void PhraseDictionaryFuzzyMatch::InitializeForInput(InputType const& inputSentence)
{
#if defined __MINGW32__
  char dirName[] = "moses.XXXXXX";
#else
  char dirName[] = "/tmp/moses.XXXXXX";
#endif // defined
  char *temp = mkdtemp(dirName);
  UTIL_THROW_IF2(temp == NULL,
		  "Couldn't create temporary directory " << dirName);

  string dirNameStr(dirName);

  string inFileName(dirNameStr + "/in");

  ofstream inFile(inFileName.c_str());

  for (size_t i = 1; i < inputSentence.GetSize() - 1; ++i) {
    inFile << inputSentence.GetWord(i);
  }
  inFile << endl;
  inFile.close();

  long translationId = inputSentence.GetTranslationId();
  string ptFileName = m_FuzzyMatchWrapper->Extract(translationId, dirNameStr);

  // populate with rules for this sentence
  PhraseDictionaryNodeMemory &rootNode = m_collection[translationId];
  FormatType format = MosesFormat;

  // data from file
  InputFileStream inStream(ptFileName);

  // copied from class LoaderStandard
  PrintUserTime("Start loading fuzzy-match phrase model");

  const StaticData &staticData = StaticData::Instance();
  const std::string& factorDelimiter = staticData.GetFactorDelimiter();


  string lineOrig;
  size_t count = 0;

  while(getline(inStream, lineOrig)) {
    const string *line;
    if (format == HieroFormat) { // reformat line
      UTIL_THROW(util::Exception, "Cannot be Hiero format");
      //line = ReformatHieroRule(lineOrig);
    } else {
      // do nothing to format of line
      line = &lineOrig;
    }

    vector<string> tokens;
    vector<float> scoreVector;

    TokenizeMultiCharSeparator(tokens, *line , "|||" );

    if (tokens.size() != 4 && tokens.size() != 5) {
      UTIL_THROW2("Syntax error at " << ptFileName << ":" << count);
    }

    const string &sourcePhraseString = tokens[0]
                                       , &targetPhraseString = tokens[1]
                                           , &scoreString        = tokens[2]
                                               , &alignString        = tokens[3];

    bool isLHSEmpty = (sourcePhraseString.find_first_not_of(" \t", 0) == string::npos);
    if (isLHSEmpty && !staticData.IsWordDeletionEnabled()) {
      TRACE_ERR( ptFileName << ":" << count << ": pt entry contains empty target, skipping\n");
      continue;
    }

    Tokenize<float>(scoreVector, scoreString);
    const size_t numScoreComponents = GetNumScoreComponents();
    if (scoreVector.size() != numScoreComponents) {
      UTIL_THROW2("Size of scoreVector != number (" << scoreVector.size() << "!="
            << numScoreComponents << ") of score components on line " << count);
    }

    UTIL_THROW_IF2(scoreVector.size() != numScoreComponents,
    		"Number of scores incorrectly specified");

    // parse source & find pt node

    // constituent labels
    Word *sourceLHS;
    Word *targetLHS;

    // source
    Phrase sourcePhrase( 0);
    // sourcePhrase.CreateFromString(Input, m_input, sourcePhraseString, factorDelimiter, &sourceLHS);
    sourcePhrase.CreateFromString(Input, m_input, sourcePhraseString, &sourceLHS);

    // create target phrase obj
    TargetPhrase *targetPhrase = new TargetPhrase(this);
    // targetPhrase->CreateFromString(Output, m_output, targetPhraseString, factorDelimiter, &targetLHS);
    targetPhrase->CreateFromString(Output, m_output, targetPhraseString, &targetLHS);

    // rest of target phrase
//.........这里部分代码省略.........

void ChartParserUnknown::Process(const Word &sourceWord, const WordsRange &range, ChartParserCallback &to)
{
  // unknown word, add as trans opt
  const StaticData &staticData = StaticData::Instance();
  const UnknownWordPenaltyProducer &unknownWordPenaltyProducer = UnknownWordPenaltyProducer::Instance();

  size_t isDigit = 0;
  if (staticData.GetDropUnknown()) {
    const Factor *f = sourceWord[0]; // TODO hack. shouldn't know which factor is surface
    const StringPiece s = f->GetString();
    isDigit = s.find_first_of("0123456789");
    if (isDigit == string::npos)
      isDigit = 0;
    else
      isDigit = 1;
    // modify the starting bitmap
  }

  Phrase* unksrc = new Phrase(1);
  unksrc->AddWord() = sourceWord;
  Word &newWord = unksrc->GetWord(0);
  newWord.SetIsOOV(true);

  m_unksrcs.push_back(unksrc);

  //TranslationOption *transOpt;
  if (! staticData.GetDropUnknown() || isDigit) {
    // loop
    const UnknownLHSList &lhsList = staticData.GetUnknownLHS();
    UnknownLHSList::const_iterator iterLHS;
    for (iterLHS = lhsList.begin(); iterLHS != lhsList.end(); ++iterLHS) {
      const string &targetLHSStr = iterLHS->first;
      float prob = iterLHS->second;

      // lhs
      //const Word &sourceLHS = staticData.GetInputDefaultNonTerminal();
      Word *targetLHS = new Word(true);

      targetLHS->CreateFromString(Output, staticData.GetOutputFactorOrder(), targetLHSStr, true);
      UTIL_THROW_IF2(targetLHS->GetFactor(0) == NULL, "Null factor for target LHS");

