Package edu.umd.cloud9.util.array

Examples of edu.umd.cloud9.util.array.ArrayListOfShorts


        if (trans != null) {
          tokenTranslations.add(new JsonPrimitive(trans));
        }
      } else {
        JsonObject tokenTrans = new JsonObject();
        HMapSFW distr = getTranslations(origQuery, token, phrasePairs, stemmed2Stemmed);
  JsonArray weights = Utils.createJsonArrayFromProbabilities(distr);
        if (weights != null) {
          tokenTrans.add("#weight", weights);
          tokenTranslations.add(tokenTrans);
        }
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    return new JUnit4TestAdapter(EnFr_CLEF06.class);
  }

  public static void main(String[] args) {
    //    HMapSFW gridAPMap = array2Map(Gridbest_AP);
    HMapSFW tenbestAPMap = array2Map(Nbest_AP.get(2));
    HMapSFW onebestAPMap = array2Map(Onebest_AP.get(1));
    HMapSFW grammarAPMap = array2Map(grammar_AP.get(0));
    HMapSFW tokenAPMap = array2Map(baseline_token_AP);
    //    System.out.println(countNumberOfImprovedTopics(tokenAPMap, gridAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, tenbestAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, onebestAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, grammarAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, tokenAPMap));
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    }
    return token;
  }

  protected HMapSFW getTranslations(String query, String token, Set<PairOfStrings> pairsInSCFG, Map<String, String> stemmed2Stemmed) {
    HMapSFW probDist = new HMapSFW();
    int f = fVocab_f2e.get(token);
    if (f <= 0) {

      // heuristic: if no translation found, include itself as only translation
      String target = (stemmed2Stemmed == null) ? token : stemmed2Stemmed.get(token);
      probDist.put(target, 1);     
      return probDist;
    }
    PriorityQueue<PairOfFloatInt> eS = f2eProbs.get(f).getTranslationsWithProbs(lexProbThreshold);
    //    LOG.info("Adding "+ eS.size() +" translations for "+token+","+f);

    float sumProbEF = 0;
    int numTrans = 0;
    //tf(e) = sum_f{tf(f)*prob(e|f)}
    while (numTrans < numTransPerToken && !eS.isEmpty()) {
      PairOfFloatInt entry = eS.poll();
      float probEF = entry.getLeftElement();
      int e = entry.getRightElement();
      String eTerm = eVocab_f2e.get(e);

      //      LOG.info("Pr("+eTerm+"|"+token+")="+probEF);

      if (probEF > 0 && e > 0 && !docLangTokenizer.isStopWord(eTerm) && (translateOnly == null || !translateOnly.equals("indri") || indriPuncPattern.matcher(eTerm).matches()) && (pairsInSCFG == null || pairsInSCFG.contains(new PairOfStrings(token,eTerm)))) {     
        // assuming our bilingual dictionary is learned from normally segmented text, but we want to use bigram tokenizer for CLIR purposes
        // then we need to convert the translations of each source token into a sequence of bigrams
        // we can distribute the translation probability equally to the each bigram
        if (bigramSegment) {
          String[] eTokens = docLangTokenizer.processContent(eTerm);
          float splitProb = probEF / eTokens.length;
          for (String eToken : eTokens) {
            // heuristic: only keep translations that are in our collection
            // exception: index might not be specified if running in --translate_only mode (in that case, we cannot run this heuristic)
            if (env == null || env.getPostingsList(eToken) != null) {
              probDist.put(eToken, splitProb);
            }
          }
          // here we add probability for tokens that we ignored in above condition,
          // but it works better (empirically) this way
          // AND it is consistent with what we would get if we did not do the index-filtering above
          // only faster
          sumProbEF += probEF;     
        }else {
    // heuristic: only keep translations that are in our collection
    // exception: index might not be specified if running in --translate_only mode (in that case, we cannot run this heuristic)
          if (env == null || env.getPostingsList(eTerm) != null) {
            probDist.increment(eTerm, probEF);
            sumProbEF += probEF;
          }
        }
        numTrans++;
      }else{
        LOG.info("Skipped target stopword/OOV " + eTerm);
      }

      // early terminate if cumulative prob. has reached specified threshold
      if (sumProbEF > cumProbThreshold || numTrans >= numTransPerToken) {
        break;
      }
    }

    // normalize weights
    for(String e : probDist.keySet()){
      probDist.put(e, probDist.get(e) / sumProbEF);
    }

