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 weka.core.Instances.randomize()

    if(m_NumAntds != -1){
      grow(data);
    }
    else{


      data.randomize(m_Random);


      // Split data into Grow and Prune     
      data.stratify(m_Folds);
  
      Instances growData=data.trainCV(m_Folds, m_Folds-1, m_Random);

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Examples of weka.core.Instances.randomize()

  RidorRule rule = new RidorRule();                                
  rule.setPredictedClass(0);       // Predict the classes other than default
    
  for(int j = 0; j < m_Shuffle; j++){
    if(m_Shuffle > 1)
      data.randomize(m_Random);
        
    rule.buildClassifier(data);
        
    double wr, w; // Worth rate and worth
    if(m_IsAllErr){

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Examples of weka.core.Instances.randomize()

      
      m_Antds = new FastVector();  
      
      /* Split data into Grow and Prune*/
      m_Random = new Random(m_Seed);
      data.randomize(m_Random);
      data.stratify(m_Folds);
      Instances growData=data.trainCV(m_Folds, m_Folds-1, m_Random);
      Instances pruneData=data.testCV(m_Folds, m_Folds-1);
      
      grow(growData);      // Build this rule

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Examples of weka.core.Instances.randomize()

  inBag[j] = new boolean[data.numInstances()];
  bagData = resampleWithWeights(data, random, inBag[j]);
      } else {
  bagData = data.resampleWithWeights(random);
  if (bagSize < data.numInstances()) {
    bagData.randomize(random);
    Instances newBagData = new Instances(bagData, 0, bagSize);
    bagData = newBagData;
  }
      }

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Examples of weka.core.Instances.randomize()

    if (m_splitThread == null) {
      final Instances dataSet = new Instances(e.getDataSet());
      m_splitThread = new Thread() {
    public void run() {
      try {
        dataSet.randomize(new Random(m_randomSeed));
        int trainSize = 
                (int)Math.round(dataSet.numInstances() * m_trainPercentage / 100);
        int testSize = dataSet.numInstances() - trainSize;
      
        Instances train = new Instances(dataSet, 0, trainSize);

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Examples of weka.core.Instances.randomize()

    throws Exception {


    FastVector predictions = new FastVector();
    Instances runInstances = new Instances(data);
    Random random = new Random(m_Seed);
    runInstances.randomize(random);
    if (runInstances.classAttribute().isNominal() && (numFolds > 1)) {
      runInstances.stratify(numFolds);
    }
    int inst = 0;
    for (int fold = 0; fold < numFolds; fold++) {

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Examples of weka.core.Instances.randomize()

      throw new Exception("No Instances set");
    }
    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    Random random = new Random(run);
    runInstances.randomize(random);
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_NumFolds);
    }
    for (int fold = 0; fold < m_NumFolds; fold++) {
      // Add in some fields to the key like run and fold number, dataset name

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