void Rule::addPred(Lex * lexer){
Predicate * pred = new Predicate(lexer);
preds.push_back(pred);//should i break out of loop when failure is made?
if (lexer->getCurrentToken()->getTokenType() == COMMA && lexer->failure == NULL){
tokens.push_back(lexer->getCurrentToken());
lexer->parseAdvance();
addPred(lexer);
}
}
void initModel(_md)
{
/* initialize vectors */
initVect(preds_m, Pred);
initVect(funcs_m, Func);
initVect(axioms_m, Axiom);
initVect(taskPtrs_m, Task*);
initVect(tmpFacts_m, Fact);
initVect(restrs_m, Restriction);
initVect(types_m, DataType);
/* create standart datatypes */
addDataType("Boolean", modelPtr);
addDataType("Integer", modelPtr);
addDataType("Real", modelPtr);
/* CONST_TRUE, CONST_FALSE */
//~ DataType* boolPtr = ptr(DataType, types_m) + DT_BOOL;
DataType* boolPtr = getTypePtr(DT_BOOL);
addBack(boolPtr->constNames, char*, getDynamicStr("True"));
addBack(boolPtr->constNames, char*, getDynamicStr("False"));
/* create arithmetic fuctions */
size_t funcNum;
DataTypeId paramTypes[] = {DT_INT, DT_INT};
for (funcNum = 0; funcNum < ARITHMETIC_FUNCTION_COUNT; ++funcNum)
{
Func func = createFunc( (char*)ARITH_FUNCTION_NAMES[funcNum],
ARITH_FUNCTION_PAR_COUNTS[funcNum],
paramTypes, DT_REAL);
addFunc(func, modelPtr);
}
/* create arithmetic relations */
size_t predNum;
for (predNum = 0; predNum < ARITHMETIC_RELATIONS_COUNT; ++predNum)
{
Pred pred = createFunc( (char*)ARITH_RELATION_NAMES[predNum],
ARITH_RELATION_PAR_COUNTS[predNum],
paramTypes, DT_BOOL);
addPred(pred, modelPtr);
}
/* other initialization */
initVect(modelPtr->errors, Error);
initVect(modelPtr->sources, Source);
modelPtr->currSrcPtr = NULL;
modelPtr->totalFactCt = 0;
modelPtr->debugStream = modelPtr->errorStream = NULL;
}
void Rule::Parse(Lex * lexer){
HeadPred(lexer);
/*if (lexer->failure != NULL){
cout << lexer->failure->toString() << endl;
return;
}
cout << "we are here" << endl;*/
if (lexer->getCurrentToken()->getTokenType() == COLON_DASH){
tokens.push_back(lexer->getCurrentToken());
lexer->parseAdvance();
addPred(lexer);
if (lexer->failure != NULL) return;
if (lexer->getCurrentToken()->getTokenType() == PERIOD){
tokens.push_back(lexer->getCurrentToken());
lexer->parseAdvance();
}
else
lexer->failure = lexer->getCurrentToken();
}
else
lexer->failure = lexer->getCurrentToken();
}
/**
* Create an FST based on an RNN
*/
void FlatBOFstBuilder::convertRNN(CRnnLM & rnnlm, VectorFst<LogArc> &fst) {
queue<NeuronFstHistory> q;
VectorFst<LogArc> new_fst;
NeuronFstHistory fsth(rnnlm.getHiddenLayerSize(),getNumBins());
FstIndex id = 0;
NeuronFstHistory new_fsth(rnnlm.getHiddenLayerSize(),getNumBins());
FstIndex new_id;
NeuronFstHistory min_backoff(rnnlm.getHiddenLayerSize(),getNumBins());
set<NeuronFstHistory>set_min_backoff;
NeuronFstHistory bo_fsth(rnnlm.getHiddenLayerSize(),getNumBins());
bool backoff = false;
vector<FstIndex> deleted;
real p = 0.00;
real p_joint = 0.00;
real entropy = 0.0;
real delta = 0.0;
vector<real> all_prob(rnnlm.getVocabSize());
vector<real> posterior(10);
map< FstIndex,set<FstIndex> > pred;
vector<bool> non_bo_pred(rnnlm.getVocabSize());
vector<int> to_be_added;
vector<int> to_be_removed;
for (int i = 0; i < rnnlm.getVocabSize(); i++) {
to_be_removed.push_back(i);
}
