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private void addDrift() { for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) { this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue()); if (// this.weights[i] >= 1.0 || this.weights[i] <= 0.0 || (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) { this.sigma[i] *= -1; } } }
private void addDrift() { for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) { this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue()); if (// this.weights[i] >= 1.0 || this.weights[i] <= 0.0 || (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) { this.sigma[i] *= -1; } } }
private void addDrift() { for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) { this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue()); if (//this.weights[i] >= 1.0 || this.weights[i] <= 0.0 || (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) { this.sigma[i] *= -1; } } }
private void addDrift() { for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) { this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue()); if (// this.weights[i] >= 1.0 || this.weights[i] <= 0.0 || (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) { this.sigma[i] *= -1; } } }
private void addDrift() { for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) { this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue()); if (// this.weights[i] >= 1.0 || this.weights[i] <= 0.0 || (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) { this.sigma[i] *= -1; } } }
protected void init(){ this.maxLTMSize = (int)(relativeLTMSizeOption.getValue() * limitOption.getValue()); this.maxSTMSize = limitOption.getValue() - this.maxLTMSize; this.stmHistory = new ArrayList<>(); this.ltmHistory = new ArrayList<>(); this.cmHistory = new ArrayList<>(); //store calculated STM distances in a matrix to avoid recalculation, are reused in the STM adaption phase this.distanceMatrixSTM = new double[limitOption.getValue()+1][limitOption.getValue()+1]; this.predictionHistories = new HashMap<>(); this.random = new Random(); }
@Override public void resetLearning() { m_n = 1; m_p = 1; m_s = 0; m_psmin = Double.MAX_VALUE; m_pmin = Double.MAX_VALUE; m_smin = Double.MAX_VALUE; minNumInstances = this.minNumInstancesOption.getValue(); warningLevel = this.warningLevelOption.getValue(); outcontrolLevel = this.outcontrolLevelOption.getValue(); }
@Override protected void initVariables(){ this.tau_size = this.tauSizeOption.getValue(); this.theta_stab = this.stabIndexSizeOption.getValue(); this.theta_diff = this.equivIndexSizeOption.getValue(); this.recentChunk = null; int ensembleSize = (int)this.memberCountOption.getValue() + MAXPERMANENT; this.ensemble = new Classifier[ensembleSize]; this.ensembleAges = new double[ensembleSize]; this.ensembleWindows = new int[ensembleSize][(int)this.evaluationSizeOption.getValue()]; }
public void initialize() { minNumInstances = this.minNumInstancesOption.getValue(); warningLevel = this.warningLevelOption.getValue(); driftLevel = this.driftLevelOption.getValue(); maxSizeConcept = this.maxSizeConceptOption.getValue(); minSizeStableConcept = this.minSizeStableConceptOption.getValue(); warnLimit = this.warnLimitOption.getValue(); storedPredictions = new byte[minSizeStableConcept]; numStoredInstances = 0; firstPos = 0; lastPos = -1; // This means storedPredictions is empty. lastWarnPos = -1; lastWarnInst = -1; instNum = 0; rddmDrift = false; this.isChangeDetected = false; resetLearning(); m_pmin = Double.MAX_VALUE; m_smin = Double.MAX_VALUE; m_psmin = Double.MAX_VALUE; }
@Override public void resetLearningImpl() { hasStarted=false; count=0; inAttrSum=null; inAttrSquaredSum=null; outAttrSum=null; outAttrSquaredSum=null; layer1Weights=null; layer2Weights=null; numericIndices=null; currentLearningRate=this.learningRatioOption.getValue(); //TODO: JD Check if random generator is somehow overridden this.classifierRandom=new Random(); this.classifierRandom.setSeed(randomSeedOption.getValue()); }
@Override public void resetLearningImpl() { this.ensemble = new ArrayList<>(); this.ensembleWeights = new ArrayList<>(); this.bkts = new ArrayList<>(); this.wkts = new ArrayList<>(); this.index = 0; this.buffer = null; this.slope = this.sigmoidSlopeOption.getValue(); this.crossingPoint = this.sigmoidCrossingPointOption.getValue(); this.pruning = this.pruningStrategyOption.getChosenIndex(); this.ensembleSize = this.ensembleSizeOption.getValue(); }
@Override public void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { this.maxMemberCount = (int) memberCountOption.getValue(); this.maxStoredCount = (int) storedCountOption.getValue(); if (this.maxMemberCount > this.maxStoredCount) { this.maxStoredCount = this.maxMemberCount; } this.chunkSize = this.chunkSizeOption.getValue(); this.numFolds = this.numFoldsOption.getValue(); this.candidateClassifier = (Classifier) getPreparedClassOption(this.learnerOption); this.candidateClassifier.resetLearning(); super.prepareForUseImpl(monitor, repository); }
@Override public void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { this.inputStream = (InstanceStream) getPreparedClassOption(this.streamOption); this.driftStream = (InstanceStream) getPreparedClassOption(this.driftstreamOption); this.random = new Random(this.randomSeedOption.getValue()); numberInstanceStream = 0; if (this.alphaOption.getValue() != 0.0) { this.widthOption.setValue((int) (1 / Math.tan(this.alphaOption.getValue() * Math.PI / 180))); } }