move LatticeMBR into moses/

1 files changed, 669 insertions, 0 deletions

diff --git a/moses/LatticeMBR.cpp b/moses/LatticeMBR.cpp
new file mode 100644
index 000000000..148b44743
--- /dev/null
+++ b/moses/LatticeMBR.cpp
@@ -0,0 +1,669 @@
+/*
+ *  LatticeMBR.cpp
+ *  moses-cmd
+ *
+ *  Created by Abhishek Arun on 26/01/2010.
+ *  Copyright 2010 __MyCompanyName__. All rights reserved.
+ *
+ */
+
+#include "LatticeMBR.h"
+#include "moses/StaticData.h"
+#include <algorithm>
+#include <set>
+
+using namespace std;
+using namespace Moses;
+
+namespace MosesCmd
+{
+
+size_t bleu_order = 4;
+float UNKNGRAMLOGPROB = -20;
+void GetOutputWords(const TrellisPath &path, vector <Word> &translation)
+{
+  const std::vector<const Hypothesis *> &edges = path.GetEdges();
+
+  // print the surface factor of the translation
+  for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
+    const Hypothesis &edge = *edges[currEdge];
+    const Phrase &phrase = edge.GetCurrTargetPhrase();
+    size_t size = phrase.GetSize();
+    for (size_t pos = 0 ; pos < size ; pos++) {
+      translation.push_back(phrase.GetWord(pos));
+    }
+  }
+}
+
+
+void extract_ngrams(const vector<Word >& sentence, map < Phrase, int >  & allngrams)
+{
+  for (int k = 0; k < (int)bleu_order; k++) {
+    for(int i =0; i < max((int)sentence.size()-k,0); i++) {
+      Phrase ngram( k+1);
+      for ( int j = i; j<= i+k; j++) {
+        ngram.AddWord(sentence[j]);
+      }
+      ++allngrams[ngram];
+    }
+  }
+}
+
+
+
+void NgramScores::addScore(const Hypothesis* node, const Phrase& ngram, float score)
+{
+  set<Phrase>::const_iterator ngramIter = m_ngrams.find(ngram);
+  if (ngramIter == m_ngrams.end()) {
+    ngramIter = m_ngrams.insert(ngram).first;
+  }
+  map<const Phrase*,float>& ngramScores = m_scores[node];
+  map<const Phrase*,float>::iterator scoreIter = ngramScores.find(&(*ngramIter));
+  if (scoreIter == ngramScores.end()) {
+    ngramScores[&(*ngramIter)] = score;
+  } else {
+    ngramScores[&(*ngramIter)] = log_sum(score,scoreIter->second);
+  }
+}
+
+NgramScores::NodeScoreIterator NgramScores::nodeBegin(const Hypothesis* node)
+{
+  return m_scores[node].begin();
+}
+
+
+NgramScores::NodeScoreIterator NgramScores::nodeEnd(const Hypothesis* node)
+{
+  return m_scores[node].end();
+}
+
+LatticeMBRSolution::LatticeMBRSolution(const TrellisPath& path, bool isMap) :
+  m_score(0.0f)
+{
+  const std::vector<const Hypothesis *> &edges = path.GetEdges();
+
+  for (int currEdge = (int)edges.size() - 1 ; currEdge >= 0 ; currEdge--) {
+    const Hypothesis &edge = *edges[currEdge];
+    const Phrase &phrase = edge.GetCurrTargetPhrase();
+    size_t size = phrase.GetSize();
+    for (size_t pos = 0 ; pos < size ; pos++) {
+      m_words.push_back(phrase.GetWord(pos));
+    }
+  }
+  if (isMap) {
+    m_mapScore = path.GetTotalScore();
+  } else {
+    m_mapScore = 0;
+  }
+}
+
+
+void LatticeMBRSolution::CalcScore(map<Phrase, float>& finalNgramScores, const vector<float>& thetas, float mapWeight)
+{
+  m_ngramScores.assign(thetas.size()-1, -10000);
+
+  map < Phrase, int > counts;
+  extract_ngrams(m_words,counts);
+
+  //Now score this translation
+  m_score = thetas[0] * m_words.size();
+
+  //Calculate the ngramScores, working in log space at first
+  for (map < Phrase, int >::iterator ngrams = counts.begin(); ngrams != counts.end(); ++ngrams) {
+    float ngramPosterior = UNKNGRAMLOGPROB;