      // add to dictionary
      TargetPhrase *targetPhrase = new TargetPhrase();
      Word &targetWord = targetPhrase->AddWord();
      targetWord.CreateUnknownWord(sourceWord);

      // scores
      float unknownScore = FloorScore(TransformScore(prob));

      targetPhrase->GetScoreBreakdown().Assign(&unknownWordPenaltyProducer, unknownScore);
      targetPhrase->Evaluate(*unksrc);

      targetPhrase->SetTargetLHS(targetLHS);
      targetPhrase->SetAlignmentInfo("0-0");
      if (staticData.IsDetailedTreeFragmentsTranslationReportingEnabled()) {
        targetPhrase->SetProperty("Tree","[ " + (*targetLHS)[0]->GetString().as_string() + " "+sourceWord[0]->GetString().as_string()+" ]");
      }

      // chart rule
      to.AddPhraseOOV(*targetPhrase, m_cacheTargetPhraseCollection, range);
    } // for (iterLHS
  } else {
    // drop source word. create blank trans opt
    float unknownScore = FloorScore(-numeric_limits<float>::infinity());

    TargetPhrase *targetPhrase = new TargetPhrase();
    // loop
    const UnknownLHSList &lhsList = staticData.GetUnknownLHS();
    UnknownLHSList::const_iterator iterLHS;
    for (iterLHS = lhsList.begin(); iterLHS != lhsList.end(); ++iterLHS) {
      const string &targetLHSStr = iterLHS->first;
      //float prob = iterLHS->second;

      Word *targetLHS = new Word(true);
      targetLHS->CreateFromString(Output, staticData.GetOutputFactorOrder(), targetLHSStr, true);
      UTIL_THROW_IF2(targetLHS->GetFactor(0) == NULL, "Null factor for target LHS");

      targetPhrase->GetScoreBreakdown().Assign(&unknownWordPenaltyProducer, unknownScore);
      targetPhrase->Evaluate(*unksrc);

      targetPhrase->SetTargetLHS(targetLHS);

      // chart rule
      to.AddPhraseOOV(*targetPhrase, m_cacheTargetPhraseCollection, range);
    }
  }
}

//.........这里部分代码省略.........
          else endPos = atoi(ij[1].c_str()) + 1;
        }

        VERBOSE(3,"XML TAG " << tagName << " (" << tagContent << ") spanning " << startPos << " to " << (endPos-1) << " complete, commence processing" << endl);

        // special tag: wall
        if (tagName == "wall") {
          size_t start = (startPos == 0) ? 0 : startPos-1;
          for(size_t pos = start; pos < endPos; pos++)
            walls.push_back( pos );
        }

        // special tag: zone
        else if (tagName == "zone") {
          if (startPos >= endPos) {
            TRACE_ERR("ERROR: zone must span at least one word: " << line << endl);
            return false;
          }
          reorderingConstraint.SetZone( startPos, endPos-1 );
        }

        // default: opening tag that specifies translation options
        else {
          if (startPos >= endPos) {
            TRACE_ERR("ERROR: tag " << tagName << " must span at least one word: " << line << endl);
            return false;
          }

          // specified translations -> vector of phrases
          // multiple translations may be specified, separated by "||"
          vector<string> altTexts = TokenizeMultiCharSeparator(ParseXmlTagAttribute(tagContent,"translation"), "||");
          if( altTexts.size() == 1 && altTexts[0] == "" )
            altTexts.pop_back(); // happens when nothing specified
          // deal with legacy annotations: "translation" was called "english"
          vector<string> moreAltTexts = TokenizeMultiCharSeparator(ParseXmlTagAttribute(tagContent,"english"), "||");
          if (moreAltTexts.size()>1 || moreAltTexts[0] != "") {
            for(vector<string>::iterator translation=moreAltTexts.begin();
                translation != moreAltTexts.end();
                translation++) {
              string t = *translation;
              altTexts.push_back( t );
            }
          }

          // specified probabilities for the translations -> vector of probs
          vector<string> altProbs = TokenizeMultiCharSeparator(ParseXmlTagAttribute(tagContent,"prob"), "||");
          if( altProbs.size() == 1 && altProbs[0] == "" )
            altProbs.pop_back(); // happens when nothing specified

          // report what we have processed so far
          VERBOSE(3,"XML TAG NAME IS: '" << tagName << "'" << endl);
          VERBOSE(3,"XML TAG TRANSLATION IS: '" << altTexts[0] << "'" << endl);
          VERBOSE(3,"XML TAG PROB IS: '" << altProbs[0] << "'" << endl);
          VERBOSE(3,"XML TAG SPAN IS: " << startPos << "-" << (endPos-1) << endl);
          if (altProbs.size() > 0 && altTexts.size() != altProbs.size()) {
            TRACE_ERR("ERROR: Unequal number of probabilities and translation alternatives: " << line << endl);
            return false;
          }

          // store translation options into members
          if (StaticData::Instance().GetXmlInputType() != XmlIgnore) {
            // only store options if we aren't ignoring them
            for (size_t i=0; i<altTexts.size(); ++i) {
              Phrase sourcePhrase; // TODO don't know what the source phrase is

              // set default probability
              float probValue = 1;
              if (altProbs.size() > 0) probValue = Scan<float>(altProbs[i]);
              // convert from prob to log-prob
              float scoreValue = FloorScore(TransformScore(probValue));

