Package org.encog.mathutil.randomize.generate

Examples of org.encog.mathutil.randomize.generate.LinearCongruentialRandom

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  public ConsistentRandomizer(final double min, final double max,
      final int seed) {
    this.max = max;
    this.min = min;
    this.seed = seed;
    this.rand = new LinearCongruentialRandom(seed);
  }
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  /**
   * Randomize the network.
   * @param network The network to randomize.
   */
  public void randomize(final BasicNetwork network) {
    this.rand = new LinearCongruentialRandom(seed);
    super.randomize(network);
  }
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   */
  public static BasicMLDataSet generate(final long seed,
      final int count, final int inputCount,
      final int idealCount, final double min, final double max) {
   
    LinearCongruentialRandom rand =
      new LinearCongruentialRandom(seed);
   
    final BasicMLDataSet result = new BasicMLDataSet();
    for (int i = 0; i < count; i++) {
      final MLData inputData = new BasicMLData(inputCount);

      for (int j = 0; j < inputCount; j++) {
        inputData.setData(j, rand.nextDouble(min, max));
      }

      final MLData idealData = new BasicMLData(idealCount);

      for (int j = 0; j < idealCount; j++) {
        idealData.setData(j, rand.nextDouble(min, max));
      }

      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      result.add(pair);
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  public static void generate(final MLDataSet training,
      final long seed,
      final int count,
      final double min, final double max) {
   
    LinearCongruentialRandom rand
      = new LinearCongruentialRandom(seed);
   
    int inputCount = training.getInputSize();
    int idealCount = training.getIdealSize();
   
    for (int i = 0; i < count; i++) {
      final MLData inputData = new BasicMLData(inputCount);

      for (int j = 0; j < inputCount; j++) {
        inputData.setData(j, rand.nextDouble(min, max));
      }

      final MLData idealData = new BasicMLData(idealCount);

      for (int j = 0; j < idealCount; j++) {
        idealData.setData(j, rand.nextDouble(min, max));
      }

      final BasicMLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      training.add(pair);
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Related Classes of org.encog.mathutil.randomize.generate.LinearCongruentialRandom

  • org.encog.mathutil.randomize.ConsistentRandomizer
  • org.encog.util.benchmark.RandomTrainingFactory
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