public void run() {
if (som == null || ((som.getLabels() == null || som.voronoiSet == null) && artterms == null))
return;
// check if termvector information is available -> if not: crawl
if (artterms == null) {
// System.out.println("do the crawl...");
// artists are contained in labels (-> collect)
Set<String> artistset = new HashSet<String>();
for (int i = 0; i < som.getNumberOfColumns(); i++) { // for each column in codebook
for (int j = 0; j < som.getNumberOfRows(); j++) { // for each row in codebook
Vector temp = (Vector) som.voronoiSet.elementAt(i * som.getNumberOfRows() + j);
// read all labels in Voronoi-Set of current map unit
for (int k = 0; k < temp.size(); k++) {
Integer labelIndex = (Integer) temp.elementAt(k);
artistset.add(som.getLabel(labelIndex.intValue()));
}
}
}
String[] artists = artistset.toArray(new String[artistset.size()]);
wtet = new WebTermExtractionThread(artists, queryconstraint);
wtet.addThreadListener(this);
wtet.start(); // -> threadEnded
} else {
System.out.println("data already present.");
this.threadEnded();
}
}
private void findUnitClusters(int[] clusterassoc, double[][] lkvalues, int[][] neighbors) {
Vector<ObjectComparablePair> topunitterms = new Vector<ObjectComparablePair>();
// sort units according to their highest value -> order to find clusters
for (int i = 0; i < lkvalues.length; i++) {
double max = Stat.max(lkvalues[i]);
if (max <= 0.) continue;
topunitterms.addElement(new ObjectComparablePair(new Integer(i), new Double(max)));
}
Collections.sort(topunitterms);
Collections.reverse(topunitterms);
Iterator<ObjectComparablePair> ocpit = topunitterms.iterator();
while (ocpit.hasNext()) {
int curunit = ((Integer) (ocpit.next().getObject())).intValue();
floodFillSimilarClusters(curunit, clusterassoc, lkvalues, neighbors, curunit);
}
// join all empty units
int firstemptyindex = -1;
for (int i = 0; i < lkvalues.length; i++) {
if (Stat.max(lkvalues[i]) <= 0.) {
if (firstemptyindex == -1) firstemptyindex = i;
else clusterassoc[i] = firstemptyindex;
}
}
}
private void floodFillSimilarClusters(
int unitpos, int[] clusterassoc, double[][] lkvalues, int[][] neighbors, int startunitpos) {
// check if already associated to other cluster (end of recursion)
if (clusterassoc[unitpos] != unitpos && clusterassoc[unitpos] != startunitpos) return;
Vector<Integer> joinedunits = new Vector<Integer>();
// check neighbors (starts with index 1) for join candidates
for (int i = 1; i < neighbors[unitpos].length; i++) {
if (neighbors[unitpos][i] != -1
&& // on the som
clusterassoc[neighbors[unitpos][i]] != startunitpos
&& // not already in cluster
clusterassoc[neighbors[unitpos][i]]
== neighbors[unitpos][i]) { // not part of any other cluster
// join two units iff summed up vector differs below threshold from both original vectors
double[] cosNormA = Vec.cosineNormalize(Vec.cloneVector(lkvalues[unitpos]));
double[] cosNormB = Vec.cosineNormalize(Vec.cloneVector(lkvalues[neighbors[unitpos][i]]));
double[] sumAB = Vec.add(lkvalues[unitpos], lkvalues[neighbors[unitpos][i]]);
double[] cosNormAB = Vec.cosineNormalize(Vec.cloneVector(sumAB));
if (Vec.euclDist(cosNormA, cosNormAB) < 0.6 && Vec.euclDist(cosNormB, cosNormAB) < 0.6) {
// similar units -> join
clusterassoc[neighbors[unitpos][i]] = startunitpos;
// write new vector to all associated
for (int k = 0; k < clusterassoc.length; k++) {
if (clusterassoc[k] == startunitpos) {
lkvalues[k] = sumAB;
}
}
// Vec.copyTo(lkvalues[unitpos], sumAB);
// Vec.copyTo(lkvalues[neighbors[unitpos][i]], sumAB);
// add to list for recursion
joinedunits.addElement(new Integer(neighbors[unitpos][i]));
}
}
}
// recursion over joined units
Iterator<Integer> iit = joinedunits.iterator();
while (iit.hasNext()) {
floodFillSimilarClusters(
iit.next().intValue(), clusterassoc, lkvalues, neighbors, startunitpos);
}
}
public void addThreadListener(ThreadListener threadlistener) {
threadlisteners.addElement(threadlistener);
}
public void threadEnded() {
if (wtet != null && artterms == null) artterms = wtet.getArtistsAndTermVectors();
int cols = som.getNumberOfColumns(), rows = som.getNumberOfRows();
// use lagus-kaski labelling technique
