本文整理了Java中edu.illinois.cs.cogcomp.core.utilities.configuration.ResourceManager.getDouble方法的一些代码示例，展示了ResourceManager.getDouble的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。ResourceManager.getDouble方法的具体详情如下：
包路径：edu.illinois.cs.cogcomp.core.utilities.configuration.ResourceManager
类名称：ResourceManager
方法名：getDouble

ResourceManager.getDouble介绍

[英]getters with default values -- won't throw exceptions
[中]具有默认值的getter不会抛出异常

代码示例

代码示例来源：origin: CogComp/cogcomp-nlp

public double getDouble(Property property) {
  return getDouble(property.key);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-core-utilities

public double getDouble(Property property) {
  return getDouble(property.key);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-phrasesim

private PhraseSim(ResourceManager config ) throws FileNotFoundException {
  loadParagram( config.getString( PhraseSimConfigurator.PARAGRAM_PATH.key ),
      config.getInt( PhraseSimConfigurator.VECTOR_DIM.key ) );
  unknownValue = config.getDouble( PhraseSimConfigurator.UNKNOWN_VALUE.key );
  zeroArray = new double[ dimension ];
  Arrays.fill( zeroArray, 0.0 );
}

代码示例来源：origin: CogComp/cogcomp-nlp

public static void initWithDefaults() throws IOException {
  ResourceManager rm = (new WordEmbeddingsConfigurator()).getDefaultConfig();
  List<String> fileNames = new LinkedList<>();
  fileNames.add(rm.getString(WordEmbeddingsConfigurator.fileNames.key));
  List<Integer> embeddingDimensionality = new LinkedList<>();
  embeddingDimensionality.add(rm.getInt(WordEmbeddingsConfigurator.dimensionalities.key));
  List<Integer> minWordAppearanceThres = new LinkedList<>();
  minWordAppearanceThres.add(rm.getInt(WordEmbeddingsConfigurator.wordNumThreshold.key));
  List<Boolean> isLowercasedEmbedding = new LinkedList<>();
  isLowercasedEmbedding.add(rm.getBoolean(WordEmbeddingsConfigurator.isLowercase.key));
  List<Double> normalizationConstant = new LinkedList<>();
  normalizationConstant.add(rm
      .getDouble(WordEmbeddingsConfigurator.normalizationConstants.key));
  List<NormalizationMethod> normalizationMethods = new LinkedList<>();
  normalizationMethods.add(NormalizationMethod.valueOf(rm
      .getString(WordEmbeddingsConfigurator.normalizationMethods.key)));
  init(fileNames, embeddingDimensionality, minWordAppearanceThres, isLowercasedEmbedding,
      normalizationConstant, normalizationMethods);
}

代码示例来源：origin: CogComp/cogcomp-nlp

public DatalessClassifierML(ResourceManager config, ConceptTree<T> conceptTree) {
  this.conceptTree = conceptTree;
  this.bottomUp = config.getBoolean(DatalessConfigurator.BottomUp_Inference.key);
  this.classifierThreshold = config.getDouble(DatalessConfigurator.classifierThreshold.key);
  this.classifierLeastK = config.getInt(DatalessConfigurator.classifierLeastK.key);
  this.classifierMaxK = config.getInt(DatalessConfigurator.classifierMaxK.key);
}

代码示例来源：origin: CogComp/cogcomp-nlp

double trainFrac = fullRm.getDouble(CorpusSplitConfigurator.TRAIN_FRACTION.key);
double testFrac = fullRm.getDouble(CorpusSplitConfigurator.TEST_FRACTION.key);

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-datalessclassification

public DatalessClassifierML(ResourceManager config, ConceptTree<T> conceptTree) {
  this.conceptTree = conceptTree;
  this.bottomUp = config.getBoolean(DatalessConfigurator.BottomUp_Inference.key);
  this.classifierThreshold = config.getDouble(DatalessConfigurator.classifierThreshold.key);
  this.classifierLeastK = config.getInt(DatalessConfigurator.classifierLeastK.key);
  this.classifierMaxK = config.getInt(DatalessConfigurator.classifierMaxK.key);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-edison

