double SpeechKMeans::Expectation(int utterance_index,
int *correctness,
vector<vector<vector<DataPoint> > > *sets) {
int u = utterance_index;
const ClusterProblem &cluster_problem =
cluster_problems_.problem(utterance_index);
const Utterance &utterance = problems_.utterance(utterance_index);
// Duplicate each state for every mode.
Viterbi viterbi(cluster_problem.num_states,
cluster_problem.num_steps,
cluster_problems_.num_modes(),
1);
viterbi.Initialize();
// Set the weights based on the current centers.
clock_t start = clock();
for (int mode = 0; mode < cluster_problems_.num_modes(); ++mode) {
for (int i = 0; i < cluster_problem.num_states; ++i) {
// const Gaussian &gaussian = gmms_[mode][cluster_problem.MapState(i)];
// for (int s = 0; s < cluster_problem.num_steps; ++s) {
// double score = -gaussian.LogLikelihood(utterance.sequence(s));
// viterbi.set_transition_score(s, i, mode, score);
// }
const DataPoint ¢er = centers_[mode][cluster_problem.MapState(i)];
for (int s = 0; s < cluster_problem.num_steps; ++s) {
double score = 0.0;
for (int j = 0; j < utterance.sequence_points(s); ++j) {
score += dist(center, utterance.sequence(s, j));
}
viterbi.set_transition_score(s, i, mode, score);
}
}
}
cerr << "TIME: score setting " << clock() - start << endl;
// Run viterbi algorithm.
viterbi.ForwardScores();
double score = viterbi.GetBestPath(&path_[u], &mode_centers_[u]);
(*correctness) = utterance.ScoreAlignment(path_[u]);
cerr << "SCORE: Correctness: " << *correctness << endl;
// Make the cluster sets.
sets->resize(cluster_problems_.num_modes());
for (int mode = 0; mode < cluster_problems_.num_modes(); ++mode) {
(*sets)[mode].resize(num_types_);
}
problems_.AlignmentClusterSet(utterance_index, path_[u], mode_centers_[u], sets);
// collapse to modes.
assert(path_[u].size() - 1 == (uint)cluster_problem.num_states);
cerr << endl;
return score;
}
/////////////////////////////////////////////////////////////////////////////
// //
// //
// Decoding //
// //
// //
/////////////////////////////////////////////////////////////////////////////
double decode (Hmm *H)
{
posterior (H);
viterbi (H);
dump_model(H);
dump_viterbi(H);
dump_posterior(H);
return H->PP;
}
void SimpleHMMExample::run() {
SimpleHMMExample::Observation o;
o.push_back(0);
o.push_back(1);
o.push_back(2);
o.push_back(3);
o.push_back(4);
viterbi(o);
};
void decode_data(uint8_t raw[RAW_SIZE], uint8_t data[DATA_SIZE], int8_t error[2]) {
uint8_t conv[CONV_SIZE];
uint8_t dec_data[RS_SIZE];
uint8_t rs[2][RS_BLOCK_SIZE];
deinterleave(raw, conv);
viterbi(conv, dec_data);
descramble_and_deinterleave(dec_data, rs);
rs_decode(rs, data, error);
}
double SpeechKMeans::GMMExpectation(int utterance_index,
vector<vector<DataPoint> > &estimators,
vector<vector<double> > &counts) {
const ClusterProblem &cluster_problem =
cluster_problems_.problem(utterance_index);
const Utterance &utterance = problems_.utterance(utterance_index);
// Duplicate each state for every mode.
Viterbi viterbi(cluster_problem.num_states,
cluster_problem.num_steps, cluster_problems_.num_modes(), 3);
viterbi.Initialize();
// Set the weights based on the current centers.
clock_t start = clock();
for (int mode = 0; mode < cluster_problems_.num_modes(); ++mode) {
for (int i = 0; i < cluster_problem.num_states; ++i) {
const DataPoint ¢er = centers_[mode][cluster_problem.MapState(i)];
for (int s = 0; s < cluster_problem.num_steps; ++s) {
double score = dist(center,
utterance.sequence(s, 0));
viterbi.set_transition_score(s, i, mode, score);
}
}
}
cerr << "score setting " << clock() - start << endl;
// Run semimarkov algorithm.
//vector<int> path;
viterbi.set_use_sum();
viterbi.ForwardScores();
double score = viterbi.GetBestScore();
viterbi.BackwardScores();
vector<vector<vector<double> > > marginals;
viterbi.Marginals(&marginals);
for (int s = 0; s < cluster_problem.num_steps; ++s) {
for (int i = 0; i < cluster_problem.num_states; ++i) {
int type = cluster_problem.MapState(i);
for (int mode = 0; mode < cluster_problems_.num_modes(); ++mode) {
double p = marginals[s][i][mode];
//cerr << p << endl;
//assert(p <= 1.0 + 1e-4);
//assert(p >= 0.0);
if (p > 1.0) p = 1.0;
//if (p > 1e-4) {
estimators[mode][type] += p * utterance.sequence(s, 0);
counts[mode][type] += p;
//}
}
}
}
return score;
}
double TaggerImpl::collins(double *collins) {
if (x_.empty()) return 0.0;
buildLattice();
viterbi(); // call for finding argmax y*
double s = 0.0;
// if correct parse, do not run forward + backward
{
size_t num = 0;
for (size_t i = 0; i < x_.size(); ++i)
if (answer_[i] == result_[i]) ++num;
if (num == x_.size()) return 0.0;
}
for (size_t i = 0; i < x_.size(); ++i) {
// answer
{
s += node_[i][answer_[i]]->cost;
for (int *f = node_[i][answer_[i]]->fvector; *f != -1; ++f)
++collins[*f + answer_[i]];
const std::vector<Path *> &lpath = node_[i][answer_[i]]->lpath;
for (const_Path_iterator it = lpath.begin(); it != lpath.end(); ++it) {
if ((*it)->lnode->y == answer_[(*it)->lnode->x]) {
for (int *f = (*it)->fvector; *f != -1; ++f)
++collins[*f +(*it)->lnode->y * ysize_ +(*it)->rnode->y];
s += (*it)->cost;
break;
}
}
}
// result
{
s -= node_[i][result_[i]]->cost;
for (int *f = node_[i][result_[i]]->fvector; *f != -1; ++f)
--collins[*f + result_[i]];
const std::vector<Path *> &lpath = node_[i][result_[i]]->lpath;
for (const_Path_iterator it = lpath.begin(); it != lpath.end(); ++it) {
if ((*it)->lnode->y == result_[(*it)->lnode->x]) {
for (int *f = (*it)->fvector; *f != -1; ++f)
--collins[*f +(*it)->lnode->y * ysize_ +(*it)->rnode->y];
s -= (*it)->cost;
break;
}
}
}
}
return -s;
}
bool TaggerImpl::parse() {
CHECK_FALSE(feature_index_->buildFeatures(this))
<< feature_index_->what();
if (x_.empty()) return true;
buildLattice();
if (nbest_ || vlevel_ >= 1) forwardbackward();
viterbi();
if (nbest_) initNbest();
return true;
}
void decode_data_debug(
uint8_t raw[RAW_SIZE], // Data to be decoded, 5200 byte (soft bit format)
uint8_t data[DATA_SIZE], // Decoded data, 256 byte
int8_t error[2], // RS decoder modules corrected errors or -1 if unrecoverable error happened
uint8_t conv[CONV_SIZE], // Deinterleaved data with SYNC removed (5132 byte, soft bit format)
uint8_t dec_data[RS_SIZE], // Viterbi decoder output (320 byte): two RS codeblock interleaved and scrambled(!)
uint8_t rs[2][RS_BLOCK_SIZE] // RS codeblocks without the leading padding 95 zeros
) {
deinterleave(raw, conv);
viterbi(conv, dec_data);
descramble_and_deinterleave(dec_data, rs);
rs_decode(rs, data, error);
}
double SpeechKMeans::UnsupExpectation(int utterance_index,
int *correctness,
vector<vector<vector<DataPoint> > > *sets) {
cerr << "Unsupervised maximization" << endl;
int u = utterance_index;
const ClusterProblem &cluster_problem =
cluster_problems_.problem(utterance_index);
const Utterance &utterance = problems_.utterance(utterance_index);
// Duplicate each state for every mode.
Viterbi viterbi(cluster_problem.num_states,
cluster_problem.num_steps,
cluster_problems_.num_types(),
1);
viterbi.Initialize();
// Set the weights based on the current centers.
clock_t start = clock();
for (int type = 0; type < cluster_problems_.num_types(); ++type) {
const DataPoint ¢er = centers_[0][type];
for (int s = 0; s < cluster_problem.num_steps; ++s) {
double score = dist(center,
utterance.sequence(s, 0));
for (int i = 0; i < cluster_problem.num_states; ++i) {
viterbi.set_transition_score(s, i, type, score);
}
}
}
cerr << "TIME: score setting " << clock() - start << endl;
// Run semimarkov algorithm.
viterbi.ForwardScores();
double score = viterbi.GetBestPath(&path_[u], &type_centers_[u]);
(*correctness) = utterance.ScoreAlignment(path_[u]);
cerr << "SCORE: Correctness: " << *correctness << endl;
// Make the cluster sets.
sets->resize(cluster_problems_.num_modes());
for (int mode = 0; mode < cluster_problems_.num_modes(); ++mode) {
(*sets)[mode].resize(num_types_);
}
problems_.AlignmentClusterSetUnsup(utterance_index, path_[u],
type_centers_[u], sets);
// collapse to modes.
cerr << endl;
return score;
}
void main_loop(const char** argv)
{
map<string, string> params;
process_cmd_line(argv, params);
Lab2VitMain mainObj(params);
while (mainObj.init_utt())
{
double logProb = viterbi(mainObj.get_graph(),
mainObj.get_gmm_probs(), mainObj.get_chart(),
mainObj.get_label_list(), mainObj.get_acous_wgt(),
mainObj.do_align());
mainObj.finish_utt(logProb);
}
mainObj.finish();
}
void SpeechKMeans::ClusterSegmentsExpectation(int utterance_index,
vector<DataPoint> *points,
vector<double> *weights) {
const ClusterProblem &cluster_problem =
cluster_problems_.problem(utterance_index);
// Duplicate each state for every mode.
