java pair使用方法_Java Pair.makePair方法代码示例[亲测有效]

import edu.berkeley.nlp.util.Pair; //导入方法依赖的package包/类

public Pair>> getLogNormalizationAndExpectedCounts(

List extends InstanceSequence> sequences, double[] w) {

int numLabels = encoding.getNumLabels();

List> counts = new ArrayList>(numLabels);

for (int l = 0; l < numLabels; l++) {

counts.add(new Counter());

}

double totalLogZ = 0.0;

Logger.startTrack(“Computing expected counts”);

int index = 0;

for (InstanceSequence s : sequences) {

double[][] alpha = inf.getAlphas(s, w);

double[][] beta = inf.getBetas(s, w);

totalLogZ += Math.log(inf.getNormalizationConstant(alpha, beta));

double[][] vertexPosteriors = inf.getVertexPosteriors(alpha, beta);

double[][][] edgePosteriors = inf.getEdgePosteriors(s, w, alpha,

beta);

for (int i = 0; i < s.getSequenceLength(); i++) {

Counter vertexFeatures = vertexExtractor.extractFeatures(s

.getVertexInstance(i));

for (int l = 0; l < numLabels; l++) {

counts.get(l).incrementAll(

vertexFeatures.scaledClone(vertexPosteriors[i][l]));

}

if (i > 0) {

for (int pl = 0; pl < numLabels; pl++) {

Counter edgeFeatures = edgeExtractor

.extractFeatures(s.getEdgeInstance(i,

encoding.getLabel(pl)));

for (int cl = 0; cl < numLabels; cl++) {

counts.get(cl)

.incrementAll(

edgeFeatures

.scaledClone(edgePosteriors[i][pl][cl]));

}

}

}

}

Logger.logs(“Processed %d/%d sentences”, ++index, sequences.size());

}

Logger.endTrack();

return Pair.makePair(totalLogZ, counts);

}