Package org.encog.neural.flat

Examples of org.encog.neural.flat.FlatNetwork.randomize()

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  }

  public static long BenchmarkEncogFlat(double[][] input, double[][] output) {
    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);

    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);
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  public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

  public static void main(final String args[]) {
   
    FlatNetwork network = new FlatNetwork(2,4,0,1,false);
    network.randomize();
   
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
   
   
    TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(network,trainingSet);
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  public static final int AVG_COUNT = 20;

  public static long benchmarkEncogFlat(double[][] input, double[][] output) {
    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);

    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);
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  public static final int AVG_COUNT = 20;

  public static long benchmarkEncogFlat(double[][] input, double[][] output) {
    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);

    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);
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