本文整理汇总了Java中edu.berkeley.nlp.util.PriorityQueue.add方法的典型用法代码示例。如果您正苦于以下问题：Java PriorityQueue.add方法的具体用法？Java PriorityQueue.add怎么用？Java PriorityQueue.add使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类edu.berkeley.nlp.util.PriorityQueue的用法示例。
在下文中一共展示了PriorityQueue.add方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: buildRankedScoreQueue
import edu.berkeley.nlp.util.PriorityQueue; //导入方法依赖的package包/类
private PriorityQueue<Pair<Integer, Integer>> buildRankedScoreQueue(
double[][] scores) {
PriorityQueue<Pair<Integer, Integer>> pq = new PriorityQueue<Pair<Integer, Integer>>();
for (int l = 0; l < scores.length; l++) {
for (int c = 0; c < scores[l].length; c++) {
pq.add(Pair.makePair(l, c), scores[l][c]);
}
}
return pq;
}
示例2: getKBestChartAndBacktrace
import edu.berkeley.nlp.util.PriorityQueue; //导入方法依赖的package包/类
public Pair<int[][][][], double[][][]> getKBestChartAndBacktrace(
InstanceSequence<V, E, L> sequence, double[] w, int k) {
int n = sequence.getSequenceLength();
int numLabels = encoding.getNumLabels();
int[][][][] bestLabels = new int[n][numLabels][][];
double[][][] bestScores = new double[n][numLabels][];
double[] startScores = scoreCalculator.getLinearVertexScores(sequence,
0, w);
for (int l = 0; l < numLabels; l++) {
bestScores[0][l] = new double[] { startScores[l] };
bestLabels[0][l] = new int[][] { new int[] { -1, 0 } };
}
for (int i = 1; i < n; i++) {
double[][] scoreMatrix = scoreCalculator.getLinearScoreMatrix(
sequence, i, w);
for (int l = 0; l < numLabels; l++) {
PriorityQueue<Pair<Integer, Integer>> pq = new PriorityQueue<Pair<Integer, Integer>>();
for (int pl = 0; pl < numLabels; pl++) {
double edgeScore = scoreMatrix[pl][l];
for (int c = 0; c < bestScores[i - 1][pl].length; c++) {
double totalScore = edgeScore
+ bestScores[i - 1][pl][c];
pq.add(Pair.makePair(pl, c), totalScore);
}
}
int cands = Math.min(k, pq.size());
bestScores[i][l] = new double[cands];
bestLabels[i][l] = new int[cands][2];
for (int c = 0; c < cands; c++) {
bestScores[i][l][c] = pq.getPriority();
Pair<Integer, Integer> backtrace = pq.next();
bestLabels[i][l][c][0] = backtrace.getFirst();
bestLabels[i][l][c][1] = backtrace.getSecond();
}
}
}
return Pair.makePair(bestLabels, bestScores);
}
示例3: printMergingStatistics
import edu.berkeley.nlp.util.PriorityQueue; //导入方法依赖的package包/类
/**
* @param grammar
* @param newGrammar
*/
public static void printMergingStatistics(Grammar grammar,
Grammar newGrammar) {
PriorityQueue<String> lexiconStates = new PriorityQueue<String>();
PriorityQueue<String> grammarStates = new PriorityQueue<String>();
short[] numSubStatesArray = grammar.numSubStates;
short[] newNumSubStatesArray = newGrammar.numSubStates;
Numberer tagNumberer = grammar.tagNumberer;
for (short state = 0; state < numSubStatesArray.length; state++) {
System.out.print("\nState " + tagNumberer.object(state) + " had "
+ numSubStatesArray[state] + " substates and now has "
+ newNumSubStatesArray[state] + ".");
if (!grammar.isGrammarTag(state)) {
lexiconStates.add((String) tagNumberer.object(state),
newNumSubStatesArray[state]);
} else {
grammarStates.add((String) tagNumberer.object(state),
newNumSubStatesArray[state]);
}
}
System.out.print("\n");
System.out.println("Lexicon: " + lexiconStates.toString());
System.out.println("Grammar: " + grammarStates.toString());
// System.out.println("after merging, we have split trees:");
// for (int i=0; i<grammar.numStates; i++) {
// System.out.println(grammar.splitTrees[i]);
// }
}