// generate summed tf vector per cluster
int[][] clustervecs = new int[cols * rows][MusicDictionary.getDictionary().size()];
int[] clustersize = new int[cols * rows];
int[] vecsum = new int[MusicDictionary.getDictionary().size()];
// for lagus kaski G2 determine r0 and r1 zone indices
int[][] r0elements = new int[cols * rows][5];
int[][] r1elements = new int[cols * rows][8];
for (int i = 0; i < cols; i++) { // for each column in codebook
for (int j = 0; j < rows; j++) { // for each row in codebook
int mappos = i * som.getNumberOfRows() + j;
// get Voronoi-Set for current map unit
Vector temp = (Vector) som.voronoiSet.elementAt(mappos);
clustersize[mappos] = temp.size();
// get this set ordered
temp = som.getPrototypesForMU(mappos, clustersize[mappos]);
int[] clustterms = new int[MusicDictionary.getDictionary().size()];
for (int k = 0; k < clustersize[mappos]; k++) {
String artist = (String) temp.elementAt(k);
int[] termvec = artterms.get(artist);
if (termvec == null) {
System.err.println("no term vector for artist " + artist);
continue;
}
Vec.addTo(clustterms, termvec);
}
// remove all terms with tf < 3 in cluster
for (int k = 0; k < clustterms.length; k++) {
if (clustterms[k] < Math.min(3, clustersize[mappos])) clustterms[k] = 0;
}
Vec.addTo(vecsum, clustterms);
// System.out.println("total "+Stat.sum(clustterms)+" terms in cluster "+mappos);
clustervecs[mappos] = clustterms; // Vec.divide(clustterms, (count>0)?count:1);
// finally, determine r0 and r1 zone elements
// for r0
r0elements[mappos][0] = mappos;
r0elements[mappos][1] = i > 0 ? (i - 1) * rows + j : -1;
r0elements[mappos][2] = i + 1 < cols ? (i + 1) * rows + j : -1;
r0elements[mappos][3] = j > 0 ? i * rows + j - 1 : -1;
r0elements[mappos][4] = j + 1 < rows ? i * rows + j + 1 : -1;
// for r1
r1elements[mappos][0] = i > 1 ? (i - 2) * rows + j : -1;
r1elements[mappos][1] = i + 2 < cols ? (i + 2) * rows + j : -1;
r1elements[mappos][2] = j > 1 ? i * rows + j - 2 : -1;
r1elements[mappos][3] = j + 2 < rows ? i * rows + j + 2 : -1;
r1elements[mappos][4] = i > 0 && j > 0 ? (i - 1) * rows + j - 1 : -1;
r1elements[mappos][5] = i + 1 < cols && j > 0 ? (i + 1) * rows + j - 1 : -1;
r1elements[mappos][6] = i > 0 && j + 1 < rows ? (i - 1) * rows + j + 1 : -1;
r1elements[mappos][7] = i + 1 < cols && j + 1 < rows ? (i + 1) * rows + j + 1 : -1;
}
}
double[] summedterms = new double[MusicDictionary.getDictionary().size()];
int[] clustertermsums = new int[cols * rows];
for (int i = 0; i < clustervecs.length; i++) {
clustertermsums[i] = Stat.sum(clustervecs[i]);
// for (int j=0; j<clustervecs[i].length; j++) {
// summedterms[j] += clustertermsums[i]>0?clustervecs[i][j]/clustertermsums[i]:0;
// }
if (clustertermsums[i] == 0) continue;
Vec.addTo(summedterms, Vec.divide(clustervecs[i], clustertermsums[i]));
}
double[][] lkvalues = new double[cols * rows][MusicDictionary.getDictionary().size()];
// determine min and max laguskaski values for value normalization between 0 and 1
double minlk = 0.01d;
double maxlk = 0.;
// for each term in each cluster
for (int i = 0; i < clustervecs.length; i++) {
// create r0 zone sum vector
// modification to reflect number of entries
double[] r0sum = new double[MusicDictionary.getDictionary().size()];
for (int j = 0; j < r0elements[i].length; j++) {
if (r0elements[i][j] == -1) continue;
if (clustertermsums[r0elements[i][j]] == 0) continue;
Vec.addTo(
r0sum, Vec.divide(clustervecs[r0elements[i][j]], clustertermsums[r0elements[i][j]]));
}
// create non-r1 zone sum vector
double[] nonr1sum = Vec.cloneVector(summedterms);
for (int j = 0; j < r1elements[i].length; j++) {
if (r1elements[i][j] == -1) continue;
if (clustertermsums[r1elements[i][j]] == 0) continue;
Vec.subtractFrom(
nonr1sum, Vec.divide(clustervecs[r1elements[i][j]], clustertermsums[r1elements[i][j]]));
}
for (int j = 0; j < clustervecs[i].length; j++) {
// // lagus kaski G0
// double fclust = clustertermsums[i]>0?clustervecs[i][j]/clustertermsums[i]:0;
// double fpen = summedterms[j];
// lagus kaski G2
double fclust = r0sum[j];
double fpen = nonr1sum[j];
if (clustervecs[i][j] == 0 // only accept words, that were on the island before G2
|| fclust == 0.
