private void jMenuItemDumpChunksActionPerformed(
java.awt.event.ActionEvent evt) { // GEN-FIRST:event_jMenuItemDumpChunksActionPerformed
if (currentResults != null) {
DocumentClassDialog d = DocumentClassDialog.showClassDialog(this, this.theProject);
DocumentClass selectedClass = d.getSelectedDocumentClass();
currentResults.dumpChunksBelongingToDocumentsWithClass(selectedClass.getId());
}
} // GEN-LAST:event_jMenuItemDumpChunksActionPerformed
public void train(BagOfWords bag, String docClassName) {
termCorpus.merge(bag);
DocumentClass docClass = docClasses.get(docClassName);
if (null == docClass) {
docClass = new DocumentClass(docClassName, keepDetailsOfDocumentsInClasses);
docClass.add(bag);
docClasses.put(docClassName, docClass);
docClassFrequencyPerTerm.addTerms(docClass.getTerms());
} else {
docClassFrequencyPerTerm.addTerms(bag.termsNotIn(docClass.getTerms()));
docClass.add(bag);
docClasses.put(docClassName, docClass);
}
trainingFinished = false;
}
private Double calculateCosinSimilairty(Map<String, Double> filteredBag, DocumentClass docClass) {
Double cosinSimilarity = null;
try {
cosinSimilarity =
VectorMath.cosineSimilarityEuclideanNorm(
// VectorMath.normlizeVectorEuclideanNorm(queryBag.getFrequencies()),
// Without stopWordFilter much better precision ~+25% better
VectorMath.normlizeVectorEuclideanNorm(filteredBag),
// Only used by BiggerExample - better result without StopWordFilter!
// Result better about 20% false
//
// VectorMath.normlizeVectorEuclideanNorm(docClass.getTermFrequencies().getFrequencies())
// Used currently with best results
// Result better about 10-20% false. Only with stopWordFilter and on real trainings
// data?!
// VectorMath.normlizeVectorEuclideanNorm(docClass.getWeightedFrequencies())
// Trial
VectorMath.normlizeVectorEuclideanNorm(
docClass.getWeightedFrequencies(getIDFTermFilter())));
} catch (InvalidObjectException e) {
e.printStackTrace();
}
return cosinSimilarity;
}
// Calculate all data for performance classification. This again adds a state what is not really
// good!
public void finishTraining() {
if (docClasses.isEmpty()) {
throw new IllegalStateException(
"Can not finish training! The classifier does not contain any document classes yet.");
}
calculateDocClassTermIDFs();
analyzeDocClassTermIDFs();
// Just to improve the performance of the later probability calculation! Pre-Initialize the
// docClasses weighted frequencies!
for (DocumentClass docClass : docClasses.values()) {
docClass.getWeightedFrequencies();
}
maxNumberAllowedTerms =
new Integer((int) (getCorpusSize() * percentageOfCorpusForAllowedTerms));
trainingFinished = true;
}
public List<Entry<String, Double>> getClassificationProbabilities(BagOfWords queryBag) {
checkFinishedTraining();
if (bagNotAlreadyAnalyzed(queryBag)) {
List<Entry<String, Double>> classifications = new ArrayList<>(docClasses.size());
// Test only if the distances differ from the cosine similarity?! No they don't!
List<Entry<String, Double>> distances = new ArrayList<>(docClasses.size());
Map<String, Double> preparedQueryBag = prepareQueryBagOfWords(queryBag);
for (DocumentClass docClass : docClasses.values()) {
Double cosinSimilarity = calculateCosinSimilairty(preparedQueryBag, docClass);
classifications.add(new SimpleEntry<>(docClass.getName(), cosinSimilarity));
Double distance = calculateDistances(preparedQueryBag, docClass);
distances.add(new SimpleEntry<>(docClass.getName(), distance));
}
lastClassifiedBag = queryBag.hashCode();
lastClassificationResult = new ArrayList<>(classifications);
// Order results. Not in own method while not clear if mor than one analyze method will be
// used (ie. KNN plus cosineSimilarity)
Collections.sort(
lastClassificationResult,
new Comparator<Entry<String, Double>>() {
@Override
public int compare(Entry<String, Double> o1, Entry<String, Double> o2) {
return o2.getValue().compareTo(o1.getValue());
}
});
// Ascending because the smallest is the most important one!
Collections.sort(
distances,
new Comparator<Entry<String, Double>>() {
@Override
public int compare(Entry<String, Double> o1, Entry<String, Double> o2) {
return o1.getValue().compareTo(o2.getValue());
}
});
// System.out.println(lastClassificationResult);
// System.out.println(distances);
}
return Collections.unmodifiableList(lastClassificationResult);
}
private Double calculateDistances(Map<String, Double> filteredBag, DocumentClass docClass) {
Double distance = null;
try {
distance =
VectorMath.distanceEuclideanNorm(
VectorMath.normlizeVectorEuclideanNorm(filteredBag),
VectorMath.normlizeVectorEuclideanNorm(
docClass.getWeightedFrequencies(getIDFTermFilter())));
} catch (Exception ex) {
ex.printStackTrace();
}
return distance;
}