+    map<Phrase,float>::const_iterator ngramPosteriorIt = finalNgramScores.find(ngrams->first);
+    if (ngramPosteriorIt != finalNgramScores.end()) {
+      ngramPosterior = ngramPosteriorIt->second;
+    }
+    size_t ngramSize = ngrams->first.GetSize();
+    m_ngramScores[ngramSize-1] = log_sum(log((float)ngrams->second) + ngramPosterior,m_ngramScores[ngramSize-1]);
+  }
+
+  //convert from log to probability and create weighted sum
+  for (size_t i = 0; i < m_ngramScores.size(); ++i) {
+    m_ngramScores[i] = exp(m_ngramScores[i]);
+    m_score += thetas[i+1] * m_ngramScores[i];
+  }
+
+
+  //The map score
+  m_score += m_mapScore*mapWeight;
+}
+
+
+void pruneLatticeFB(Lattice & connectedHyp, map < const Hypothesis*, set <const Hypothesis* > > & outgoingHyps, map<const Hypothesis*, vector<Edge> >& incomingEdges,
+                    const vector< float> & estimatedScores, const Hypothesis* bestHypo, size_t edgeDensity, float scale)
+{
+
+  //Need hyp 0 in connectedHyp - Find empty hypothesis
+  VERBOSE(2,"Pruning lattice to edge density " << edgeDensity << endl);
+  const Hypothesis* emptyHyp = connectedHyp.at(0);
+  while (emptyHyp->GetId() != 0) {
+    emptyHyp = emptyHyp->GetPrevHypo();
+  }
+  connectedHyp.push_back(emptyHyp); //Add it to list of hyps
+
+  //Need hyp 0's outgoing Hyps
+  for (size_t i = 0; i < connectedHyp.size(); ++i) {
+    if (connectedHyp[i]->GetId() > 0 && connectedHyp[i]->GetPrevHypo()->GetId() == 0)
+      outgoingHyps[emptyHyp].insert(connectedHyp[i]);
+  }
+
+  //sort hyps based on estimated scores - do so by copying to multimap
+  multimap<float, const Hypothesis*> sortHypsByVal;
+  for (size_t i =0; i < estimatedScores.size(); ++i) {
+    sortHypsByVal.insert(make_pair(estimatedScores[i], connectedHyp[i]));
+  }
+
+  multimap<float, const Hypothesis*>::const_iterator it = --sortHypsByVal.end();
+  float bestScore = it->first;
+  //store best score as score of hyp 0
+  sortHypsByVal.insert(make_pair(bestScore, emptyHyp));
+
+
+  IFVERBOSE(3) {
+    for (multimap<float, const Hypothesis*>::const_iterator it = --sortHypsByVal.end(); it != --sortHypsByVal.begin(); --it) {
+      const Hypothesis* currHyp =  it->second;
+      cerr << "Hyp " << currHyp->GetId() << ", estimated score: " << it->first << endl;
+    }
+  }
+
+
+  set <const Hypothesis*> survivingHyps; //store hyps that make the cut in this
+
+  VERBOSE(2, "BEST HYPO TARGET LENGTH : " << bestHypo->GetSize() << endl)
+  size_t numEdgesTotal = edgeDensity * bestHypo->GetSize(); //as per Shankar, aim for (density * target length of MAP solution) arcs
+  size_t numEdgesCreated = 0;
+  VERBOSE(2, "Target edge count: " << numEdgesTotal << endl);
+
+  float prevScore = -999999;
+
+  //now iterate over multimap
+  for (multimap<float, const Hypothesis*>::const_iterator it = --sortHypsByVal.end(); it != --sortHypsByVal.begin(); --it) {
+    float currEstimatedScore = it->first;
+    const Hypothesis* currHyp =  it->second;
+
+    if (numEdgesCreated >= numEdgesTotal && prevScore > currEstimatedScore) //if this hyp has equal estimated score to previous, include its edges too
+      break;
+
+    prevScore = currEstimatedScore;
+    VERBOSE(3, "Num edges created : "<< numEdgesCreated << ", numEdges wanted " << numEdgesTotal << endl)
+    VERBOSE(3, "Considering hyp " << currHyp->GetId() << ", estimated score: " << it->first << endl)
+
+    survivingHyps.insert(currHyp); //CurrHyp made the cut
+
+    // is its best predecessor already included ?