              WordsRange range(startPos,endPos-1); // span covered by phrase
              TargetPhrase targetPhrase;
              targetPhrase.CreateFromString(Output, outputFactorOrder,altTexts[i],factorDelimiter, NULL);

              targetPhrase.SetXMLScore(scoreValue);
              targetPhrase.Evaluate(sourcePhrase);

              XmlOption *option = new XmlOption(range,targetPhrase);
              CHECK(option);

              res.push_back(option);
            }
            altTexts.clear();
            altProbs.clear();
          }
        }
      }
    }
  }
  // we are done. check if there are tags that are still open
  if (tagStack.size() > 0) {
    TRACE_ERR("ERROR: some opened tags were never closed: " << line << endl);
    return false;
  }

  // return de-xml'ed sentence in line
  line = cleanLine;
  return true;
}

 bool operator() (const TargetPhrase &a, const TargetPhrase &b) {
   return a.GetFutureScore() > b.GetFutureScore();
 }

TargetPhraseVectorPtr PhraseDecoder::DecodeCollection(
  TargetPhraseVectorPtr tpv, BitWrapper<> &encodedBitStream,
  const Phrase &sourcePhrase, bool topLevel, bool eval)
{

  bool extending = tpv->size();
  size_t bitsLeft = encodedBitStream.TellFromEnd();

  typedef std::pair<size_t, size_t> AlignPointSizeT;

  std::vector<int> sourceWords;
  if(m_coding == REnc) {
    for(size_t i = 0; i < sourcePhrase.GetSize(); i++) {
      std::string sourceWord
      = sourcePhrase.GetWord(i).GetString(*m_input, false);
      unsigned idx = GetSourceSymbolId(sourceWord);
      sourceWords.push_back(idx);
    }
  }

  unsigned phraseStopSymbol = 0;
  AlignPoint alignStopSymbol(-1, -1);

  std::vector<float> scores;
  std::set<AlignPointSizeT> alignment;

  enum DecodeState { New, Symbol, Score, Alignment, Add } state = New;

  size_t srcSize = sourcePhrase.GetSize();

  TargetPhrase* targetPhrase = NULL;
  while(encodedBitStream.TellFromEnd()) {

    if(state == New) {
      // Creating new TargetPhrase on the heap
      tpv->push_back(TargetPhrase());
      targetPhrase = &tpv->back();

      alignment.clear();
      scores.clear();

      state = Symbol;
    }

    if(state == Symbol) {
      unsigned symbol = m_symbolTree->Read(encodedBitStream);
      if(symbol == phraseStopSymbol) {
        state = Score;
      } else {
        if(m_coding == REnc) {
          std::string wordString;
          size_t type = GetREncType(symbol);

          if(type == 1) {
            unsigned decodedSymbol = DecodeREncSymbol1(symbol);
            wordString = GetTargetSymbol(decodedSymbol);
          } else if (type == 2) {
            size_t rank = DecodeREncSymbol2Rank(symbol);
            size_t srcPos = DecodeREncSymbol2Position(symbol);

            if(srcPos >= sourceWords.size())
              return TargetPhraseVectorPtr();

            wordString = GetTargetSymbol(GetTranslation(sourceWords[srcPos], rank));
            if(m_phraseDictionary.m_useAlignmentInfo) {
              size_t trgPos = targetPhrase->GetSize();
              alignment.insert(AlignPoint(srcPos, trgPos));
            }
          } else if(type == 3) {
            size_t rank = DecodeREncSymbol3(symbol);
            size_t srcPos = targetPhrase->GetSize();

            if(srcPos >= sourceWords.size())
              return TargetPhraseVectorPtr();

            wordString = GetTargetSymbol(GetTranslation(sourceWords[srcPos], rank));
            if(m_phraseDictionary.m_useAlignmentInfo) {
              size_t trgPos = srcPos;
              alignment.insert(AlignPoint(srcPos, trgPos));
            }
          }

          Word word;
          word.CreateFromString(Output, *m_output, wordString, false);
          targetPhrase->AddWord(word);
        } else if(m_coding == PREnc) {
          // if the symbol is just a word
          if(GetPREncType(symbol) == 1) {
            unsigned decodedSymbol = DecodePREncSymbol1(symbol);

            Word word;
            word.CreateFromString(Output, *m_output,
                                  GetTargetSymbol(decodedSymbol), false);
            targetPhrase->AddWord(word);
          }
          // if the symbol is a subphrase pointer
          else {
            int left = DecodePREncSymbol2Left(symbol);
            int right = DecodePREncSymbol2Right(symbol);
            unsigned rank = DecodePREncSymbol2Rank(symbol);
//.........这里部分代码省略.........

