List of usage examples for weka.classifiers.functions SMO setOptions
public void setOptions(String[] options) throws Exception
From source file:de.tudarmstadt.ukp.dkpro.spelling.experiments.hoo2012.featureextraction.AllFeaturesExtractor.java
License:Apache License
private Classifier getClassifier() throws Exception { Classifier cl = null;/*from w w w . j a v a 2s. c o m*/ // Build and evaluate classifier // The options given correspond to the default settings in the WEKA GUI if (classifier.equals("smo")) { SMO smo = new SMO(); smo.setOptions(Utils.splitOptions( "-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); cl = smo; } else if (classifier.equals("j48")) { J48 j48 = new J48(); j48.setOptions(new String[] { "-C", "0.25", "-M", "2" }); cl = j48; } else if (classifier.equals("naivebayes")) { cl = new NaiveBayes(); } else if (classifier.equals("randomforest")) { RandomForest rf = new RandomForest(); rf.setOptions(Utils.splitOptions("-I 10 -K 0 -S 1")); cl = rf; } return cl; }
From source file:elh.eus.absa.WekaWrapper.java
License:Open Source License
/** * @param traindata/*from w w w. j av a 2 s. c om*/ * @param testdata * @param id : whether the first attribute represents de instance id and should be filtered out for classifying * @throws Exception */ public WekaWrapper(Instances traindata, Instances testdata, boolean id) throws Exception { // classifier weka.classifiers.functions.SMO SVM = new weka.classifiers.functions.SMO(); SVM.setOptions(weka.core.Utils.splitOptions("-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 " + "-K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); setTraindata(traindata); setTestdata(testdata); // first attribute reflects instance id, delete it when building the classifier if (id) { //filter Remove rm = new Remove(); rm.setAttributeIndices("1"); // remove 1st attribute // meta-classifier FilteredClassifier fc = new FilteredClassifier(); fc.setFilter(rm); fc.setClassifier(SVM); setMLclass(fc); } else { setMLclass(SVM); } }
From source file:etc.aloe.cscw2013.TrainingImpl.java
License:Open Source License
@Override public WekaModel train(ExampleSet examples) { System.out.println("SMO Options: " + SMO_OPTIONS); SMO smo = new SMO(); try {/*from w w w .j a v a 2s .com*/ smo.setOptions(Utils.splitOptions(SMO_OPTIONS)); } catch (Exception ex) { System.err.println("Unable to configure SMO."); System.err.println("\t" + ex.getMessage()); return null; } //Build logistic models if desired smo.setBuildLogisticModels(isBuildLogisticModel()); Classifier classifier = smo; if (useCostTraining) { CostSensitiveClassifier cost = new CostSensitiveClassifier(); cost.setClassifier(smo); CostMatrix matrix = new CostMatrix(2); matrix.setElement(0, 0, 0); matrix.setElement(0, 1, falsePositiveCost); matrix.setElement(1, 0, falseNegativeCost); matrix.setElement(1, 1, 0); cost.setCostMatrix(matrix); classifier = cost; System.out.print("Wrapping SMO in CostSensitiveClassifier " + matrix.toMatlab()); if (useReweighting) { cost.setMinimizeExpectedCost(false); System.out.println(" using re-weighting."); } else { cost.setMinimizeExpectedCost(true); System.out.println(" using min-cost criterion."); } } try { System.out.print("Training SMO on " + examples.size() + " examples... "); classifier.buildClassifier(examples.getInstances()); System.out.println("done."); WekaModel model = new WekaModel(classifier); return model; } catch (Exception ex) { System.err.println("Unable to train SMO."); System.err.println("\t" + ex.getMessage()); return null; } }
From source file:farm_ads.MyClassifier.java
public Classifier classifierSMO(Instances instances) throws Exception { SMO classifier = new SMO(); classifier.setOptions(weka.core.Utils.splitOptions( "-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); classifier.buildClassifier(instances); return classifier; }
From source file:org.dkpro.similarity.algorithms.ml.ClassifierSimilarityMeasure.java
License:Open Source License
public static Classifier getClassifier(WekaClassifier classifier) throws IllegalArgumentException { try {/*from w w w. j ava 2s .c o m*/ switch (classifier) { case NAIVE_BAYES: return new NaiveBayes(); case J48: J48 j48 = new J48(); j48.setOptions(new String[] { "-C", "0.25", "-M", "2" }); return j48; case SMO: SMO smo = new SMO(); smo.setOptions(Utils.splitOptions( "-C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\"")); return smo; case LOGISTIC: Logistic logistic = new Logistic(); logistic.setOptions(Utils.splitOptions("-R 1.0E-8 -M -1")); return logistic; default: throw new IllegalArgumentException("Classifier " + classifier + " not found!"); } } catch (Exception e) { throw new IllegalArgumentException(e); } }
From source file:org.uclab.mm.icl.llc.config.RecognizerType.java
License:Apache License
/** * Returns the corresponding recognizer//from www.j a va 2s.c o m * @param rec recognizer type to return * @param userID user ID to set * @return instance of the corresponding recognizer */ public LLCRecognizer getRecognizer(long userID) { RecognizerType rec = this.values()[value]; switch (rec) { case SER: String[] labels = { "Anger", "Happiness", "Sadness" }; String path = FileUtil.getRootPath() + "/training/modeldataV2.7.txt"; SMO svm = new SMO(); // Define Classifier with Weka try { svm.setOptions(weka.core.Utils.splitOptions( "-C 1.0 -L 0.0010 -P 1.0E-12 -N 1 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.RBFKernel -C 250007 -G 0.01\"")); svm.setFilterType(new SelectedTag(SMO.FILTER_STANDARDIZE, SMO.TAGS_FILTER)); } catch (Exception e) { e.printStackTrace(); } ExtClassification classifier = new ExtClassification(path, 78 * 2, labels, svm); AudioEmotionRecognizer aer = new AudioEmotionRecognizer(classifier, path, userID); return aer; case ER: return new AudioEmotionRecognizerV(userID); case IAR: return new InertialActivityRecognizer(userID); case VAR: return new VideoActivityRecognizer(userID); case LR: //get user loc coord / label with userID by restful service return new GPSLocationRecognizer(userID); case FR: return new FoodRecognizer(userID); } return null; }