vector<real> to_be_added_prob;
FstIndex n_added = 0;
FstIndex n_processed = 0;
FstIndex next_n_added = 0;
FstIndex next_n_processed = 0;
FstIndex n_backoff = 0;
FstIndex n_only_backoff = 0;
int v = rnnlm.getVocabSize();
int w = 0;
// Initialize
rnnlm.copyHiddenLayerToInput();
// printNeurons(rnnlm.getInputLayer(),0,10);
// Initial state ( 0 | hidden layer after </s>)
printNeurons(rnnlm.getHiddenLayer(),0,10);
fsth.setFstHistory(rnnlm, *dzer);
fsth.setLastWord(0);
q.push(fsth);
addFstState(id, new NeuronFstHistory(fsth), fst);
fst.SetStart(INIT_STATE);
// Final state (don't care about the associated discrete representation)
fst.AddState();
fst.SetFinal(FINAL_STATE, LogWeight::One());
/*posterior.at(INIT_STATE) = MY_LOG_ONE;*/
min_backoff.setLastWord(-1);
computeEntropyAndConditionals(entropy, all_prob, rnnlm, min_backoff);
min_backoff = getBackoff(rnnlm, min_backoff, set_min_backoff, all_prob, to_be_removed);
cout << "MIN BACKOFF " << min_backoff.toString() << endl;
set_min_backoff.insert(min_backoff);
// addFstState(id, min_backoff, fst);
// q.push(min_backoff);
// Estimate number of backoff loop to bound the backoff path length
// float ratioa = 0.0;
// float ratiob = 0.0;
float ratio = 0.0;
// for (int i=0; i < min_backoff.getNumDims(); i++) {
// if (min_backoff.getDim(i) == 1) {
// ratioa++;
// }
// if (fsth.getDim(i) == 1) {
// ratiob++;
// }
// }
// ratioa /= min_backoff.getNumDims();
// ratiob /= min_backoff.getNumDims();
// ratio = (ratioa*(1.0-ratiob))+(ratiob*(1.0-ratioa));
ratio=1.0;
// printf("ratio=%f\t%i BO loops\n", ratio, n_bo_loops);
//foreach state in the queue
while (!q.empty()) {
fsth = q.front();
q.pop();
id = h2state[&fsth];
state2h.push_back(new NeuronFstHistory(fsth));
if (id == FINAL_STATE) { continue; }
dprintf(1,"-- STUDY STATE %li = %s\n", id, fsth.toString().c_str());
/* try { posterior.at(id) = MY_LOG_ONE; }
catch (exception e) {
posterior.resize((int) (posterior.size()*1.5)+1);
posterior.at(id) = MY_LOG_ONE;
}*/
computeEntropyAndConditionals(entropy, all_prob, rnnlm, fsth);
//compute BO in advance and check if it is a min BO node
bo_fsth = getBackoff(rnnlm, fsth, set_min_backoff, all_prob, to_be_removed);
if (bo_fsth == fsth) { bo_fsth = min_backoff; }
//foreach w (ie, foreach word of each class c)
//test if the edge has to kept or removed
backoff = false; //no backoff yet since no edge has been removed
for (w=0; w < rnnlm.getVocabSize(); w++) {
p = all_prob[w];
/*p_joint = exp(-posterior[id]-p);*/
p_joint = exp(-p);
delta = -1.0*p_joint*log2(p_joint);
//accept edge if this leads to a minimum
//relative gain of the entropy
dprintf(2,"P = %e \tP_joint = %e \tH = %e \tDelta =%e \tDelta H = %.6f %%\n",exp(-p), p_joint, entropy, delta, 100.0*delta/entropy);
if (set_min_backoff.find(fsth) != set_min_backoff.end() || (delta > pruning_threshold*entropy)) {
// if ((fsth == min_backoff) || (delta > pruning_threshold*entropy)) {
next_n_added++;
to_be_added.push_back(w);
to_be_added_prob.push_back(p);
dprintf(2,"\tACCEPT [%li] -- %i (%s) / %f --> ...\t(%e > %e)\n", id, w, rnnlm.getWordString(w), p, delta, pruning_threshold*entropy);
// to_be_removed.push_back(w);
}
//backoff
else {
// to_be_removed.push_back(w);
backoff = true;
dprintf(2,"\tPRUNE [%li] -- %i / %f --> ...\n", id, w, p);