void PhrasePairFeature::EvaluateWithSourceContext(const InputType &input
    , const InputPath &inputPath
    , const TargetPhrase &targetPhrase
    , const StackVec *stackVec
    , ScoreComponentCollection &scoreBreakdown
    , ScoreComponentCollection *estimatedFutureScore) const
{
  const Phrase& source = inputPath.GetPhrase();
  if (m_simple) {
    ostringstream namestr;
    namestr << "pp_";
    namestr << source.GetWord(0).GetFactor(m_sourceFactorId)->GetString();
    for (size_t i = 1; i < source.GetSize(); ++i) {
      const Factor* sourceFactor = source.GetWord(i).GetFactor(m_sourceFactorId);
      namestr << ",";
      namestr << sourceFactor->GetString();
    }
    namestr << "~";
    namestr << targetPhrase.GetWord(0).GetFactor(m_targetFactorId)->GetString();
    for (size_t i = 1; i < targetPhrase.GetSize(); ++i) {
      const Factor* targetFactor = targetPhrase.GetWord(i).GetFactor(m_targetFactorId);
      namestr << ",";
      namestr << targetFactor->GetString();
    }

    scoreBreakdown.SparsePlusEquals(namestr.str(),1);
  }
  if (m_domainTrigger) {
    const Sentence& isnt = static_cast<const Sentence&>(input);
    const bool use_topicid = isnt.GetUseTopicId();
    const bool use_topicid_prob = isnt.GetUseTopicIdAndProb();

    // compute pair
    ostringstream pair;
    pair << source.GetWord(0).GetFactor(m_sourceFactorId)->GetString();
    for (size_t i = 1; i < source.GetSize(); ++i) {
      const Factor* sourceFactor = source.GetWord(i).GetFactor(m_sourceFactorId);
      pair << ",";
      pair << sourceFactor->GetString();
    }
    pair << "~";
    pair << targetPhrase.GetWord(0).GetFactor(m_targetFactorId)->GetString();
    for (size_t i = 1; i < targetPhrase.GetSize(); ++i) {
      const Factor* targetFactor = targetPhrase.GetWord(i).GetFactor(m_targetFactorId);
      pair << ",";
      pair << targetFactor->GetString();
    }

    if (use_topicid || use_topicid_prob) {
      if(use_topicid) {
        // use topicid as trigger
        const long topicid = isnt.GetTopicId();
        stringstream feature;
        feature << "pp_";
        if (topicid == -1)
          feature << "unk";
        else
          feature << topicid;

        feature << "_";
        feature << pair.str();
        scoreBreakdown.SparsePlusEquals(feature.str(), 1);
      } else {
        // use topic probabilities
        const vector<string> &topicid_prob = *(isnt.GetTopicIdAndProb());
        if (atol(topicid_prob[0].c_str()) == -1) {
          stringstream feature;
          feature << "pp_unk_";
          feature << pair.str();
          scoreBreakdown.SparsePlusEquals(feature.str(), 1);
        } else {
          for (size_t i=0; i+1 < topicid_prob.size(); i+=2) {
            stringstream feature;
            feature << "pp_";
            feature << topicid_prob[i];
            feature << "_";
            feature << pair.str();
            scoreBreakdown.SparsePlusEquals(feature.str(), atof((topicid_prob[i+1]).c_str()));
          }
        }
      }
    } else {
      // range over domain trigger words
      const long docid = isnt.GetDocumentId();
      for (set<string>::const_iterator p = m_vocabDomain[docid].begin(); p != m_vocabDomain[docid].end(); ++p) {
        string sourceTrigger = *p;
        ostringstream namestr;
        namestr << "pp_";
        namestr << sourceTrigger;
        namestr << "_";
        namestr << pair.str();
        scoreBreakdown.SparsePlusEquals(namestr.str(),1);
      }
    }
  }
  if (m_sourceContext) {
    const Sentence& isnt = static_cast<const Sentence&>(input);

    // range over source words to get context
    for(size_t contextIndex = 0; contextIndex < isnt.GetSize(); contextIndex++ ) {
//.........这里部分代码省略.........

void WordTranslationFeature::EvaluateWithSourceContext(const InputType &input
    , const InputPath &inputPath
    , const TargetPhrase &targetPhrase
    , const StackVec *stackVec
    , ScoreComponentCollection &scoreBreakdown
    , ScoreComponentCollection *estimatedScores) const
{
  const Sentence& sentence = static_cast<const Sentence&>(input);
  const AlignmentInfo &alignment = targetPhrase.GetAlignTerm();

  // process aligned words
  for (AlignmentInfo::const_iterator alignmentPoint = alignment.begin(); alignmentPoint != alignment.end(); alignmentPoint++) {
    const Phrase& sourcePhrase = inputPath.GetPhrase();
    int sourceIndex = alignmentPoint->first;
    int targetIndex = alignmentPoint->second;
    Word ws = sourcePhrase.GetWord(sourceIndex);
    if (m_factorTypeSource == 0 && ws.IsNonTerminal()) continue;
    Word wt = targetPhrase.GetWord(targetIndex);
    if (m_factorTypeSource == 0 && wt.IsNonTerminal()) continue;
    StringPiece sourceWord = ws.GetFactor(m_factorTypeSource)->GetString();
    StringPiece targetWord = wt.GetFactor(m_factorTypeTarget)->GetString();
    if (m_ignorePunctuation) {
      // check if source or target are punctuation
      char firstChar = sourceWord[0];
      CharHash::const_iterator charIterator = m_punctuationHash.find( firstChar );
      if(charIterator != m_punctuationHash.end())
        continue;
      firstChar = targetWord[0];
      charIterator = m_punctuationHash.find( firstChar );
      if(charIterator != m_punctuationHash.end())
        continue;
    }

    if (!m_unrestricted) {
      if (FindStringPiece(m_vocabSource, sourceWord) == m_vocabSource.end())
        sourceWord = "OTHER";
      if (FindStringPiece(m_vocabTarget, targetWord) == m_vocabTarget.end())
        targetWord = "OTHER";
    }