Viterbi viterbi(cluster_problem.num_states,
cluster_problem.num_steps,
cluster_problem.num_hidden(0), 1);
viterbi.Initialize();
// Set the weights based on the current centers.
clock_t start = clock();
for (int s = 0; s < cluster_problem.num_steps; ++s) {
for (int c = 0; c < cluster_problems_.num_hidden(0); ++c) {
double score = distances_[utterance_index]->get_distance(s, c);
viterbi.set_score(s, c, score);
}
}
cerr << "TIME: score setting " << clock() - start << endl;
// Run semimarkov algorithm.
viterbi.ForwardScores();
vector<int> path;
vector<int> centers;
viterbi.GetBestPath(&path, ¢ers);
//double weight = 0.0;
int state = 0;
for (int s = 0; s < cluster_problem.num_steps; ++s) {
points->push_back(problems_.center(centers[state]).point());
// weight += distances_[utterance_index]->get_distance(s,
// centers[state]);
if (s >= path[state + 1]) {
//weights->push_back(weight);
points->push_back(problems_.center(centers[state]).point());
++state;
//weight = 0.0;
}
}
}
void run_benchmark( void *vargs ) {
struct bench_args_t *args = (struct bench_args_t *)vargs;
#ifdef GEM5_HARNESS
mapArrayToAccelerator(
MACHSUITE_VITERBI_VITERBI, "obs", (void*)&args->obs, sizeof(args->obs));
mapArrayToAccelerator(
MACHSUITE_VITERBI_VITERBI, "path", (void*)&args->path, sizeof(args->path));
mapArrayToAccelerator(
MACHSUITE_VITERBI_VITERBI, "transition", (void*)&args->transition,
sizeof(args->transition));
mapArrayToAccelerator(
MACHSUITE_VITERBI_VITERBI, "emission", (void*)&args->emission,
sizeof(args->emission));
mapArrayToAccelerator(
MACHSUITE_VITERBI_VITERBI, "init", (void*)&args->init,
sizeof(args->init));
invokeAcceleratorAndBlock(MACHSUITE_VITERBI_VITERBI);
#else
viterbi( args->obs, args->init, args->transition, args->emission, args->path );
#endif
}
bool Viterbi::analyze(Lattice *lattice) const {
if (!lattice || !lattice->sentence()) {
return false;
}
if (!initPartial(lattice)) {
return false;
}
if (lattice->has_request_type(MECAB_NBEST) ||
lattice->has_request_type(MECAB_MARGINAL_PROB)) {
if (!viterbiWithAllPath(lattice)) {
return false;
}
} else {
if (!viterbi(lattice)) {
return false;
}
}
if (!forwardbackward(lattice)) {
return false;
}
if (!buildBestLattice(lattice)) {
return false;
}
if (!buildAllLattice(lattice)) {
return false;
}
if (!initNBest(lattice)) {
return false;
}
return true;
}
int main() {
int i, j, k;
int Obs[numObs];
float transMat[numStates*numObs], obsLik[numStates*numObs];
int finalState;
finalState = 2;
srandom(1);
for(i=0;i<numObs;i++){
Obs[i] = i;
}
for(j=0;j<numStates;j++){
for(i=0;i<numObs;i++){
transMat[j*numObs + i] = RR();
obsLik[j*numObs + i] = RR();
}
}
finalState = viterbi(Obs, transMat, obsLik);
printf("final == %d\n", finalState);
return 0;
}
/******************************************************************************
Checkforminimumduration() :
inputs : Observation Space (pointer to all training feature vectors) same as Sequence of Observations,
allMixtureMeans of GMMs - pointer to means of gmms,
allMixtureVars of GMMs, (*numStates), numMixEachState, Transition matrix (not using currently),
Emission probability matrix Bj(Ot), Initial probabilities (Pi)
outputs : check for minimum duration and drops any state which has less than MIN_DUR frames
******************************************************************************/
int* CheckMinDuration(VECTOR_OF_F_VECTORS *features, int *hiddenStateSeq, int *numStates,
VECTOR_OF_F_VECTORS *allMixtureMeans, VECTOR_OF_F_VECTORS *allMixtureVars, int *numMixEachState,
int totalNumFeatures, float **B, int *numElemEachState, float *T1[], int *T2[], float *Pi,
float *mixtureWeight){
FindNumberOfElemInEachState(hiddenStateSeq, numStates, totalNumFeatures, numElemEachState, Pi);
// check min duration constraint
int i = 0, j = 0, s = 0, d= 0, mixCount = 0;
for(s = 0; s < *numStates; s++){
if( numElemEachState[s] < MIN_DUR ){
printf("dropping state: %d ....does not contain enough elements...\n", s);
//drop the state
VECTOR_OF_F_VECTORS *tempAllMixtureMeans, *tempAllMixtureVars;
tempAllMixtureMeans = (VECTOR_OF_F_VECTORS *) calloc(MAX_NUM_MIX * (*numStates) , sizeof(VECTOR_OF_F_VECTORS));
tempAllMixtureVars = (VECTOR_OF_F_VECTORS *) calloc(MAX_NUM_MIX * (*numStates) , sizeof(VECTOR_OF_F_VECTORS));
for(i = 0; i < MAX_NUM_MIX * (*numStates); i++){
tempAllMixtureMeans[i] = (F_VECTOR *) AllocFVector(DIM);
tempAllMixtureVars[i] = (F_VECTOR *) AllocFVector(DIM);
}
// COPY ORIGINAL MEANS AND VARS INTO TEMPS
int totalMix = 0;
for(i = 0; i < *numStates; i++)
totalMix += numMixEachState[i];
for(i = 0; i < totalMix; i++){
for(d = 0; d < DIM; d++){
tempAllMixtureMeans[i]->array[d] = allMixtureMeans[i]->array[d];
tempAllMixtureMeans[i]->numElements = DIM;
tempAllMixtureVars[i]->array[d] = allMixtureVars[i]->array[d];
tempAllMixtureMeans[i]->numElements = DIM;
}
}
// copy from temp to original means and vars array
int mix_s = numMixEachState[s];
for(j = 0; j < *numStates - 1; j++){
if(j >= s)
numMixEachState[j] = numMixEachState[j+1];
}
for(j = 0; j < *numStates - 1; j++){
if(j < s){
Pi[j] = (float) log((float)numElemEachState[j]/totalNumFeatures);
mixCount = 0;
for(i = 0; i < j; i++)
mixCount += numMixEachState[i];
for(i = mixCount; i < mixCount + numMixEachState[j]; i++){
for(d = 0; d < DIM; d++){
allMixtureMeans[i]->array[d] = tempAllMixtureMeans[i]->array[d];
allMixtureMeans[i]->numElements = DIM;
allMixtureVars[i]->array[d] = tempAllMixtureVars[i]->array[d];
allMixtureMeans[i]->numElements = DIM;
}
}
}
else if(j >= s){
mixCount = 0;
numElemEachState[j] = numElemEachState[j+1];
Pi[j] = (float) log((float)numElemEachState[j]/totalNumFeatures);
//printf("Pi[%d] : %f\n", j, Pi[j]);
for(i = 0; i < j; i++)
mixCount += numMixEachState[i];
for(i = mixCount; i < mixCount + numMixEachState[j]; i++){
for(d = 0; d < DIM; d++){
allMixtureMeans[i]->array[d] = tempAllMixtureMeans[i + mix_s]->array[d];
allMixtureMeans[i]->numElements = DIM;
allMixtureVars[i]->array[d] = tempAllMixtureVars[i + mix_s]->array[d];
allMixtureMeans[i]->numElements = DIM;
}
}
}
}
// FREE TEMP VECTORS
for(i = 0; i < MAX_NUM_MIX * (*numStates); i++){
free(tempAllMixtureMeans[i]);
free(tempAllMixtureVars[i]);
}
free(tempAllMixtureMeans);
free(tempAllMixtureVars);
// change number of states by one
*numStates = *numStates - 1;
// first compute new posterior probs for each element
ComputePosteriorProb(features, B, allMixtureMeans, allMixtureVars, numStates, numMixEachState,
totalNumFeatures, mixtureWeight);
// Now call viterbi alginment again
return viterbi( features, numStates, allMixtureMeans, allMixtureVars, numMixEachState,
totalNumFeatures, B, numElemEachState, T1, T2,
Pi, mixtureWeight);
}//matches if
}
return hiddenStateSeq;
}
int main(int argc, char *argv[])
{
if (argc != 3) exit(-1);
load_trans_prob();
std::ifstream emissin(argv[1]);
std::ofstream fout(argv[2]);
std::string line;
while (getline(emissin, line))
{
int N = 100;
std::vector<Phone> seq;
size_t pre = 0, next;
std::string seq_id;
next = line.find(' ', pre);
seq_id = line.substr(pre, next - pre);
fout << seq_id << " " << N << std::endl;
pre = next+1;
int cnt = std::stoi(line.substr(pre));
std::cout << seq_id << "\t" << cnt << std::endl;
while(cnt--)
{
Phone tmp;
getline(emissin, line);
std::istringstream ss(line);
for (int i=0; i<48; i++)
ss >> tmp.features[i];
seq.push_back(tmp);
}
std::vector<int> ans(seq.size(), 0);
for (int i=0; i<seq.size(); i++)
{
float max = -1e50;
int max_index = 0;
