Java PropertiesUtils.getInt方法代码示例

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * A main method for training and evaluating the postprocessor.
 * 
 * @param args
 */
public static void main(String[] args) {
  // Strips off hyphens
  Properties options = StringUtils.argsToProperties(args, optionArgDefs());
  if (options.containsKey("help") || args.length == 0) {
    System.err.println(usage(GermanPostprocessor.class.getName()));
    System.exit(-1);
  }

  int nThreads = PropertiesUtils.getInt(options, "nthreads", 1);
  GermanPreprocessor preProcessor = new GermanPreprocessor();
  GermanPostprocessor postProcessor = new GermanPostprocessor(options);
  
  CRFPostprocessor.setup(postProcessor, preProcessor, options);
  CRFPostprocessor.execute(nThreads, preProcessor, postProcessor);    
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * A main method for training and evaluating the postprocessor.
 * 
 * @param args
 */
public static void main(String[] args) {
  // Strips off hyphens
  Properties options = StringUtils.argsToProperties(args, optionArgDefs());
  if (options.containsKey("help") || args.length == 0) {
    System.err.println(usage(FrenchPostprocessor.class.getName()));
    System.exit(-1);
  }

  int nThreads = PropertiesUtils.getInt(options, "nthreads", 1);
  FrenchPreprocessor preProcessor = new FrenchPreprocessor();
  FrenchPostprocessor postProcessor = new FrenchPostprocessor(options);
  
  CRFPostprocessor.setup(postProcessor, preProcessor, options);
  CRFPostprocessor.execute(nThreads, preProcessor, postProcessor);    
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * A main method for training and evaluating the postprocessor.
 * 
 * @param args
 */
public static void main(String[] args) {
  // Strips off hyphens
  Properties options = StringUtils.argsToProperties(args, optionArgDefs());
  if (options.containsKey("help") || args.length == 0) {
    System.err.println(usage(EnglishPostprocessor.class.getName()));
    System.exit(-1);
  }

  int nThreads = PropertiesUtils.getInt(options, "nthreads", 1);
  EnglishPreprocessor preProcessor = new EnglishPreprocessor();
  EnglishPostprocessor postProcessor = new EnglishPostprocessor(options);
  
  CRFPostprocessor.setup(postProcessor, preProcessor, options);
  CRFPostprocessor.execute(nThreads, preProcessor, postProcessor);    
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * A main method for training and evaluating the postprocessor.
 * 
 * @param args
 */
public static void main(String[] args) {
  // Strips off hyphens
  Properties options = StringUtils.argsToProperties(args, optionArgDefs());
  if (options.containsKey("help") || args.length == 0) {
    System.err.println(usage(SpanishPostprocessor.class.getName()));
    System.exit(-1);
  }

  int nThreads = PropertiesUtils.getInt(options, "nthreads", 1);
  SpanishPreprocessor preProcessor = new SpanishPreprocessor();
  SpanishPostprocessor postProcessor = new SpanishPostprocessor(options);
  
  CRFPostprocessor.setup(postProcessor, preProcessor, options);
  CRFPostprocessor.execute(nThreads, preProcessor, postProcessor);    
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * Constructor.
 * 
 * @param args
 */
public TargetFunctionWordInsertion(String...args) {
  Properties options = FeatureUtils.argsToProperties(args);
  if (args.length < 2) {
    throw new RuntimeException("Must specify source and target unigram counts files");
  }
  System.err.println("Loading TargetFunctionWordInsertion template...");
  String sourceFilename = options.getProperty("sourceFile");
  String targetFilename = options.getProperty("targetFile");
  this.rankCutoff = PropertiesUtils.getInt(options, "rankCutoff", DEFAULT_RANK_CUTOFF);
  
  System.err.println("Source words:");
  sourceFunctionWordSet = loadCountsFile(sourceFilename);
  System.err.println("Target words:");
  targetFunctionWordSet = loadCountsFile(targetFilename);
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * 
 * @param args
 * @throws IOException
 */
public static void main(String[] args) throws IOException {
  if (args.length < 1) {
    System.err.print(usage());
    System.exit(-1);
  }
  
  Properties options = StringUtils.argsToProperties(args, argDefs());
  int ngramOrder = PropertiesUtils.getInt(options, "order", BLEUMetric.DEFAULT_MAX_NGRAM_ORDER);
  boolean disableTokenization = PropertiesUtils.getBool(options, "no-nist", false);
  String metric = options.getProperty("metric", "bleu");

  String[] refs = options.getProperty("").split("\\s+");
  List<List<Sequence<IString>>> referencesList = MetricUtils.readReferences(refs, ! disableTokenization);
  System.err.printf("Metric: %s with %d references%n", metric, referencesList.get(0).size());
  
