Java Code Examples for com.carrotsearch.hppc.IntFloatHashMap

The following examples show how to use com.carrotsearch.hppc.IntFloatHashMap. These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
public BoostedComp(IntIntHashMap boostedDocs, ScoreDoc[] scoreDocs, float maxScore) {
    this.boostedMap = new IntFloatHashMap(boostedDocs.size()*2);

    for(int i=0; i<scoreDocs.length; i++) {
        if(boostedDocs.containsKey(scoreDocs[i].doc)) {
            boostedMap.put(scoreDocs[i].doc, maxScore+boostedDocs.get(scoreDocs[i].doc));
        } else {
            break;
        }
    }
}
 
Example 2
Source Project: lucene-solr   Source File: ReRankCollector.java    License: Apache License 2.0 5 votes vote down vote up
public BoostedComp(IntIntHashMap boostedDocs, ScoreDoc[] scoreDocs, float maxScore) {
  this.boostedMap = new IntFloatHashMap(boostedDocs.size()*2);

  for(int i=0; i<scoreDocs.length; i++) {
    final int idx;
    if((idx = boostedDocs.indexOf(scoreDocs[i].doc)) >= 0) {
      boostedMap.put(scoreDocs[i].doc, maxScore+boostedDocs.indexGet(idx));
    } else {
      break;
    }
  }
}
 
Example 3
Source Project: lucene-solr   Source File: IntFloatDynamicMap.java    License: Apache License 2.0 5 votes vote down vote up
/**
 * Create map with expected max value of key.
 * Although the map will automatically do resizing to be able to hold key {@code >= expectedKeyMax}.
 * But putting key much larger than {@code expectedKeyMax} is discourage since it can leads to use LOT OF memory.
 */
public IntFloatDynamicMap(int expectedKeyMax, float emptyValue) {
  this.threshold = threshold(expectedKeyMax);
  this.maxSize = expectedKeyMax;
  this.emptyValue = emptyValue;
  if (useArrayBased(expectedKeyMax)) {
    upgradeToArray();
  } else {
    this.hashMap = new IntFloatHashMap(mapExpectedElements(expectedKeyMax));
  }
}
 
Example 4
Source Project: SFA   Source File: BOSSVSClassifier.java    License: GNU General Public License v3.0 5 votes vote down vote up
@Override
public Score fit(final TimeSeries[] trainSamples) {
  // generate test train/split for cross-validation
  generateIndices(trainSamples);

  Score bestScore = null;
  int bestCorrectTraining = 0;

  int minWindowLength = 10;
  int maxWindowLength = getMax(trainSamples, MAX_WINDOW_LENGTH);

  // equi-distance sampling of windows
  ArrayList<Integer> windows = new ArrayList<>();
  double count = Math.sqrt(maxWindowLength);
  double distance = ((maxWindowLength - minWindowLength) / count);
  for (int c = minWindowLength; c <= maxWindowLength; c += distance) {
    windows.add(c);
  }

  for (boolean normMean : NORMALIZATION) {
    // train the shotgun models for different window lengths
    Ensemble<BossVSModel<IntFloatHashMap>> model = fitEnsemble(
            windows.toArray(new Integer[]{}), normMean, trainSamples);
    Double[] labels = predict(model, trainSamples);
    Predictions pred = evalLabels(trainSamples, labels);

    if (bestCorrectTraining <= pred.correct.get()) {
      bestCorrectTraining = pred.correct.get();
      bestScore = model.getHighestScoringModel().score;
      bestScore.training = pred.correct.get();
      this.model = model;
    }
  }

  // return score
  return bestScore;
}
 
Example 5
Source Project: SFA   Source File: BOSSVS.java    License: GNU General Public License v3.0 5 votes vote down vote up
protected void initMatrix(
    final ObjectObjectHashMap<Double, IntFloatHashMap> matrix,
    final Set<Double> uniqueLabels,
    final BagOfPattern[] bag) {
  for (Double label : uniqueLabels) {
    IntFloatHashMap stat = matrix.get(label);
    if (stat == null) {
      matrix.put(label, new IntFloatHashMap(bag[0].bag.size() * bag.length));
    } else {
      stat.clear();
    }
  }
}
 
Example 6
Source Project: SFA   Source File: BOSSVS.java    License: GNU General Public License v3.0 4 votes vote down vote up
public ObjectObjectHashMap<Double, IntFloatHashMap> createTfIdf(
    final BagOfPattern[] bagOfPatterns,
    final Set<Double> uniqueLabels) {
  int[] sampleIndices = createIndices(bagOfPatterns.length);
  return createTfIdf(bagOfPatterns, sampleIndices, uniqueLabels);
}