for (int j=0; j<48; j++)
{
if (seq[i].features[j] > max)
{
max = seq[i].features[j];
max_index = j;
}
}
ans[i] = max_index;
}
int start, end;
for (start=0; start < seq.size(); start++)
if (ans[start] != 37)
break;
for (end = seq.size(); end>0; end--)
if (ans[end-1] != 37)
break;
Phone tmp;
for (int i=0; i<48; i++)
tmp.features[i] = 0;
std::vector<Phone> newseq;
cnt = 0;
for (int i=start; i<end; i++)
{
if (i != start && ans[i] != ans[i-1])
{
for (int j=0; j<48; j++)
tmp.features[j] = std::log(tmp.features[j] / cnt);
newseq.push_back(tmp);
for (int j=0; j<48; j++)
tmp.features[j] = 0;
cnt = 0;
}
for (int j=0; j<48; j++)
tmp.features[j] += std::exp(seq[i].features[j]);
cnt ++;
}
viterbi(newseq, N, fout);
}
}
void mexFunction(
int nlhs,
mxArray *plhs[],
int nrhs,
const mxArray *prhs[]
)
{
double a_matrix_in[4][4];/* 2 dimensional C array to pass to workFcn() */
double *delta_in_matrix;/* 2 dimensional C array to pass to workFcn() */
double *observation_probs_matrix;/* 2 dimensional C array to pass to workFcn() */
double *psi_matrix;/* 2 dimensional C array to pass to workFcn() */
double duration_sum_in[4];/* 2 dimensional C array to pass to workFcn() */
double duration_probs_matrix[4][150];/* 2 dimensional C array to pass to workFcn() */
int actual_T;
int fake_T_extended;
int actual_N;
int max_duration_D_val;
int row,col; /* loop indices */
int m,n; /* temporary array size holders */
/* Step 1: Error Checking Step 1a: is nlhs 1? If not,
* generate an error message and exit mexample (mexErrMsgTxt
* does this for us!) */
if (nlhs!=3)
mexErrMsgTxt("mexample requires 3 output argument.");
/* Step 1b: is nrhs 2? */
if (nrhs!=9)
mexErrMsgTxt("mexample requires 9 input arguments");
actual_T = mxGetM(observation_probs);
actual_N = mxGetN(observation_probs);
max_duration_D_val = mxGetScalar(max_duration_D);
/* Step 2: Allocate memory for return argument(s) */
delta_out = mxCreateDoubleMatrix((actual_T+max_duration_D_val-1), actual_N, mxREAL);
psi_out = mxCreateDoubleMatrix((actual_T+max_duration_D_val-1), actual_N, mxREAL);
psi_duration = mxCreateDoubleMatrix((actual_T+max_duration_D_val-1), actual_N, mxREAL);
/* Step 3: Convert ARRAY_IN to a 2x2 C array
* MATLAB stores a two-dimensional matrix in memory as a one-
* dimensional array. If the matrix is size MxN, then the
* first M elements of the one-dimensional array correspond to
* the first column of the matrix, and the next M elements
* correspond to the second column, etc. The following loop
* converts from MATLAB format to C format: */
for (col=0; col < mxGetN(a_matrix); col++){
for (row=0; row < mxGetM(a_matrix); row++){
a_matrix_in[row][col] =(mxGetPr(a_matrix))[row+col*mxGetM(a_matrix)];
}
}
for (col=0; col < mxGetM(duration_sum); col++){
duration_sum_in[col] =(mxGetPr(duration_sum))[col];
}
delta_in_matrix = mxGetPr(delta);
observation_probs_matrix = mxGetPr(observation_probs);
psi_matrix = mxGetPr(psi);
/* for (col=0; col < mxGetN(delta); col++){
* // for (row=0; row < mxGetM(delta); row++){
* //
* //
* // observation_probs_matrix[row][col] =(mxGetPr(observation_probs))[row+col*mxGetM(observation_probs)];
* // psi_matrix[row][col] =(mxGetPr(psi))[row+col*mxGetM(psi)];
* // }
* // }*/
for (col=0; col < mxGetN(duration_probs); col++){
for (row=0; row < mxGetM(duration_probs); row++){
duration_probs_matrix[row][col] =(mxGetPr(duration_probs))[row+col*mxGetM(duration_probs)];
}
}
/* mxGetPr returns a pointer to the real part of the array
* ARRAY_IN. In the line above, it is treated as the one-
* dimensional array mentioned in the previous comment. */
/* Step 4: Call workFcn function */
viterbi(actual_N,actual_T,a_matrix_in,max_duration_D_val,delta_in_matrix,observation_probs_matrix,duration_probs_matrix,psi_matrix,mxGetPr(psi_duration),duration_sum_in);
memcpy ( mxGetPr(delta_out), delta_in_matrix, actual_N*(actual_T+max_duration_D_val-1)*8);
memcpy ( mxGetPr(psi_out), psi_matrix, actual_N*(actual_T+max_duration_D_val-1)*8);
}
int two_states_coin_toss()
{
model my_model;
state model_states[2];
double symbols_head_state[2]={1.0,0.0};
double trans_prob_head_state[2]={0.5,0.5};
double trans_prob_head_state_rev[2]={0.5,0.5};
int trans_id_head_state[2]={0,1};
double symbols_tail_state[2]={0.0,1.0};
double trans_prob_tail_state[2]={0.5,0.5};
double trans_prob_tail_state_rev[2]={0.5,0.5};
int trans_id_tail_state[2]={0,1};
sequence_t *my_output;
double log_p_viterbi, log_p_forward;
double **forward_alpha;
double forward_scale[10];
int *viterbi_path;
int i;
/* flags indicating whether a state is silent */
int silent_array[2] = {0,0};
my_model.model_type = 0;
/* initialise head state */
model_states[0].pi = 0.5;
model_states[0].b=symbols_head_state;
model_states[0].out_states=2;
model_states[0].out_a=trans_prob_head_state;
model_states[0].out_id=trans_id_head_state;
model_states[0].in_states=2;
model_states[0].in_id=trans_id_head_state;
model_states[0].in_a=trans_prob_head_state_rev;
model_states[0].fix=1;
/* initialise tail state */
model_states[1].pi = 0.5;
model_states[1].b=symbols_tail_state;
model_states[1].out_states=2;
model_states[1].out_id=trans_id_tail_state;
model_states[1].out_a=trans_prob_tail_state;
model_states[1].in_states=2;
model_states[1].in_id=trans_id_tail_state;
model_states[1].in_a=trans_prob_tail_state_rev;
model_states[1].fix=1;
/* initialise model */
my_model.N=2;
my_model.M=2;
my_model.s=model_states;
my_model.prior=-1;
my_model.silent = silent_array;
fprintf(stdout,"transition matrix:\n");
model_A_print(stdout,&my_model,""," ","\n");
fprintf(stdout,"observation symbol matrix:\n");
model_B_print(stdout,&my_model,""," ","\n");
my_output=model_generate_sequences(&my_model,0,10,10,100);
sequence_print(stdout,my_output);
/* try viterbi algorithm in a clear situation */
viterbi_path=viterbi(&my_model,
my_output->seq[0],
my_output->seq_len[0],
&log_p_viterbi);
if (viterbi_path==NULL)
{fprintf(stderr,"viterbi failed!"); return 1;}
fprintf(stdout,"viterbi:\n");
for(i=0;i<my_output->seq_len[0];i++){
printf(" %d, ", viterbi_path[i]);
}
printf("\n");
fprintf(stdout,
"log-p of this sequence (viterbi algorithm): %f\n",
log_p_viterbi);
/* allocate matrix for forward algorithm */
fprintf(stdout,"applying forward algorithm to the sequence...");
forward_alpha=stat_matrix_d_alloc(10,2);
if (forward_alpha==NULL)
{
fprintf(stderr,"\n could not alloc forward_alpha matrix\n");
return 1;
}
/* run foba_forward */
if (foba_forward(&my_model,
my_output->seq[0],
my_output->seq_len[0],
forward_alpha,
forward_scale,
&log_p_forward))
{
fprintf(stderr,"foba_logp failed!");
stat_matrix_d_free(&forward_alpha);
return 1;
}
/* alpha matrix */
fprintf(stdout,"Done.\nalpha matrix from forward algorithm:\n");
matrix_d_print(stdout,forward_alpha,10,2,""," ","\n");
fprintf(stdout,"log-p of this sequence (forward algorithm): %f\n",log_p_forward);
/* clean up */
sequence_free(&my_output);
free(viterbi_path);
stat_matrix_d_free(&forward_alpha);
return 0;
}
void mexFunction(
int nlhs,
mxArray *plhs[],
int nrhs,
const mxArray *prhs[]
)
{
double a_matrix_in[4][4];/* 2 dimensional C array to pass to workFcn() */
double *delta_in_matrix;/* 2 dimensional C array to pass to workFcn() */
double *observation_probs_matrix;/* 2 dimensional C array to pass to workFcn() */
double *psi_matrix;/* 2 dimensional C array to pass to workFcn() */
double duration_probs_matrix[4][150];/* 2 dimensional C array to pass to workFcn() */
int actual_T;
int actual_N;
int max_duration_D_val;