+    if (survivingHyps.find(currHyp->GetPrevHypo()) != survivingHyps.end()) { //yes, then add an edge
+      vector <Edge>& edges = incomingEdges[currHyp];
+      Edge winningEdge(currHyp->GetPrevHypo(),currHyp,scale*(currHyp->GetScore() - currHyp->GetPrevHypo()->GetScore()),currHyp->GetCurrTargetPhrase());
+      edges.push_back(winningEdge);
+      ++numEdgesCreated;
+    }
+
+    //let's try the arcs too
+    const ArcList *arcList = currHyp->GetArcList();
+    if (arcList != NULL) {
+      ArcList::const_iterator iterArcList;
+      for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
+        const Hypothesis *loserHypo = *iterArcList;
+        const Hypothesis* loserPrevHypo = loserHypo->GetPrevHypo();
+        if (survivingHyps.find(loserPrevHypo) != survivingHyps.end()) { //found it, add edge
+          double arcScore = loserHypo->GetScore() - loserPrevHypo->GetScore();
+          Edge losingEdge(loserPrevHypo, currHyp, arcScore*scale, loserHypo->GetCurrTargetPhrase());
+          vector <Edge>& edges = incomingEdges[currHyp];
+          edges.push_back(losingEdge);
+          ++numEdgesCreated;
+        }
+      }
+    }
+
+    //Now if a successor node has already been visited, add an edge connecting the two
+    map < const Hypothesis*, set < const Hypothesis* > >::const_iterator outgoingIt = outgoingHyps.find(currHyp);
+
+    if (outgoingIt != outgoingHyps.end()) {//currHyp does have successors
+      const set<const Hypothesis*> & outHyps = outgoingIt->second; //the successors
+      for (set<const Hypothesis*>::const_iterator outHypIts = outHyps.begin(); outHypIts != outHyps.end(); ++outHypIts) {
+        const Hypothesis* succHyp = *outHypIts;
+
+        if (survivingHyps.find(succHyp) == survivingHyps.end()) //Have we encountered the successor yet?
+          continue; //No, move on to next
+
+        //Curr Hyp can be : a) the best predecessor  of succ b) or an arc attached to succ
+        if (succHyp->GetPrevHypo() == currHyp) { //best predecessor
+          vector <Edge>& succEdges = incomingEdges[succHyp];
+          Edge succWinningEdge(currHyp, succHyp, scale*(succHyp->GetScore() - currHyp->GetScore()), succHyp->GetCurrTargetPhrase());
+          succEdges.push_back(succWinningEdge);
+          survivingHyps.insert(succHyp);
+          ++numEdgesCreated;
+        }
+
+        //now, let's find an arc
+        const ArcList *arcList = succHyp->GetArcList();
+        if (arcList != NULL) {
+          ArcList::const_iterator iterArcList;
+          //QUESTION: What happens if there's more than one loserPrevHypo?
