本文整理汇总了Java中it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap类的典型用法代码示例。如果您正苦于以下问题:Java Object2DoubleOpenHashMap类的具体用法?Java Object2DoubleOpenHashMap怎么用?Java Object2DoubleOpenHashMap使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
Object2DoubleOpenHashMap类属于it.unimi.dsi.fastutil.objects包,在下文中一共展示了Object2DoubleOpenHashMap类的20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,您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: getVector
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public Object2DoubleMap<String> getVector(Language lang, String value) {
Object2DoubleMap<String> uriWeightMap = new Object2DoubleOpenHashMap<>();
try {
Search luceneSearch = search();
final String field = getContentFieldName(lang);
TopScoreDocCollector docsCollector = luceneSearch.search(value, field);
ScoreDoc[] scoreDocs = docsCollector.topDocs().scoreDocs;
double score = 0.0;
for(int i=0;i<scoreDocs.length;++i) {
int docID = scoreDocs[i].doc;
score = scoreDocs[i].score;
Document document = luceneSearch.getDocumentWithDocID(docID);
String uri = document.get(fieldNameURI);
uriWeightMap.put(uri, score);
}
return uriWeightMap;
} catch(IOException x) {
throw new RuntimeException(x);
}
}
开发者ID:jmccrae,项目名称:naisc,代码行数:25,代码来源:Corpus.java
示例2: testExtractFeatures
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Test of extractFeatures method, of class CLESAFeatureExtractor.
*/
@Test
public void testExtractFeatures() {
System.out.println("extractFeatures");
LangStringPair facet = new LangStringPair(Language.ENGLISH, Language.GERMAN, "test", "Test");
String id1 = "id1";
String id2 = "id2";
Corpus corpus = mock(Corpus.class);
Object2DoubleMap<String> v1 = new Object2DoubleOpenHashMap<>();
v1.put("a", 0.2);
v1.put("b", 0.3);
Object2DoubleMap<String> v2 = new Object2DoubleOpenHashMap<>();
v2.put("a", 0.5);
when(corpus.getVector(Language.ENGLISH, "test")).thenReturn(v1);
when(corpus.getVector(Language.GERMAN, "Test")).thenReturn(v2);
CLESAFeatureExtractor instance = new CLESAFeatureExtractor(corpus);
double[] expResult = new double[] { .2*.5 / Math.sqrt((.2*.2 +.3*0.3)*(0.5*0.5)) };
double[] result = instance.extractFeatures(facet, id1, id2);
assertArrayEquals(expResult, result, 0.0);
}
开发者ID:jmccrae,项目名称:naisc,代码行数:23,代码来源:CLESAFeatureExtractorTest.java
示例3: main
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public static void main(String[] args) throws Exception {
Table table = Table.read().csv(CsvReadOptions
.builder("../data/movielens.data")
.separator('\t'));
double supportThreshold = .25;
double confidenceThreshold = .5;
double interestThreshold = .5;
AssociationRuleMining model = new AssociationRuleMining(table.shortColumn("user"), table.shortColumn("movie")
, supportThreshold);
FrequentItemset frequentItemsetModel = new FrequentItemset(table.shortColumn("user"), table.shortColumn
("movie"), supportThreshold);
Object2DoubleOpenHashMap<IntRBTreeSet> confidenceMap = frequentItemsetModel.confidenceMap();
Table interestingRuleTable = model.interest(confidenceThreshold, interestThreshold, confidenceMap);
interestingRuleTable = interestingRuleTable.sortDescendingOn("Interest", "Antecedent");
out(interestingRuleTable);
}
开发者ID:jtablesaw,项目名称:tablesaw,代码行数:23,代码来源:AssociationRuleMiningExample.java
示例4: BinomialNonRedundancyUserReranker
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Constructor.
*
* @param recommendation input recommendation to be re-ranked
* @param maxLength number of items to be greedily selected
*/
public BinomialNonRedundancyUserReranker(Recommendation<U, I> recommendation, int maxLength) {
super(recommendation, maxLength);
ubm = binomialModel.getModel(recommendation.getUser());
featureCount = new Object2IntOpenHashMap<>();
featureCount.defaultReturnValue(0);
patienceNow = new Object2DoubleOpenHashMap<>();
patienceLater = new Object2DoubleOpenHashMap<>();
ubm.getFeatures().forEach(f -> {
patienceNow.put(f, ubm.patience(0, f, cutoff));
patienceLater.put(f, ubm.patience(1, f, cutoff));
});
}
开发者ID:RankSys,项目名称:RankSys,代码行数:22,代码来源:BinomialNonRedundancyReranker.java
示例5: run
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Executes the PageRank algorithm by setting the PAGERANK property on all nodes.