    if (m_simple) {
      // construct feature name
      util::StringStream featureName;
      featureName << m_description << "_";
      featureName << sourceWord;
      featureName << "~";
      featureName << targetWord;
      scoreBreakdown.SparsePlusEquals(featureName.str(), 1);
    }
    if (m_domainTrigger && !m_sourceContext) {
      const bool use_topicid = sentence.GetUseTopicId();
      const bool use_topicid_prob = sentence.GetUseTopicIdAndProb();
      if (use_topicid || use_topicid_prob) {
        if(use_topicid) {
          // use topicid as trigger
          const long topicid = sentence.GetTopicId();
          util::StringStream feature;
          feature << m_description << "_";
          if (topicid == -1)
            feature << "unk";
          else
            feature << topicid;

          feature << "_";
          feature << sourceWord;
          feature << "~";
          feature << targetWord;
          scoreBreakdown.SparsePlusEquals(feature.str(), 1);
        } else {
          // use topic probabilities
          const vector<string> &topicid_prob = *(input.GetTopicIdAndProb());
          if (atol(topicid_prob[0].c_str()) == -1) {
            util::StringStream feature;
            feature << m_description << "_unk_";
            feature << sourceWord;
            feature << "~";
            feature << targetWord;
            scoreBreakdown.SparsePlusEquals(feature.str(), 1);
          } else {
            for (size_t i=0; i+1 < topicid_prob.size(); i+=2) {
              util::StringStream feature;
              feature << m_description << "_";
              feature << topicid_prob[i];
              feature << "_";
              feature << sourceWord;
              feature << "~";
              feature << targetWord;
              scoreBreakdown.SparsePlusEquals(feature.str(), atof((topicid_prob[i+1]).c_str()));
            }
          }
        }
      } else {
        // range over domain trigger words (keywords)
        const long docid = input.GetDocumentId();
        for (boost::unordered_set<std::string>::const_iterator p = m_vocabDomain[docid].begin(); p != m_vocabDomain[docid].end(); ++p) {
          string sourceTrigger = *p;
          util::StringStream feature;
          feature << m_description << "_";
          feature << sourceTrigger;
          feature << "_";
//.........这里部分代码省略.........

//.........这里部分代码省略.........
        }

        // assemble remaining information about tag
        size_t startPos = openedTag.second.first;
        string tagContent = openedTag.second.second;
        size_t endPos = wordPos;

        // span attribute overwrites position
        string span = ParseXmlTagAttribute(tagContent,"span");
        if (! span.empty()) {
          vector<string> ij = Tokenize(span, "-");
          if (ij.size() != 1 && ij.size() != 2) {
            TRACE_ERR("ERROR: span attribute must be of the form \"i-j\" or \"i\": " << line << endl);
            return false;
          }
          startPos = atoi(ij[0].c_str());
          if (ij.size() == 1) endPos = startPos + 1;
          else endPos = atoi(ij[1].c_str()) + 1;
        }

        VERBOSE(3,"XML TAG " << tagName << " (" << tagContent << ") spanning " << startPos << " to " << (endPos-1) << " complete, commence processing" << endl);

        if (startPos >= endPos) {
          TRACE_ERR("ERROR: tag " << tagName << " must span at least one word: " << line << endl);
          return false;
        }

        // may be either a input span label ("label"), or a specified output translation "translation"
        string label = ParseXmlTagAttribute(tagContent,"label");
        string translation = ParseXmlTagAttribute(tagContent,"translation");

        // specified label
        if (translation.length() == 0 && label.length() > 0) {
          WordsRange range(startPos,endPos-1); // really?
          XMLParseOutput item(label, range);
          sourceLabels.push_back(item);
        }

        // specified translations -> vector of phrases, separated by "||"
        if (translation.length() > 0 && StaticData::Instance().GetXmlInputType() != XmlIgnore) {
          vector<string> altTexts = TokenizeMultiCharSeparator(translation, "||");
          vector<string> altLabel = TokenizeMultiCharSeparator(label, "||");
          vector<string> altProbs = TokenizeMultiCharSeparator(ParseXmlTagAttribute(tagContent,"prob"), "||");
          //TRACE_ERR("number of translations: " << altTexts.size() << endl);
          for (size_t i=0; i<altTexts.size(); ++i) {
            // set target phrase
            TargetPhrase targetPhrase;
            targetPhrase.CreateFromString(Output, outputFactorOrder,altTexts[i],factorDelimiter, NULL);

            // set constituent label
            string targetLHSstr;
            if (altLabel.size() > i && altLabel[i].size() > 0) {
              targetLHSstr = altLabel[i];
            } else {
              const UnknownLHSList &lhsList = StaticData::Instance().GetUnknownLHS();
              UnknownLHSList::const_iterator iterLHS = lhsList.begin();
              targetLHSstr = iterLHS->first;
            }
            Word *targetLHS = new Word(true);
            targetLHS->CreateFromString(Output, outputFactorOrder, targetLHSstr, true);
            CHECK(targetLHS->GetFactor(0) != NULL);
            targetPhrase.SetTargetLHS(targetLHS);

            // not tested
            Phrase sourcePhrase = this->GetSubString(WordsRange(startPos,endPos-1));

