List of usage examples for weka.core Instance setClassValue
public void setClassValue(String value);
From source file:put.semantic.fcanew.ml.WekaClassifier.java
@Override public void addExample(Map<String, Double> features, boolean accept) { Instance i = makeInstance(features); i.setClassValue(classes.elementAt(accept ? 1 : 0).toString()); if (!accept) { i.setWeight(getRejectedWeight()); }/* w w w . ja va2 s . c o m*/ instances.add(i); tableModel.fireTableRowsInserted(instances.numInstances() - 1, instances.numInstances() - 1); }
From source file:tr.gov.ulakbim.jDenetX.classifiers.CoOzaBagASHT.java
License:Open Source License
/** * This is the main classification function that is used by the GUI *///w w w.java 2 s . c o m public double[] getVotesForInstance(Instance inst) { DoubleVector combinedVote = new DoubleVector(); DoubleVector confidenceVec = new DoubleVector(); double[] ensembleVotes = new double[inst.numClasses()]; double qbcEntropy = 0.0; int success = 0; int alpha1 = 1; int alpha2 = 1; for (int i = 0; i < this.ensemble.length; i++) { DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst)); if (vote.sumOfValues() > 0.0) { vote.normalize(); confidenceVec.addValues(vote); if ((this.useWeightOption != null) && this.useWeightOption.isSet()) { vote.scaleValues(1.0 / (this.error[i] * this.error[i])); //System.out.println("Ensemble : " + i + " Error: " + this.error[i]); } combinedVote.addValues(vote); } // //Ignore the classifiers which have high error ratio // if (this.error[i] < 0.23) { // // this is the votes of the ensembles for the classes // success++; ensembleVotes[combinedVote.maxIndex()] += combinedVote.getValue(combinedVote.maxIndex()); } } //For confidence measure add to the pool and in order to fit the confidence value between 0 and 1 divide by success val //System.out.println("Confidence " + combinedVote.getValue(combinedVote.maxIndex())); if ((confidenceVec.getValue(combinedVote.maxIndex())) >= confidenceThreshold) { qbcEntropy = queryByCommitee(ensembleVotes, inst.numClasses(), success); double activeLearningRatio = (qbcEntropy) * (combinedVote.getValue(combinedVote.maxIndex()) / this.ensemble.length); inst.setClassValue(combinedVote.maxIndex()); //Set the class value of the instance instConfPool.addVotedInstance(inst, combinedVote.getValue(combinedVote.maxIndex()), activeLearningRatio); instConfCount++; } return combinedVote.getArrayRef(); }
From source file:tr.gov.ulakbim.jDenetX.classifiers.CoOzaBagASHT.java
License:Open Source License
/** * This is the main classification function that is used by the GUI *//*from w ww. j ava 2 s . c o m*/ public double[] getVotesForInstanceOrig(Instance inst) { DoubleVector combinedVote = new DoubleVector(); double[] ensembleVotes = new double[inst.numClasses()]; double qbcEntropy = 0.0; int success = 0; for (int i = 0; i < this.ensemble.length; i++) { DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst)); // This will call the HoeffdingTree's getVotesForInstance Function if (vote.sumOfValues() > 0.0) { vote.normalize(); if ((this.useWeightOption != null) && this.useWeightOption.isSet()) { vote.scaleValues(1.0 / (this.error[i] * this.error[i])); System.out.println("Ensemble : " + i + " Error: " + this.error[i]); } // //Ignore the ensembles which have high error ratio // if (this.error[i] < 0.3) { combinedVote.addValues(vote); } } // // this is the votes of the ensembles for the classes // if (this.error[i] < 0.3) { success++; ensembleVotes[combinedVote.maxIndex()] += combinedVote.getValue(combinedVote.maxIndex()); } } // For confidence measure add to the pool and in order to fit the confidence value between 0 and 1 divide by success val if ((combinedVote.getValue(combinedVote.maxIndex()) / success) >= confidenceThreshold) { qbcEntropy = queryByCommitee(ensembleVotes, inst.numClasses(), 0); System.out.println("QBC Entropy: " + qbcEntropy); double activeLearningRatio = (qbcEntropy) + (combinedVote.getValue(combinedVote.maxIndex()) / this.ensemble.length); inst.setClassValue(combinedVote.maxIndex()); instConfPool.addVotedInstance(inst, combinedVote.getValue(combinedVote.maxIndex()), activeLearningRatio); } return combinedVote.getArrayRef(); }