*/
public void run() {
try (Neo4jTransactionManager transactionManager = new Neo4jTransactionManager(graphDatabase)) {
// Initialize the PageRank value of each node to 1/numberOfVertices
initializeValues();
newPrValues = new Object2DoubleOpenHashMap<>(prValues.size());
for (int iteration = 0; iteration < maxIterations; iteration++) {
newDanglingSum = 0.0;
for (Node node : prValues.keySet()) {
computeNewValue(node);
}
swapAfterIteration();
}
writeValues(transactionManager);
}
}
开发者ID:atlarge-research,项目名称:graphalytics-platforms-neo4j,代码行数:23,代码来源:PageRankComputation.java
示例6: getGenericRelatedness
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
private double getGenericRelatedness(int wid1, int wid2, Object2DoubleOpenHashMap<Pair<Integer,Integer>> cache, String url){
if (wid2 < wid1) {
int tmp = wid2;
wid2 = wid1;
wid2 = tmp;
}
Pair <Integer,Integer> p = new Pair<Integer,Integer>(wid1,wid2);
if (!cache.containsKey(p))
cache.put(p, queryJsonRel(wid1, wid2, url));
return cache.getDouble(p);
}
开发者ID:marcocor,项目名称:smaph,代码行数:13,代码来源:WATRelatednessComputer.java
示例7: getRankerConfig
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public static RankerConfig getRankerConfig(Configuration rankerConfig,
Injector injector) {
int pageSize = 24;
if (rankerConfig.asMap().containsKey(ConfigKey.RANKER_PAGE_SIZE.get())) {
rankerConfig.getInt(ConfigKey.RANKER_PAGE_SIZE.get());
}
Object2DoubleMap<String> defaults = new Object2DoubleOpenHashMap<>();
Configuration defaultConfig = rankerConfig.getConfig("blendingDefaults");
for (String key : defaultConfig.keys()) {
defaults.put(key, defaultConfig.getDouble(key));
}
List<Configuration> expanders = ExpanderUtilities.getEntityExpandersConfig(rankerConfig);
return new PercentileBlendingRankerConfig(pageSize, defaults, expanders, injector, rankerConfig);
}
开发者ID:grouplens,项目名称:samantha,代码行数:15,代码来源:PercentileBlendingRankerConfig.java
示例8: getRankerConfig
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public static RankerConfig getRankerConfig(Configuration rankerConfig,
Injector injector) {
int pageSize = 24;
if (rankerConfig.asMap().containsKey(ConfigKey.RANKER_PAGE_SIZE.get())) {
rankerConfig.getInt(ConfigKey.RANKER_PAGE_SIZE.get());
}
Object2DoubleMap<String> defaults = new Object2DoubleOpenHashMap<>();
Configuration defaultConfig = rankerConfig.getConfig("blendingDefaults");
for (String key : defaultConfig.keys()) {
defaults.put(key, defaultConfig.getDouble(key));
}
List<Configuration> expanders = ExpanderUtilities.getEntityExpandersConfig(rankerConfig);
return new FieldBlendingRankerConfig(pageSize, defaults, expanders, injector, rankerConfig);
}
开发者ID:grouplens,项目名称:samantha,代码行数:15,代码来源:FieldBlendingRankerConfig.java
示例9: getPredictorConfig
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public static PredictorConfig getPredictorConfig(Configuration predictorConfig,
Injector injector) {
Object2DoubleMap<String> defaults = new Object2DoubleOpenHashMap<>();
Configuration defaultConfig = predictorConfig.getConfig("blendingDefaults");
for (String key : defaultConfig.keys()) {
defaults.put(key, defaultConfig.getDouble(key));
}
List<Configuration> expanders = ExpanderUtilities.getEntityExpandersConfig(predictorConfig);
return new FieldBlendingPredictorConfig(predictorConfig,
predictorConfig.getConfig(ConfigKey.ENTITY_DAOS_CONFIG.get()),
injector, expanders, defaults, predictorConfig.getString("daoConfigKey"));
}
开发者ID:grouplens,项目名称:samantha,代码行数:13,代码来源:FieldBlendingPredictorConfig.java
示例10: getTriggeredFeatures
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public List<ObjectNode> getTriggeredFeatures(List<ObjectNode> bases) {
Object2DoubleMap<String> item2score = new Object2DoubleOpenHashMap<>();
int numInter = 0;