+          for (iterArcList = arcList->begin() ; iterArcList != arcList->end() ; ++iterArcList) {
+            const Hypothesis *loserHypo = *iterArcList;
+            const Hypothesis* loserPrevHypo = loserHypo->GetPrevHypo();
+            if (loserPrevHypo == currHyp) { //found it
+              vector <Edge>& succEdges = incomingEdges[succHyp];
+              double arcScore = loserHypo->GetScore() - currHyp->GetScore();
+              Edge losingEdge(currHyp, succHyp,scale* arcScore, loserHypo->GetCurrTargetPhrase());
+              succEdges.push_back(losingEdge);
+              ++numEdgesCreated;
+            }
+          }
+        }
+      }
+    }
+  }
+
+  connectedHyp.clear();
+  for (set <const Hypothesis*>::iterator it =  survivingHyps.begin(); it != survivingHyps.end(); ++it) {
+    connectedHyp.push_back(*it);
+  }
+
+  VERBOSE(2, "Done! Num edges created : "<< numEdgesCreated << ", numEdges wanted " << numEdgesTotal << endl)
+
+  IFVERBOSE(3) {
+    cerr << "Surviving hyps: " ;
+    for (set <const Hypothesis*>::iterator it =  survivingHyps.begin(); it != survivingHyps.end(); ++it) {
+      cerr << (*it)->GetId() << " ";
+    }
+    cerr << endl;
+  }
+
+
+}
+
+void calcNgramExpectations(Lattice & connectedHyp, map<const Hypothesis*, vector<Edge> >& incomingEdges,
+                           map<Phrase, float>& finalNgramScores, bool posteriors)
+{
+
+  sort(connectedHyp.begin(),connectedHyp.end(),ascendingCoverageCmp); //sort by increasing source word cov
+
+  /*cerr << "Lattice:" << endl;
+  for (Lattice::const_iterator i = connectedHyp.begin(); i != connectedHyp.end(); ++i) {
+      const Hypothesis* h = *i;
+      cerr << *h << endl;
+      const vector<Edge>& edges = incomingEdges[h];
+      for (size_t e = 0; e < edges.size(); ++e) {
+          cerr << edges[e];
+      }
+  }*/
+
+  map<const Hypothesis*, float> forwardScore;
+  forwardScore[connectedHyp[0]] = 0.0f; //forward score of hyp 0 is 1 (or 0 in logprob space)
+  set< const Hypothesis *> finalHyps; //store completed hyps
+
+  NgramScores ngramScores;//ngram scores for each hyp
+
+  for (size_t i = 1; i < connectedHyp.size(); ++i) {
+    const Hypothesis* currHyp = connectedHyp[i];
+    if (currHyp->GetWordsBitmap().IsComplete()) {
+      finalHyps.insert(currHyp);
+    }
+
+    VERBOSE(3, "Processing hyp: " << currHyp->GetId() << ", num words cov= " << currHyp->GetWordsBitmap().GetNumWordsCovered() <<  endl)
+
+    vector <Edge> & edges = incomingEdges[currHyp];
+    for (size_t e = 0; e < edges.size(); ++e) {
+      const Edge& edge = edges[e];
+      if (forwardScore.find(currHyp) == forwardScore.end()) {
+        forwardScore[currHyp] = forwardScore[edge.GetTailNode()] + edge.GetScore();
+        VERBOSE(3, "Fwd score["<<currHyp->GetId()<<"] = fwdScore["<<edge.GetTailNode()->GetId() << "] + edge Score: " << edge.GetScore() << endl)
+      } else {
+        forwardScore[currHyp] = log_sum(forwardScore[currHyp], forwardScore[edge.GetTailNode()] + edge.GetScore());
+        VERBOSE(3, "Fwd score["<<currHyp->GetId()<<"] += fwdScore["<<edge.GetTailNode()->GetId() << "] + edge Score: " << edge.GetScore() << endl)
+      }
+    }
+
+    //Process ngrams now
+    for (size_t j =0 ; j < edges.size(); ++j) {
+      Edge& edge = edges[j];
+      const NgramHistory & incomingPhrases = edge.GetNgrams(incomingEdges);
+
+      //let's first score ngrams introduced by this edge
+      for (NgramHistory::const_iterator it = incomingPhrases.begin(); it != incomingPhrases.end(); ++it) {
+        const Phrase& ngram = it->first;
+        const PathCounts& pathCounts = it->second;
+        VERBOSE(4, "Calculating score for: " << it->first << endl)
+
+        for (PathCounts::const_iterator pathCountIt = pathCounts.begin(); pathCountIt != pathCounts.end(); ++pathCountIt) {
+          //Score of an n-gram is forward score of head node of leftmost edge + all edge scores
+          const Path&  path = pathCountIt->first;
+          //cerr << "path count for " << ngram << " is " << pathCountIt->second << endl;
+          float score = forwardScore[path[0]->GetTailNode()];
+          for (size_t i = 0; i < path.size(); ++i) {
+            score += path[i]->GetScore();
+          }
+          //if we're doing expectations, then the number of times the ngram
+          //appears on the path is relevant.