            // get probability
            float probValue = 1;
            if (altProbs.size() > i && altProbs[i].size() > 0) {
              probValue = Scan<float>(altProbs[i]);
            }
            // convert from prob to log-prob
            float scoreValue = FloorScore(TransformScore(probValue));
            targetPhrase.SetXMLScore(scoreValue);
            targetPhrase.Evaluate(sourcePhrase);

            // set span and create XmlOption
            WordsRange range(startPos+1,endPos);
            XmlOption *option = new XmlOption(range,targetPhrase);
            CHECK(option);
            xmlOptions.push_back(option);

            VERBOSE(2,"xml translation = [" << range << "] " << targetLHSstr << " -> " << altTexts[i] << " prob: " << probValue << endl);
          }
          altTexts.clear();
          altProbs.clear();
        }
      }
    }
  }
  // we are done. check if there are tags that are still open
  if (tagStack.size() > 0) {
    TRACE_ERR("ERROR: some opened tags were never closed: " << line << endl);
    return false;
  }

  // return de-xml'ed sentence in line
  line = cleanLine;
  return true;
}

void Tokenize(SourcePhrase &sourcePhrase, TargetPhrase &targetPhrase, char *line, OnDiskWrapper &onDiskWrapper, int numScores, vector<float> &misc)
{
	size_t scoreInd = 0;
	
	// MAIN LOOP
	size_t stage = 0;
	/*	0 = source phrase
	 1 = target phrase
	 2 = scores
	 3 = align
	 4 = count
	 */
	char *tok = strtok (line," ");
	while (tok != NULL)
	{
		if (0 == strcmp(tok, "|||"))
		{
			++stage;
		}
		else
		{
			switch (stage)
			{
				case 0:
				{
					Tokenize(sourcePhrase, tok, true, true, onDiskWrapper);
					break;
				}
				case 1:
				{
					Tokenize(targetPhrase, tok, false, true, onDiskWrapper);
					break;
				}
				case 2:
				{
					float score = Moses::Scan<float>(tok);
					targetPhrase.SetScore(score, scoreInd);
					++scoreInd;
					break;
				}
				case 3:
				{
					targetPhrase.Create1AlignFromString(tok);
					break;
				}
				case 4:
					++stage;
					break;
				case 5:					
				{ // count info. Only store the 2nd one
					float val = Moses::Scan<float>(tok);
					misc[0] = val;
					++stage;
					break;
				}
				default:
					assert(false);
					break;
			}
		}
		
		tok = strtok (NULL, " ");
	} // while (tok != NULL)
	
	assert(scoreInd == numScores);
	targetPhrase.SortAlign();
	
} // Tokenize()

bool PhraseDictionaryNewFormat::Load(const std::vector<FactorType> &input
																			 , const std::vector<FactorType> &output
																			 , std::istream &inStream
																			 , const std::vector<float> &weight
																			 , size_t tableLimit
																			 , const LMList &languageModels
																			 , float weightWP)
{
	PrintUserTime("Start loading new format pt model");
	
	const StaticData &staticData = StaticData::Instance();
	const std::string& factorDelimiter = staticData.GetFactorDelimiter();
	
	VERBOSE(2,"PhraseDictionaryNewFormat: input=" << m_inputFactors << "  output=" << m_outputFactors << std::endl);
	
	string line;
	size_t count = 0;
	
	while(getline(inStream, line))
	{
		vector<string> tokens;
		vector<float> scoreVector;
		
		TokenizeMultiCharSeparator(tokens, line , "|||" );
					
		if (tokens.size() != 4 && tokens.size() != 5)
		{
			stringstream strme;
			strme << "Syntax error at " << m_filePath << ":" << count;
			UserMessage::Add(strme.str());
			abort();
		}
		
		const string &sourcePhraseString	= tokens[0]
								, &targetPhraseString	= tokens[1]
								, &alignString				= tokens[2]
								, &scoreString				= tokens[3];

		bool isLHSEmpty = (sourcePhraseString.find_first_not_of(" \t", 0) == string::npos);
		if (isLHSEmpty && !staticData.IsWordDeletionEnabled()) {
			TRACE_ERR( m_filePath << ":" << count << ": pt entry contains empty target, skipping\n");
			continue;
		}
		
		Tokenize<float>(scoreVector, scoreString);
		if (scoreVector.size() != m_numScoreComponent)
		{
			stringstream strme;
			strme << "Size of scoreVector != number (" <<scoreVector.size() << "!=" <<m_numScoreComponent<<") of score components on line " << count;
			UserMessage::Add(strme.str());
			abort();
		}
		assert(scoreVector.size() == m_numScoreComponent);
		
		// parse source & find pt node
		
		// head word
		Word sourceLHS, targetLHS;

		// source
		Phrase sourcePhrase(Input);
		sourcePhrase.CreateFromStringNewFormat(Input, input, sourcePhraseString, factorDelimiter, sourceLHS);
		
		// create target phrase obj
		TargetPhrase *targetPhrase = new TargetPhrase(Output);
		targetPhrase->CreateFromStringNewFormat(Output, output, targetPhraseString, factorDelimiter, targetLHS);
		
		// alignment
		list<pair<size_t,size_t> > alignmentInfo;
		CreateAlignmentInfo(alignmentInfo, alignString);

		// rest of target phrase
		targetPhrase->SetAlignmentInfo(alignmentInfo);
		targetPhrase->SetTargetLHS(targetLHS);
		//targetPhrase->SetDebugOutput(string("New Format pt ") + line);
		
