Examples of edu.umd.cloud9.io.map.HMapIFW

edu.umd.cloud9.io.map.HMapIFW

Writable representing a map where keys are ints and values are floats.

One notable feature of this class is the ability to support lazy decoding, controlled by the {@link #setLazyDecodeFlag(boolean)} method. In lazydecoding mode, when an object of this type is deserialized, key-value pairs are not inserted into the map, but rather held in arrays. The reduces memory used in cases where random access to values is not required. In lazy decoding mode, the raw keys and values may be fetched by the {@link #getKeys()} and{@link #getValues()} methods, respectively. The map can be subsequentlypopulated with the {@link #decode()} method.
@author Jimmy Lin

   *     mapping from F-terms to their df values
   * @return
   *     mapping from E-terms to their computed df values
   */
  public static HMapIFW translateDFTable(Vocab eVocabSrc, Vocab fVocabTrg, TTable_monolithic_IFAs e2f_probs, HMapSIW dfs){
    HMapIFW transDfTable = new HMapIFW();
    for(int e=1;e<eVocabSrc.size();e++){
      int[] fS = null;
      try {
        fS = e2f_probs.get(e).getTranslations(0.0f);
      } catch (Exception e1) {
        e1.printStackTrace();
      }
      float df=0;
      for(int f : fS){
        float probEF = e2f_probs.get(e, f);
        String fTerm = fVocabTrg.get(f);
        if(!dfs.containsKey(fTerm)){  //only if word is in the collection, can it contribute to the df values.
          continue;
        }      
        float df_f = dfs.get(fTerm);
        df+=(probEF*df_f);
      }
      transDfTable.put(e, df);
    }
    return transDfTable;
  }

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    for(String term : terms){
      term2Tf.increment(term);
    }


    //translated tf values
    HMapIFW transTermTf = new HMapIFW();
    for(Entry<String> entry : term2Tf.entrySet()){
      String fTerm = entry.getKey();
      int tf = entry.getValue();
      // transTermTf won't be updated if fTerm not in vocab
      transTermTf = CLIRUtils.updateTFsByTerm(fTerm, tf, transTermTf, eVocabSrc, eVocabTrg, fVocabSrc, fVocabTrg, e2f_Probs, f2e_Probs, eTok, sLogger);

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      if (numTerms < MIN_SIZE) {
        reporter.incrCounter(Docs.SHORT, 1);
        return;
      }


      HMapIFW tfS = new HMapIFW();
      // We simply use the source-language doc length since the ratio of doc length to average doc
      // length is unlikely to change significantly (not worth complicating the pipeline)
      int docLen = CLIRUtils.translateTFs(doc, tfS, eVocabSrc, eVocabTrg, fVocabSrc, fVocabTrg,
          e2f_Probs, f2e_Probs, tokenizer, LOG);

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      }  


      DefaultFrequencySortedDictionary dict = new DefaultFrequencySortedDictionary(new Path(env.getIndexTermsData()), new Path(env.getIndexTermIdsData()), new Path(env.getIndexTermIdMappingData()), fs2);
      DfTableArray dfTable = new DfTableArray(new Path(dfByIntFile), fs2);


      HMapIFW transDfTable = CLIRUtils.translateDFTable(eVocab_e2f, fVocab_e2f, en2DeProbs, dict, dfTable);


      SequenceFile.Writer writer = SequenceFile.createWriter(fs2, conf, new Path(transDfFile), IntWritable.class, FloatWritable.class);
      for(MapIF.Entry term : transDfTable.entrySet()){
        reporter.incrCounter(DF.TransDf, 1);
        writer.append(new IntWritable(term.getKey()), new FloatWritable(term.getValue()));
      }
      writer.close();
    }

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      return s;
    }


    public void map(IntWritable docno, WeightedIntDocVector docvectorIn,
        OutputCollector<IntWritable, NBitSignature> output, Reporter reporter) throws IOException {
      HMapIFW docvector = docvectorIn.getWeightedTerms();
      FloatAsBytesWritable value;


      for (int i = 0; i < randomUnitVectors.size(); i++) {
        value = (FloatAsBytesWritable) randomUnitVectors.get(i);
        double dprod = dotProduct(docvector, value);