int row,col; /* loop indices */
int m,n; /* temporary array size holders */
/* Step 1: Error Checking Step 1a: is nlhs 1? If not,
* generate an error message and exit mexample (mexErrMsgTxt
* does this for us!) */
if (nlhs!=3)
mexErrMsgTxt("mexample requires 3 output argument.");
/* Step 1b: is nrhs 2? */
if (nrhs!=8)
mexErrMsgTxt("mexample requires 8 input arguments");
actual_T = mxGetM(observation_probs);
actual_N = mxGetN(observation_probs);
/* Step 2: Allocate memory for return argument(s) */
delta_out = mxCreateDoubleMatrix(actual_T, actual_N, mxREAL);
psi_out = mxCreateDoubleMatrix(actual_T, actual_N, mxREAL);
psi_duration = mxCreateDoubleMatrix(actual_T, actual_N, mxREAL);
/* Step 3: Convert ARRAY_IN to a 2x2 C array
* MATLAB stores a two-dimensional matrix in memory as a one-
* dimensional array. If the matrix is size MxN, then the
* first M elements of the one-dimensional array correspond to
* the first column of the matrix, and the next M elements
* correspond to the second column, etc. The following loop
* converts from MATLAB format to C format: */
for (col=0; col < mxGetN(a_matrix); col++){
for (row=0; row < mxGetM(a_matrix); row++){
a_matrix_in[row][col] =(mxGetPr(a_matrix))[row+col*mxGetM(a_matrix)];
}
}
delta_in_matrix = mxGetPr(delta);
observation_probs_matrix = mxGetPr(observation_probs);
psi_matrix = mxGetPr(psi);
for (col=0; col < mxGetN(duration_probs); col++){
for (row=0; row < mxGetM(duration_probs); row++){
duration_probs_matrix[row][col] =(mxGetPr(duration_probs))[row+col*mxGetM(duration_probs)];
}
}
max_duration_D_val = mxGetScalar(max_duration_D);
/* mxGetPr returns a pointer to the real part of the array
* ARRAY_IN. In the line above, it is treated as the one-
* dimensional array mentioned in the previous comment. */
/* Step 4: Call workFcn function */
viterbi(actual_N,actual_T,a_matrix_in,max_duration_D_val,delta_in_matrix,observation_probs_matrix,duration_probs_matrix,psi_matrix,mxGetPr(psi_duration));
memcpy ( mxGetPr(delta_out), delta_in_matrix, actual_N*actual_T*8);
memcpy ( mxGetPr(psi_out), psi_matrix, actual_N*actual_T*8);
/* workFcn(twoDarray,mxGetPr(VECTOR_IN), mxGetPr(VECTOR_OUT));
*
* workFcn will fill VECTOR_OUT with the return values for
* mexample, so the MEX-function is done! To use it, compile
* it according to the instructions in the Application Program
* Interface Guide. Then, in MATLAB, type
*
* c=mexample([1 2; 3 4],[1 2])
*
* This will return the same as
*
* c=det([1 2; 3 4]) * [1 2]
*
*/
}
/**
* For one network phi and one experiment extracted from the total data matrix X,
* for one experiments states Gx use the viterbi algorithm
* N: number of nodes
* T: number of time points
* R: number of replicates
* X: data matrix (N x TxR)
* GS: optim state matrix (N x TxR), initialised in R and given as argument
* G: state matrix (N x sum_s(M_s)); for each stimulus s, there are M_s states
* Glen:
* TH: theta parameter matrix (N x 4)
* tps: timepoint vector (T)
* stimgrps: vector containing the stimulus indices
* numexperimentsx: number of experiments (equals length of R and Ms)
* hmmit: number of hmmiterations
* Ms: number of system states for each experiment
*/
double hmmsearch(int *phi, const int N, const int *Tx, const int *Rx,
const double *X, int *GS,
int *G, int Glen, double *TH,
const int *tps,
const int *stimids, const int *stimgrps,
const int numexperiments, const int hmmit, int *Ms)
{
// fixed for the moment, number of iterations in the em-algorithm
int ncol_GS=0;
int numstims, idstart=0, Gstart=0;
for(int i=0; i!=numexperiments; ++i) {
ncol_GS += Tx[i]*Rx[i];
}
// printf("~~~~~ \n ncol_GS: %d\n",ncol_GS);
// init A, TH and L
// sort of a hack for getting the - infinity value
double temp = 1.0;
double infinity = -1 * (temp / (temp - 1.0));
int M = Glen/N; // total number of system states
// allocate the transition probability matrix, for all experiments
int A_sz = 0;
for(int i=0; i!=numexperiments; ++i) {
A_sz += pow(Ms[i],2);
}
// make a vector of doubles, holding the transition probabilities
double *A = malloc(A_sz * sizeof(double));
init_A(A, Ms, numexperiments); // init transition probabilities (sparse MxM matrix)
/* main loop: for each iteration in hmmiterations:
* perform viterbi for each experiment separately
* get the parameters TH and updates for A and GS for all experiments combined
*/
int Mexp, T, R;
int startA=0, startX=0, startG=0;
double *Aexp = NULL;
double *Xexp = NULL;
int *Gexp = NULL;
int *GSexp = NULL;
int allR, allT;
double Lik = -1*infinity;
double Likold = -1*infinity;
int nLikEqual = 0;
// keep track of the last 5 likelihood differences
// if switching behaviour occurs, it can be seen here
// take differences
int K = 6;
double diff, difftmp;
//double *diffvec = calloc(K-1, sizeof(double));
double *diffvec = malloc(K * sizeof(double));
int *toswitch = calloc(N, sizeof(int));
int nsw=0, maxsw=10;
// new state matrix object
for(int it=0; it!=hmmit; ++it) {
allR=0;
allT=0;
startA=0;
startX=0;
startG=0;
// find switchable rows in TH
nsw = find_switchable(TH, N, toswitch);
if(nsw>0) {
maxsw = min(nsw, maxsw);
}
/* here the experiment loop
* extract each experiment from X according to R and stimgrps and run the hmm
* indices of columns of X to be selected:
* expind is equivalent to the experiment index
* this defines the stimuli to take from stimgrps
*/
for(int expind=0; expind!=numexperiments; expind++) {
//print_intmatrix(toswitch, 1, N);
// if inconsistencies occur in the gamma matrix,
// try switching the theta parameters upto
// maxsw times to reduce the number of inconsitencies
//maxsw = min(nsw, N);
T = Tx[expind];
R = Rx[expind];
allR += R;
allT += T;
Mexp = Ms[expind];
for(int swind=0; swind!=maxsw; ++swind) {
// extract the sub-transition matrix:
Aexp = realloc(Aexp, Mexp*Mexp * sizeof(double));
extract_transitionmatrix(A, Aexp, Mexp, startA);
// extract the sub-data matrix
Xexp = realloc(Xexp, N*T*R*sizeof(double));
extract_datamatrix(X, Xexp, N, T, R, startX);
// extract the sub-state matrix
Gexp = realloc(Gexp, N*Mexp*sizeof(int));
extract_statematrix(G, Gexp, N, Mexp, startG);
// extract the sub-optimstate matrix
GSexp = realloc(GSexp, N*T*R*sizeof(int));
extract_statematrix(GS, GSexp, N, T*R, startX);
// run the viterbi
viterbi(T, Mexp, Xexp, Gexp, TH, N, R, Aexp, GSexp);
// consitency check
int inc = is_consistent(phi, GSexp, Gexp, N, T, R);
if(inc>0 && nsw>0) {
//printf("Inconsitensies in state series. Repeat HMM with modified thetaprime.\n");
// select a row to switch randomly
int rnum = rand() % nsw; // a random number between 1 and nsw
int hit = 0, ii=0;
for(ii=0; ii!=N; ++ii) {
if(toswitch[ii]!=0) {
if(hit==rnum) {
break;
}
hit++;
}
}
switch_theta_row(TH, ii, N);
// remove node as possible switch node
toswitch[ii] = 0;
nsw--;
} else {
// update the GSnew matrix
update_statematrix(GS, GSexp, startX, N, T, R);
// update the new transition prob matrix
update_transitionmatrix(A, Aexp, Mexp, startA);
break;
}
} // switch loop end
// increment the experiment start indices
startA += pow(Mexp, 2);
startX += N*T*R;
startG += N*Mexp;
} // experiment loop end
// number of replicates are the same in each experiment, since
// pad-columns containing NAs were added
allR = allR/numexperiments;
// M-step
// update the theta matrix
estimate_theta(X, GS, TH, N, allT, allR);
// calculate the new likelihood
Lik = calculate_likelihood(X, GS, TH, N, allT, allR); //Liktmp$L
diff = fabs((fabs(Lik) - fabs(Likold)));