		// component score, for n-best output
		std::transform(scoreVector.begin(),scoreVector.end(),scoreVector.begin(),TransformScore);
		std::transform(scoreVector.begin(),scoreVector.end(),scoreVector.begin(),FloorScore);
		
		targetPhrase->SetScoreChart(GetFeature(), scoreVector, weight, languageModels);
		
		// count info for backoff
		if (tokens.size() >= 6)
			targetPhrase->CreateCountInfo(tokens[5]);

		TargetPhraseCollection &phraseColl = GetOrCreateTargetPhraseCollection(sourcePhrase, *targetPhrase);
		AddEquivPhrase(phraseColl, targetPhrase);
		
		count++;
	}
	
	// cleanup cache
	
	// sort each target phrase collection
	m_collection.Sort(m_tableLimit);
	
	return true;
}

ChartTranslationOption::ChartTranslationOption(const TargetPhrase &targetPhrase)
  :m_targetPhrase(targetPhrase)
  ,m_scoreBreakdown(targetPhrase.GetScoreBreakdown())
{
}

  void PhraseDictionaryFuzzyMatch::InitializeForInput(InputType const& inputSentence)
  {
    char dirName[] = "/tmp/moses.XXXXXX";
    char *temp = mkdtemp(dirName);
    CHECK(temp);
    string dirNameStr(dirName);
    
    string inFileName(dirNameStr + "/in");
    
    ofstream inFile(inFileName.c_str());
    
    for (size_t i = 1; i < inputSentence.GetSize() - 1; ++i)
    {
      inFile << inputSentence.GetWord(i);
    }
    inFile << endl;
    inFile.close();
        
    long translationId = inputSentence.GetTranslationId();
    string ptFileName = m_FuzzyMatchWrapper->Extract(translationId, dirNameStr);

    // populate with rules for this sentence
    PhraseDictionaryNodeSCFG &rootNode = m_collection[translationId];
    FormatType format = MosesFormat;
        
    // data from file
    InputFileStream inStream(ptFileName);
    
    // copied from class LoaderStandard
    PrintUserTime("Start loading fuzzy-match phrase model");
    
    const StaticData &staticData = StaticData::Instance();
    const std::string& factorDelimiter = staticData.GetFactorDelimiter();
    
    
    string lineOrig;
    size_t count = 0;
    
    while(getline(inStream, lineOrig)) {  //mgjang std add
      const string *line;
      if (format == HieroFormat) { // reformat line
        assert(false);
        //line = ReformatHieroRule(lineOrig);
      }
      else
      { // do nothing to format of line
        line = &lineOrig;
      }
      
      vector<string> tokens;
      vector<float> scoreVector;
      
      TokenizeMultiCharSeparator(tokens, *line , "|||" );
      
      if (tokens.size() != 4 && tokens.size() != 5) {
        stringstream strme;
        strme << "Syntax error at " << ptFileName << ":" << count;
        UserMessage::Add(strme.str());
		LOGE("[mgjang] before abort\n");
        abort();
      }
      
      const string &sourcePhraseString = tokens[0]
      , &targetPhraseString = tokens[1]
      , &scoreString        = tokens[2]
      , &alignString        = tokens[3];
      
      bool isLHSEmpty = (sourcePhraseString.find_first_not_of(" \t", 0) == string::npos);
      if (isLHSEmpty && !staticData.IsWordDeletionEnabled()) {
        TRACE_ERR( ptFileName << ":" << count << ": pt entry contains empty target, skipping\n");
        continue;
      }
      
      Tokenize<float>(scoreVector, scoreString);
      const size_t numScoreComponents = GetFeature()->GetNumScoreComponents();
      if (scoreVector.size() != numScoreComponents) {
        stringstream strme;
        strme << "Size of scoreVector != number (" << scoreVector.size() << "!="
        << numScoreComponents << ") of score components on line " << count;
        UserMessage::Add(strme.str());
		LOGE("[mgjang] before abort\n");
        abort();
      }
      CHECK(scoreVector.size() == numScoreComponents);
      
      // parse source & find pt node
      
      // constituent labels
      Word sourceLHS, targetLHS;
      
      // source
      Phrase sourcePhrase( 0);
      sourcePhrase.CreateFromStringNewFormat(Input, *m_input, sourcePhraseString, factorDelimiter, sourceLHS);
      
      // create target phrase obj
      TargetPhrase *targetPhrase = new TargetPhrase();
      targetPhrase->CreateFromStringNewFormat(Output, *m_output, targetPhraseString, factorDelimiter, targetLHS);
      
      // rest of target phrase
      targetPhrase->SetAlignmentInfo(alignString);
//.........这里部分代码省略.........

bool RuleTableLoaderCompact::LoadRuleSection(
  LineReader &reader,
  const std::vector<Word> &vocab,
  const std::vector<Phrase> &sourcePhrases,
  const std::vector<Phrase> &targetPhrases,
  const std::vector<size_t> &targetLhsIds,
  const std::vector<const AlignmentInfo *> &alignmentSets,
  RuleTableTrie &ruleTable)
{
  // Read rule count.
  reader.ReadLine();
  const size_t ruleCount = std::atoi(reader.m_line.c_str());