public static void initWithDefaults() throws IOException {
  ResourceManager rm = (new WordEmbeddingsConfigurator()).getDefaultConfig();
  List<String> fileNames = new LinkedList<>();
  fileNames.add(rm.getString(WordEmbeddingsConfigurator.fileNames.key));
  List<Integer> embeddingDimensionality = new LinkedList<>();
  embeddingDimensionality.add(rm.getInt(WordEmbeddingsConfigurator.dimensionalities.key));
  List<Integer> minWordAppearanceThres = new LinkedList<>();
  minWordAppearanceThres.add(rm.getInt(WordEmbeddingsConfigurator.wordNumThreshold.key));
  List<Boolean> isLowercasedEmbedding = new LinkedList<>();
  isLowercasedEmbedding.add(rm.getBoolean(WordEmbeddingsConfigurator.isLowercase.key));
  List<Double> normalizationConstant = new LinkedList<>();
  normalizationConstant.add(rm
      .getDouble(WordEmbeddingsConfigurator.normalizationConstants.key));
  List<NormalizationMethod> normalizationMethods = new LinkedList<>();
  normalizationMethods.add(NormalizationMethod.valueOf(rm
      .getString(WordEmbeddingsConfigurator.normalizationMethods.key)));
  init(fileNames, embeddingDimensionality, minWordAppearanceThres, isLowercasedEmbedding,
      normalizationConstant, normalizationMethods);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-corpusreaders

double trainFrac = fullRm.getDouble(CorpusSplitConfigurator.TRAIN_FRACTION.key);
double testFrac = fullRm.getDouble(CorpusSplitConfigurator.TEST_FRACTION.key);

代码示例来源：origin: CogComp/cogcomp-nlp

/**
 * reads parameters from configuration file named by m_propertiesFile loads
 * stopwords, sets xmlrpc client if appropriate
 * 
 * @throws IllegalArgumentException
 * @throws IOException
 */
protected void configure(ResourceManager rm_) throws IllegalArgumentException, IOException {
  // boolean useWordSim = rm_.getBoolean(Constants.USE_WORDSIM); // if
  // false, use WNSim (older
  // package)
  entailmentThreshold = rm_.getDouble(SimConfigurator.WORD_ENTAILMENT_THRESHOLD.key);
  computeSimpleScore = rm_.getBoolean(SimConfigurator.USE_SIMPLE_SCORE.key);
  String wordComparator = rm_.getString(SimConfigurator.WORD_METRIC);
  wordSim = new WordSim(rm_, wordComparator);
}

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-corpusreaders

double trainFrac = fullRm.getDouble(CorpusSplitConfigurator.TRAIN_FRACTION.key);
double devFrac = fullRm.getDouble(CorpusSplitConfigurator.DEV_FRACTION.key);
double testFrac = fullRm.getDouble(CorpusSplitConfigurator.TEST_FRACTION.key);

代码示例来源：origin: CogComp/cogcomp-nlp

double trainFrac = fullRm.getDouble(CorpusSplitConfigurator.TRAIN_FRACTION.key);
double devFrac = fullRm.getDouble(CorpusSplitConfigurator.DEV_FRACTION.key);
double testFrac = fullRm.getDouble(CorpusSplitConfigurator.TEST_FRACTION.key);

代码示例来源：origin: CogComp/cogcomp-nlp

private void initialize(ResourceManager rm_, Comparator<String, EntailmentResult> comparator) throws IOException {
  ResourceManager fullRm = new SimConfigurator().getConfig(rm_);
  double threshold = fullRm.getDouble(SimConfigurator.LLM_ENTAILMENT_THRESHOLD.key);
  tokenizer = new IllinoisTokenizer();
  this.comparator = comparator;
  filter = new WordListFilter(fullRm);
  neAligner = new Aligner<String, EntailmentResult>(new NEComparator(), filter);
  aligner = new Aligner<String, EntailmentResult>(comparator, filter);
  scorer = new GreedyAlignmentScorer<String>(threshold);
}

代码示例来源：origin: CogComp/cogcomp-nlp

double randomNoiseLevel = rm.getDouble(NerBaseConfigurator.RANDOM_NOISE_LEVEL);
double omissionRate = rm.getDouble(NerBaseConfigurator.OMISSION_RATE);
param.featurePruningThreshold = rm.getDouble(NerBaseConfigurator.FEATUREPRUNINGTHRESHOLD);

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-mlner

double randomNoiseLevel = rm.getDouble(NerBaseConfigurator.RANDOM_NOISE_LEVEL);
double omissionRate = rm.getDouble(NerBaseConfigurator.OMISSION_RATE);

代码示例来源：origin: edu.illinois.cs.cogcomp/illinois-ner

double randomNoiseLevel = rm.getDouble(NerBaseConfigurator.RANDOM_NOISE_LEVEL);
double omissionRate = rm.getDouble(NerBaseConfigurator.OMISSION_RATE);
param.featurePruningThreshold = rm.getDouble(NerBaseConfigurator.FEATUREPRUNINGTHRESHOLD);