List of usage examples for weka.clusterers MakeDensityBasedClusterer MakeDensityBasedClusterer
public MakeDensityBasedClusterer()
From source file:adams.flow.transformer.WekaCrossValidationClustererEvaluator.java
License:Open Source License
/** * Executes the flow item./* www . ja v a 2 s. co m*/ * * @return null if everything is fine, otherwise error message */ @Override protected String doExecute() { String result; Instances data; weka.clusterers.Clusterer cls; int folds; MakeDensityBasedClusterer make; double log; result = null; try { // evaluate classifier cls = getClustererInstance(); if (cls == null) throw new IllegalStateException("Clusterer '" + getClusterer() + "' not found!"); data = (Instances) m_InputToken.getPayload(); folds = m_Folds; if (folds == -1) folds = data.numInstances(); if (!(cls instanceof DensityBasedClusterer)) { make = new MakeDensityBasedClusterer(); make.setClusterer(cls); cls = make; } log = ClusterEvaluation.crossValidateModel((DensityBasedClusterer) cls, data, folds, new Random(m_Seed)); m_OutputToken = new Token(new WekaClusterEvaluationContainer(log)); } catch (Exception e) { m_OutputToken = null; result = handleException("Failed to cross-validate clusterer: ", e); } if (m_OutputToken != null) updateProvenance(m_OutputToken); return result; }
From source file:br.com.ufu.lsi.rebfnetwork.RBFNetwork.java
License:Open Source License
/** * Builds the classifier//www.ja v a 2s . c om * * @param instances the training data * @throws Exception if the classifier could not be built successfully */ public void buildClassifier(Instances instances) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(instances); // remove instances with missing class instances = new Instances(instances); instances.deleteWithMissingClass(); // only class? -> build ZeroR model if (instances.numAttributes() == 1) { System.err.println( "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!"); m_ZeroR = new weka.classifiers.rules.ZeroR(); m_ZeroR.buildClassifier(instances); return; } else { m_ZeroR = null; } m_standardize = new Standardize(); m_standardize.setInputFormat(instances); instances = Filter.useFilter(instances, m_standardize); SimpleKMeans sk = new SimpleKMeans(); sk.setNumClusters(m_numClusters); sk.setSeed(m_clusteringSeed); MakeDensityBasedClusterer dc = new MakeDensityBasedClusterer(); dc.setClusterer(sk); dc.setMinStdDev(m_minStdDev); m_basisFilter = new ClusterMembership(); m_basisFilter.setDensityBasedClusterer(dc); m_basisFilter.setInputFormat(instances); Instances transformed = Filter.useFilter(instances, m_basisFilter); if (instances.classAttribute().isNominal()) { m_linear = null; m_logistic = new Logistic(); m_logistic.setRidge(m_ridge); m_logistic.setMaxIts(m_maxIts); m_logistic.buildClassifier(transformed); } else { m_logistic = null; m_linear = new LinearRegression(); m_linear.setAttributeSelectionMethod( new SelectedTag(LinearRegression.SELECTION_NONE, LinearRegression.TAGS_SELECTION)); m_linear.setRidge(m_ridge); m_linear.buildClassifier(transformed); } }
From source file:milk.classifiers.MIRBFNetwork.java
License:Open Source License
public Exemplars transform(Exemplars ex) throws Exception { // Throw all the instances together Instances data = new Instances(ex.exemplar(0).getInstances()); for (int i = 0; i < ex.numExemplars(); i++) { Exemplar curr = ex.exemplar(i);//w w w . j a va2 s. c o m double weight = 1.0 / (double) curr.getInstances().numInstances(); for (int j = 0; j < curr.getInstances().numInstances(); j++) { Instance inst = (Instance) curr.getInstances().instance(j).copy(); inst.setWeight(weight); data.add(inst); } } double factor = (double) data.numInstances() / (double) data.sumOfWeights(); for (int i = 0; i < data.numInstances(); i++) { data.instance(i).setWeight(data.instance(i).weight() * factor); } SimpleKMeans kMeans = new SimpleKMeans(); kMeans.setNumClusters(m_num_clusters); MakeDensityBasedClusterer clust = new MakeDensityBasedClusterer(); clust.setClusterer(kMeans); m_clm.setDensityBasedClusterer(clust); m_clm.setIgnoredAttributeIndices("" + (ex.exemplar(0).idIndex() + 1)); m_clm.setInputFormat(data); // Use filter and discard result Instances tempData = Filter.useFilter(data, m_clm); tempData = new Instances(tempData, 0); tempData.insertAttributeAt(ex.exemplar(0).getInstances().attribute(0), 0); // Go through exemplars and add them to new dataset Exemplars newExs = new Exemplars(tempData); for (int i = 0; i < ex.numExemplars(); i++) { Exemplar curr = ex.exemplar(i); Instances temp = Filter.useFilter(curr.getInstances(), m_clm); temp.insertAttributeAt(ex.exemplar(0).getInstances().attribute(0), 0); for (int j = 0; j < temp.numInstances(); j++) { temp.instance(j).setValue(0, curr.idValue()); } newExs.add(new Exemplar(temp)); } //System.err.println("Finished transforming"); //System.err.println(newExs); return newExs; }