// count number of equal differences in the last 10 likelihoods
// if all 5 differences are equal, then stop
int stopit=0, count = 0; // if all diffs are equal, stopit remains 1 and the loop is aborted
// check if diffvec is filled
if(it>=K) {
// check elements in diffvec
for(int k=0; k!=(K-1); ++k) {
if(k>0) {
if(fabs(diffvec[k]-difftmp)<=0.001) {
count++;
//stopit = 0;
}
}
// remember the original value at current position
difftmp = diffvec[k];
// shift next value left one position
if(k<K) {
diffvec[k] = diffvec[k+1];
} else {
// update the new difference at last position
diffvec[k] = diff;
}
}
if(count==(K-1)) {
stopit = 1;
// make sure that the higher likelihood is taken
// if switching occurs
if(Likold>Lik) {
stopit = 0;
}
}
} else {
// if not filled then add elements
diffvec[it] = diff;
}
// another termination criterion: count if liklihood terms do not change
if(Lik==Likold) {
nLikEqual++;
} else {
nLikEqual = 0;
Likold = Lik;
}
// abort baum-welch, if Lik does not change anymore
if(nLikEqual>=10 || stopit==1) {
break;
}
}
free(toswitch);
free(diffvec);
free(GSexp);
free(Gexp);
free(Xexp);
free(Aexp);
free(A);
return Lik;
}
int main(int argc, char *argv[])
{
char *configfile = NULL;
FILE *fin, *bin;
char *linebuf = NULL;
size_t buflen = 0;
int iterations = 3;
int mode = 3;
int c;
float d;
float *loglik;
float p;
int i, j, k;
opterr = 0;
while ((c = getopt(argc, argv, "c:n:hp:")) != -1) {
switch (c) {
case 'c':
configfile = optarg;
break;
case 'h':
usage();
exit(EXIT_SUCCESS);
case 'n':
iterations = atoi(optarg);
break;
case 'p':
mode = atoi(optarg);
if (mode != 1 && mode != 2 && mode != 3) {
fprintf(stderr, "illegal mode: %d\n", mode);
exit(EXIT_FAILURE);
}
break;
case '?':
fprintf(stderr, "illegal options\n");
exit(EXIT_FAILURE);
default:
abort();
}
}
if (configfile == NULL) {
fin = stdin;
} else {
fin = fopen(configfile, "r");
if (fin == NULL) {
handle_error("fopen");
}
}
i = 0;
while ((c = getline(&linebuf, &buflen, fin)) != -1) {
if (c <= 1 || linebuf[0] == '#')
continue;
if (i == 0) {
if (sscanf(linebuf, "%d", &nstates) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
prior = (float *) malloc(sizeof(float) * nstates);
if (prior == NULL) handle_error("malloc");
trans = (float *) malloc(sizeof(float) * nstates * nstates);
if (trans == NULL) handle_error("malloc");
xi = (float *) malloc(sizeof(float) * nstates * nstates);
if (xi == NULL) handle_error("malloc");
pi = (float *) malloc(sizeof(float) * nstates);
if (pi == NULL) handle_error("malloc");
} else if (i == 1) {
if (sscanf(linebuf, "%d", &nobvs) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
obvs = (float *) malloc(sizeof(float) * nstates * nobvs);
if (obvs == NULL) handle_error("malloc");
gmm = (float *) malloc(sizeof(float) * nstates * nobvs);
if (gmm == NULL) handle_error("malloc");
} else if (i == 2) {
/* read initial state probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nstates; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
prior[j] = logf(d);
}
fclose(bin);
} else if (i <= 2 + nstates) {
/* read state transition probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nstates; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
trans[IDX((i - 3),j,nstates)] = logf(d);
}
fclose(bin);
} else if (i <= 2 + nstates * 2) {
/* read output probabilities */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < nobvs; j++) {
if (fscanf(bin, "%f", &d) != 1) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
obvs[IDX((i - 3 - nstates),j,nobvs)] = logf(d);
}
fclose(bin);
} else if (i == 3 + nstates * 2) {
if (sscanf(linebuf, "%d %d", &nseq, &length) != 2) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
data = (int *) malloc (sizeof(int) * nseq * length);
if (data == NULL) handle_error("malloc");
} else if (i <= 3 + nstates * 2 + nseq) {
/* read data */
bin = fmemopen(linebuf, buflen, "r");
if (bin == NULL) handle_error("fmemopen");
for (j = 0; j < length; j++) {
if (fscanf(bin, "%d", &k) != 1 || k < 0 || k >= nobvs) {
fprintf(stderr, "config file format error: %d\n", i);
freeall();
exit(EXIT_FAILURE);
}
data[(i - 4 - nstates * 2) * length + j] = k;
}
fclose(bin);
}
i++;
}
fclose(fin);
if (linebuf) free(linebuf);
if (i < 4 + nstates * 2 + nseq) {
fprintf(stderr, "configuration incomplete.\n");
freeall();
exit(EXIT_FAILURE);
}
if (mode == 3) {
loglik = (float *) malloc(sizeof(float) * nseq);
if (loglik == NULL) handle_error("malloc");
for (i = 0; i < iterations; i++) {
init_count();
for (j = 0; j < nseq; j++) {
loglik[j] = forward_backward(data + length * j, length, 1);
}
p = sumf(loglik, nseq);
update_prob();
printf("iteration %d log-likelihood: %.4f\n", i + 1, p);
printf("updated parameters:\n");
printf("# initial state probability\n");
for (j = 0; j < nstates; j++) {
printf(" %.4f", exp(prior[j]));
}
printf("\n");
printf("# state transition probability\n");
for (j = 0; j < nstates; j++) {
for (k = 0; k < nstates; k++) {
printf(" %.4f", exp(trans[IDX(j,k,nstates)]));
}
printf("\n");
}
printf("# state output probility\n");
for (j = 0; j < nstates; j++) {
for (k = 0; k < nobvs; k++) {
printf(" %.4f", exp(obvs[IDX(j,k,nobvs)]));
}
printf("\n");
}
printf("\n");
}
free(loglik);
} else if (mode == 2) {
for (i = 0; i < nseq; i++) {
viterbi(data + length * i, length);
}
} else if (mode == 1) {
loglik = (float *) malloc(sizeof(float) * nseq);
if (loglik == NULL) handle_error("malloc");
for (i = 0; i < nseq; i++) {
loglik[i] = forward_backward(data + length * i, length, 0);
}
p = sumf(loglik, nseq);
for (i = 0; i < nseq; i++)
printf("%.4f\n", loglik[i]);
printf("total: %.4f\n", p);
free(loglik);
}
freeall();
return 0;
}
bool CTrigramModel::adjusting_homonyms_coef(string FileName) const
{
FILE* fp = fopen(FileName.c_str(), "r");
if (!fp)
{
fprintf (stderr, "cannot open %s\n", FileName.c_str());
return false;
};
char buffer[10000];
int pos=0, neg=0, not_amb=0;
int SentNo = 0;
while (fgets(buffer, 10000, fp))
{
SentNo++;
vector<string> words;
vector<CWordIntepretation> tags;
vector<WORD> refs;
StringTokenizer tok(buffer, " \t\r\n");
for (size_t i=0; tok(); i++)
{
string t = tok.val();
if (i%2==0)
{
words.push_back(t);
}
else
{
WORD ti=find_tag(t);
if (ti == UnknownTag)
{
fprintf (stderr, "unknown tag \"%s\" LineNo=%i\n", t.c_str(), SentNo);
return false;
}
refs.push_back(ti);
}
}
if (words.empty()) continue;
if ( (SentNo%100)== 0)
printf ("\r%i ", SentNo);
if (!viterbi(words, tags))
return false;
for (size_t i=0; i<words.size(); i++)
{
WORD ref = refs[i];
if (tags[i].m_TagId2 == UnknownTag)
not_amb++;
if ( (tags[i].m_TagId1==ref)
|| (tags[i].m_TagId2==ref)
)
pos++;
else
{
string gs = m_RegisteredTags[tags[i].m_TagId1];
if (tags[i].m_TagId2 != UnknownTag)
gs += "["+m_RegisteredTags[tags[i].m_TagId2]+"]";
string rs = m_RegisteredTags[ref];
for (int j=(int)i-5; j<(int)min(i+5, words.size()); j++)
{
if (j<0) { continue; }
if (j != i)
fprintf(stderr, "%s ", words[j].c_str());
else
fprintf(stderr, "*%s* [guess %s ref %s] ", words[i].c_str(), gs.c_str(), rs.c_str() );
}
fprintf(stderr, "\n");
neg++;
}
}
//fprintf (stderr, "dump %d\n",pos+neg);
}
printf ("\n");
fprintf (stderr, "%d (%d+%d) words tagged, accuracy %7.3f%%, recall %7.3f%%\n",
pos+neg, pos, neg, 100.0*(prob_t)pos/(prob_t)(pos+neg), 100.0*(prob_t)not_amb/(prob_t)(pos+neg));
fclose (fp);
return true;
}
void testBaumwelch(){
int i, error, tl,z,z1,z2;
double log_p,first_prob1,first_prob2, first_prob;
double *proba;
int *path;
int* real_path;