  // Read rules and add to table.
  const size_t numScoreComponents = ruleTable.GetNumScoreComponents();
  std::vector<float> scoreVector(numScoreComponents);
  std::vector<size_t> tokenPositions;
  for (size_t i = 0; i < ruleCount; ++i) {
    reader.ReadLine();

    tokenPositions.clear();
    FindTokens(tokenPositions, reader.m_line);

    const char *charLine = reader.m_line.c_str();

    // The first three tokens are IDs for the source phrase, target phrase,
    // and alignment set.
    const int sourcePhraseId = std::atoi(charLine+tokenPositions[0]);
    const int targetPhraseId = std::atoi(charLine+tokenPositions[1]);
    const int alignmentSetId = std::atoi(charLine+tokenPositions[2]);

    const Phrase &sourcePhrase = sourcePhrases[sourcePhraseId];
    const Phrase &targetPhrasePhrase = targetPhrases[targetPhraseId];
    const Word *targetLhs = new Word(vocab[targetLhsIds[targetPhraseId]]);
    Word sourceLHS("X"); // TODO not implemented for compact
    const AlignmentInfo *alignNonTerm = alignmentSets[alignmentSetId];

    // Then there should be one score for each score component.
    for (size_t j = 0; j < numScoreComponents; ++j) {
      float score = std::atof(charLine+tokenPositions[3+j]);
      scoreVector[j] = FloorScore(TransformScore(score));
    }
    if (reader.m_line[tokenPositions[3+numScoreComponents]] != ':') {
      std::stringstream msg;
      msg << "Size of scoreVector != number ("
          << scoreVector.size() << "!=" << numScoreComponents
          << ") of score components on line " << reader.m_lineNum;
      UserMessage::Add(msg.str());
      return false;
    }

    // The remaining columns are currently ignored.

    // Create and score target phrase.
    TargetPhrase *targetPhrase = new TargetPhrase(targetPhrasePhrase);
    targetPhrase->SetAlignNonTerm(alignNonTerm);
    targetPhrase->SetTargetLHS(targetLhs);
    targetPhrase->SetSourcePhrase(sourcePhrase);

    targetPhrase->Evaluate(sourcePhrase, ruleTable.GetFeaturesToApply());

    // Insert rule into table.
    TargetPhraseCollection &coll = GetOrCreateTargetPhraseCollection(
                                     ruleTable, sourcePhrase, *targetPhrase, &sourceLHS);
    coll.Add(targetPhrase);
  }

  return true;
}

  const TargetPhraseCollection*
     PhraseDictionaryInterpolated::GetTargetPhraseCollection(const Phrase& src) const {

    delete m_targetPhrases;
    m_targetPhrases = new TargetPhraseCollection();
    PhraseSet allPhrases;
    vector<PhraseSet> phrasesByTable(m_dictionaries.size());
    for (size_t i = 0; i < m_dictionaries.size(); ++i) {
      const TargetPhraseCollection* phrases = m_dictionaries[i]->GetTargetPhraseCollection(src);
      if (phrases) {
        for (TargetPhraseCollection::const_iterator j = phrases->begin(); 
          j != phrases->end(); ++j) {
          allPhrases.insert(*j);
          phrasesByTable[i].insert(*j);
        }
      }
    }
    ScoreComponentCollection sparseVector;
    for (PhraseSet::const_iterator i = allPhrases.begin(); i != allPhrases.end(); ++i) {
      TargetPhrase* combinedPhrase = new TargetPhrase((Phrase)**i);
      //combinedPhrase->ResetScore();
      //cerr << *combinedPhrase << " " << combinedPhrase->GetScoreBreakdown() << endl;
      combinedPhrase->SetSourcePhrase((*i)->GetSourcePhrase());
      combinedPhrase->SetAlignTerm(&((*i)->GetAlignTerm()));
      combinedPhrase->SetAlignNonTerm(&((*i)->GetAlignTerm()));
      Scores combinedScores(GetFeature()->GetNumScoreComponents());
      for (size_t j = 0; j < phrasesByTable.size(); ++j) {
        PhraseSet::const_iterator tablePhrase = phrasesByTable[j].find(combinedPhrase);
        if (tablePhrase != phrasesByTable[j].end()) {
          Scores tableScores = (*tablePhrase)->GetScoreBreakdown()
            .GetScoresForProducer(GetFeature());
          //cerr << "Scores from " << j << " table: ";
          for (size_t k = 0; k < tableScores.size()-1; ++k) {
            //cerr << tableScores[k] << "(" << exp(tableScores[k]) << ") ";
            combinedScores[k] += m_weights[k][j] * exp(tableScores[k]);
            //cerr << m_weights[k][j] * exp(tableScores[k]) << " ";
          }
          //cerr << endl;
        }
      }
      //map back to log space
      //cerr << "Combined ";
      for (size_t k = 0; k < combinedScores.size()-1; ++k) {
        //cerr << combinedScores[k] << " ";
        combinedScores[k] = log(combinedScores[k]);
        //cerr << combinedScores[k] << " ";
      }
      //cerr << endl;
      combinedScores.back() = 1; //assume last is penalty
      combinedPhrase->SetScore(
        GetFeature(),
        combinedScores,
        sparseVector,
        m_weightT,
        m_weightWP,
        *m_languageModels);
      //cerr << *combinedPhrase << " " << combinedPhrase->GetScoreBreakdown() <<  endl;
      m_targetPhrases->Add(combinedPhrase);
    }

    m_targetPhrases->Prune(true,m_tableLimit);


    return m_targetPhrases;
  }