From source file:tr.gov.ulakbim.jDenetX.classifiers.LeveragingBag.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); //Output Codes if (this.initMatrixCodes == true) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0;/*from w w w. j av a 2 s.c o m*/ numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) numberOnes++; else numberZeros++; } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; double w = 1.0; double mt = 0.0; Instance weightedInst = (Instance) inst.copy(); /*for (int i = 0; i < this.ensemble.length; i++) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()] ); } if(!this.ensemble[i].correctlyClassifies(weightedInst)) { mt++; } }*/ //update w w = this.weightShrinkOption.getValue(); //1.0 +mt/2.0; //Train ensemble of classifiers for (int i = 0; i < this.ensemble.length; i++) { int k = MiscUtils.poisson(w, this.classifierRandom); if (k > 0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) if (this.ADError[i].getEstimation() > ErrEstim) Change = true; } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); } } }
From source file:tr.gov.ulakbim.jDenetX.classifiers.LeveragingBagHalf.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); //Output Codes if (this.initMatrixCodes == true) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0;//w w w. j a v a 2s. com numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) numberOnes++; else numberZeros++; } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; double w = 1.0; double mt = 0.0; Instance weightedInst = (Instance) inst.copy(); //Train ensemble of classifiers for (int i = 0; i < this.ensemble.length; i++) { int k = this.classifierRandom.nextBoolean() ? 0 : (int) this.weightShrinkOption.getValue(); //half bagging if (k > 0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) if (this.ADError[i].getEstimation() > ErrEstim) Change = true; } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); } } }
From source file:tr.gov.ulakbim.jDenetX.classifiers.LeveragingBagWT.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); //Output Codes if (this.initMatrixCodes == true) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0;/*from w ww . j av a 2 s. com*/ numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) numberOnes++; else numberZeros++; } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; double w = 1.0; double mt = 0.0; Instance weightedInst = (Instance) inst.copy(); //update w w = this.weightShrinkOption.getValue(); //Train ensemble of classifiers for (int i = 0; i < this.ensemble.length; i++) { int k = 1 + MiscUtils.poisson(w, this.classifierRandom); if (k > 0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) if (this.ADError[i].getEstimation() > ErrEstim) Change = true; } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); } } }
From source file:tr.gov.ulakbim.jDenetX.classifiers.LeveragingSubag.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); //Output Codes if (this.initMatrixCodes == true) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0;/* ww w .jav a 2s .c o m*/ numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) numberOnes++; else numberZeros++; } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; double w = 1.0; double mt = 0.0; Instance weightedInst = (Instance) inst.copy(); //Train ensemble of classifiers for (int i = 0; i < this.ensemble.length; i++) { int k = MiscUtils.poisson(1, this.classifierRandom); k = (k > 0) ? (int) this.weightShrinkOption.getValue() : 0; if (k > 0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) if (this.ADError[i].getEstimation() > ErrEstim) Change = true; } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); } } }