+          size_t count = posteriors ? 1 : pathCountIt->second;
+          for (size_t k = 0; k < count; ++k) {
+            ngramScores.addScore(currHyp,ngram,score);
+          }
+        }
+      }
+
+      //Now score ngrams that are just being propagated from the history
+      for (NgramScores::NodeScoreIterator it = ngramScores.nodeBegin(edge.GetTailNode());
+           it != ngramScores.nodeEnd(edge.GetTailNode()); ++it) {
+        const Phrase & currNgram = *(it->first);
+        float currNgramScore = it->second;
+        VERBOSE(4, "Calculating score for: " << currNgram << endl)
+
+        // For posteriors, don't double count ngrams
+        if (!posteriors || incomingPhrases.find(currNgram) == incomingPhrases.end()) {
+          float score = edge.GetScore() + currNgramScore;
+          ngramScores.addScore(currHyp,currNgram,score);
+        }
+      }
+
+    }
+  }
+
+  float Z = 9999999; //the total score of the lattice
+
+  //Done - Print out ngram posteriors for final hyps
+  for (set< const Hypothesis *>::iterator finalHyp = finalHyps.begin(); finalHyp != finalHyps.end(); ++finalHyp) {
+    const Hypothesis* hyp = *finalHyp;
+
+    for (NgramScores::NodeScoreIterator it = ngramScores.nodeBegin(hyp); it != ngramScores.nodeEnd(hyp); ++it) {
+      const Phrase& ngram = *(it->first);
+      if (finalNgramScores.find(ngram) == finalNgramScores.end()) {
+        finalNgramScores[ngram] = it->second;
+      } else {
+        finalNgramScores[ngram] = log_sum(it->second,  finalNgramScores[ngram]);
+      }
+    }
+
+    if (Z == 9999999) {
+      Z = forwardScore[hyp];
+    } else {
+      Z = log_sum(Z, forwardScore[hyp]);
+    }
+  }
+
+  //Z *= scale;  //scale the score
+
+  for (map<Phrase, float>::iterator finalScoresIt = finalNgramScores.begin();  finalScoresIt != finalNgramScores.end(); ++finalScoresIt) {
+    finalScoresIt->second =  finalScoresIt->second - Z;
+    IFVERBOSE(2) {
+      VERBOSE(2,finalScoresIt->first << " [" << finalScoresIt->second << "]" << endl);
+    }
+  }
+
+}
+
+const NgramHistory& Edge::GetNgrams(map<const Hypothesis*, vector<Edge> > & incomingEdges)
+{
+
+  if (m_ngrams.size() > 0)
+    return m_ngrams;
+
+  const Phrase& currPhrase = GetWords();
+  //Extract the n-grams local to this edge
+  for (size_t start = 0; start < currPhrase.GetSize(); ++start) {
+    for (size_t end = start; end < start + bleu_order; ++end) {
+      if (end < currPhrase.GetSize()) {
+        Phrase edgeNgram(end-start+1);
+        for (size_t index = start; index <= end; ++index) {
+          edgeNgram.AddWord(currPhrase.GetWord(index));
+        }
+        //cout << "Inserting Phrase : " << edgeNgram << endl;
+        vector<const Edge*> edgeHistory;
+        edgeHistory.push_back(this);
+        storeNgramHistory(edgeNgram, edgeHistory);
+      } else {
+        break;
+      }
+    }
+  }
+
+  map<const Hypothesis*, vector<Edge> >::iterator it = incomingEdges.find(m_tailNode);
+  if (it != incomingEdges.end()) { //node has incoming edges
+    vector<Edge> & inEdges = it->second;
+
+    for (vector<Edge>::iterator edge = inEdges.begin(); edge != inEdges.end(); ++edge) {//add the ngrams straddling prev and curr edge
+      const NgramHistory & edgeIncomingNgrams = edge->GetNgrams(incomingEdges);
+      for (NgramHistory::const_iterator edgeInNgramHist = edgeIncomingNgrams.begin(); edgeInNgramHist != edgeIncomingNgrams.end(); ++edgeInNgramHist) {