int *path1;
int* real_path1;
int *path2;
int* real_path2;
model *mo = NULL;
sequence_t *my_output, *your_output;
int seqlen = 1000;
tl = 150;
mo = malloc(sizeof(model));
if (mo==NULL) {fprintf(stderr,"Null Pointer in malloc(model).\n");}
real_path = malloc(seqlen*sizeof(double));
if(!real_path){ printf("real_path hat kein platz gekriegt\n");}
real_path1 = malloc(seqlen*sizeof(double));
if(!real_path1){ printf("real_path hat kein platz gekriegt\n");}
real_path2 = malloc(seqlen*sizeof(double));
if(!real_path2){ printf("real_path hat kein platz gekriegt\n");}
/* generate a model with variable number of states*/
generateModel(mo, 5);
/*generate a random sequence*/
my_output = model_label_generate_sequences(mo, 0, seqlen, 10, seqlen);
for (i=0; i<seqlen; i++){
printf("%d", my_output->state_labels[0][i]);
}
printf("\n");
/*viterbi*/
path = viterbi(mo, my_output->seq[0], my_output->seq_len[0], &first_prob);
path1 = viterbi(mo, my_output->seq[1], my_output->seq_len[1], &first_prob1);
path2 = viterbi(mo, my_output->seq[2], my_output->seq_len[2], &first_prob2);
printf("\n viterbi-path\n");
z=0;
z1=0;
z2=0;
for (i=0; i<my_output->seq_len[0]; i++){
if (path1[i] != -1) {
real_path1[z1]=path1[i];
z1++;
printf("%d", path1[i]);
}
else printf("hallo");
if (path2[i] != -1) {
real_path2[z2]=path2[i];
z2++;
printf("%d", path2[i]);
}
else printf("hallo");
if (path[i] != -1) {
real_path[z]=path[i];
z++;
printf("%d", path[i]);
}
}
printf("\n");
printf("log-prob: %g\n",first_prob);
my_output->state_labels[0]=real_path;
my_output->state_labels[1]=real_path1;
my_output->state_labels[2]=real_path2;
for (i=0;i<seqlen;i++)
printf("realpath[%i]=%i",i,real_path[i]);
proba = malloc(sizeof(double)*tl);
printf("No of Sequences = %d", my_output->seq_number);
your_output = model_label_generate_sequences(mo, 0, seqlen, 1, seqlen);
error = gradient_descent(&mo, your_output, .02, i);
path = viterbi(mo, my_output->seq[0], my_output->seq_len[0], &log_p);
free(path);
/*reestimate_baum_welch_label(mo, my_output);*/
/*reestimate_baum_welch(mo, my_output);*/
/*reruns viterbi to check the training*/
printf("run viterbi second\n");
path = viterbi(mo, my_output->seq[0], my_output->seq_len[0], &log_p);
for (i=0; i<(my_output->seq_len[0]*mo->N); i++){
if (path[i] != -1) {printf("%d", path[i]);}
}
printf("\n");
printf("log-prob: %g\n",log_p);
/* freeing memory */
model_free(&mo);
free(path);
/*printf("sequence_free success: %d\n", */sequence_free(&my_output)/*)*/;
free(my_output);
}
// viterbi algorithm
LABEL viterbi_loss(PATTERN x, LABEL y, STRUCTMODEL *sm, STRUCT_LEARN_PARM *sparm, int loss_bool)
{
/*
// record the path (end in last state) points to this state will make max value
int viterbi_table[x.frame_num][STATE_TYPES]; // t from 1 to x.frame_num
float max_val_now[STATE_TYPES];
float max_val_last[STATE_TYPES];
//initial table
int w;
int j;
for (j = 0; j < x.frame_num; j++){
for (w = 0; w < STATE_TYPES; ++w)
viterbi_table[j][w] = 0;
}
for (w = 0; w < STATE_TYPES; ++w){
max_val_now[w] = 0;
max_val_last[w] = 0;
}
double* Wobserve = & ( sm->w[1] );
double* Wtrans = & ( sm->w[OBSERVE_ELEMENT_DIM*STATE_TYPES + 1] );
// + OBSERVE_ELEMENT_DIM : add dummy hint
//printf(" w[OBSERVE_ELEMENT_DIM*STATE_TYPES + 1] : %lf \n", sm->w[OBSERVE_ELEMENT_DIM*STATE_TYPES + 2]);
int t;
for (t = 0; t < x.frame_num; ++t){
int s;
// observation element in time t
if (t == 0){
for (s = 0; s < STATE_TYPES; ++s){
// for each state type s
// Wobserve*psi
int i;
for (i = 0; i < OBSERVE_ELEMENT_DIM; ++i)
max_val_now[s] += Wobserve[s*OBSERVE_ELEMENT_DIM + i]*x.observe_object[t][i];
if (loss_bool)
{
if (s != y.state_object[t])
// max_val_now[s] += 1.0/(double)(y.frame_num);
max_val_now[s] ++;
}
}
}
else{
for (s = 0; s < STATE_TYPES; ++s){
// for each state type s
double temp_max_value = -1000000;
int max_path_index = 0;
int p;
// for every path p cal max_value(p) last time + Wtrans*psi@[a -> b]
for (p = 0; p < STATE_TYPES; ++p){
double path_value = 0;
path_value = max_val_last[p] + Wtrans[p*STATE_TYPES + s]*1.0 ; // ex : a->b
if (path_value > temp_max_value){
temp_max_value = path_value;
max_path_index = p;
}
}
// build viterbi table
viterbi_table[t][s] = max_path_index;
max_val_now[s] = temp_max_value;
// add Wobserve*psi @ time t
int i;
for (i = 0; i < OBSERVE_ELEMENT_DIM; ++i)
max_val_now[s] += Wobserve[s*OBSERVE_ELEMENT_DIM + i]*x.observe_object[t][i];
if (loss_bool){
if (s != y.state_object[t])
// max_val_now[s] += 1.0/(double)(y.frame_num);
max_val_now[s]++;
}
}
}
//printf("time %d:\n",t);
for (w = 0; w < STATE_TYPES; ++w)
{
//printf(" %d %d \n",w, max_val_now[w]);
max_val_last[w] = max_val_now[w];
}
}
// find max path
int max_end_index = 0;
float max = -1000000;
int s;
for (s = 0; s < STATE_TYPES; ++s){
if (max_val_now[s] > max)
{
max = max_val_now[s];
max_end_index = s;
}
}
//printf("max : %f \n", max);
// back trace max path and build y_max
LABEL y_max;
y_max.state_object = (int *)malloc(sizeof(int)*MAX_ELEMENT_IN_OBSERVE);
y_max.speaker = (char *)malloc(sizeof(char)*20);
y_max.frame_num = x.frame_num;
y_max.state_object[y_max.frame_num - 1] = max_end_index;
int predict_state = max_end_index;
for (t = x.frame_num - 1; t > 0; --t){
predict_state = viterbi_table[t][predict_state];
y_max.state_object[t-1] = predict_state;
}*/
double** emit = new double*[x.frame_num];
for (int i = 0; i < x.frame_num; i++){
emit[i] = new double[STATE_TYPES];
}
double** trans = new double*[STATE_TYPES];
for (int i = 0; i < STATE_TYPES; i++){
trans[i] = new double[STATE_TYPES];
}
for (int i = 0; i < x.frame_num; i++){
for (int j = 0; j < STATE_TYPES; j++){
int sum = 0;
for (int k = 0; k < OBSERVE_ELEMENT_DIM; k++){
sum += sm->w[j*OBSERVE_ELEMENT_DIM+k] * x.observe_object[i][k];
}
emit[i][j] = exp(sum);
}
}
for (int i = 0; i < STATE_TYPES; i++){
for (int j = 0; j < STATE_TYPES; j++){
trans[i][j] = exp(sm->w[STATE_TYPES * OBSERVE_ELEMENT_DIM + i * STATE_TYPES + j]);
}
}
double* start = new double[STATE_TYPES];
for (int i = 0; i < STATE_TYPES; i++){
start[i] = sparm->start_probability[i];
}
double* end = new double[STATE_TYPES];
for (int i = 0; i < STATE_TYPES; i++){
end[i] = sparm->end_probability[i];
}
auto ret = viterbi(x.frame_num, STATE_TYPES, emit, trans, start, end);
LABEL y_max;
y_max.state_object = new int[MAX_ELEMENT_IN_OBSERVE];
y_max.frame_num = ret.size();
y_max.speaker = (char*)malloc(sizeof(char)*20);;
int i = 0;
//cout << "addr of sizePsi:" << &sm->sizePsi <<endl;
for (auto it = ret.begin(); it != ret.end(); it++){
//cerr << i << ":" << *it << " addr of state_object:" << &(y_max.state_object[i]) <<endl;
y_max.state_object[i] = *it;
/*if (sm->sizePsi != OBSERVE_ELEMENT_DIM*STATE_TYPES + STATE_TYPES*STATE_TYPES){
cout << "missmatch:" <<i << endl;
cout << &(y_max->state_object[i]) << endl;
break;
}*/
i++;
}
/*for (int i = 0; i < y_max.frame_num; i++){
cout << y_max.state_object[i] << " ";
}*/
//cout << "\nviterbi_8\n";
//sm->sizePsi = OBSERVE_ELEMENT_DIM*STATE_TYPES + STATE_TYPES*STATE_TYPES ;
//cout << endl;
return (y_max);
}
void
MP1::
viterbi(CorpusCache& cache){
double& alpha=alpha_;
for(auto& sp: cache){
vector<vector<double>> target_probs(sp.m,vector<double>(sp.l,0.0));
vector<vector<pair<int,int>>> target_best(sp.m,
vector<pair<int,int>>(sp.m));
alpha/=sp.n*sp.l;
for(int j=0;j<sp.m;j++){
for(int jlen=0;jlen<sp.l;jlen++){
for(int i=0;i<sp.n;i++){
for(int ilen=0;ilen<sp.l;ilen++){
if(sp(i,ilen,j,jlen)!=(void*)0){
if(target_probs[j][jlen]<
