training algorithm, and send the output to the console.
        Parameters:
        train - The training method to use.
        network - The network to train.
        trainingSet - The training set.
        minutes - The number of minutes to train for.
      • trainToError

        public static void trainToError(MLMethod method,                MLDataSet dataSet,                double error)
        Train the method, to a specific error, send the output to the console.
        Parameters:
        method - The method to train.
        dataSet - The training set to use.
        error - The error level to train to.
      • trainToError

        public static void trainToError(MLTrain train,                double error)
        Train to a specific error, using the specified training method, send the output to the console.
        Parameters:
        train - The training method.
        error - The desired error level.
      • loadEGB2Memory

        public static MLDataSet loadEGB2Memory(File filename)
      • convertCSV2Binary

        public static void convertCSV2Binary(String csvFile,                     String binFile,                     int inputCount,                     int outputCount,                     boolean headers)
        Convert a CSV file to a binary training file.
        Parameters:
        csvFile - The binary file.
        binFile - The binary file.
        inputCount - The number of input values.
        outputCount - The number of output values.
        headers - True, if there are headers on the CSV.
      • convertCSV2Binary

        public static void convertCSV2Binary(File csvFile,                     CSVFormat format,                     File binFile,                     int[] input,                     int[] ideal,                     boolean headers)
      • calculateRegressionError

        public static double calculateRegressionError(MLRegression method,                              MLDataSet data)

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