ParallelExtract.java
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package extractors;
import is2.data.Cluster;
import is2.data.DataF;
import is2.data.Edges;
import is2.data.F2SF;
import is2.data.FV;
import is2.data.Instances;
import is2.data.Long2IntInterface;
import java.util.ArrayList;
import java.util.concurrent.Callable;
/**
* @author Bernd Bohnet, 30.08.2009
*
* This class implements a parallel feature extractor.
*/
final public class ParallelExtract implements Callable<Object>
{
// the data space of the weights for a dependency tree
final DataF d;
// the data extractor does the actual work
final Extractor extractor;
private Instances is;
private int i;
private F2SF para;
private Cluster cluster;
private Long2IntInterface li;
public ParallelExtract(Extractor e, Instances is, int i, DataF d, F2SF para,Cluster cluster, Long2IntInterface li) {
this.is =is;
extractor=e;
this.d =d;
this.i=i;
this.para=para;
this.cluster = cluster;
this.li=li;
}
public static class DSet {
int w1,w2;
}
public Object call() {
try {
F2SF f= para;
short[] pos=is.pposs[i];
int[] forms=is.forms[i];
int[] lemmas=is.plemmas[i];
short[][] feats=is.feats[i];
int length = pos.length;
long[] svs = new long[250];
int type=extractor.getType();
while (true) {
DSet set = get();
if (set ==null) break;
int w1=set.w1;
int w2=set.w2;
f.clear();
int n =extractor.basic(pos, forms, w1, w2,cluster, f);
d.pl[w1][w2]=f.getScoreF();
short[] labels = Edges.get(pos[w1], pos[w2],false);
float[][] lab = d.lab[w1][w2];
extractor.firstm(is, i, w1, w2, 0, cluster, svs);
if (labels!=null) {
for (int l = labels.length - 1; l >= 0; l--) {
short label = labels[l];
f.clear();
for(int k=svs.length-1;k>=0;k--) if (svs[k]>0) f.add(li.l2i(svs[k]+label*type));
lab[label][0]=f.getScoreF();
}
}
labels = Edges.get(pos[w1], pos[w2],true);
if (labels!=null) {
for (int l = labels.length - 1; l >= 0; l--) {
int label = labels[l];
f.clear();
for(int k=svs.length-1;k>=0;k--) if (svs[k]>0) f.add(li.l2i(svs[k]+label*type));
lab[label][1]=f.getScoreF();
}
}
int s = w1<w2 ? w1 : w2;
int e = w1<w2 ? w2 : w1;
int sg = w1<w2 ? w1 : 0;
int eg = w1<w2 ? length : w1+1;
for(int m=s;m<e;m++) {
for(int dir=0;dir<2;dir++) {
labels = Edges.get(pos[w1], pos[w2],dir==1);
float lab2[]= new float[labels.length];
int g = (m==s||e==m) ? -1 : m;
extractor.siblingm(is,i,pos,forms,lemmas,feats, w1, w2, g, 0, cluster, svs,n);
for (int l = labels.length - 1; l >= 0; l--) {
int label = labels[l];
f.clear();
for(int k=svs.length-1;k>=0;k--) {
if (svs[k]>0) f.add(li.l2i(svs[k]+label*type));
}
lab2[l] = (float)f.score;//f.getScoreF();
}
d.sib[w1][w2][m][dir]=lab2;
}
}
for(int m=sg;m<eg;m++) {
for(int dir=0;dir<2;dir++) {
labels = Edges.get(pos[w1], pos[w2],dir==1);
float[] lab2 = new float[labels.length];
int g = (m==s||e==m) ? -1 : m;
extractor.gcm(is, i, w1,w2,g, 0, cluster, svs);
for (int l = labels.length - 1; l >= 0; l--) {
int label = labels[l];
f.clear();
for(int k=svs.length-1;k>=0;k--) {
if (svs[k]>0) f.add(li.l2i(svs[k]+label*type));
}
lab2[l] = f.getScoreF();
}
d.gra[w1][w2][m][dir] =lab2;
}
}
}
} catch(Exception e ) {
e.printStackTrace();
}
return null;
}
static ArrayList<DSet> sets = new ArrayList<DSet>();
private DSet get() {
synchronized (sets) {
if (sets.size()==0) return null;
return sets.remove(sets.size()-1);
}
}
static public void add(int w1, int w2){
DSet ds =new DSet();
ds.w1=w1;
ds.w2=w2;
sets.add(ds);
}
}