    //    LOG.info("Translations of "+token+"="+probDist);

    return probDist;
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    }
    return cnt;
  }

  private static HMapSFW array2Map(String[] array) {
    HMapSFW map = new HMapSFW();
    for ( int i = 0; i < array.length; i += 2 ) {
      map.put(array[i], Float.parseFloat(array[i+1]));
    }
    return map;
  }
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      sLogger = logger;
    }

    //sLogger.setLevel(Level.DEBUG);

    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(int e : tfTable.keySet()){
      // retrieve term string, tf and df
      String eTerm = eVocab.get(e);
      float tf = tfTable.get(e);
      float df = dfTable.get(e);

      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);

      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);
      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }   
      }
    }

    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }
View Full Code Here

      sLogger = logger;
    }

    //sLogger.setLevel(Level.DEBUG);

    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(int e : tfTable.keySet()){
      // retrieve term string, tf and df
      String eTerm = eVocab.get(e);
      float tf = tfTable.get(e);
      float df = dfTable.get(eTerm);

      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);

      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);
      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }  
      }
    }

    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }
View Full Code Here

      sLogger = logger;
    }

    //    sLogger.setLevel(Level.DEBUG);

    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(edu.umd.cloud9.util.map.MapIF.Entry entry : tfTable.entrySet()){
      // retrieve term string, tf and df
      String eTerm = eVocab.get(entry.getKey());
      float tf = entry.getValue();
      int eId = dict.getId(eTerm);
      if(eId < 1){    //OOV
        continue;
      }
      int df = dfTable.getDf(eId);
      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);
      if(df<1){
        sLogger.warn("Suspicious DF WARNING = "+eTerm+" "+tf+" "+df+" "+score);
      }

      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);

      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }   
      }
    }

    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }
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  public static HMapSFW createTermDocVector(int docLen, HMapSIW tfTable, ScoringModel scoringModel, FrequencySortedDictionary dict, DfTableArray dfTable, boolean isNormalize, Logger sLogger) {
    if(sLogger == null){
      sLogger = logger;
    }

    HMapSFW v = new HMapSFW();
    float normalization=0;
    for(edu.umd.cloud9.util.map.MapKI.Entry<String> entry : tfTable.entrySet()){
      // retrieve term string, tf and df
      String eTerm = entry.getKey();
      float tf = entry.getValue();
      int eId = dict.getId(eTerm);
      if(eId < 1){    //OOV
        continue;
      }
      int df = dfTable.getDf(eId);
      // compute score via scoring model
      float score = ((Bm25) scoringModel).computeDocumentWeight(tf, df, docLen);
      if(df<1){
        sLogger.warn("Suspicious DF WARNING = "+eTerm+" "+tf+" "+df+" "+score);
      }

      sLogger.debug(eTerm+" "+tf+" "+df+" "+score);

      if(score>0){
        v.put(eTerm, score);
        if(isNormalize){
          normalization+=Math.pow(score, 2);
        }  
      }
    }

    // length-normalize doc vector
    if(isNormalize){
      normalization = (float) Math.sqrt(normalization);
      for(Entry<String> e : v.entrySet()){
        v.put(e.getKey(), e.getValue()/normalization);
      }
    }
    return v;
  }
View Full Code Here

    Configuration conf = IntegrationUtils.getBespinConfiguration();
    FileSystem fs = FileSystem.get(conf);

    SequenceFile.Reader reader;
    IntWritable key = new IntWritable();
    HMapSFW value = new HMapSFW();

    reader = new SequenceFile.Reader(fs.getConf(),
        SequenceFile.Reader.file(new Path(galagoIndex + "/test_wt-term-doc-vectors/part-00000")));
    reader.next(key, value);
    System.out.println("galagoTerm1\n"+key+";"+value);
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    Configuration conf = IntegrationUtils.getBespinConfiguration();
    FileSystem fs = FileSystem.get(conf);

    SequenceFile.Reader reader;
    IntWritable key = new IntWritable();
    HMapSFW value = new HMapSFW();

    reader = new SequenceFile.Reader(fs.getConf(),
        SequenceFile.Reader.file(new Path(opennlpIndex + "/test_wt-term-doc-vectors/part-00000")));
    reader.next(key, value);
    System.out.println("opennlpterm1\n"+key+";"+value);
View Full Code Here

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