void ReferenceSet::AddLine(size_t sentenceId, const StringPiece& line, Vocab& vocab)
{
//cerr << line << endl;
NgramCounter ngramCounts;
list<WordVec> openNgrams;
size_t length = 0;
//tokenize & count
for (util::TokenIter<util::SingleCharacter, true> j(line, util::SingleCharacter(' ')); j; ++j) {
const Vocab::Entry* nextTok = &(vocab.FindOrAdd(*j));
++length;
openNgrams.push_front(WordVec());
for (list<WordVec>::iterator k = openNgrams.begin(); k != openNgrams.end(); ++k) {
k->push_back(nextTok);
++ngramCounts[*k];
}
if (openNgrams.size() >= kBleuNgramOrder) openNgrams.pop_back();
}
//merge into overall ngram map
for (NgramCounter::const_iterator ni = ngramCounts.begin();
ni != ngramCounts.end(); ++ni) {
size_t count = ni->second;
//cerr << *ni << " " << count << endl;
if (ngramCounts_.size() <= sentenceId) ngramCounts_.resize(sentenceId+1);
NgramMap::iterator totalsIter = ngramCounts_[sentenceId].find(ni->first);
if (totalsIter == ngramCounts_[sentenceId].end()) {
ngramCounts_[sentenceId][ni->first] = pair<size_t,size_t>(count,count);
} else {
ngramCounts_[sentenceId][ni->first].first = max(count, ngramCounts_[sentenceId][ni->first].first); //clip
ngramCounts_[sentenceId][ni->first].second += count; //no clip
}
}
//length
if (lengths_.size() <= sentenceId) lengths_.resize(sentenceId+1);
//TODO - length strategy - this is MIN
if (!lengths_[sentenceId]) {
lengths_[sentenceId] = length;
} else {
lengths_[sentenceId] = min(length,lengths_[sentenceId]);
}
//cerr << endl;
}
FeatureStatsType HgBleuScorer::Score(const Edge& edge, const Vertex& head, vector<FeatureStatsType>& bleuStats)
{
NgramCounter ngramCounts;
size_t childId = 0;
size_t wordId = 0;
size_t contextId = 0; //position within left or right context
const VertexState* vertexState = NULL;
bool inLeftContext = false;
bool inRightContext = false;
list<WordVec> openNgrams;
const Vocab::Entry* currentWord = NULL;
while (wordId < edge.Words().size()) {
currentWord = edge.Words()[wordId];
if (currentWord != NULL) {
++wordId;
} else {
if (!inLeftContext && !inRightContext) {
//entering a vertex
assert(!vertexState);
vertexState = &(vertexStates_[edge.Children()[childId]]);
++childId;
if (vertexState->leftContext.size()) {
inLeftContext = true;
contextId = 0;
currentWord = vertexState->leftContext[contextId];
} else {
//empty context
vertexState = NULL;
++wordId;
continue;
}
} else {
//already in a vertex
++contextId;
if (inLeftContext && contextId < vertexState->leftContext.size()) {
//still in left context
currentWord = vertexState->leftContext[contextId];
} else if (inLeftContext) {
//at end of left context
if (vertexState->leftContext.size() == kBleuNgramOrder-1) {
//full size context, jump to right state
openNgrams.clear();
inLeftContext = false;
inRightContext = true;
contextId = 0;
currentWord = vertexState->rightContext[contextId];
} else {
//short context, just ignore right context
inLeftContext = false;
vertexState = NULL;
++wordId;
continue;
}
} else {
//in right context
if (contextId < vertexState->rightContext.size()) {
currentWord = vertexState->rightContext[contextId];
} else {
//leaving vertex
inRightContext = false;
vertexState = NULL;
++wordId;
continue;
}
}
}
}
assert(currentWord);
if (graph_.IsBoundary(currentWord)) continue;
openNgrams.push_front(WordVec());
openNgrams.front().reserve(kBleuNgramOrder);
for (list<WordVec>::iterator k = openNgrams.begin(); k != openNgrams.end(); ++k) {
k->push_back(currentWord);
//Only insert ngrams that cross boundaries
if (!vertexState || (inLeftContext && k->size() > contextId+1)) ++ngramCounts[*k];
}
if (openNgrams.size() >= kBleuNgramOrder) openNgrams.pop_back();
}
//Collect matches
//This edge
//cerr << "edge ngrams" << endl;
UpdateMatches(ngramCounts, bleuStats);
//Child vertexes
for (size_t i = 0; i < edge.Children().size(); ++i) {
//cerr << "vertex ngrams " << edge.Children()[i] << endl;
for (size_t j = 0; j < bleuStats.size(); ++j) {
bleuStats[j] += vertexStates_[edge.Children()[i]].bleuStats[j];
}
}
FeatureStatsType sourceLength = head.SourceCovered();
size_t referenceLength = references_.Length(sentenceId_);
FeatureStatsType effectiveReferenceLength =
sourceLength / totalSourceLength_ * referenceLength;
bleuStats[bleuStats.size()-1] = effectiveReferenceLength;
//backgroundBleu_[backgroundBleu_.size()-1] =
// backgroundRefLength_ * sourceLength / totalSourceLength_;
FeatureStatsType bleu = sentenceLevelBackgroundBleu(bleuStats, backgroundBleu_);