From source file:tr.gov.ulakbim.jDenetX.classifiers.OzaBoostAdwin.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { int numClasses = inst.numClasses(); // Set log (k-1) and (k-1) for SAMME Method if (this.sammeOption.isSet()) { this.Km1 = numClasses - 1; this.logKm1 = Math.log(this.Km1); this.initKm1 = false; }/*from ww w. j a v a 2s. co m*/ //Output Codes if (this.initMatrixCodes) { this.matrixCodes = new int[this.ensemble.length][inst.numClasses()]; for (int i = 0; i < this.ensemble.length; i++) { int numberOnes; int numberZeros; do { // until we have the same number of zeros and ones numberOnes = 0; numberZeros = 0; for (int j = 0; j < numClasses; j++) { int result = 0; if (j == 1 && numClasses == 2) { result = 1 - this.matrixCodes[i][0]; } else { result = (this.classifierRandom.nextBoolean() ? 1 : 0); } this.matrixCodes[i][j] = result; if (result == 1) numberOnes++; else numberZeros++; } } while ((numberOnes - numberZeros) * (numberOnes - numberZeros) > (this.ensemble.length % 2)); } this.initMatrixCodes = false; } boolean Change = false; double lambda_d = 1.0; Instance weightedInst = (Instance) inst.copy(); for (int i = 0; i < this.ensemble.length; i++) { double k = this.pureBoostOption.isSet() ? lambda_d : MiscUtils.poisson(lambda_d * this.Km1, this.classifierRandom); if (k > 0.0) { if (this.outputCodesOption.isSet()) { weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); } weightedInst.setWeight(inst.weight() * k); this.ensemble[i].trainOnInstance(weightedInst); } boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(weightedInst); if (correctlyClassifies) { this.scms[i] += lambda_d; lambda_d *= this.trainingWeightSeenByModel / (2 * this.scms[i]); } else { this.swms[i] += lambda_d; lambda_d *= this.trainingWeightSeenByModel / (2 * this.swms[i]); } double ErrEstim = this.ADError[i].getEstimation(); if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1)) if (this.ADError[i].getEstimation() > ErrEstim) Change = true; } if (Change) { numberOfChangesDetected++; double max = 0.0; int imax = -1; for (int i = 0; i < this.ensemble.length; i++) { if (max < this.ADError[i].getEstimation()) { max = this.ADError[i].getEstimation(); imax = i; } } if (imax != -1) { this.ensemble[imax].resetLearning(); //this.ensemble[imax].trainOnInstance(inst); this.ADError[imax] = new ADWIN((double) this.deltaAdwinOption.getValue()); this.scms[imax] = 0; this.swms[imax] = 0; } } }
From source file:tr.gov.ulakbim.jDenetX.classifiers.OzaBoostAdwin.java
License:Open Source License
public double[] getVotesForInstanceBinary(Instance inst) { double combinedVote[] = new double[(int) inst.numClasses()]; Instance weightedInst = (Instance) inst.copy(); if (this.initMatrixCodes) { for (int i = 0; i < this.ensemble.length; i++) { //Replace class by OC weightedInst.setClassValue((double) this.matrixCodes[i][(int) inst.classValue()]); double vote[]; vote = this.ensemble[i].getVotesForInstance(weightedInst); //Binary Case int voteClass = 0; if (vote.length == 2) { voteClass = (vote[1] > vote[0] ? 1 : 0); }//from w w w . j ava 2 s . co m //Update votes for (int j = 0; j < inst.numClasses(); j++) { if (this.matrixCodes[i][j] == voteClass) { combinedVote[j] += getEnsembleMemberWeight(i); } } } } return combinedVote; }
From source file:tr.gov.ulakbim.jDenetX.streams.clustering.RandomRBFGeneratorEvents.java
License:Open Source License
public Instance nextInstance() { numGeneratedInstances++;/* ww w . j av a 2s. c o m*/ eventScheduler(); //make room for thge classlabel double[] values_new = new double[numAttsOption.getValue() + 1]; double[] values = null; int clusterChoice = -1; if (instanceRandom.nextDouble() > noiseLevelOption.getValue()) { clusterChoice = chooseWeightedElement(); values = kernels.get(clusterChoice).generator.sample(instanceRandom).toDoubleArray(); } else { //get ranodm noise point values = getNewSample(); } if (Double.isNaN(values[0])) { System.out.println("Instance corrupted:" + numGeneratedInstances); } System.arraycopy(values, 0, values_new, 0, values.length); Instance inst = new DenseInstance(1.0, values_new); inst.setDataset(getHeader()); if (clusterChoice == -1) { inst.setClassValue(-1); } else { inst.setClassValue(kernels.get(clusterChoice).generator.getId()); //Do we need micro cluster representation if have overlapping clusters? //if(!overlappingOption.isSet()) kernels.get(clusterChoice).addInstance(inst); } // System.out.println(numGeneratedInstances+": Overlap is"+updateOverlaps()); return inst; }