List of usage examples for weka.core Instance copy
Object copy();
From source file:tr.gov.ulakbim.jDenetX.classifiers.OzaBagASHOT.java
License:Open Source License
@Override public void trainOnInstanceImpl(Instance inst) { final int trueClass = (int) inst.classValue(); //System.out.println("Ensemble Length " + this.ensemble.length); for (int i = 0; i < this.ensemble.length; i++) { final int k = MiscUtils.poisson(1.0, this.classifierRandom); if (k > 0) { final Instance weightedInst = (Instance) inst.copy(); weightedInst.setWeight(inst.weight() * k); if (Utils.maxIndex(this.ensemble[i].getVotesForInstance(inst)) == trueClass) { this.error[i] += alpha * (0.0 - this.error[i]); //EWMA } else { this.error[i] += alpha * (1.0 - this.error[i]); //EWMA }/*from w w w . ja va 2 s.c o m*/ this.ensemble[i].trainOnInstance(weightedInst); } // System.out.println("ClassifierRandom: " + k); //System.out.println("EWMA Error Ensemble "+i+" "+ this.error[i]); if (this.error[i] > 0.6) { System.out.println("Error is " + this.error[i]); System.out.println("Ensemble " + i); System.err.println("Warning!!!!!"); } } }
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; }/* w w w . j a va 2 s .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 ww w . j a va2 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.tasks.EvaluateInterleavedTestThenTrain.java
License:Open Source License
@Override protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) { Classifier learner = (Classifier) getPreparedClassOption(this.learnerOption); InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption); ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption( this.evaluatorOption); learner.setModelContext(stream.getHeader()); int maxInstances = this.instanceLimitOption.getValue(); long instancesProcessed = 0; int maxSeconds = this.timeLimitOption.getValue(); int secondsElapsed = 0; monitor.setCurrentActivity("Evaluating learner...", -1.0); LearningCurve learningCurve = new LearningCurve("learning evaluation instances"); File dumpFile = this.dumpFileOption.getFile(); PrintStream immediateResultStream = null; if (dumpFile != null) { try {/*from w w w .j av a 2 s . c o m*/ if (dumpFile.exists()) { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true); } else { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open immediate result file: " + dumpFile, ex); } } boolean firstDump = true; boolean preciseCPUTiming = TimingUtils.enablePreciseTiming(); long evaluateStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); long lastEvaluateStartTime = evaluateStartTime; double RAMHours = 0.0; while (stream.hasMoreInstances() && ((maxInstances < 0) || (instancesProcessed < maxInstances)) && ((maxSeconds < 0) || (secondsElapsed < maxSeconds))) { Instance trainInst = stream.nextInstance(); Instance testInst = (Instance) trainInst.copy(); int trueClass = (int) trainInst.classValue(); testInst.setClassMissing(); double[] prediction = learner.getVotesForInstance(testInst); evaluator.addClassificationAttempt(trueClass, prediction, testInst.weight()); learner.trainOnInstance(trainInst); instancesProcessed++; if (instancesProcessed % this.sampleFrequencyOption.getValue() == 0) { long evaluateTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); double time = TimingUtils.nanoTimeToSeconds(evaluateTime - evaluateStartTime); double timeIncrement = TimingUtils.nanoTimeToSeconds(evaluateTime - lastEvaluateStartTime); double RAMHoursIncrement = learner.measureByteSize() / (1024.0 * 1024.0 * 1024.0); //GBs RAMHoursIncrement *= (timeIncrement / 3600.0); //Hours RAMHours += RAMHoursIncrement; lastEvaluateStartTime = evaluateTime; learningCurve.insertEntry(new LearningEvaluation( new Measurement[] { new Measurement("learning evaluation instances", instancesProcessed), new Measurement("evaluation time (" + (preciseCPUTiming ? "cpu " : "") + "seconds)", time), new Measurement("model cost (RAM-Hours)", RAMHours) }, evaluator, learner)); if (immediateResultStream != null) { if (firstDump) { immediateResultStream.println(learningCurve.headerToString()); firstDump = false; } immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1)); immediateResultStream.flush(); } } if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } long estimatedRemainingInstances = stream.estimatedRemainingInstances(); if (maxInstances > 0) { long maxRemaining = maxInstances - instancesProcessed; if ((estimatedRemainingInstances < 0) || (maxRemaining < estimatedRemainingInstances)) { estimatedRemainingInstances = maxRemaining; } } monitor.setCurrentActivityFractionComplete(estimatedRemainingInstances < 0 ? -1.0 : (double) instancesProcessed / (double) (instancesProcessed + estimatedRemainingInstances)); if (monitor.resultPreviewRequested()) { monitor.setLatestResultPreview(learningCurve.copy()); } secondsElapsed = (int) TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - evaluateStartTime); } } if (immediateResultStream != null) { immediateResultStream.close(); } return learningCurve; }
From source file:tr.gov.ulakbim.jDenetX.tasks.EvaluatePrequential.java
License:Open Source License