sp(i,ilen,j,jlen)->prob*alpha){
target_probs[j][jlen]=
sp(i,ilen,j,jlen)->prob*alpha;
target_best[j][jlen]=make_pair(i,ilen);
}
if(sp(i,ilen,j,jlen)->prob>1){
cerr<<"wth prob>1 : "
<<sp(i,ilen,j,jlen)->prob<<endl;
}
}
}
}
//cout<<target_probs[j][jlen]<<" ";
}
//cout<<endl;
}
//cout<<endl;
for(int j=0;j<sp.m;j++){
for(int jlen=0;jlen<sp.l;jlen++){
cerr<<"("<<j<<","<<jlen<<")=>("
<<target_best[j][jlen].first
<<" "<<target_best[j][jlen].second<<") ";
}
cerr<<endl;
}
//viterbi
vector<pair<double,int> > viterbi(sp.m,pair<double,int>(0.0,0));
for(int i=0;i<sp.l&&i<sp.m;i++){
viterbi[i]=pair<double,int>(target_probs[0][i],-1);
}
for(int i=1;i<(int)viterbi.size();i++){
for(int j=1;j<=sp.l&&i-j>=0;j++){
if(viterbi[i-j].first*target_probs[i-j+1][j-1]>viterbi[i].first)
viterbi[i]=
make_pair(viterbi[i-j].first*target_probs[i-j+1][j-1],i-j);
}
}
int pos=sp.m-1;
string sequence="";
string source="";
while(pos>=0){
sequence=to_string(viterbi[pos].second+1)+","+to_string(pos)
+" "+sequence;
cout<<viterbi[pos].second+1<<","<<pos-viterbi[pos].second<<endl;
pair<int,int> srcpair=
target_best[viterbi[pos].second+1][pos-viterbi[pos].second];
source=to_string(srcpair.first)+","+
to_string(srcpair.second)
+" "+source;
pos=viterbi[pos].second;
}
cout<<"best seg:"<<sequence<<endl;
cout<<" src:"<<source<<endl;
}
}
void MP1::
expectation(CorpusCache& cache){
double& alpha=alpha_;
double alphaCount=0;
for(auto& sp: cache){
vector<vector<double>> target_probs(sp.m,vector<double>(sp.l,0.0));
alpha/=sp.n*sp.l;
for(int j=0;j<sp.m;j++){
for(int jlen=0;jlen<sp.l;jlen++){
for(int i=0;i<sp.n;i++){
for(int ilen=0;ilen<sp.l;ilen++){
if(sp(i,ilen,j,jlen)!=(void*)0){
target_probs[j][jlen]+=
(sp(i,ilen,j,jlen)->prob*alpha);
if(sp(i,ilen,j,jlen)->prob>1){
cerr<<"wth prob>1 : "
<<sp(i,ilen,j,jlen)->prob<<endl;
}
}
}
}
//cout<<target_probs[j][jlen]<<" ";
}
//cout<<endl;
}
//cout<<endl;
for(int j=0;j<sp.m;j++){
for(int jlen=0;jlen<sp.l;jlen++){
if(target_probs[j][jlen]>1)
cerr<<"error :"<<target_probs[j][jlen]<<endl;
if(target_probs[j][0]==0){
target_probs[j][0]=1E-7;
//cerr<<"reset error target["<<j<<",0]"<<endl;
}
}
}
vector<LogProb> forward(sp.m,0.0),backward(sp.m,0.0);
if(specs.logEM){
for(auto& i:forward)i=-1E10;
for(auto& i:backward)i=-1E10;
}
//forward[i] is the posterior probability of target words of 1...i+1
for(int i=0;i<sp.l&&i<sp.m;i++)
forward[i]=target_probs[0][i];
for(int i=1;i<(int)forward.size();i++){
for(int j=1;j<=sp.l&&i-j>=0;j++){
forward[i]+=forward[i-j]*(LogProb)target_probs[i-j+1][j-1];
}
}
//backward[i] is the posterior probability of target words of i+1...m
for(int i=0;i<sp.l&&i<sp.m;i++)
backward[sp.m-i-1]=target_probs[sp.m-i-1][i];
for(int i=sp.m-2;i>=0;i--){
for(int j=1;j<=sp.l&&i+j<sp.m;j++){
backward[i]+=(LogProb)target_probs[i][j-1]*backward[i+j];
}
}
//viterbi
vector<pair<double,int> > viterbi(sp.m,pair<double,int>(0.0,0));
for(int i=0;i<sp.l&&i<sp.m;i++)
viterbi[i]=pair<double,int>(target_probs[0][i],-1);
for(int i=1;i<(int)forward.size();i++){
for(int j=1;j<=sp.l&&i-j>=0;j++){
if(viterbi[i-j].first*target_probs[i-j+1][j-1]>viterbi[i].first)
viterbi[i]=
make_pair(viterbi[i-j].first*target_probs[i-j+1][j-1],i-j);
}
}
int pos=sp.m-1;
string sequence="";
while(pos>=0){
sequence=to_string(pos)+" "+sequence;
pos=viterbi[pos].second;
}
//cout<<"best seg:"<<sequence<<endl;
//make sure forward[sp.m-1]==backward[0];
assert(backward[0]>(LogProb)0);
if(abs(forward[sp.m-1]-backward[0])>=(LogProb)1e-5*backward[0])
cerr<<forward[sp.m-1]<<", "<<backward[0]<<endl;
assert(abs(forward[sp.m-1]-backward[0])<(LogProb)1e-5*backward[0]);
//cerr<<"backward[0]:"<<backward[0]<<endl;
//collect fractional count for each phrase pair
//fraccount=forward[j]*backward[j+jlen]*p(t|s)/backward[0];
for(int j=0;j<sp.m;j++){
for(int jlen=0;jlen<sp.l&&j+jlen+1<=sp.m;jlen++){
double segprob=0;
LogProb before=1;
LogProb after=1;
if(j>0)before=forward[j-1];
if(j+jlen+1<sp.m)after=backward[j+jlen+1];
segprob=before*after*(LogProb)target_probs[j][jlen]
/backward[0];
if(segprob>1||segprob<=0){
//cerr<<"segprob "<<segprob<<","<<j<<","<<jlen<<endl;
}
if(segprob<=0)continue;
for(int i=0;i<sp.n;i++){
for(int ilen=0;ilen<sp.l&&ilen+i+1<=sp.n;ilen++){
if(sp(i,ilen,j,jlen)!=(void*)0){
double count=sp(i,ilen,j,jlen)->prob*segprob*alpha
/target_probs[j][jlen];
sp(i,ilen,j,jlen)->count+=count;
//cerr<<"before update"<<endl;
//sp(i,ilen,j,jlen)->fractype.print(cerr);
sp(i,ilen,j,jlen)->fractype.updateLog(count);
//cerr<<"after update"<<endl;
//sp(i,ilen,j,jlen)->fractype.print(cerr);
alphaCount+=count;
if(count>1+1e-5)
cerr<<i<<","<<ilen<<","<<j
<<","<<jlen<<" ["<<sp.m<<","<<sp.n<<"]"
<<",count "<<count
<<", target probs "
<<target_probs[j][jlen]<<endl;
}
}
}
}
}
alpha*=sp.n*sp.l;
}
//cerr<<alphaCount<<","<<cache.size()<<endl;
alpha=alphaCount/(alphaCount+cache.size());
}
/******************************************************************************
mergingAndClustering() : Clustering of GMMs and Realignment; Merging of states
inputs :
features - pointer to all feature vectors, allMixtureMeans, allMixtureVars
numstates, numMixEachState, totalNumFeatures,
posterior - posterior probability matrix, mixtureElemCount - Element count in each mixture
outputs : Perform viterbi realignment, clustering and merging of states or GMMs
******************************************************************************/
void ClusteringAndMerging(VECTOR_OF_F_VECTORS *features, VECTOR_OF_F_VECTORS *allMixtureMeans,
VECTOR_OF_F_VECTORS *allMixtureVars, int *numStates, int *numMixEachState,
int totalNumFeatures, float **posterior, float **mixtureElemCount,
int *numElemEachState, float *Pi, float *mixtureWeight)
{
// local Variable declaration
int VQIter = 7, GMMIter = 23, mergeIter=0, maxMergeIter = 16;
int viterbiIter = 5;
int i = 0, j = 0, k = 0,s= 0, flag = 1;
float *T1[MAX_NUM_STATES];
int *T2[MAX_NUM_STATES];
int *newStateSeq;
float *deltaBIC[MAX_NUM_STATES];
for(i = 0; i < (*numStates); i++){
deltaBIC[i] = (float *)calloc((*numStates), sizeof(float));
}
for(i = 0; i < (*numStates); i++){
T1[i] = (float *)calloc(totalNumFeatures, sizeof(float));
T2[i] = (int *)calloc(totalNumFeatures, sizeof(int));
}
// Merge two GMM States or two mixture models
//perform the steps repeatedly till there are no more states to be merged
while(flag && mergeIter < maxMergeIter){
printf("\nClustering and Merging Iteration: %d...........\n", mergeIter);
printf("starting with, States: %d \n", *numStates);
mergeIter++;
for(s = 0; s < *numStates; s++)
printf("state[%d] : %d \n", s, numElemEachState[s]);
for(i = 0; i < viterbiIter; i++){
//perform Viterbi Realignment
printf("performing viterbi realignment....\n");
newStateSeq = viterbi( features , numStates, allMixtureMeans, allMixtureVars, numMixEachState,
totalNumFeatures, posterior, numElemEachState, T1, T2,
Pi, mixtureWeight);
printf("Viterbi realignment has successfully completed...\n");
//find number of elements in each state
//FindNumberOfElemInEachState(newStateSeq, numStates, totalNumFeatures, numElemEachState, Pi);
//perform GMM clustering for all states
for(s = 0; s < (*numStates); s++){
// find all elements present in state s using viterbi_realignment
int count = 0, d = 0; // to count number of features in a state
for(j = 0; j < totalNumFeatures; j++){
if(newStateSeq[j] == s){
for(d = 0; d < DIM; d++){
featuresForClustering[count]->array[d] = features[j]->array[d];
featuresForClustering[count]->numElements = DIM;
}
count++;//increment count
