@Override public Instance instance(int index) { return (Instance) m_Instances.elementAt(index); }
FastVector[] ass=ap.getAllTheRules(); for (FastVector rule : ass) { if (rule == null) continue; System.out.println("---> " + rule); for (int i = 0; i < rule.size(); ++i) { Object o = rule.elementAt(i); if (o instanceof AprioriItemSet) { System.out.println(((AprioriItemSet) o).toString(data)); } else { System.out.println("rule: "+o); } } }
String classLabel_i = (String)this.classesList.elementAt(i); stringBuffer.append(classLabel_i);
Attribute classAtt = (Attribute) attributeInfo.elementAt(0);
Attribute classAtt = (Attribute) attributeInfo.elementAt(0);
protected static ExperimentResults resultsFromEvaluation(Evaluation eval, String authorCSV, List<String> documentTitles){ ExperimentResults results = new ExperimentResults(); FastVector predictions = eval.predictions(); String[] authors = getAuthorsFromAttributeString(authorCSV); //for each document for (int i = 0; i<predictions.size(); i++){ NominalPrediction prediction = (NominalPrediction)predictions.elementAt(i); String actual = authors[(int)prediction.actual()]; double[] probabilities = prediction.distribution(); Map<String,Double> probMap = new HashMap<String,Double>(); //for each potential author... for (int j = 0; j< probabilities.length; j++){ probMap.put(authors[j], probabilities[j]); } results.addDocResult(new DocResult(documentTitles.get(i),actual,probMap)); } return results; }
public static Instances instancesFromDataMap(DataMap datamap){ Instances instances = null; FastVector attributes = createFastVector(datamap.getFeatures(),datamap.getDataMap().keySet()); int numfeatures = attributes.size(); instances = new Instances("Instances",attributes,datamap.numDocuments()); //for each author... for (String author : datamap.getDataMap().keySet()){ ConcurrentHashMap<String,DocumentData> authormap = datamap.getDataMap().get(author); //for each document... for (String doctitle : authormap.keySet()){ Instance instance = new SparseInstance(numfeatures); ConcurrentHashMap<Integer,FeatureData> documentData = authormap.get(doctitle).getDataValues(); //for each index we have a value for for (Integer index : documentData.keySet()){ instance.setValue((Attribute)attributes.elementAt(index), documentData.get(index).getValue()); } instance.setValue((Attribute)attributes.elementAt(attributes.size()-1), author); instances.add(instance); } } return instances; }
for (int j = 0; j < scores.size(); j++){ Double score = scores.get(j); instance.setValue((Attribute) attrs.elementAt(j), score);
for (int j = 0; j < scores.size(); j++){ Double score = scores.get(j); instance.setValue((Attribute) attrs.elementAt(j), score);
Attribute classAtt = (Attribute) attributeInfo.elementAt(0);
Attribute classAtt = (Attribute) attributeInfo.elementAt(0);
for (int i = 0; i < features.size(); i++){ Double score = features.get(i); instance.setValue((Attribute) attrs.elementAt(i), score);
vals[i] = ((Instance) vector.elementAt(i)).value(distAttIndex); newVector.addElement(vector.elementAt(sortedIndices[i])); vals[i] = -((Instance) vector.elementAt(i)).value(tfidfAttIndex); newVector.addElement(vector.elementAt(sortedIndices[i])); vals[i] = 1 - ((Instance) vector.elementAt(i)).value(probsAttIndex); newVector.addElement(vector.elementAt(sortedIndices[i])); Instance currentInstance = (Instance) vector.elementAt(i); Instance inst = (Instance) vector.elementAt(startInd); if ((inst.value(tfidfAttIndex) != currentInstance .value(tfidfAttIndex))