@Override protected Object doMainTask(TaskMonitor monitor, ObjectRepository repository) { Classifier learner = (Classifier) getPreparedClassOption(this.learnerOption); InstanceStream stream = (InstanceStream) getPreparedClassOption(this.streamOption); ClassificationPerformanceEvaluator evaluator = (ClassificationPerformanceEvaluator) getPreparedClassOption( this.evaluatorOption); //New for prequential methods if (evaluator instanceof WindowClassificationPerformanceEvaluator) { ((WindowClassificationPerformanceEvaluator) evaluator).setWindowWidth(widthOption.getValue()); }/*w w w. ja v a2 s .c om*/ if (evaluator instanceof EWMAClassificationPerformanceEvaluator) { ((EWMAClassificationPerformanceEvaluator) evaluator).setalpha(alphaOption.getValue()); } if (evaluator instanceof FadingFactorClassificationPerformanceEvaluator) { ((FadingFactorClassificationPerformanceEvaluator) evaluator).setalpha(alphaOption.getValue()); } //End New for prequential methods learner.setModelContext(stream.getHeader()); int maxInstances = this.instanceLimitOption.getValue(); long instancesProcessed = 0; int maxSeconds = this.timeLimitOption.getValue(); int secondsElapsed = 0; monitor.setCurrentActivity("Evaluating learner...", -1.0); LearningCurve learningCurve = new LearningCurve("learning evaluation instances"); File dumpFile = this.dumpFileOption.getFile(); PrintStream immediateResultStream = null; if (dumpFile != null) { try { if (dumpFile.exists()) { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile, true), true); } else { immediateResultStream = new PrintStream(new FileOutputStream(dumpFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open immediate result file: " + dumpFile, ex); } } //File for output predictions File outputPredictionFile = this.outputPredictionFileOption.getFile(); PrintStream outputPredictionResultStream = null; if (outputPredictionFile != null) { try { if (outputPredictionFile.exists()) { outputPredictionResultStream = new PrintStream(new FileOutputStream(outputPredictionFile, true), true); } else { outputPredictionResultStream = new PrintStream(new FileOutputStream(outputPredictionFile), true); } } catch (Exception ex) { throw new RuntimeException("Unable to open prediction result file: " + outputPredictionFile, ex); } } boolean firstDump = true; boolean preciseCPUTiming = TimingUtils.enablePreciseTiming(); long evaluateStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); long lastEvaluateStartTime = evaluateStartTime; double RAMHours = 0.0; while (stream.hasMoreInstances() && ((maxInstances < 0) || (instancesProcessed < maxInstances)) && ((maxSeconds < 0) || (secondsElapsed < maxSeconds))) { Instance trainInst = stream.nextInstance(); Instance testInst = (Instance) trainInst.copy(); int trueClass = (int) trainInst.classValue(); testInst.setClassMissing(); double[] prediction = learner.getVotesForInstance(testInst); // Output prediction if (outputPredictionFile != null) { outputPredictionResultStream.println(Utils.maxIndex(prediction) + "," + trueClass); } evaluator.addClassificationAttempt(trueClass, prediction, testInst.weight()); learner.trainOnInstance(trainInst); instancesProcessed++; if (instancesProcessed % this.sampleFrequencyOption.getValue() == 0) { long evaluateTime = TimingUtils.getNanoCPUTimeOfCurrentThread(); double time = TimingUtils.nanoTimeToSeconds(evaluateTime - evaluateStartTime); double timeIncrement = TimingUtils.nanoTimeToSeconds(evaluateTime - lastEvaluateStartTime); double RAMHoursIncrement = learner.measureByteSize() / (1024.0 * 1024.0 * 1024.0); //GBs RAMHoursIncrement *= (timeIncrement / 3600.0); //Hours RAMHours += RAMHoursIncrement; lastEvaluateStartTime = evaluateTime; learningCurve.insertEntry(new LearningEvaluation( new Measurement[] { new Measurement("learning evaluation instances", instancesProcessed), new Measurement("evaluation time (" + (preciseCPUTiming ? "cpu " : "") + "seconds)", time), new Measurement("model cost (RAM-Hours)", RAMHours) }, evaluator, learner)); if (immediateResultStream != null) { if (firstDump) { immediateResultStream.println(learningCurve.headerToString()); firstDump = false; } immediateResultStream.println(learningCurve.entryToString(learningCurve.numEntries() - 1)); immediateResultStream.flush(); } } if (instancesProcessed % INSTANCES_BETWEEN_MONITOR_UPDATES == 0) { if (monitor.taskShouldAbort()) { return null; } long estimatedRemainingInstances = stream.estimatedRemainingInstances(); if (maxInstances > 0) { long maxRemaining = maxInstances - instancesProcessed; if ((estimatedRemainingInstances < 0) || (maxRemaining < estimatedRemainingInstances)) { estimatedRemainingInstances = maxRemaining; } } monitor.setCurrentActivityFractionComplete(estimatedRemainingInstances < 0 ? -1.0 : (double) instancesProcessed / (double) (instancesProcessed + estimatedRemainingInstances)); if (monitor.resultPreviewRequested()) { monitor.setLatestResultPreview(learningCurve.copy()); } secondsElapsed = (int) TimingUtils .nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread() - evaluateStartTime); } } if (immediateResultStream != null) { immediateResultStream.close(); } if (outputPredictionResultStream != null) { outputPredictionResultStream.close(); } return learningCurve; }
From source file:zhaop.textmining.proj.MultiFilter.java
License:Open Source License
/** * processes the given instance (may change the provided instance) and returns * the modified version.//from w ww . j av a 2 s .c om * * @param instance * the instance to process * @return the modified data * @throws Exception * in case the processing goes wrong */ protected Instance process(Instance instance) throws Exception { Instance result; int i; result = (Instance) instance.copy(); for (i = 0; i < getFilters().length; i++) { getFilter(i).input(result); result = getFilter(i).output(); } return result; }