|| fpen == 0.) continue;
Double ftc = new Double(fclust * fclust / fpen);
// find max for normalization
if (ftc > maxlk) maxlk = ftc;
// smooth -> only values with score >= minlk
if (ftc >= minlk) lkvalues[i][j] = ftc;
}
}
// // calculate all pairwise distances matrix of cos norm vectors
// Vector<Double> dists = new Vector<Double>();
// for (int i=0; i<lkvalues.length-1; i++) {
// for (int j=i+1; j<lkvalues.length; j++) {
// double[] cosNormA = Vec.cosineNormalize(Vec.cloneVector(lkvalues[i]));
// double[] cosNormB = Vec.cosineNormalize(Vec.cloneVector(lkvalues[j]));
// dists.addElement(new Double(Vec.euclDist(cosNormA, cosNormB)));
// }
// }
// Collections.sort(dists);
// System.out.println("sorted pairwise distances of all clusters");
// Iterator<Double> dit = dists.iterator();
// while (dit.hasNext()) {
// System.out.println(dit.next());
// }
// init cluster formation map -> every unit is its own cluster
int[] clusterassociations = new int[cols * rows];
for (int i = 0; i < clusterassociations.length; i++) {
clusterassociations[i] = i;
}
// copy original lkvalues (for coloring later)
double[][] origlkvalues = new double[cols * rows][MusicDictionary.getDictionary().size()];
for (int i = 0; i < cols * rows; i++) {
for (int j = 0; j < MusicDictionary.getDictionary().size(); j++) {
origlkvalues[i][j] = lkvalues[i][j];
}
}
// find coherent regions on SOM
findUnitClusters(clusterassociations, lkvalues, r0elements);
// // print clusterassoc map
// System.out.println("clusterassociations");
// for (int j=0; j<rows; j++) { // for each row in codebook
// for (int i=0; i<cols; i++) { // for each column in codebook
// int mappos = i*som.getNumberOfRows()+j;
// System.out.print(clusterassociations[mappos]+" ");
// }
// System.out.println("");
// }
Vector[] clusterterms = new Vector[cols * rows];
for (int i = 0; i < lkvalues.length; i++) {
clusterterms[i] = new Vector();
for (int j = 0; j < lkvalues[i].length; j++) {
if (lkvalues[i][j] > minlk) {
ObjectComparablePair ocp =
new ObjectComparablePair(
MusicDictionary.getDictionary().elementAt(j),
new Double(
Math.min(maxlk, lkvalues[i][j]))); // use old max as upper bound for all value
clusterterms[i].addElement(ocp);
}
// if (lkvalues[i][j] > maxlk) maxlk = lkvalues[i][j];
}
Collections.sort(clusterterms[i]);
Collections.reverse(clusterterms[i]);
}
Vector<Vector<String>> mdmlabels = new Vector<Vector<String>>();
// calc normalized lagus kaski
for (int i = 0; i < cols; i++) { // for each column in codebook
for (int j = 0; j < rows; j++) { // for each row in codebook
int mappos = i * som.getNumberOfRows() + j;
Vector<String> unitterms = new Vector<String>();
// get terms for current map unit
for (int k = 0; k < clusterterms[mappos].size() && k < maxTermsPerUnit; k++) {
ObjectComparablePair ocp = (ObjectComparablePair) (clusterterms[mappos].elementAt(k));
double laguskaski = ((Double) (ocp.getComparable())).doubleValue();
String word = (String) (ocp.getObject());
if (k < minTermsPerUnit || laguskaski > minlk) {
double normlk = (laguskaski - minlk) / (maxlk - minlk);
String wordandval = word + "_" + normlk; // TextTool.doubleToString(laguskaski, 3)+")";
unitterms.addElement(wordandval);
}
}
mdmlabels.addElement(unitterms);
}
}
som.setMDM(this);
this.setLabels(mdmlabels);
this.setClusterAssociations(clusterassociations);
this.setNeighborhood(r0elements);
if (colorByPCA) {
Vector[] colorclusterterms = new Vector[cols * rows];
for (int i = 0; i < origlkvalues.length; i++) {
colorclusterterms[i] = new Vector();
for (int j = 0; j < origlkvalues[i].length; j++) {
if (origlkvalues[i][j] > minlk) {
ObjectComparablePair ocp =
new ObjectComparablePair(
