List of randomize() Examples

Examples of randomize()

com.heatonresearch.aifh.examples.ca.mergelife.physics.MergePhysics.randomize()
{@inheritDoc}
com.heatonresearch.aifh.examples.ca.mergelife.physics.Physics.randomize()
Randomize the physics to random values.
com.heatonresearch.aifh.examples.ca.mergelife.universe.Universe.randomize()
Randomize the universe. @param rnd A random number generator.
com.heatonresearch.aifh.examples.ca.mergelife.universe.UniverseRunner.randomize()
Randomize the universe grid. @param rnd Random number generator.
ise.mace.simulations.evolution.SimulationGenome.randomize()
org.encog.mathutil.randomize.ConsistentRandomizer.randomize()
Generate a random number based on the range specified in the constructor. @param d The range randomizer ignores this value. @return The random number.
org.encog.mathutil.randomize.ConstRandomizer.randomize()
Generate a random number based on the range specified in the constructor. @param d The range randomizer ignores this value. @return The random number.
org.encog.mathutil.randomize.Distort.randomize()
Distort the random number by the factor that was specified in the constructor. @param d The number to distort. @return The result.
org.encog.mathutil.randomize.GaussianRandomizer.randomize()
Generate a random number. @param d The input value, not used. @return The random number.
org.encog.mathutil.randomize.Randomizer.randomize()
Randomize the synapses and bias values in the basic network based on an array, modify the array. Previous values may be used, or they may be discarded, depending on the randomizer. @param network A network to randomize.
org.encog.ml.prg.extension.ProgramExtensionTemplate.randomize()
Randomize this actual tree node. @param rnd Random number generator. @param desiredType The desired type of the randomization, if allowed. @param actual The actual program node to randomize. @param minValue The minimum value to use for randomization. @param maxValue The maximum value to use for randomization.
org.encog.neural.flat.FlatNetwork.randomize()
Perform a simple randomization of the weights of the neural network between -1 and 1.
org.jwildfire.create.tina.random.AbstractRandomGenerator.randomize()
org.jwildfire.create.tina.random.MarsagliaRandomGenerator.randomize()
org.rsbot.script.wrappers.RSTile.randomize()
Randomizes this tile. @param maxXDeviation Max X distance from tile x. @param maxYDeviation Max Y distance from tile y. @return The randomized tile
org.spout.vanilla.world.generator.nether.object.GlowstonePatchObject.randomize()
org.spout.vanilla.world.generator.normal.object.BlockPatchObject.randomize()
org.spout.vanilla.world.generator.normal.object.CactusStackObject.randomize()
org.spout.vanilla.world.generator.normal.object.DungeonObject.randomize()
org.spout.vanilla.world.generator.normal.object.HugeMushroomObject.randomize()
org.spout.vanilla.world.generator.normal.object.SugarCaneStackObject.randomize()
org.spout.vanilla.world.generator.normal.object.tree.TreeObject.randomize()
org.spout.vanilla.world.generator.structure.StructurePiece.randomize()
org.spout.vanilla.world.generator.theend.object.SpireObject.randomize()
prolog.core.Cat.randomize()
weka.core.Instances.randomize()
Shuffles the instances in the set so that they are ordered randomly. @param random a random number generator

Examples of org.encog.mathutil.randomize.Distort.randomize()


  private void optionPerturb(RandomizeNetworkDialog dialog) {
    double percent = dialog.getPerturbPercent().getValue();


    Distort distort = new Distort(percent);
    distort.randomize((BasicNetwork) this.method);
    setDirty(true);
  }


  private void optionGaussian(RandomizeNetworkDialog dialog) {
    double mean = dialog.getMean().getValue();

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Examples of org.encog.mathutil.randomize.GaussianRandomizer.randomize()

  private void optionGaussian(RandomizeNetworkDialog dialog) {
    double mean = dialog.getMean().getValue();
    double dev = dialog.getDeviation().getValue();


    GaussianRandomizer g = new GaussianRandomizer(mean, dev);
    g.randomize((BasicNetwork) this.method);
    setDirty(true);
  }


  private void optionRandomize(RandomizeNetworkDialog dialog) {
    Randomizer r = null;

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Examples of org.encog.mathutil.randomize.Randomizer.randomize()


          double weight = this.network.getWeight(fromLayer,
              fromNeuron, toNeuron);


          if (randomize) {
            weight = d.randomize(weight);
          }


          newWeights[weightsIndex++] = weight;
        }
      }

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Examples of org.encog.mathutil.randomize.Randomizer.randomize()


  public static FlatNetwork createNetwork() {
    BasicNetwork network = EncogUtility
        .simpleFeedForward(2, 4, 0, 1, false);
    Randomizer randomizer = new ConsistentRandomizer(-1, 1);
    randomizer.randomize(network);
    return network.getStructure().getFlat().clone();
  }


  public static void main(String[] args) {

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Examples of org.encog.mathutil.randomize.Randomizer.randomize()

          .getHigh().getValue(), false);
      break;
    }


    if (r != null) {
      r.randomize((BasicNetwork) this.method);
      setDirty(true);
    }
  }


  private void performQuery() {

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Examples of org.encog.ml.prg.extension.ProgramExtensionTemplate.randomize()

      }
    }


    // now actually create the node
    final ProgramNode result = new ProgramNode(program, temp, children);
    temp.randomize(rnd, types, result, getMinConst(), getMaxConst());
    return result;
  }


  /**
   * Create a terminal node.

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

  }


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

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

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

  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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