/**
* Computes the empirical distribution using data read from a URL.
*
* @param url url of the input file
* @throws IOException if an IO error occurs
*/
public void load(URL url) throws IOException {
BufferedReader in = new BufferedReader(new InputStreamReader(url.openStream()));
try {
DataAdapter da = new StreamDataAdapter(in);
try {
da.computeStats();
} catch (IOException ioe) {
// don't wrap exceptions which are already IOException
throw ioe;
} catch (RuntimeException rte) {
// don't wrap RuntimeExceptions
throw rte;
} catch (Exception e) {
throw MathRuntimeException.createIOException(e);
}
if (sampleStats.getN() == 0) {
throw MathRuntimeException.createEOFException("URL {0} contains no data", url);
}
in = new BufferedReader(new InputStreamReader(url.openStream()));
fillBinStats(in);
loaded = true;
} finally {
try {
in.close();
} catch (IOException ex) {
// ignore
}
}
}
/**
* Fills binStats array (second pass through data file).
*
* @param in object providing access to the data
* @throws IOException if an IO error occurs
*/
private void fillBinStats(Object in) throws IOException {
// Load array of bin upper bounds -- evenly spaced from min - max
double min = sampleStats.getMin();
double max = sampleStats.getMax();
double delta = (max - min) / (Double.valueOf(binCount)).doubleValue();
double[] binUpperBounds = new double[binCount];
binUpperBounds[0] = min + delta;
for (int i = 1; i < binCount - 1; i++) {
binUpperBounds[i] = binUpperBounds[i - 1] + delta;
}
binUpperBounds[binCount - 1] = max;
// Initialize binStats ArrayList
if (!binStats.isEmpty()) {
binStats.clear();
}
for (int i = 0; i < binCount; i++) {
SummaryStatistics stats = new SummaryStatistics();
binStats.add(i, stats);
}
// Filling data in binStats Array
DataAdapterFactory aFactory = new DataAdapterFactory();
DataAdapter da = aFactory.getAdapter(in);
try {
da.computeBinStats(min, delta);
} catch (IOException ioe) {
// don't wrap exceptions which are already IOException
throw ioe;
} catch (RuntimeException rte) {
// don't wrap RuntimeExceptions
throw rte;
} catch (Exception e) {
throw MathRuntimeException.createIOException(e);
}
// Assign upperBounds based on bin counts
upperBounds = new double[binCount];
upperBounds[0] = ((double) binStats.get(0).getN()) / (double) sampleStats.getN();
for (int i = 1; i < binCount - 1; i++) {
upperBounds[i] =
upperBounds[i - 1] + ((double) binStats.get(i).getN()) / (double) sampleStats.getN();
}
upperBounds[binCount - 1] = 1.0d;
}
/**
* Calculates and returns the arrival rate of the traces in piList
*
* @return double
*/
public double getArrivalRate() {
double arrivalRate = 0;
if (arrivalStats.getN() > 0 && arrivalStats.getMean() != 0) {
// mean arrivalRate is 1 divided by the mean of the inter-arrival
// times
arrivalRate = 1 / arrivalStats.getMean();
}
return arrivalRate;
}
/** 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;
}
}
}
/**
* 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");
}
/**
* 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;
}
}
}