+        const Phrase& edgeIncomingNgram = edgeInNgramHist->first;
+        const PathCounts &  edgeIncomingNgramPaths = edgeInNgramHist->second;
+        size_t back = min(edgeIncomingNgram.GetSize(), edge->GetWordsSize());
+        const Phrase&  edgeWords = edge->GetWords();
+        IFVERBOSE(3) {
+          cerr << "Edge: "<< *edge <<endl;
+          cerr << "edgeWords: " << edgeWords << endl;
+          cerr << "edgeInNgram: " << edgeIncomingNgram << endl;
+        }
+
+        Phrase edgeSuffix(ARRAY_SIZE_INCR);
+        Phrase ngramSuffix(ARRAY_SIZE_INCR);
+        GetPhraseSuffix(edgeWords,back,edgeSuffix);
+        GetPhraseSuffix(edgeIncomingNgram,back,ngramSuffix);
+
+        if (ngramSuffix == edgeSuffix) { //we've got the suffix of previous edge
+          size_t  edgeInNgramSize =  edgeIncomingNgram.GetSize();
+
+          for (size_t i = 0; i < GetWordsSize() && i + edgeInNgramSize < bleu_order ; ++i) {
+            Phrase newNgram(edgeIncomingNgram);
+            for (size_t j = 0; j <= i ; ++j) {
+              newNgram.AddWord(GetWords().GetWord(j));
+            }
+            VERBOSE(3, "Inserting New Phrase : " << newNgram << endl)
+
+            for (PathCounts::const_iterator pathIt = edgeIncomingNgramPaths.begin(); pathIt !=  edgeIncomingNgramPaths.end(); ++pathIt) {
+              Path newNgramPath = pathIt->first;
+              newNgramPath.push_back(this);
+              storeNgramHistory(newNgram, newNgramPath, pathIt->second);
+            }
+          }
+        }
+      }
+    }
+  }
+  return m_ngrams;
+}
+
+//Add the last lastN words of origPhrase to targetPhrase
+void Edge::GetPhraseSuffix(const Phrase&  origPhrase, size_t lastN, Phrase& targetPhrase) const
+{
+  size_t origSize = origPhrase.GetSize();
+  size_t startIndex = origSize - lastN;
+  for (size_t index = startIndex; index < origPhrase.GetSize(); ++index) {
+    targetPhrase.AddWord(origPhrase.GetWord(index));
+  }
+}
+
+bool Edge::operator< (const Edge& compare ) const
+{
+  if (m_headNode->GetId() < compare.m_headNode->GetId())
+    return true;
+  if (compare.m_headNode->GetId() < m_headNode->GetId())
+    return false;
+  if (m_tailNode->GetId() < compare.m_tailNode->GetId())
+    return true;
+  if (compare.m_tailNode->GetId() < m_tailNode->GetId())
+    return false;
+  return GetScore() <  compare.GetScore();
+}
+
+ostream& operator<< (ostream& out, const Edge& edge)
+{
+  out << "Head: " << edge.m_headNode->GetId() << ", Tail: " << edge.m_tailNode->GetId() << ", Score: " << edge.m_score << ", Phrase: " << edge.m_targetPhrase << endl;
+  return out;
+}
+
+bool ascendingCoverageCmp(const Hypothesis* a, const Hypothesis* b)
+{
+  return a->GetWordsBitmap().GetNumWordsCovered() <  b->GetWordsBitmap().GetNumWordsCovered();
+}
+
+void getLatticeMBRNBest(Manager& manager, TrellisPathList& nBestList,
+                        vector<LatticeMBRSolution>& solutions, size_t n)
+{
+  const StaticData& staticData = StaticData::Instance();
+  std::map < int, bool > connected;
+  std::vector< const Hypothesis *> connectedList;
+  map<Phrase, float> ngramPosteriors;
+  std::map < const Hypothesis*, set <const Hypothesis*> > outgoingHyps;
+  map<const Hypothesis*, vector<Edge> > incomingEdges;
+  vector< float> estimatedScores;
+  manager.GetForwardBackwardSearchGraph(&connected, &connectedList, &outgoingHyps, &estimatedScores);
