void BuffImplementation::deactivate(bool removeModifiers) {
ManagedReference<CreatureObject*> strongRef = creature.get().get();
if (strongRef == NULL)
return;
try {
if (removeModifiers) {
removeAttributeModifiers();
removeSkillModifiers();
removeStates();
}
if (creature.get()->isPlayerCreature())
sendDestroyTo(cast<CreatureObject*>(creature.get().get()));
if (!endMessage.isEmpty())
creature.get()->sendSystemMessage(endMessage);
if (!endFlyFile.isEmpty())
creature.get()->showFlyText(endFlyFile, endFlyAux, endFlyRed, endFlyGreen, endFlyBlue);
clearBuffEvent();
} catch (Exception& e) {
error(e.getMessage());
e.printStackTrace();
}
}
/* update - Performs a single update on the HMM model for the given data. */
int update(Model **m, Hmm **tmp_hmm, RMat *data, Real **postpC, Real log_D,
int *c_ls, int ll, int cc, enum training_mode training_mode,
int upper_triangular) {
/* tmp_hmm[c] is scratch space. It must have at least as many states as
m[c]->hmm. */
int c, l, u;
int *xu;
for (c = 0; c<cc; c++) {
if (g_lastData!=data||ll>g_ll||m[c]->uu>g_uu) {
g_lastData = data;
initGlobals(ll, m[c]->uu, data);
}
zeroHMMlinear(tmp_hmm[c]);
for (l = 0; l<ll; l++) {
switch (training_mode) {
case HMM_ML:
if (c!=c_ls[l]) continue;
for (u = 0; u<m[c]->uu; u++) {
FFM_logL(m[c]->ffm[u], g_logL[l][u], data[l]);
}
HMM_updateModel(m[c]->hmm, tmp_hmm[c], g_logL[l], g_gamma[l], log_D,
0.0, -1, -1, training_mode);
break;
case HMM_DT:
for (u = 0; u<m[c]->uu; u++) {
FFM_logL(m[c]->ffm[u], g_logL[l][u], data[l]);
//if(g_logL[l][u][tt-1] != NEGATIVE_INFINITY) flag = 1;
}
HMM_updateModel(m[c]->hmm, tmp_hmm[c], g_logL[l], g_gamma[l], log_D,
my_exp(postpC[c][l]), c, c_ls[l], training_mode);
break;
default: panic("unrecognized training mode");
}
}
xu = (int *)safe_malloc(sizeof(int) * m[c]->uu);
if (!normaliseHMMlinear(tmp_hmm[c], upper_triangular, training_mode, xu)) {
assert(training_mode == HMM_DT);
free(xu);
return FALSE;
}
copyHMM(m[c]->hmm, tmp_hmm[c]);
for (u = 0; u<m[c]->uu; u++) {
switch (training_mode) {
case HMM_ML:
assert(FFM_maximise(m[c]->ffm[u], data, g_gamma_r[u], ll, log_D,
NULL, c, c_ls));
break;
case HMM_DT:
if (!FFM_maximise(m[c]->ffm[u], data, g_gamma_r[u], ll, log_D,
postpC[c], c, c_ls)){
free(xu);
return FALSE;
}
break;
default: panic("unrecognized training mode");
}
}
/* Remove redundant states by shifting the memory if necessary. */
for(u = 0; u < m[c]->uu; u ++)
if(xu[u]) break;
if(u < m[c]->uu)
removeStates(m[c], xu, tmp_hmm[c]);
free(xu);
}
return TRUE;
}
/**
* 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;
}
