public Options getOptions() { if (this.options == null) { this.options = new Options(); Option[] myOptions = discoverOptionsViaReflection(); for (Option option : myOptions) { this.options.addOption(option); } } return this.options; }
@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()]; }
@Override public void resetLearning() { m_n = 1.0; m_sum = 0.0; m_p = 0.0; m_s = 0.0; z_t = 0.0; lambda = this.lambdaOption.getValue(); }
private Rule newRule(int ID) { Rule r=new Rule.Builder(). threshold(this.pageHinckleyThresholdOption.getValue()). alpha(this.pageHinckleyAlphaOption.getValue()). changeDetection(this.DriftDetectionOption.isSet()). predictionFunction(this.predictionFunctionOption.getChosenIndex()). statistics(new double[3]). id(ID). amRules(this).build(); r.getBuilder().setOwner(r); return r; }
@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 setRandomSeed(int s) { this.randomSeed = s; if (this.randomSeedOption != null) { // keep option consistent this.randomSeedOption.setValue(s); } }
public TargetMean(TargetMeanData td) { this(); this.n = td.n; this.sum = td.sum; this.errorSum = td.errorSum; this.nError = td.nError; this.fadingErrorFactor = td.fadingErrorFactor; this.fadingErrorFactorOption.setValue(td.fadingErrorFactorOptionValue); }
@Override public void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { this.fileSource = sourceTypeOption.getValue(); this.hasStarted = false; } }
public RandomRBFGeneratorEvents() { noiseInClusterOption.set(); // eventDeleteCreateOption.set(); // eventMergeSplitOption.set(); }
public FloatOption(String name, char cliChar, String purpose, double defaultVal, double minVal, double maxVal) { super(name, cliChar, purpose); this.defaultVal = defaultVal; this.minVal = minVal; this.maxVal = maxVal; resetToDefault(); }
public Options getOptions() { if (this.options == null) { this.options = new Options(); Option[] myOptions = discoverOptionsViaReflection(); for (Option option : myOptions) { this.options.addOption(option); } } return this.options; }
@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(); }
public TargetMeanData(TargetMean tm) { this.n = tm.n; this.sum = tm.sum; this.errorSum = tm.errorSum; this.nError = tm.nError; this.fadingErrorFactor = tm.fadingErrorFactor; if (tm.fadingErrorFactorOption != null) this.fadingErrorFactorOptionValue = tm.fadingErrorFactorOption.getValue(); else this.fadingErrorFactorOptionValue = 0.99; }
public void setRandomSeed(int s) { this.randomSeed = s; if (this.randomSeedOption != null) { // keep option consistent this.randomSeedOption.setValue(s); } }
public TargetMeanData(TargetMean tm) { this.n = tm.n; this.sum = tm.sum; this.errorSum = tm.errorSum; this.nError = tm.nError; this.fadingErrorFactor = tm.fadingErrorFactor; if (tm.fadingErrorFactorOption != null) this.fadingErrorFactorOptionValue = tm.fadingErrorFactorOption.getValue(); else this.fadingErrorFactorOptionValue = 0.99; }