List of usage examples for weka.core Instance deleteAttributeAt
public void deleteAttributeAt(int position);
From source file:moa.streams.clustering.FileStream.java
License:Apache License
protected boolean readNextInstanceFromFile() { try {//ww w . j a v a 2 s . co m if (this.instances.readInstance(this.fileReader)) { Instance rawInstance = this.instances.instance(0); //remove dataset from instance so we can delete attributes rawInstance.setDataset(null); for (int i = removeAttributes.length - 1; i >= 0; i--) { rawInstance.deleteAttributeAt(removeAttributes[i]); } //set adjusted dataset for instance rawInstance.setDataset(filteredDataset); if (normalizeOption.isSet() && valuesMinMaxDiff != null) { for (int i = 0; i < rawInstance.numAttributes(); i++) { if (valuesMinMaxDiff.get(i)[2] != 1 && // Already normalized valuesMinMaxDiff.get(i)[2] != 0 && // Max. value is 0 (unable to be normalized) i != rawInstance.classIndex()) { // Class label is not subject to be normalized double v = rawInstance.value(i); v = (v - valuesMinMaxDiff.get(i)[0]) / valuesMinMaxDiff.get(i)[2]; rawInstance.setValue(i, v); } } } this.lastInstanceRead = rawInstance; this.instances.delete(); // keep instances clean this.numInstancesRead++; return true; } if (this.fileReader != null) { this.fileReader.close(); this.fileReader = null; } return false; } catch (IOException ioe) { throw new RuntimeException("ArffFileStream failed to read instance from stream.", ioe); } }
From source file:mulan.transformations.BinaryRelevanceTransformation.java
License:Open Source License
/** * Remove all label attributes except labelToKeep * @param instance // ww w .j ava 2s . c om * @param labelToKeep * @return transformed Instance */ public Instance transformInstance(Instance instance, int labelToKeep) { Instance newInstance = DataUtils.createInstance(instance, instance.numAttributes()); newInstance.setDataset(null); int numPredictors = instance.numAttributes() - numOfLabels; int skipLabel = 0; for (int labelIndex = 0; labelIndex < numOfLabels; labelIndex++) { if (labelIndex == labelToKeep) { skipLabel++; continue; } newInstance.deleteAttributeAt(numPredictors + skipLabel); } return newInstance; }
From source file:nl.bioinf.roelen.thema11.classifier_tools.ClassifierUser.java
License:Open Source License
/** * use the classifier to test the sequences in a genbank or fasta file for boundaries * @param fileLocation the location of the genbank of fasta file * @param classifier the classifier to use * @return /* w w w . j a va 2s .c om*/ */ public static ArrayList<ClassifiedNucleotide> getPossibleBoundaries(String fileLocation, Classifier classifier) { ArrayList<Gene> genesFromFile = new ArrayList<>(); ArrayList<ClassifiedNucleotide> classifiedNucleotides = new ArrayList<>(); //read from fasta if (fileLocation.toUpperCase().endsWith(".FASTA") || fileLocation.toUpperCase().endsWith(".FA") || fileLocation.toUpperCase().endsWith(".FAN")) { genesFromFile.addAll(readFasta(fileLocation)); } //read from genbank else if (fileLocation.toUpperCase().endsWith(".GENBANK") || fileLocation.toUpperCase().endsWith(".GB")) { GenBankReader gbr = new GenBankReader(); gbr.readFile(fileLocation); GenbankResult gbresult = gbr.getResult(); genesFromFile = gbresult.getGenes(); } //get the test data HashMap<String, ArrayList<IntronExonBoundaryTesterResult>> geneTestResults; geneTestResults = TestGenes.testForIntronExonBoundaries(genesFromFile, 1); ArrayList<InstanceToClassify> instanceNucs = new ArrayList<>(); try { //write our results to a temporary file File tempArrf = File.createTempFile("realSet", ".arff"); ArffWriter.write(tempArrf.getAbsolutePath(), geneTestResults, null); //get data ConverterUtils.DataSource source = new ConverterUtils.DataSource(tempArrf.getAbsolutePath()); //SET DATA AND OPTIONS Instances data = source.getDataSet(); for (int i = 0; i < data.numInstances(); i++) { Instance in = data.instance(i); //get the name of the gene or sequence tested String nameOfInstance = in.stringValue(in.numAttributes() - 3); //get the tested position int testedPosition = (int) in.value(in.numAttributes() - 2); //set the class as