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()

      // theInstances.stratify(10);
        
      CVincreased = false;
      cvr = new Random(getSeed());
      trainCopy = new Instances(m_theInstances);
      trainCopy.randomize(cvr);
      templl = 0.0;
      for (i = 0; i < numFolds; i++) {
  Instances cvTrain = trainCopy.trainCV(numFolds, i, cvr);
  if (num_clusters > cvTrain.numInstances()) {
    break CLUSTER_SEARCH;

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Examples of weka.core.Instances.randomize()

            newInstance.setClassValue( instance.classValue( ) );
            dataSubSet.add( newInstance );
          }
        }


        dataSubSet.randomize(random);
        // Remove a percentage of the instances
  Instances originalDataSubSet = dataSubSet;
  dataSubSet.randomize(random);
        RemovePercentage rp = new RemovePercentage();
        rp.setPercentage( m_RemovedPercentage );

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Examples of weka.core.Instances.randomize()

        }
        for (int k = 0; k < subsets[j].numInstances(); k++) {
          data.add(subsets[j].instance(k));
        }  
        data.compactify(); 
        data.randomize(rand);
        m_classifiers[i][j].buildClassifier(data, i, j, 
            m_fitLogisticModels,
            m_numFolds, m_randomSeed);
      }
    }

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Examples of weka.core.Instances.randomize()

  // create new train/test sources
  if (splitPercentage > 0) {
    testSetPresent = true;
    Instances tmpInst = trainSource.getDataSet(actualClassIndex);
    if (!preserveOrder)
      tmpInst.randomize(new Random(seed));
    int trainSize = 
            (int) Math.round(tmpInst.numInstances() * splitPercentage / 100);
    int testSize  = tmpInst.numInstances() - trainSize;
    Instances trainInst = new Instances(tmpInst, 0, trainSize);
    Instances testInst  = new Instances(tmpInst, trainSize, testSize);

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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_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()

    Instances newData = new Instances(data);
    m_BaseFormat = new Instances(data, 0);
    newData.deleteWithMissingClass();


    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal()) {
      newData.stratify(m_NumFolds);
    }


    // Create meta level

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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);
    runInstances.randomize(new Random(run));
    /*if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_StepSize);
    }*/


    // Tell the resultproducer to send results to us

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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()

    // weights for root nodes of each fold for prepruning and postpruning.
    int expansion = 0;


    Random random = new Random(m_Seed);
    Instances cvData = new Instances(data);
    cvData.randomize(random);
    cvData = new Instances(cvData,0,(int)(cvData.numInstances()*m_SizePer)-1);
    cvData.stratify(m_numFoldsPruning);


    Instances[] train = new Instances[m_numFoldsPruning];
    Instances[] test = new Instances[m_numFoldsPruning];

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