/** Prints one decomposition to stdout */
void printDecomposition(const Alphabet& alphabet, const decomposition_t& decomposition)
{
assert(alphabet.size() == decomposition.size());
bool first = true;
for (Alphabet::size_type i = 0; i < alphabet.size(); ++i) {
if (decomposition[i] > 0) {
if (!first) {
cout << " ";
} else {
first = false;
}
cout << alphabet.getName(i) << decomposition[i];
}
}
}
otws_t OTWS_Load(const char *cfg_file) {
// allocate config parser and set default config.
ltp_configure *cfg = new ltp_configure();
cfg->set_cfg("target", "test");
cfg->set_cfg("agenda", "1");
cfg->set_cfg("model", "./");
cfg->set_cfg("dict", "./");
// load config.
if (-1 == cfg->load_cfg(cfg_file)) {
WARNING_LOG("Failed to load config file");
delete cfg;
return NULL;
}
OTWS_Engine *engine = new OTWS_Engine();
// allocate alphabet
Alphabet *features = new HashDict();
Alphabet *labels = new HashDict();
Alphabet *words = new HashDict();
Parameter *param = new CParameter(0, 0);
// load model.
Model *model = new Model();
model->registAlphabet("FEATURES", features);
model->registAlphabet("LABELS", labels);
model->registParameter("PARAMETER", param);
model->loadModel(cfg->config("model").c_str());
TRACE_LOG("Loading model is done.");
TRACE_LOG("Num Features: %d", features->size());
TRACE_LOG("Num Labels: %d", labels->size());
Extractor *extractor = new SegmentExtractor(
cfg->config("dict").c_str(),
features, labels, words);
Decoder *decoder = new SegmentDecoder(model, 1);
engine->model = model;
engine->extractor = extractor;
engine->decoder = decoder;
return reinterpret_cast<otws_t>(engine);
}
// ----------------------------------------------
// PLAIN DECODER
// ----------------------------------------------
SegmentDecoder::SegmentDecoder(
Model *model,
int agenda) {
this->m_Model = model;
this->m_Agenda = agenda;
Alphabet *labelAlpha = model->getAlphabet("LABELS");
this->m_NumLabels = labelAlpha->size();
m_Legal = new int *[m_NumLabels + 1];
for (int i = 0; i <= m_NumLabels; ++ i) {
char prev = 'X';
if (i < m_NumLabels)
prev = labelAlpha->rlookup(i)[0];
m_Legal[i] = new int[m_NumLabels];
for (int j = 0; j < m_NumLabels; ++ j) {
char curr = labelAlpha->rlookup(j)[0];
m_Legal[i][j] = 0;
if ((prev == 'X' || prev == 'S' || prev == 'E')
&& (curr == 'S' || curr == 'B'))
m_Legal[i][j] = 1;
if ((prev == 'M' || prev == 'B') &&
(curr == 'M' || curr == 'E'))
m_Legal[i][j] = 1;
}
}
}
ProfileFelsenstein::ProfileFelsenstein(const MultSeqAlignment &A,
const Alphabet &alphabet,
const AlphabetMap &alphabetMap)
: alignment(A), alphabet(alphabet), numAlpha(alphabet.size()),
alphabetMap(alphabetMap)
{
// ctor
}
unsigned convertNmerCode(unsigned rawCode,int seqLength,const Alphabet &alphabet)
{
Sequence S;
S.resize(seqLength);
unsigned base=alphabet.size();
for(int i=seqLength-1 ; i>=0 ; --i) {
unsigned digit=rawCode%base;
Symbol s=(int)digit;
S[i]=s;
rawCode/=base;
}
return S.asInt(alphabet,0,S.getLength());
}
void Segmentor::readEmbeddings(Alphabet &alpha, const string& inFile, NRMat<dtype>& emb) {
static ifstream inf;
if (inf.is_open()) {
inf.close();
inf.clear();
}
inf.open(inFile.c_str());
static string strLine, curWord;
static int wordId;
//find the first line, decide the wordDim;
while (1) {
if (!my_getline(inf, strLine)) {
break;
}
if (!strLine.empty())
break;
}
int unknownId = alpha.from_string(m_classifier.fe.unknownkey);
static vector<string> vecInfo;
split_bychar(strLine, vecInfo, ' ');
int wordDim = vecInfo.size() - 1;
std::cout << "embedding dim is " << wordDim << std::endl;
emb.resize(alpha.size(), wordDim);
emb = 0.0;
curWord = normalize_to_lowerwithdigit(vecInfo[0]);
wordId = alpha.from_string(curWord);
hash_set<int> indexers;
dtype sum[wordDim];
int count = 0;
bool bHasUnknown = false;
if (wordId >= 0) {
count++;
if (unknownId == wordId)
bHasUnknown = true;
indexers.insert(wordId);
for (int idx = 0; idx < wordDim; idx++) {
dtype curValue = atof(vecInfo[idx + 1].c_str());
sum[idx] = curValue;
emb[wordId][idx] = curValue;
}
} else {
for (int idx = 0; idx < wordDim; idx++) {
sum[idx] = 0.0;
}
}
while (1) {
if (!my_getline(inf, strLine)) {
break;
}
if (strLine.empty())
continue;
split_bychar(strLine, vecInfo, ' ');
if (vecInfo.size() != wordDim + 1) {
std::cout << "error embedding file" << std::endl;
}
curWord = normalize_to_lowerwithdigit(vecInfo[0]);
wordId = alpha.from_string(curWord);
if (wordId >= 0) {
count++;
if (unknownId == wordId)
bHasUnknown = true;
indexers.insert(wordId);
for (int idx = 0; idx < wordDim; idx++) {
dtype curValue = atof(vecInfo[idx + 1].c_str());
sum[idx] += curValue;
emb[wordId][idx] += curValue;
}
}
}
if (!bHasUnknown) {
for (int idx = 0; idx < wordDim; idx++) {
emb[unknownId][idx] = sum[idx] / count;
}
count++;
std::cout << unknownkey << " not found, using averaged value to initialize." << std::endl;
}
int oovWords = 0;
int totalWords = 0;
for (int id = 0; id < alpha.size(); id++) {
if (indexers.find(id) == indexers.end()) {
oovWords++;
for (int idx = 0; idx < wordDim; idx++) {
emb[id][idx] = emb[unknownId][idx];
}
}
totalWords++;
}
std::cout << "OOV num is " << oovWords << ", total num is " << alpha.size() << ", embedding oov ratio is " << oovWords * 1.0 / alpha.size()
<< std::endl;
}