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   *     FileSystem object
   * @return
   *     mapping from term ids to df values
   */
  public static HMapIFW readTransDfTable(Path path, FileSystem fs) {
    HMapIFW transDfTable = new HMapIFW();
    try {
      SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, fs.getConf());


      IntWritable key = (IntWritable) reader.getKeyClass().newInstance();
      FloatWritable value = (FloatWritable) reader.getValueClass().newInstance();


      while (reader.next(key, value)) {
        transDfTable.put(key.get(), value.get());
        //        logger.info(key.get()+"-->"+value.get());
        key = (IntWritable) reader.getKeyClass().newInstance();
        value = (FloatWritable) reader.getValueClass().newInstance();
      }
      reader.close();

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   *     ttable E-->F (i.e., Pr(f|e))
   * @return
   *     mapping from E-terms to their computed df values
   */
  public static HMapIFW translateDFTable(Vocab eVocabSrc, Vocab fVocabTrg, TTable_monolithic_IFAs e2f_probs, FrequencySortedDictionary dict, DfTableArray dfTable){
    HMapIFW transDfTable = new HMapIFW();
    for(int e=1;e<eVocabSrc.size();e++){
      int[] fS = e2f_probs.get(e).getTranslations(0.0f);
      float df=0;
      for(int f : fS){
        float probEF = e2f_probs.get(e, f);
        String fTerm = fVocabTrg.get(f);
        int id = dict.getId(fTerm); 
        if(id != -1){
          float df_f = dfTable.getDf(id);        
          df += (probEF*df_f);
        }else{
          logger.debug(fTerm+" not in dict");
        }
      }
      transDfTable.put(e, df);
    }
    return transDfTable;
  }

View Full Code Here

   *     mapping from F-terms to their df values
   * @return
   *     mapping from E-terms to their computed df values
   */
  public static HMapIFW translateDFTable(Vocab eVocabSrc, Vocab fVocabTrg, TTable_monolithic_IFAs e2f_probs, HMapSIW dfs){
    HMapIFW transDfTable = new HMapIFW();
    for(int e=1;e<eVocabSrc.size();e++){
      int[] fS = null;
      try {
        fS = e2f_probs.get(e).getTranslations(0.0f);
      } catch (Exception e1) {
        e1.printStackTrace();
      }
      float df=0;
      for(int f : fS){
        float probEF = e2f_probs.get(e, f);
        String fTerm = fVocabTrg.get(f);
        if(!dfs.containsKey(fTerm)){  //only if word is in the collection, can it contribute to the df values.
          continue;
        }      
        float df_f = dfs.get(fTerm);
        df+=(probEF*df_f);
      }
      transDfTable.put(e, df);
    }
    return transDfTable;
  }

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   *     FileSystem object
   * @return
   *     mapping from term ids to df values
   */
  public static HMapIFW readTransDfTable(Path path, FileSystem fs) {
    HMapIFW transDfTable = new HMapIFW();
    try {
      SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, fs.getConf());


      IntWritable key = (IntWritable) reader.getKeyClass().newInstance();
      FloatWritable value = (FloatWritable) reader.getValueClass().newInstance();


      while (reader.next(key, value)) {
        transDfTable.put(key.get(), value.get());
        //        logger.info(key.get()+"-->"+value.get());
        key = (IntWritable) reader.getKeyClass().newInstance();
        value = (FloatWritable) reader.getValueClass().newInstance();
      }
      reader.close();

View Full Code Here

   *     ttable E-->F (i.e., Pr(f|e))
   * @return
   *     mapping from E-terms to their computed df values
   */
  public static HMapIFW translateDFTable(Vocab eVocabSrc, Vocab fVocabTrg, TTable_monolithic_IFAs e2f_probs, FrequencySortedDictionary dict, DfTableArray dfTable){
    HMapIFW transDfTable = new HMapIFW();
    for(int e=1;e<eVocabSrc.size();e++){
      int[] fS = e2f_probs.get(e).getTranslations(0.0f);
      float df=0;
      for(int f : fS){
        float probEF = e2f_probs.get(e, f);
        String fTerm = fVocabTrg.get(f);
        int id = dict.getId(fTerm); 
        if(id != -1){
          float df_f = dfTable.getDf(id);        
          df += (probEF*df_f);
        }else{
          logger.debug(fTerm+" not in dict");
        }
      }
      transDfTable.put(e, df);
    }
    return transDfTable;
  }

View Full Code Here

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