public double getValue(Document doc) throws EventGenerationException {
double wordCount = wordCounter.getValue(doc);
double sentenceCount = sentenceCounter.getValue(doc);
EventSet syllables = syllablesDriver.createEventSet(doc);
for (int i = syllables.size() - 1; i >= 0; i--) {
if (Integer.parseInt(syllables.eventAt(i).toString()) < 3) {
syllables.removeEvent(syllables.eventAt(i));
}
}
double complexWordsCount = syllables.size();
return 0.4 * (wordCount / sentenceCount + 100 * complexWordsCount / wordCount);
}
public void build(EventSet e) {
for (int i = 0; i < e.size(); i++) {
Event start = e.eventAt(i);
if (!root.isEventInLevel(start)) {
insertAtRoot(start, e, i);
} else {
insertBelowRoot(start, e, i);
}
}
root.key = null;
}
private void insertAtRoot(Event start, EventSet e, int offset) {
root.addEventToLevel(start);
XEDictionaryNode node;
node = root;
int j = offset;
while (j < e.size() - 1) {
node = node.get(e.eventAt(j));
j++;
// System.out.println("Adding Event: " + e.eventAt(j));
node.addEventToLevel(e.eventAt(j));
}
}
private void insertBelowRoot(Event start, EventSet e, int offset) {
XEDictionaryNode node;
node = root;
// System.out.println("Event at offset: " + e.eventAt(offset));
node = node.get(e.eventAt(offset));
int j = offset;
boolean matches = true; // match the events up to a given level
while (matches && (j < e.size() - 1)) {
j++;
if (node.isEventInLevel(e.eventAt(j))) {
// System.out.println("Match at level: " + e.eventAt(j));
node = node.get(e.eventAt(j));
} else {
matches = false;
}
}
for (int i = j; i < e.size(); i++) {
// System.out.println("Adding Event: " + e.eventAt(i));
node.addEventToLevel(e.eventAt(i));
node = node.get(e.eventAt(i));
}
}
private double meanEntropy(EventSet e1, EventSet e2, int windowSize) {
double totalEntropy = 0;
int trials = 0;
if (windowSize > e1.size() - 1) {
windowSize = e1.size();
}
// for (int j = 0; j <= e1.size() - windowSize; j++) {
XEDictionary xed = new XEDictionary();
EventSet dictionary;
dictionary = window(e1, 0, windowSize);
xed.build(dictionary);
for (int i = 0; i <= e2.size() - windowSize; i++) {
totalEntropy += xed.find(window(e2, i, windowSize));
trials++;
}
// }
return totalEntropy / trials;
}
public int find(EventSet e) {
int matchlength = 0;
boolean matched = false;
XEDictionaryNode node = root;
while ((matchlength < e.size()) && !matched) {
if (node.isEventInLevel(e.eventAt(matchlength))) {
node = node.get(e.eventAt(matchlength));
matchlength++;
} else {
matched = true;
}
}
return matchlength;
}
/**
* Culls the test set using the relevant Events extracted from the training data.<br>
*
* @param relevantEvents the features from the EventSets which are going to be evaluated
* @param eventSetsToCull The test documents to be culled
* @return the culled test documents
* @throws Exception
*/
public List<EventSet> cullWithRespectToTraining(
List<EventSet> relevantEvents, List<EventSet> eventSetsToCull, CumulativeFeatureDriver cfd)
throws Exception {
List<EventSet> relevant = relevantEvents;
int numOfFeatureClasses = eventSetsToCull.size() - 1; // -1 to compensate for removing metadata
int i;
List<EventSet> culledUnknownEventSets = new LinkedList<EventSet>();
// remove the metadata prior to culling
EventSet metadata = eventSetsToCull.remove(eventSetsToCull.size() - 1);
// make sure all unknown sets would have only events that appear in the
// known sets
// UNLESS the event set contains a sole numeric value event - in that
// case take it anyway
for (i = 0; i < numOfFeatureClasses; i++) {
if (cfd.featureDriverAt(i).isCalcHist()) {
// initialize set of relevant events
EventSet es = relevant.get(i);
Set<String> relevantEventsString = new HashSet<String>(es.size());
for (Event e : es) relevantEventsString.add(e.getEvent());
// remove all non-relevant events from unknown event sets
EventSet unknown;
Event e;
unknown = eventSetsToCull.get(i);
Iterator<Event> iterator = unknown.iterator();
Event next = null;
// the test doc may not contain a given feature (ie it might not
// have any semi-colons)
if (iterator.hasNext()) next = (Event) iterator.next();
// while it has more of a feature
while (iterator.hasNext()) {
// copy the feature
e = next;
boolean remove = true;
// check to see if the feature is relevant
for (int l = 0; l < unknown.size(); l++) {
try {
if (e.equals(relevantEvents.get(i).eventAt(l))) {
remove = false; // if it is, break
break;
}
} catch (IndexOutOfBoundsException iobe) {
remove = true; // it is not relevant if we reach this point.
break;
}
}
// remove the feature if it isn't relevant
if (remove) {
iterator.remove();
}
// grab the next feature
next = iterator.next();
}
// add the culled event set
culledUnknownEventSets.add(unknown);
} else { // one unique numeric event
// add non-histogram if it is in the relevantEventSets list
boolean isRelevant = false;
for (EventSet res : relevantEvents) {
if (res.getEventSetID().equals(eventSetsToCull.get(i).getEventSetID())) {
isRelevant = true;
break;
}
}
if (isRelevant) culledUnknownEventSets.add(eventSetsToCull.get(i));
}
}
eventSetsToCull.add(metadata);
culledUnknownEventSets.add(metadata);
return culledUnknownEventSets;
}