MusicDictionary.getDictionary().elementAt(j),
new Double(
Math.min(
maxlk,
origlkvalues[i][j]))); // use old max as upper bound for all value
colorclusterterms[i].addElement(ocp);
}
}
Collections.sort(colorclusterterms[i]);
Collections.reverse(colorclusterterms[i]);
}
HashSet<String> remainingwords = new HashSet<String>();
Hashtable<String, Double>[] mdmvalues = new Hashtable[cols * rows];
// calc normalized lagus kaski
for (int i = 0; i < cols; i++) { // for each column in codebook
for (int j = 0; j < rows; j++) { // for each row in codebook
int mappos = i * som.getNumberOfRows() + j;
mdmvalues[mappos] = new Hashtable<String, Double>();
Vector<String> unitterms = new Vector<String>();
// get terms for current map unit
for (int k = 0; k < colorclusterterms[mappos].size() && k < maxTermsPerUnit; k++) {
ObjectComparablePair ocp =
(ObjectComparablePair) (colorclusterterms[mappos].elementAt(k));
double laguskaski = ((Double) (ocp.getComparable())).doubleValue();
String word = (String) (ocp.getObject());
if (k < minTermsPerUnit || laguskaski > minlk) {
double normlk = (laguskaski - minlk) / (maxlk - minlk);
// remainingwords.add(word);
// mdmvalues[mappos].put(word, new Double(laguskaski));
}
if (k < 4) {
remainingwords.add(word);
mdmvalues[mappos].put(word, new Double(laguskaski));
}
}
mdmlabels.addElement(unitterms);
}
}
// // remove all words that never occur
// Vector<double[]> relevantDimensions = new Vector<double[]>();
// double[][] featuredims = new Matrix(lkvalues).transpose().getArray();
// for (int i=0; i<featuredims.length; i++) {
// if (Stat.sum(featuredims[i]) > 0.1) {
// relevantDimensions.addElement(featuredims[i]);
// }
// }
// double[][] reduceddims = new double[relevantDimensions.size()][lkvalues.length];
// for (int i=0; i<relevantDimensions.size(); i++) {
// reduceddims[i] = relevantDimensions.elementAt(i);
// }
// cellColors = PCAProjectionToColor.getColorsForFeatures(new
// Matrix(reduceddims).transpose().getArray());
// ---------------------------
// construct new vocabulary vector from remaining words
// ignore all empty vectors (discard cells w/o entries)
String[] remainingvocab = remainingwords.toArray(new String[0]);
Hashtable<Integer, double[]> reducedVectorMapping = new Hashtable<Integer, double[]>();
int[] colorarraymapping = new int[cols * rows];
int usefulfeats = 0;
for (int i = 0; i < cols * rows; i++) {
double[] nufeat = HashtableTool.getDoubleVectorRepresentation(mdmvalues[i], remainingvocab);
if (Stat.max(nufeat) == 0.) {
colorarraymapping[i] = -1;
} else {
reducedVectorMapping.put(new Integer(usefulfeats), nufeat);
colorarraymapping[i] = usefulfeats;
usefulfeats++;
}
}
// recreate nufeature set
double[][] nufeatures = new double[usefulfeats][remainingvocab.length];
for (int i = 0; i < nufeatures.length; i++) {
nufeatures[i] = Vec.cosineNormalize(reducedVectorMapping.get(new Integer(i)));
}
// System.out.println(TextTool.toMatlabFormat(nufeatures));
PCA pca = new PCA(nufeatures, 20);
Color[] reducedSetCellColors =
SammonsMappingToColor.getColorsForFeatures(pca.getPCATransformedDataAsDoubleArray());
// recreate Color assignment
cellColors = new Color[colorarraymapping.length];
for (int i = 0; i < cellColors.length; i++) {
if (colorarraymapping[i] == -1) cellColors[i] = Color.white;
else cellColors[i] = reducedSetCellColors[colorarraymapping[i]];
}
// cellColors = SammonsMappingToColor.getColorsForFeatures(new PCA(lkvalues,
// 20).getPCATransformedDataAsDoubleArray());
} else {
cellColors = new Color[cols * rows];
}
for (Enumeration e = threadlisteners.elements(); e.hasMoreElements(); ) {
ThreadListener tl = (ThreadListener) (e.nextElement());
tl.threadEnded();
}
}