  LineNumberReader reader = new LineNumberReader(new InputStreamReader(
      System.in));
  int sourceInputId = 0;
  for (String line; (line = reader.readLine()) != null; ++sourceInputId) {
    line = disableTokenization ? line : NISTTokenizer.tokenize(line);
    Sequence<IString> translation = IStrings.tokenize(line);
    double score = getScore(translation, referencesList.get(sourceInputId), ngramOrder, metric);
    System.out.printf("%.4f%n", score);
  }
  System.err.printf("Scored %d input segments%n", sourceInputId);
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * Constructor for reflection loading discriminative lexicalized reordering.
 * 
 * @param args
 */
public LexicalReorderingFeaturizer(String...args) {
  Properties options = FeatureUtils.argsToProperties(args);
  this.dynamic = PropertiesUtils.getBool(options, "dynamic", false);
  if (dynamic) {
    this.discriminativeSet = null;
    this.mlrt = null;
    this.featureTags = Arrays.stream(LexicalReorderingTable.msdBidirectionalPositionMapping).map(m -> 
    String.format("%s:%s", FEATURE_PREFIX, m)).toArray(String[]::new);
    this.useAlignmentConstellations = false;
    this.useClasses = false;
    this.countFeatureIndex = -1;
    this.lexicalCutoff = 0;

  } else {
    this.discriminativeSet = new ArrayList<>(Arrays.asList(LexicalReorderingTable.ReorderingTypes.values()));
    this.useAlignmentConstellations = options.containsKey("conditionOnConstellations");
    this.countFeatureIndex = PropertiesUtils.getInt(options, "countFeatureIndex", -1);
    // Which reordering classes to extract
    if (options.containsKey("classes")) {
      String[] typeStrings = options.getProperty("classes").split("-");
      discriminativeSet = new ArrayList<>();
      for (String type : typeStrings) {
        discriminativeSet.add(LexicalReorderingTable.ReorderingTypes.valueOf(type));
      }
    }
    // Use class-based feature representations
    this.useClasses = options.containsKey("useClasses");
    if (useClasses) {
      sourceMap = SourceClassMap.getInstance();
      targetMap = TargetClassMap.getInstance();
    }
    this.mlrt = null;
    this.featureTags = null;
    this.lexicalCutoff = PropertiesUtils.getInt(options, "lexicalCutoff", 0);
  }
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * Constructor for reflection loading.
 * 
 * @param args
 */
public RuleIndicator(String... args) {
  Properties options = FeatureUtils.argsToProperties(args);
  this.addLexicalizedRule = options.containsKey("addLexicalized");
  this.addClassBasedRule = options.containsKey("addClassBased");

  this.countFeatureIndex = PropertiesUtils.getInt(options, "countFeatureIndex", -1);
  if (addClassBasedRule) {
    sourceMap = SourceClassMap.getInstance();
    targetMap = TargetClassMap.getInstance();
  }
  this.lexicalCutoff = PropertiesUtils.getInt(options, "lexicalCutoff", 0);
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
private static boolean validateCommandLine(String[] args) {
  // Command line parsing
  Properties options = StringUtils.argsToProperties(args, argDefs());

  VERBOSE = options.containsKey("v");
  SRC_FILE = options.getProperty("s", null);
  OPTS_FILE = options.getProperty("o", null);
  XSD_FILE = options.getProperty("x", null);
  FIRST_ID = PropertiesUtils.getInt(options, "f", Integer.MIN_VALUE);
  LAST_ID = PropertiesUtils.getInt(options,"l",Integer.MAX_VALUE);

  return true;
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
public POSTaggerAnnotator(String annotatorName, Properties props) {
  String posLoc = props.getProperty(annotatorName + ".model");
  if (posLoc == null) {
    posLoc = DefaultPaths.DEFAULT_POS_MODEL;
  }
  boolean verbose = PropertiesUtils.getBool(props, annotatorName + ".verbose", false);
  this.pos = loadModel(posLoc, verbose);
  this.maxSentenceLength = PropertiesUtils.getInt(props, annotatorName + ".maxlen", Integer.MAX_VALUE);
  this.nThreads = PropertiesUtils.getInt(props, annotatorName + ".nthreads", PropertiesUtils.getInt(props, "nthreads", 1));
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
  if (args.length < 1) {
    System.err.print(usage());
    System.exit(-1);
  }

