Java Code Examples for it.unimi.dsi.fastutil.ints.Int2DoubleMap#put()

@Override
public Int2DoubleMap asMap() {
	Int2DoubleMap mapRow = new Int2DoubleOpenHashMap(array.length);
	for (int i = 0; i < array.length; i++) {
		double value = array[i];
		if (isNotDefault(value)) {
			mapRow.put(i, value);
		}
	}
	return mapRow;
}
 

@Test
public void testAsMap() {
	Int2DoubleMap map = new Int2DoubleOpenHashMap();
	map.put(1, 0.5);
	map.put(2, 0.4);
	Int2DoubleRow row = createRow(map, 3);
	assertThat(row.asMap()).hasSize(2);
	assertThat(row.asMap()).containsEntry(1, 0.5);
	assertThat(row.asMap()).containsEntry(2, 0.4);
}
 

@Test
public void testGetOrDefault() {
	Int2DoubleMap map = new Int2DoubleOpenHashMap();
	map.put(1, 0.5);
	map.put(2, 0.4);
	Int2DoubleRow row = createRow(map, 3);
	assertThat(row.getOrDefault(0)).isEqualTo(0.0);
	assertThat(row.getOrDefault(1)).isEqualTo(0.5);
	assertThat(row.getOrDefault(2)).isEqualTo(0.4);
}
 

@Test
public void testValues() {
	Int2DoubleMap map = new Int2DoubleOpenHashMap();
	map.put(1, 0.5);
	map.put(2, 0.4);
	Int2DoubleRow row = createRow(map, 3);
	assertThat(row.values()).contains(0.5, 0.4);
}
 

public LearningInstance featurize(JsonNode entity, boolean update) {
    Map<String, List<Feature>> feaMap = new HashMap<>();
    for (FeatureExtractor extractor : featureExtractors) {
        feaMap.putAll(extractor.extract(entity, update,
                indexSpace));
    }
    Int2DoubleMap feas = new Int2DoubleOpenHashMap();
    for (String fea : features) {
        if (feaMap.containsKey(fea)) {
            for (Feature feature : feaMap.get(fea)) {
                feas.put(feature.getIndex(), feature.getValue());
            }
        }
    }
    double label = StandardLearningInstance.defaultLabel;
    double weight = StandardLearningInstance.defaultWeight;
    if (entity.has(labelName)) {
        label = entity.get(labelName).asDouble();
    } else if (feaMap.containsKey(labelName)) {
        label = feaMap.get(labelName).get(0).getIndex();
    }
    if (entity.has(weightName)) {
        weight = entity.get(weightName).asDouble();
    } else if (feaMap.containsKey(weightName)) {
        weight = feaMap.get(weightName).get(0).getValue();
    }
    String group = null;
    if (groupKeys != null && groupKeys.size() > 0) {
        group = FeatureExtractorUtilities.composeConcatenatedKey(entity, groupKeys);
    }
    return new StandardLearningInstance(feas, label, weight, group);
}
 

/**
 * Tests the global probability method (BinomialModel::p).
 */
@Test
public void testGlobalModel() {
    Int2DoubleMap expectedGlobalP = new Int2DoubleOpenHashMap();
    expectedGlobalP.put(1, 0.8);
    expectedGlobalP.put(2, 0.2);
    
    BinomialModel<Integer, Integer, Integer> bm = new BinomialModel<>(false, Stream.empty(), preferences, featureData, 0.0);
    
    assertEquals(expectedGlobalP.keySet(), bm.getFeatures());
    expectedGlobalP.forEach((f, p) -> assertEquals(p, bm.p(f), 0.0001));
}
 

/**
 * Tests the local (user) probability method with alpha = 0.0.
 */
@Test
public void testLocalModelAlpha00() {
    Int2DoubleMap expectedLocalP = new Int2DoubleOpenHashMap();
    expectedLocalP.put(1, 0.8);
    expectedLocalP.put(2, 0.2);
    
    checkLocalModel(0.0, expectedLocalP);
}
 

/**
 * Tests the local (user) probability method with alpha = 0.5.
 */
@Test
public void testLocalModelAlpha05() {
    Int2DoubleMap expectedLocalP = new Int2DoubleOpenHashMap();
    expectedLocalP.put(1, 0.9);
    expectedLocalP.put(2, 0.1);
    
    checkLocalModel(0.5, expectedLocalP);
}
 

/**
 * Tests the local (user) probability method with alpha = 1.0.
 */
@Test
public void testLocalModelAlpha10() {
    Int2DoubleMap expectedLocalP = new Int2DoubleOpenHashMap();
    expectedLocalP.put(1, 1.0);
    
    checkLocalModel(1.0, expectedLocalP);
}