missing, because we want to find it in.setMissing((in.numAttributes() - 1)); Instance instanceNoExtras = new Instance(in); //delete the name and position, they are irrelevant for classifying instanceNoExtras.deleteAttributeAt(instanceNoExtras.numAttributes() - 2); instanceNoExtras.deleteAttributeAt(instanceNoExtras.numAttributes() - 2); InstanceToClassify ic = new InstanceToClassify(instanceNoExtras, testedPosition, nameOfInstance); instanceNucs.add(ic); } for (InstanceToClassify ic : instanceNucs) { Instance in = ic.getInstance(); in.setDataset(data); data.setClassIndex(data.numAttributes() - 1); //classify our instance classifier.classifyInstance(in); //save the likelyhood something is part of something double likelyhoodBoundary = classifier.distributionForInstance(in)[0]; double likelyhoodNotBoundary = classifier.distributionForInstance(in)[1]; //create a classified nucleotide and give it the added data ClassifiedNucleotide cn = new ClassifiedNucleotide(likelyhoodBoundary, likelyhoodNotBoundary, ic.getName(), ic.getPosition()); classifiedNucleotides.add(cn); } } catch (IOException ex) { Logger.getLogger(ClassifierUser.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(ClassifierUser.class.getName()).log(Level.SEVERE, null, ex); } return classifiedNucleotides; }
From source file:org.knime.knip.suise.node.boundarymodel.contourdata.IRI.java
License:Open Source License
private Instances toSingleInstanceDataset(Instances miData, Instances flatData) throws Exception { MultiInstanceToPropositional convertToProp = new MultiInstanceToPropositional(); convertToProp.setInputFormat(miData); for (int i = 0; i < miData.numInstances(); i++) { convertToProp.input(miData.instance(i)); }/* w ww. j a v a 2 s . c om*/ convertToProp.batchFinished(); if (flatData == null) { flatData = convertToProp.getOutputFormat(); flatData.deleteAttributeAt(0); // remove the bag index attribute } Instance processed; while ((processed = convertToProp.output()) != null) { processed.setDataset(null); processed.deleteAttributeAt(0); // remove the bag index attribute flatData.add(processed); } // remove class attribute // flatData.setClassIndex(-1); // flatData.deleteAttributeAt(flatData.numAttributes() - 1); // set weights int instanceIdx = 0; for (Instance bag : miData) { for (Instance instance : bag.relationalValue(1)) { flatData.get(instanceIdx).setWeight(instance.weight()); instanceIdx++; } } return flatData; }
From source file:sg.edu.nus.comp.nlp.ims.classifiers.CMultiClassesSVM.java
License:Open Source License
/** * generate instances for classifier classIdx * * @param p_Instances//from w w w . j a va2 s. c o m * input instances * @param p_ClassIndex * class index * @param p_ID2Classes * instance ids * @return new instances */ protected Instances genInstances(Instances p_Instances, double p_ClassIndex, Hashtable<String, ArrayList<Double>> p_ID2Classes) { Instances newInsts = new Instances(this.m_OutputFormat, 0); for (int i = 0; i < p_Instances.numInstances(); i++) { Instance inst = p_Instances.instance(i); Instance newInst = null; if (SparseInstance.class.isInstance(inst)) { newInst = new SparseInstance(inst); } else { newInst = new Instance(inst); } if (newInst.value(p_Instances.classIndex()) == p_ClassIndex) { newInst.setValue(inst.classIndex(), 1); } else { if (p_ID2Classes == null || !p_ID2Classes.get(inst.stringValue(this.m_IndexOfID)) .contains(new Double(p_ClassIndex))) { newInst.setValue(inst.classIndex(), 0); } else { continue; } } newInst.deleteAttributeAt(this.m_IndexOfID); newInst.setDataset(newInsts); newInsts.add(newInst); } return newInsts; }
From source file:sg.edu.nus.comp.nlp.ims.classifiers.CMultiClassesSVM.java
License:Open Source License
/** * filter instance//from www. j ava 2 s. c o m * * @param p_Instance * input instance * @return filtered instance */ protected Instance filterInstance(Instance p_Instance) { Instance retVal = null; if (SparseInstance.class.isInstance(p_Instance)) { retVal = new SparseInstance(p_Instance); } else { retVal = new Instance(p_Instance); } if (this.m_IndexOfID >= 0) { retVal.deleteAttributeAt(this.m_IndexOfID); } return retVal; }