  Properties options = StringUtils.argsToProperties(args, argDefs());
  int BLEUOrder = PropertiesUtils.getInt(options, "order", BLEUMetric.DEFAULT_MAX_NGRAM_ORDER);
  boolean doSmooth = PropertiesUtils.getBool(options, "smooth", false);
  boolean disableTokenization = PropertiesUtils.getBool(options, "no-nist", false);
  boolean doCased = PropertiesUtils.getBool(options, "cased", false);

  // Setup the metric tokenization scheme. Applies to both the references and
  // hypotheses
  if (doCased) NISTTokenizer.lowercase(false);
  NISTTokenizer.normalize( ! disableTokenization);

  // Load the references
  String[] refs = options.getProperty("").split("\\s+");
  System.out.printf("Metric: BLEU-%d with %d references%n", BLEUOrder, refs.length);
  List<List<Sequence<IString>>> referencesList = MetricUtils.readReferences(refs, true);

  // For backwards compatibility
  doSmooth |= System.getProperty("smoothBLEU") != null;

  BLEUMetric<IString, String> bleu = new BLEUMetric<IString, String>(referencesList, BLEUOrder,
        doSmooth);
  BLEUMetric<IString, String>.BLEUIncrementalMetric incMetric = bleu
      .getIncrementalMetric();

  LineNumberReader reader = new LineNumberReader(new InputStreamReader(
      System.in));
  for (String line; (line = reader.readLine()) != null; ) {
    line = NISTTokenizer.tokenize(line);
    Sequence<IString> translation = IStrings.tokenize(line);
    ScoredFeaturizedTranslation<IString, String> tran = new ScoredFeaturizedTranslation<IString, String>(
        translation, null, 0);
    incMetric.add(tran);
  }
  // Check for an incomplete set of translations
  if (reader.getLineNumber() < referencesList.size()) {
    System.err.printf("WARNING: Translation candidate file is shorter than references (%d/%d)%n", 
        reader.getLineNumber(), referencesList.size());
  }
  reader.close();

  double[] ngramPrecisions = incMetric.ngramPrecisions();
  System.out.printf("BLEU = %.3f, ", 100 * incMetric.score());
  for (int i = 0; i < ngramPrecisions.length; i++) {
    if (i != 0) {
      System.out.print("/");
    }
    System.out.printf("%.3f", ngramPrecisions[i] * 100);
  }
  System.out.printf(" (BP=%.3f, ratio=%.3f %d/%d)%n", incMetric
      .brevityPenalty(), ((1.0 * incMetric.candidateLength()) / incMetric
      .effectiveReferenceLength()), incMetric.candidateLength(), incMetric
      .effectiveReferenceLength());

  System.out.printf("%nPrecision Details:%n");
  double[][] precCounts = incMetric.ngramPrecisionCounts();
  for (int i = 0; i < ngramPrecisions.length; i++) {
    System.out.printf("\t%d:%d/%d%n", i, (int) precCounts[i][0], (int) precCounts[i][1]);
  }
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
 * 
 * @param args
 * @throws IOException
 */
public static void main(String[] args) throws IOException {
  if (args.length < 2) {
    System.err.print(usage());
    System.exit(-1);
  }

  Properties options = StringUtils.argsToProperties(args, argDefs());
  int BLEUOrder = PropertiesUtils.getInt(options, "order", BLEUMetric.DEFAULT_MAX_NGRAM_ORDER);
  boolean doCased = PropertiesUtils.getBool(options, "cased", false);

  // Setup the metric tokenization scheme. Applies to both the references and
  // hypotheses
  if (doCased) NISTTokenizer.lowercase(false);

  // Load the references
  String[] parameters = options.getProperty("").split("\\s+");
  String[] refs = new String[parameters.length - 1];
  System.arraycopy(parameters, 1, refs, 0, refs.length);
  List<InputProperties> inputProperties = InputProperties.parse(new File(parameters[0]));
  List<List<Sequence<IString>>> referencesList = MetricUtils.readReferences(refs, true);
  
  Map<String,BLEUMetric<IString, String>.BLEUIncrementalMetric> metrics = 
      BLEUGenreEvaluator.run(referencesList, inputProperties, BLEUOrder, System.in);

  for (Map.Entry<String,BLEUMetric<IString, String>.BLEUIncrementalMetric> entry : metrics.entrySet()) {
    String genre = entry.getKey();
    BLEUMetric<IString, String>.BLEUIncrementalMetric incMetric = entry.getValue();
    System.out.printf("Genre: %s%n", genre);
    double[] ngramPrecisions = incMetric.ngramPrecisions();
    System.out.printf("BLEU = %.3f, ", 100 * incMetric.score());
    for (int i = 0; i < ngramPrecisions.length; i++) {
      if (i != 0) {
        System.out.print("/");
      }
      System.out.printf("%.3f", ngramPrecisions[i] * 100);
    }
    System.out.printf(" (BP=%.3f, ratio=%.3f %d/%d)%n", incMetric
        .brevityPenalty(), ((1.0 * incMetric.candidateLength()) / incMetric
            .effectiveReferenceLength()), incMetric.candidateLength(), incMetric
            .effectiveReferenceLength());