}
}
extern VECTOR_OF_F_VECTORS *mixtureMeans, *mixtureVars;
extern float probScaleFactor;
extern int varianceNormalize, ditherMean;
int numFeatures = count;
//printf("count: %d num: %d\n", count, numElemEachState[s]);
int numMix = numMixEachState[s];
// Cluster using GMM
ComputeGMM(featuresForClustering, numFeatures, mixtureMeans, mixtureVars,
mixtureElemCount[s], numMix , VQIter, GMMIter, probScaleFactor,
ditherMean, varianceNormalize, time(NULL));
int mixCount = 0, k=0;
//store current mean and variance into all means and variance
for(j = 0; j < s; j++)
mixCount += numMixEachState[j];
for(j = mixCount; j < mixCount + numMixEachState[s]; j++){
for(k = 0; k < DIM; k++){
allMixtureMeans[j]->array[k] = mixtureMeans[j-mixCount]->array[k];
allMixtureVars[j]->array[k] = mixtureVars[j-mixCount]->array[k];
}
float mix_wt = mixtureElemCount[s][j-mixCount] / numElemEachState[s];
mixtureWeight[j] = mix_wt;
}
}//GMM Clustering for each state has completed
//calculate posterior probabilities
PrintAllDetails(numStates, numMixEachState, numElemEachState,
mixtureElemCount, allMixtureMeans, mixtureWeight);
ComputePosteriorProb(features, posterior, allMixtureMeans, allMixtureVars, numStates,
numMixEachState, totalNumFeatures, mixtureWeight);
}
// PrintAllDetails(numStates, numMixEachState, numElemEachState,
// mixtureElemCount, allMixtureMeans, mixtureWeight);
// Calculate BIC Value between Each state
CalculateBIC(deltaBIC, features , allMixtureMeans, allMixtureVars, numStates, totalNumFeatures,
newStateSeq, numMixEachState , numElemEachState, posterior);
int min_i =0, min_j = 0;
float min = 99999999;
// find minimum delta BIC states
for(j = 0; j < *numStates; j++){
for(k = j+1; k < *numStates; k++){
if(deltaBIC[j][k] < min){
min = deltaBIC[j][k];
min_i = j;
min_j = k;
}
}
}
printf("min_i: %d min_j: %d minDBIC: %f\n", min_i, min_j, deltaBIC[min_i][min_j]);
// if no two states can be merged set the flag to False to terminate process
if(deltaBIC[min_i][min_j] >= 0){
printf("We need to stop now ..... Final no of states: %d\n", *numStates);
flag = 0;
}
// Merge two states
if(flag)
MergeTwoStates( min_i, min_j, allMixtureMeans, allMixtureVars, numStates, numMixEachState,
numElemEachState, Pi, totalNumFeatures,
features, newStateSeq, mixtureWeight);
/*printf("Means after Merging of two states.................\n");
PrintAllDetails(numStates, numMixEachState, numElemEachState,
mixtureElemCount, allMixtureMeans);*/
for(i = 0; i < *numStates; i++)
printf("mix[%d]: %d\n", i, numMixEachState[i]);
}
//WRITE PLOT_File to plot distribution of each data point
writePlotFile(posterior, totalNumFeatures, numStates);
//WRITE RTTM FILE
writeRTTMFile(newStateSeq, numStates, totalNumFeatures, numElemEachState);
}
// process the given utterance
// - handles multiple pronunciations
// - handles optional symbols (typically silence+fillers)
Alignment *ViterbiX::processUtterance(VLexUnit &vLexUnitTranscription, bool bMultiplePronunciations,
VLexUnit &vLexUnitOptional, MatrixBase<float> &mFeatures, double *dUtteranceLikelihood, int &iErrorCode) {
//double dTimeBegin = TimeUtils::getTimeMilliseconds();
// make sure there is at least one lexical unit to align to
if (vLexUnitTranscription.empty()) {
iErrorCode = ERROR_CODE_EMPTY_TRANSCRIPTION;
return NULL;
}
// create the HMM-graph
int iNodes = -1;
int iEdges = -1;
FBNodeHMM *nodeInitial = NULL;
FBNodeHMM *nodeFinal = NULL;
HMMGraph *hmmGraph = new HMMGraph(m_phoneSet,m_lexiconManager,
m_hmmManager,m_hmmManager,bMultiplePronunciations,vLexUnitOptional);
FBNodeHMM **nodes = hmmGraph->create(vLexUnitTranscription,&iNodes,&iEdges,&nodeInitial,&nodeFinal);
if (nodes == NULL) {
delete hmmGraph;
iErrorCode = ERROR_CODE_UNABLE_TO_CREATE_HMM_GRAPH;
return NULL;
}
delete hmmGraph;
assert(nodeInitial->iDistanceEnd % NUMBER_HMM_STATES == 0);
// there can't be fewer feature vectors than HMM-states in the composite
int iFeatures = mFeatures.getRows();
if (iFeatures < nodeInitial->iDistanceEnd) {
HMMGraph::destroy(nodes,iNodes);
iErrorCode = ERROR_CODE_INSUFFICIENT_NUMBER_FEATURE_VECTORS;
return NULL;
}
// reset the emission probability computation (to avoid using cached computations that are outdated)
VHMMStateDecoding vHMMState;
for(int i=0 ; i < iNodes ; ++i) {
for(FBEdgeHMM *edge = nodes[i]->edgeNext ; edge != NULL ; edge = edge->edgePrev) {
assert(edge->hmmStateEstimation != NULL);
vHMMState.push_back((HMMStateDecoding*)edge->hmmStateEstimation);
}
}
m_hmmManager->resetHMMEmissionProbabilityComputation(vHMMState);
// do the actual Viterbi pass
int iErrorCodeFB = -1;
VTrellisNode *trellis = viterbi(mFeatures,iNodes,nodes,iEdges,
nodeInitial,nodeFinal,m_fPruningBeam,&iErrorCodeFB);
if (trellis == NULL) {
HMMGraph::destroy(nodes,iNodes);
iErrorCode = iErrorCodeFB;
return NULL;
}
// get the maximum viterbi score from the terminal edges
double dViterbiBest = -DBL_MAX;
FBEdgeHMM *edgeBest = NULL;
for(FBEdgeHMM *edge = nodeFinal->edgePrev ; edge != NULL ; edge = edge->edgeNext) {
if (trellis[(iFeatures-1)*iEdges+edge->iEdge].dViterbi > dViterbiBest) {
edgeBest = edge;
dViterbiBest = trellis[(iFeatures-1)*iEdges+edge->iEdge].dViterbi;
}
}
assert(edgeBest != NULL);
// utterance likelihood
assert(dViterbiBest != -DBL_MAX);
assert(edgeBest != NULL);
double dLikelihoodUtterance = dViterbiBest;
//countUnusedPositions(trellis,iFeatures,iNodes);
Alignment *alignment = new Alignment(ALIGNMENT_TYPE_VITERBI);
FBEdgeHMM *edgeTmp = edgeBest;
int iState = 0;
int iFrameEnd = -1;
int iStatesLeft = 1;
LexUnit *lexUnit = NULL;
for(int t = iFeatures-1 ; t >= 0 ; --t) {
FBEdgeHMM *edgePrevBest = NULL;
double dViterbiPrevBest = -DBL_MAX;
// self-transition
if (trellis[t*iEdges+edgeTmp->iEdge].dViterbi > dViterbiPrevBest) {
dViterbiPrevBest = trellis[t*iEdges+edgeTmp->iEdge].dViterbi;
edgePrevBest = edgeTmp;
}
// previous nodes
for(FBEdgeHMM *edge = edgeTmp->nodePrev->edgePrev ; edge != NULL ; edge = edge->edgeNext) {
if (trellis[t*iEdges+edge->iEdge].dViterbi != -DBL_MAX) {
if (trellis[t*iEdges+edge->iEdge].dViterbi > dViterbiPrevBest) {
dViterbiPrevBest = trellis[t*iEdges+edge->iEdge].dViterbi;
edgePrevBest = edge;
}
}
}
HMMStateDecoding *hmmStateDecoding = (HMMStateDecoding*)edgePrevBest->hmmStateUpdate;
// state-level alignment
VStateOcc *vStateOcc = new VStateOcc;
vStateOcc->push_back(Alignment::newStateOcc(hmmStateDecoding->getId(),1.0));
alignment->addFrameAlignmentFront(vStateOcc);
// word-level alignment
if (hmmStateDecoding->getState() != iState) {
--iStatesLeft;
}
iState = hmmStateDecoding->getState();
if (iStatesLeft == 0) {
iStatesLeft = (int)(edgePrevBest->lexUnit->vPhones.size()*NUMBER_HMM_STATES);
if (lexUnit != NULL) {
alignment->addLexUnitAlignmentFront(t+1,iFrameEnd,lexUnit);
}
iFrameEnd = t;
lexUnit = edgePrevBest->lexUnit;
}
edgeTmp = edgePrevBest;
}
if ((iStatesLeft == 1) && (lexUnit != NULL)) {
alignment->addLexUnitAlignmentFront(0,iFrameEnd,lexUnit);
}
// TODO if multiple pronunciations are allowed the alternatives at each edge might be wrong due to the
// path recombination in HMMGraph, this needs to be addressed
//double dTimeEnd = TimeUtils::getTimeMilliseconds();
//double dTimeSeconds = (dTimeEnd-dTimeBegin)/1000.0;
//printf("alignment: %12.4f (%12.4f seconds)\n",dViterbiBest,dTimeSeconds);
// clean-up
deleteTrellis(trellis);
HMMGraph::destroy(nodes,iNodes);
*dUtteranceLikelihood = dLikelihoodUtterance;
iErrorCode = UTTERANCE_PROCESSED_SUCCESSFULLY;
return alignment;
}