}
//print
if (next_n_processed % 100000 == 0) {
fprintf(stderr, "\rH=%.5f / N proc'd=%li / N added=%li (%.5f %%) / N bo=%li (%.5f %%) / %li/%li Nodes (%2.1f %%) / N min BO=%i", entropy, n_processed, n_added, ((float) n_added/ (float)n_processed)*100.0, n_backoff, ((float) n_backoff/ (float)n_added)*100.0, id, id+q.size(), 100.0 - (float) (100.0*id/(id+q.size())), (int) set_min_backoff.size());
}
next_n_processed++;
// }
}
//Set a part of the new FST history
new_fsth.setFstHistory(rnnlm, *dzer);
//if at least one word is backing off
if (backoff) {
n_backoff++;
if (to_be_added.size() == 0) {
n_only_backoff++;
}
if (addFstState(new_id, new NeuronFstHistory(bo_fsth), fst)) {
q.push(bo_fsth);
try { non_bo_pred.at(new_id) = false; }
catch (exception e) {
non_bo_pred.resize(new_id+(int) (non_bo_pred.size()*0.5)+1);
non_bo_pred.at(new_id) = false;
}
}
dprintf(1,"BACKOFF\t[%li]\t(%s)\n-------\t[%li]\t(%s)\n", id, fsth.toString().c_str(), new_id, bo_fsth.toString().c_str());
fst.AddArc(id, LogArc(EPSILON, EPSILON, LogWeight::Zero(), new_id));
addPred(pred, new_id, id);
}
vector<real>::iterator it_p = to_be_added_prob.begin();
for (vector<int>::iterator it = to_be_added.begin(); it != to_be_added.end(); ++it) {
w = *it;
p = *it_p;
if (w == 0) {
fst.AddArc(id, LogArc(FstWord(w),FstWord(w),p,FINAL_STATE));
dprintf(1,"EDGE [%li] (%s)\n---- %i (%s) / %f -->\n---- [%li] FINAL STATE)\n\n", id, fsth.toString().c_str(), FstWord(w), rnnlm.getWordString(w), p, FINAL_STATE);
}
//accept edge
else {
new_fsth.setLastWord(w);
//if sw not in the memory
//then add a new state for sw in the FST and push sw in the queue
if (addFstState(new_id, new NeuronFstHistory(new_fsth), fst)) {
q.push(new_fsth);
try { non_bo_pred.at(new_id) = true; }
catch (exception e) {
non_bo_pred.resize(new_id+(int) (non_bo_pred.size()*0.5)+1);
non_bo_pred.at(new_id) = true;
}
}
else { /* already exists */ }
//add the edge in the FST
non_bo_pred.at(new_id) = true;
fst.AddArc(id, LogArc(FstWord(w),FstWord(w),p,new_id));
dprintf(1,"EDGE [%li] (%s)\n---- %i (%s) / %f -->\n---- [%li] (%s)\n\n", id, fsth.toString().c_str(), FstWord(w), rnnlm.getWordString(w), p, new_id, new_fsth.toString().c_str());
// posterior.at(new_id) += posterior[id]*p;
}
/*if (posterior[id]+p < LogWeight::Zero().Value()) {
p_joint = exp(-posterior[id]-p);
entropy -= p_joint*log2(p_joint);
}*/
++it_p;
}
n_added = next_n_added;
n_processed = next_n_processed;
//reset queues
to_be_added.clear();
to_be_added_prob.clear();
// to_be_removed.clear();
}
cout << endl;
//compute backoff weights
deleted = compactBackoffNodes(fst, pred, non_bo_pred);
computeAllBackoff(fst, pred);
//remove useless nodes
removeStates(fst, new_fst, deleted);
fst.DeleteStates();
fst = new_fst;
//Fill the table of symbols
SymbolTable dic("dictionnary");
dic.AddSymbol("*", 0);
for (int i=0; i<rnnlm.getVocabSize(); i++) {
dic.AddSymbol(string(rnnlm.getWordString(i)), i+1);
}
fst.SetInputSymbols(&dic);
fst.SetOutputSymbols(&dic);
//printf("H=%.5f / N proc'd=%li / N added=%li (%.5f %%) %li/%li Nodes (%2.1f %%)\n", entropy, n_processed, n_added, ((float) n_added/ (float)n_processed)*100.0, id, id+q.size(), 100.0 - (float) (100.0*id/(id+q.size())));
cout << "END" << endl;
}