    System.out.printf("%nPrecision Details:%n");
    double[][] precCounts = incMetric.ngramPrecisionCounts();
    for (int i = 0; i < ngramPrecisions.length; i++) {
      System.out.printf("\t%d:%d/%d%n", i, (int) precCounts[i][0], (int) precCounts[i][1]);
    }
    System.out.println();
  }
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
/**
   * Start the service.
   * 
   * @param args
   */
  public static void main(String[] args) {
    Properties options = StringUtils.argsToProperties(args, optionArgDefs());
    int port = PropertiesUtils.getInt(options, "p", DEFAULT_HTTP_PORT);
    boolean loadMockServlet = PropertiesUtils.getBool(options, "m", false);
    boolean localHost = PropertiesUtils.getBool(options, "l", false);
    String uiFile = options.getProperty("u", "debug.html");
    String resourcePath = options.getProperty("r", ".");

    // Parse arguments
    String argList = options.getProperty("",null);
    String[] parsedArgs = argList == null ? null : argList.split("\\s+");
    if (parsedArgs == null || parsedArgs.length != 1) {
      System.out.println(usage());
      System.exit(-1);
    }
    String phrasalIniFile = parsedArgs[0];
    
    // Setup the jetty server
    Server server = new Server();

    // Jetty 8 way of configuring the server
//    Connector connector = new SelectChannelConnector();
//    connector.setPort(port);
//    server.addConnector(connector);

//  Jetty9 way of configuring the server
    ServerConnector connector = new ServerConnector(server);
    connector.setPort(port);
    server.addConnector(connector);

    if (localHost) {
      connector.setHost(DEBUG_URL);
    }
    
    // Setup the servlet context
    ServletContextHandler context = new ServletContextHandler(ServletContextHandler.SESSIONS);
    context.setContextPath("/");
 
    // Add Phrasal servlet
    PhrasalServlet servlet = loadMockServlet ? new PhrasalServlet() : new PhrasalServlet(phrasalIniFile);
    context.addServlet(new ServletHolder(servlet), SERVLET_ROOT);

    // TODO(spenceg): gzip compression causes an encoding problem for unicode characters
    // on the client. Not sure if the compression or decompression is the problem.
//    EnumSet<DispatcherType> dispatches = EnumSet.of(DispatcherType.REQUEST, DispatcherType.ASYNC);
//    context.addFilter(new FilterHolder(new IncludableGzipFilter()), "/t", dispatches);

    // Add debugging web-page
    ResourceHandler resourceHandler = new ResourceHandler();
    resourceHandler.setWelcomeFiles(new String[]{ uiFile });
    resourceHandler.setResourceBase(resourcePath);

    HandlerList handlers = new HandlerList();
    handlers.setHandlers(new Handler[] { resourceHandler, context });
    server.setHandler(handlers);
    
    // Start the service
    try {
      logger.info("Starting PhrasalService on port: " + String.valueOf(port));
      server.start();
      server.join();
    } catch (Exception e) {
      logger.error("Servlet crashed. Service shutting down.");
      e.printStackTrace();
    }
  }
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
public NERCustomAnnotator(String name, Properties properties) {
    this(NERClassifierCombiner.createNERClassifierCombiner(name, properties), false,
            PropertiesUtils.getInt(properties, name + ".nthreads", PropertiesUtils.getInt(properties, "nthreads", 1)),
            PropertiesUtils.getLong(properties, name + ".maxtime", -1),
            PropertiesUtils.getInt(properties, name + ".maxlength", Integer.MAX_VALUE));
}
 

import edu.stanford.nlp.util.PropertiesUtils; //导入方法依赖的package包/类
public OwnPOSTaggerAnnotator(String annotatorName, Properties props) {
  boolean verbose = PropertiesUtils.getBool(props, annotatorName + ".verbose", false);
  this.pos = loadModel(annotatorName, verbose);
  this.maxSentenceLength = PropertiesUtils.getInt(props, annotatorName + ".maxlen", Integer.MAX_VALUE);
  this.nThreads = PropertiesUtils.getInt(props, annotatorName + ".nthreads", PropertiesUtils.getInt(props, "nthreads", 1));
}