for (ObjectNode inter : bases) {
double weight = 1.0;
if (inter.has(weightAttr)) {
weight = inter.get(weightAttr).asDouble();
}
String key = FeatureExtractorUtilities.composeConcatenatedKey(inter, feaAttrs);
if (weight >= 0.5 && featureKnnModel != null) {
getNeighbors(item2score, featureKnnModel, key, weight);
}
if (weight < 0.5 && featureKdnModel != null) {
getNeighbors(item2score, featureKdnModel, key, weight);
}
numInter++;
if (numInter >= maxInter) {
break;
}
}
List<ObjectNode> results = new ArrayList<>();
for (Map.Entry<String, Double> entry : item2score.entrySet()) {
ObjectNode entity = Json.newObject();
Map<String, String> attrVals = FeatureExtractorUtilities.decomposeKey(entry.getKey());
for (Map.Entry<String, String> ent : attrVals.entrySet()) {
entity.put(ent.getKey(), ent.getValue());
}
entity.put(scoreAttr, entry.getValue());
results.add(entity);
}
results.sort(SortingUtilities.jsonFieldReverseComparator(scoreAttr));
return results;
}
开发者ID:grouplens,项目名称:samantha,代码行数:34,代码来源:KnnModelFeatureTrigger.java
示例11: getFactorFeatures
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public Object2DoubleMap<String> getFactorFeatures(int minSupport) {
int numFeas = indexSpace.getKeyMapSize(SVDFeatureKey.FACTORS.get());
Object2DoubleMap<String> fea2sup = new Object2DoubleOpenHashMap<>();
for (int i=0; i<numFeas; i++) {
String feature = (String)indexSpace.getKeyForIndex(SVDFeatureKey.FACTORS.get(), i);
if (indexSpace.containsKey(SVDFeatureKey.BIASES.get(), feature)) {
int idx = indexSpace.getIndexForKey(SVDFeatureKey.BIASES.get(), feature);
double support = variableSpace.getScalarVarByNameIndex(SVDFeatureKey.SUPPORT.get(), idx);
if (support >= minSupport) {
fea2sup.put(feature, support);
}
}
}
return fea2sup;
}
开发者ID:grouplens,项目名称:samantha,代码行数:16,代码来源:SVDFeature.java
示例12: interestingRules
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public List<AssociationRule> interestingRules(double confidenceThreshold,
double interestThreshold,
Object2DoubleOpenHashMap<IntRBTreeSet> confidenceMap) {
List<AssociationRule> rules = model.learn(confidenceThreshold);
for (AssociationRule rule : rules) {
double interest = rule.confidence - confidenceMap.getDouble(rule.consequent);
if (Math.abs(interest) < interestThreshold) {
rules.remove(rule);
}
}
return rules;
}
开发者ID:jtablesaw,项目名称:tablesaw,代码行数:13,代码来源:AssociationRuleMining.java
示例13: UserPM
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
public UserPM(Recommendation<U, I> recommendation, int maxLength) {
super(recommendation, maxLength);
this.ubm = binomialModel.getModel(recommendation.getUser());
this.featureCount = new Object2DoubleOpenHashMap<>();
featureCount.defaultReturnValue(0.0);
this.probNorm = new Object2DoubleOpenHashMap<>();
recommendation.getItems()
.forEach(i -> featureData.getItemFeatures(i.v1).sequential()
.forEach(fv -> probNorm.addTo(fv.v1, i.v2)));
this.lcf = getLcf();
}
开发者ID:RankSys,项目名称:RankSys,代码行数:13,代码来源:PM.java
示例14: AbstractSalesDiversityMetric
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Constructor
*
* @param cutoff maximum length of the recommendation lists that is evaluated
* @param disc ranking discount model
* @param rel relevance model
*/
public AbstractSalesDiversityMetric(int cutoff, RankingDiscountModel disc, RelevanceModel<U, I> rel) {
this.cutoff = cutoff;
this.disc = disc;
this.rel = rel;
this.itemCount = new Object2DoubleOpenHashMap<>();
this.itemCount.defaultReturnValue(0.0);
this.itemWeight = new Object2DoubleOpenHashMap<>();
this.itemWeight.defaultReturnValue(0.0);
this.freeNorm = 0;
this.numUsers = 0;
}
开发者ID:RankSys,项目名称:RankSys,代码行数:20,代码来源:AbstractSalesDiversityMetric.java
示例15: evaluate
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Returns a score for the recommendation list.