return bleu;
}
void Viterbi(const Graph& graph, const SparseVector& weights, float bleuWeight, const ReferenceSet& references , size_t sentenceId, const std::vector<FeatureStatsType>& backgroundBleu, HgHypothesis* bestHypo)
{
BackPointer init(NULL,kMinScore);
vector<BackPointer> backPointers(graph.VertexSize(),init);
HgBleuScorer bleuScorer(references, graph, sentenceId, backgroundBleu);
vector<FeatureStatsType> winnerStats(kBleuNgramOrder*2+1);
for (size_t vi = 0; vi < graph.VertexSize(); ++vi) {
// cerr << "vertex id " << vi << endl;
FeatureStatsType winnerScore = kMinScore;
const Vertex& vertex = graph.GetVertex(vi);
const vector<const Edge*>& incoming = vertex.GetIncoming();
if (!incoming.size()) {
//UTIL_THROW(HypergraphException, "Vertex " << vi << " has no incoming edges");
//If no incoming edges, vertex is a dead end
backPointers[vi].first = NULL;
backPointers[vi].second = kMinScore;
} else {
//cerr << "\nVertex: " << vi << endl;
for (size_t ei = 0; ei < incoming.size(); ++ei) {
//cerr << "edge id " << ei << endl;
FeatureStatsType incomingScore = incoming[ei]->GetScore(weights);
for (size_t i = 0; i < incoming[ei]->Children().size(); ++i) {
size_t childId = incoming[ei]->Children()[i];
//UTIL_THROW_IF(backPointers[childId].second == kMinScore,
// HypergraphException, "Graph was not topologically sorted. curr=" << vi << " prev=" << childId);
incomingScore = max(incomingScore + backPointers[childId].second, kMinScore);
}
vector<FeatureStatsType> bleuStats(kBleuNgramOrder*2+1);
// cerr << "Score: " << incomingScore << " Bleu: ";
// if (incomingScore > nonbleuscore) {nonbleuscore = incomingScore; nonbleuid = ei;}
FeatureStatsType totalScore = incomingScore;
if (bleuWeight) {
FeatureStatsType bleuScore = bleuScorer.Score(*(incoming[ei]), vertex, bleuStats);
if (isnan(bleuScore)) {
cerr << "WARN: bleu score undefined" << endl;
cerr << "\tVertex id : " << vi << endl;
cerr << "\tBleu stats : ";
for (size_t i = 0; i < bleuStats.size(); ++i) {
cerr << bleuStats[i] << ",";
}
cerr << endl;
bleuScore = 0;
}
//UTIL_THROW_IF(isnan(bleuScore), util::Exception, "Bleu score undefined, smoothing problem?");
totalScore += bleuWeight * bleuScore;
// cerr << bleuScore << " Total: " << incomingScore << endl << endl;
//cerr << "is " << incomingScore << " bs " << bleuScore << endl;
}
if (totalScore >= winnerScore) {
//We only store the feature score (not the bleu score) with the vertex,
//since the bleu score is always cumulative, ie from counts for the whole span.
winnerScore = totalScore;
backPointers[vi].first = incoming[ei];
backPointers[vi].second = incomingScore;
winnerStats = bleuStats;
}
}
//update with winner
//if (bleuWeight) {
//TODO: Not sure if we need this when computing max-model solution
if (backPointers[vi].first) {
bleuScorer.UpdateState(*(backPointers[vi].first), vi, winnerStats);
}
}
// cerr << "backpointer[" << vi << "] = (" << backPointers[vi].first << "," << backPointers[vi].second << ")" << endl;
}
//expand back pointers
GetBestHypothesis(graph.VertexSize()-1, graph, backPointers, bestHypo);
//bleu stats and fv
//Need the actual (clipped) stats
//TODO: This repeats code in bleu scorer - factor out
bestHypo->bleuStats.resize(kBleuNgramOrder*2+1);
NgramCounter counts;
list<WordVec> openNgrams;
for (size_t i = 0; i < bestHypo->text.size(); ++i) {
const Vocab::Entry* entry = bestHypo->text[i];
if (graph.IsBoundary(entry)) continue;
openNgrams.push_front(WordVec());
for (list<WordVec>::iterator k = openNgrams.begin(); k != openNgrams.end(); ++k) {
k->push_back(entry);
++counts[*k];
}
if (openNgrams.size() >= kBleuNgramOrder) openNgrams.pop_back();
}
for (NgramCounter::const_iterator ngi = counts.begin(); ngi != counts.end(); ++ngi) {
size_t order = ngi->first.size();
size_t count = ngi->second;
bestHypo->bleuStats[(order-1)*2 + 1] += count;
bestHypo->bleuStats[(order-1) * 2] += min(count, references.NgramMatches(sentenceId,ngi->first,true));
}
bestHypo->bleuStats[kBleuNgramOrder*2] = references.Length(sentenceId);
}
int set_LL_data_IQ(int deviceID, int channelNum, int length, unsigned short* addr, unsigned short* count,
unsigned short* trigger1, unsigned short * trigger2, unsigned short* repeat){
//Convert data pointers to vectors and passed through
return APSRack_.set_LL_data(deviceID, channelNum, WordVec(addr, addr+length), WordVec(count, count+length),
WordVec(trigger1, trigger1+length), WordVec(trigger2, trigger2+length), WordVec(repeat, repeat+length));
}