+  pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores, manager.GetBestHypothesis(), staticData.GetLatticeMBRPruningFactor(),staticData.GetMBRScale());
+  calcNgramExpectations(connectedList, incomingEdges, ngramPosteriors,true);
+
+  vector<float> mbrThetas = staticData.GetLatticeMBRThetas();
+  float p = staticData.GetLatticeMBRPrecision();
+  float r = staticData.GetLatticeMBRPRatio();
+  float mapWeight = staticData.GetLatticeMBRMapWeight();
+  if (mbrThetas.size() == 0) { //thetas not specified on the command line, use p and r instead
+    mbrThetas.push_back(-1); //Theta 0
+    mbrThetas.push_back(1/(bleu_order*p));
+    for (size_t i = 2; i <= bleu_order; ++i) {
+      mbrThetas.push_back(mbrThetas[i-1] / r);
+    }
+  }
+  IFVERBOSE(2) {
+    VERBOSE(2,"Thetas: ");
+    for (size_t i = 0; i < mbrThetas.size(); ++i) {
+      VERBOSE(2,mbrThetas[i] << " ");
+    }
+    VERBOSE(2,endl);
+  }
+  TrellisPathList::const_iterator iter;
+  size_t ctr = 0;
+  LatticeMBRSolutionComparator comparator;
+  for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter, ++ctr) {
+    const TrellisPath &path = **iter;
+    solutions.push_back(LatticeMBRSolution(path,iter==nBestList.begin()));
+    solutions.back().CalcScore(ngramPosteriors,mbrThetas,mapWeight);
+    sort(solutions.begin(), solutions.end(), comparator);
+    while (solutions.size() > n) {
+      solutions.pop_back();
+    }
+  }
+  VERBOSE(2,"LMBR Score: " << solutions[0].GetScore() << endl);
+}
+
+vector<Word> doLatticeMBR(Manager& manager, TrellisPathList& nBestList)
+{
+
+  vector<LatticeMBRSolution> solutions;
+  getLatticeMBRNBest(manager, nBestList, solutions,1);
+  return solutions.at(0).GetWords();
+}
+
+const TrellisPath doConsensusDecoding(Manager& manager, TrellisPathList& nBestList)
+{
+  static const int BLEU_ORDER = 4;
+  static const float SMOOTH = 1;
+
+  //calculate the ngram expectations
+  const StaticData& staticData = StaticData::Instance();
+  std::map < int, bool > connected;
+  std::vector< const Hypothesis *> connectedList;
+  map<Phrase, float> ngramExpectations;
+  std::map < const Hypothesis*, set <const Hypothesis*> > outgoingHyps;
+  map<const Hypothesis*, vector<Edge> > incomingEdges;
+  vector< float> estimatedScores;
+  manager.GetForwardBackwardSearchGraph(&connected, &connectedList, &outgoingHyps, &estimatedScores);
+  pruneLatticeFB(connectedList, outgoingHyps, incomingEdges, estimatedScores, manager.GetBestHypothesis(), staticData.GetLatticeMBRPruningFactor(),staticData.GetMBRScale());
+  calcNgramExpectations(connectedList, incomingEdges, ngramExpectations,false);
+
+  //expected length is sum of expected unigram counts
+  //cerr << "Thread " << pthread_self() <<  " Ngram expectations size: " << ngramExpectations.size() << endl;
+  float ref_length = 0.0f;
+  for (map<Phrase,float>::const_iterator ref_iter = ngramExpectations.begin();
+       ref_iter != ngramExpectations.end(); ++ref_iter) {
+    //cerr << "Ngram: " << ref_iter->first << " score: " <<
+    //    ref_iter->second << endl;
+    if (ref_iter->first.GetSize() == 1) {
+      ref_length += exp(ref_iter->second);
+      //    cerr << "Expected for " << ref_iter->first << " is " << exp(ref_iter->second) << endl;
+    }
+  }
+
+  VERBOSE(2,"REF Length: " << ref_length << endl);
+
+  //use the ngram expectations to rescore the nbest list.
