@Test
public void testMemoryLeakage() {
long memoryBaseline = 0;
SummaryStatistics stats = new SummaryStatistics();
int numRuns = 25;
for (int r = 0; r < numRuns; r++) {
if (r == 1 || r == (numRuns - 1)) {
memoryBaseline = MemoryAssess.getMemoryUse();
stats.addValue(memoryBaseline);
// System.out.println("Memory before run "+(r+1)+": " + memoryBaseline / 1024 + " KB");
}
for (int t = 0; t < 1000; t++) {
graph.addVertex(null);
graph.rollback();
TitanTransaction tx = graph.newTransaction();
tx.addVertex();
tx.rollback();
}
if (r == 1 || r == (numRuns - 1)) {
memoryBaseline = MemoryAssess.getMemoryUse();
stats.addValue(memoryBaseline);
// System.out.println("Memory after run " + (r + 1) + ": " + memoryBaseline / 1024 + " KB");
}
clopen();
}
System.out.println(
"Average: " + stats.getMean() + " Std. Dev: " + stats.getStandardDeviation());
assertTrue(stats.getStandardDeviation() < stats.getMin());
}
@Override
public double[] getResult() {
double[] result = new double[3];
result[0] = overallRes / overallQueries;
result[1] = sumStats.getStandardDeviation();
result[2] = sumStats.getVariance();
return result;
}
public static void main(String[] args) {
Ordering ordering = new Ordering();
ordering.prepare("ww3d", SequenceType.allen);
SummaryStatistics ss = new SummaryStatistics();
for (int i = 0; i < 100; ++i) {
double value = ordering.experiment(SequenceType.allen, true);
// double value = ordering.orderingExperiment("ww3d", SequenceType.allen, true);
System.out.println("..." + value);
ss.addValue(value);
}
System.out.println("Summary " + ss.getMean() + " -- " + ss.getStandardDeviation());
}
/**
* Generates a random value from this distribution.
*
* @return the random value.
* @throws IllegalStateException if the distribution has not been loaded
*/
public double getNextValue() throws IllegalStateException {
if (!loaded) {
throw MathRuntimeException.createIllegalStateException("distribution not loaded");
}
// Start with a uniformly distributed random number in (0,1)
double x = Math.random();
// Use this to select the bin and generate a Gaussian within the bin
for (int i = 0; i < binCount; i++) {
if (x <= upperBounds[i]) {
SummaryStatistics stats = binStats.get(i);
if (stats.getN() > 0) {
if (stats.getStandardDeviation() > 0) { // more than one obs
return randomData.nextGaussian(stats.getMean(), stats.getStandardDeviation());
} else {
return stats.getMean(); // only one obs in bin
}
}
}
}
throw new MathRuntimeException("no bin selected");
}
private GeoSegment getQQLineSegment() {
SummaryStatistics stats = new SummaryStatistics();
for (int i = 0; i < sortedData.length; i++) {
stats.addValue(sortedData[i]);
}
double sd = stats.getStandardDeviation();
double mean = stats.getMean();
double min = stats.getMin();
double max = stats.getMax();
// qq line: y = (1/sd)x - mean/sd
GeoPoint startPoint = new GeoPoint(cons);
startPoint.setCoords(min, (min / sd) - mean / sd, 1.0);
GeoPoint endPoint = new GeoPoint(cons);
endPoint.setCoords(max, (max / sd) - mean / sd, 1.0);
GeoSegment seg = new GeoSegment(cons, startPoint, endPoint);
seg.calcLength();
return seg;
}
/** Iterate through the graph, calculating rank. */
protected void iterateGraph(final int max_iterations) {
final double[] rankList = new double[nodeList.length];
// either run through N iterations, or until the standard
// error converges below a threshold
for (int k = 0; k < max_iterations; k++) {
distStats.clear();
// calculate the next rank for each node
for (int i = 0; i < nodeList.length; i++) {
final Node n1 = nodeList[i];
double rank = 0.0D;
for (Node n2 : n1.edges) {
rank += n2.rank / (double) n2.edges.size();
}
rank *= TEXTRANK_DAMPING_FACTOR;
rank += 1.0D - TEXTRANK_DAMPING_FACTOR;
rankList[i] = rank;
distStats.addValue(Math.abs(n1.rank - rank));
// System.out.println("node : " + n1.key + " rank : " + Math.abs((n1.rank - rank)));
}
final double standard_error =
distStats.getStandardDeviation() / Math.sqrt((double) distStats.getN());
// swap in new rank values
for (int i = 0; i < nodeList.length; i++) {
nodeList[i].rank = rankList[i];
}
if (standard_error < STANDARD_ERROR_THRESHOLD) {
break;
}
}
}
/**
* Calculates the rank weighting scores for all the nodes in the graph. Iterative calculates over
* the graph until convergence at the standard error threshold or until max iterations.
*
* @param maxIterations Max number of iterations allowed for calculating rank scores
* @param language Language of the text to calculate rank weighting scores for Available
* languages: Icelandic and English
*/
public void weigthingScore(int maxIterations, Language language) {
LinkedList<Node> nodes = new LinkedList<Node>();
// Add nodes to LinkedList, we need them to stay in order
for (int i = 0; i < nodeList.length; i++) {
nodes.add(nodeList[i]);
}
/*
WS(Vi) = ( 1 - d) + d * Sum(VjIn) ________Wij________ * WS(Vj)
Sum(Vk outVj) Wjk
*/
for (int k = 0; k < maxIterations; k++) {
distStats.clear();
// Use dynamic programming to calculate the scores
double previousWSScore[] = new double[nodes.size()];
// Read in scores already calculated for nodes
for (Node s : nodeList) {
previousWSScore[nodes.indexOf(s)] = s.rank;
}
// For all nodes in the graph
for (Node sentence_i : nodes) {
double resultSumVji = 0;
// For all in-coming edges of Vi
for (Node sentence_j : sentence_i.edgesIN) {
// Do not compare a sentence to it self, we do not allow self voting here
if (!sentence_j.value.text.equalsIgnoreCase(sentence_i.value.text)) {
// Calculate the sum of all similarity measurements
// from all Vj nodes with outgoing edges to Vk nodes, see Wjk in equation
double sumWjk = getSumWjk(sentence_j, language);
if (sumWjk != 0) {
double Wji = 0.0;
if (language.equals(Language.ICELANDIC)) {
// Calculate Wij, similarity between two sentences
Wji = sentence_i.similarity(sentence_j);
} else if (language.equals(Language.ENGLISH)) {
// Calculate Wij, similarity between two sentences
Wji = sentence_i.similarityEN(sentence_j);
}
// Get the score for the previous node
double WSVj = previousWSScore[nodes.indexOf(sentence_j)];
// Sum all (j in Vj)
resultSumVji += ((Wji / sumWjk) * WSVj);
}
}
}
// Calculate weighting score WS(Vi)
double WSVi = (1.0 - TEXTRANK_DAMPING_FACTOR) + TEXTRANK_DAMPING_FACTOR * resultSumVji;
distStats.addValue(Math.abs(sentence_i.rank - WSVi));
sentence_i.rank = WSVi;
}
// Calculate the Standard Error of the Mean
final double standard_error =
distStats.getStandardDeviation() / Math.sqrt((double) distStats.getN());
// if std error of the mean is less than threshold
// the graph has converged and we break
if (standard_error < STANDARD_ERROR_THRESHOLD) {
break;
}
}
}
/** Calculate a threshold for the ranked results. */
public double getRankThreshold() {
return distStats.getMean() + (distStats.getStandardDeviation() * INCLUSIVE_COEFF);
}