*
* @param recommendation recommendation list
* @return score of the metric to the recommendation
*/
@Override
public double evaluate(Recommendation<U, I> recommendation) {
RelevanceModel.UserRelevanceModel<U, I> userRelModel = relModel.getModel(recommendation.getUser());
UserIntentModel<U, I, F> uim = intentModel.getModel(recommendation.getUser());
DoubleAdder erria = new DoubleAdder();
Object2DoubleMap<F> pNoPrevRel = new Object2DoubleOpenHashMap<>();
pNoPrevRel.defaultReturnValue(0.0);
uim.getIntents().forEach(f -> pNoPrevRel.put(f, 1.0));
AtomicInteger rank = new AtomicInteger();
recommendation.getItems().stream().limit(cutoff).forEach(iv -> {
if (userRelModel.isRelevant(iv.v1)) {
double gain = userRelModel.gain(iv.v1);
uim.getItemIntents(iv.v1).forEach(f -> {
double red = pNoPrevRel.getDouble(f);
erria.add(uim.pf_u(f) * gain * red / (1.0 + rank.intValue()));
pNoPrevRel.put(f, red * (1 - gain));
});
}
rank.incrementAndGet();
});
return erria.doubleValue();
}
开发者ID:RankSys,项目名称:RankSys,代码行数:33,代码来源:ERRIA.java
示例16: UserERRRelevanceModel
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Constructor.
*
* @param user user whose relevance model is created
*/
public UserERRRelevanceModel(U user) {
this.gainMap = new Object2DoubleOpenHashMap<>();
gainMap.defaultReturnValue(0.0);
testData.getUserPreferences(user)
.filter(iv -> iv.v2 >= threshold)
.forEach(iv -> gainMap.put(iv.v1, (Math.pow(2, iv.v2) - 1) / Math.pow(2, maxPreference)));
}
开发者ID:RankSys,项目名称:RankSys,代码行数:14,代码来源:ERRIA.java
示例17: getItemAspectModel
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
@Override
public ItemAspectModel<I, F> getItemAspectModel(List<Tuple2od<I>> items) {
Object2DoubleOpenHashMap<F> probNorm = new Object2DoubleOpenHashMap<>();
items.forEach(iv -> getItemIntents(iv.v1).forEach(f -> probNorm.addTo(f, iv.v2)));
return (iv, f) -> iv.v2 / probNorm.getDouble(f);
}
开发者ID:RankSys,项目名称:RankSys,代码行数:8,代码来源:ScoresAspectModel.java
示例18: UserAlphaXQuAD
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
/**
* Constructor.
*
* @param recommendation input recommendation to be re-ranked
* @param maxLength maximum length to be re-ranked with xQuAD
*/
public UserAlphaXQuAD(Recommendation<U, I> recommendation, int maxLength) {
super(recommendation, maxLength);
this.uam = aspectModel.getModel(recommendation.getUser());
this.iam = uam.getItemAspectModel(recommendation.getItems());
this.redundancy = new Object2DoubleOpenHashMap<>();
this.redundancy.defaultReturnValue(1.0);
}
开发者ID:RankSys,项目名称:RankSys,代码行数:15,代码来源:AlphaXQuAD.java
示例19: getItemAspectModel
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
@Override
public ItemAspectModel<I, F> getItemAspectModel(List<Tuple2od<I>> items) {
Object2DoubleOpenHashMap<F> probNorm = new Object2DoubleOpenHashMap<>();
items.forEach(iv -> getItemIntents(iv.v1)
.forEach(f -> {
if (iv.v2 > probNorm.getOrDefault(f, 0.0)) {
probNorm.put(f, iv.v2);
}
}));
return (iv, f) -> (Math.pow(2, iv.v2 / probNorm.getDouble(f)) - 1) / 2.0;
}
开发者ID:RankSys,项目名称:RankSys,代码行数:13,代码来源:ScoresRelevanceAspectModel.java
示例20: init
import it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap; //导入依赖的package包/类
private void init() {
featureNorms = new Object2DoubleOpenHashMap<>();
featureData.getAllFeatures().forEach(f -> {
int count = featureData.getFeatureItems(f)
.map(Tuple2::v1)
.mapToInt(totalData::numUsers)
.sum();
featureNorms.put(f, count);
});
}
开发者ID:RankSys,项目名称:RankSys,代码行数:11,代码来源:FeatureIntentModel.java
注:本文中的it.unimi.dsi.fastutil.objects.Object2DoubleOpenHashMap类示例整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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