+  TrellisPathList::const_iterator iter;
+  TrellisPathList::const_iterator best = nBestList.end();
+  float bestScore = -100000;
+  //cerr << "nbest list size: " << nBestList.GetSize() << endl;
+  for (iter = nBestList.begin() ; iter != nBestList.end() ; ++iter) {
+    const TrellisPath &path = **iter;
+    vector<Word> words;
+    map<Phrase,int> ngrams;
+    GetOutputWords(path,words);
+    /*for (size_t i = 0; i < words.size(); ++i) {
+        cerr << words[i].GetFactor(0)->GetString() << " ";
+    }
+    cerr << endl;
+    */
+    extract_ngrams(words,ngrams);
+
+    vector<float> comps(2*BLEU_ORDER+1);
+    float logbleu = 0.0;
+    float brevity = 0.0;
+    int hyp_length = words.size();
+    for (int i = 0; i < BLEU_ORDER; ++i) {
+      comps[2*i] = 0.0;
+      comps[2*i+1] = max(hyp_length-i,0);
+    }
+
+    for (map<Phrase,int>::const_iterator hyp_iter = ngrams.begin();
+         hyp_iter != ngrams.end(); ++hyp_iter) {
+      map<Phrase,float>::const_iterator ref_iter = ngramExpectations.find(hyp_iter->first);
+      if (ref_iter != ngramExpectations.end()) {
+        comps[2*(hyp_iter->first.GetSize()-1)] += min(exp(ref_iter->second), (float)(hyp_iter->second));
+      }
+
+    }
+    comps[comps.size()-1] = ref_length;
+    /*for (size_t i = 0; i < comps.size(); ++i) {
+        cerr << comps[i] << " ";
+    }
+    cerr << endl;
+    */
+
+    float score = 0.0f;
+    if (comps[0] != 0) {
+      for (int i=0; i<BLEU_ORDER; i++) {
+        if ( i > 0 ) {
+          logbleu += log((float)comps[2*i]+SMOOTH)-log((float)comps[2*i+1]+SMOOTH);
+        } else {
+          logbleu += log((float)comps[2*i])-log((float)comps[2*i+1]);
+        }
+      }
+      logbleu /= BLEU_ORDER;
+      brevity = 1.0-(float)comps[comps.size()-1]/comps[1]; // comps[comps_n-1] is the ref length, comps[1] is the test length
+      if (brevity < 0.0) {
+        logbleu += brevity;
+      }
+      score =  exp(logbleu);
+    }
+
+    //cerr << "score: " << score << " bestScore: " << bestScore <<  endl;
+    if (score > bestScore) {
+      bestScore = score;
+      best = iter;
+      VERBOSE(2,"NEW BEST: " << score << endl);
+      //for (size_t i = 0; i < comps.size(); ++i) {
+      //    cerr << comps[i] << " ";
+      //}
+      //cerr << endl;
+    }
+  }
+
+  assert (best != nBestList.end());
+  return **best;
+  //vector<Word> bestWords;
+  //GetOutputWords(**best,bestWords);
+  //return bestWords;
+}
+
+}
+
+