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Diffstat (limited to 'gi/pf/transliterations.cc')
| -rw-r--r-- | gi/pf/transliterations.cc | 334 |
1 files changed, 0 insertions, 334 deletions
diff --git a/gi/pf/transliterations.cc b/gi/pf/transliterations.cc deleted file mode 100644 index b2996f65..00000000 --- a/gi/pf/transliterations.cc +++ /dev/null @@ -1,334 +0,0 @@ -#include "transliterations.h" - -#include <iostream> -#include <vector> - -#include "boost/shared_ptr.hpp" - -#include "backward.h" -#include "filelib.h" -#include "tdict.h" -#include "trule.h" -#include "filelib.h" -#include "ccrp_nt.h" -#include "m.h" -#include "reachability.h" - -using namespace std; -using namespace std::tr1; - -struct TruncatedConditionalLengthModel { - TruncatedConditionalLengthModel(unsigned max_src_size, unsigned max_trg_size, double expected_src_to_trg_ratio) : - plens(max_src_size+1, vector<prob_t>(max_trg_size+1, 0.0)) { - for (unsigned i = 1; i <= max_src_size; ++i) { - prob_t z = prob_t::Zero(); - for (unsigned j = 1; j <= max_trg_size; ++j) - z += (plens[i][j] = prob_t(0.01 + exp(Md::log_poisson(j, i * expected_src_to_trg_ratio)))); - for (unsigned j = 1; j <= max_trg_size; ++j) - plens[i][j] /= z; - //for (unsigned j = 1; j <= max_trg_size; ++j) - // cerr << "P(trg_len=" << j << " | src_len=" << i << ") = " << plens[i][j] << endl; - } - } - - // return p(tlen | slen) for *chunks* not full words - inline const prob_t& operator()(int slen, int tlen) const { - return plens[slen][tlen]; - } - - vector<vector<prob_t> > plens; -}; - -struct CondBaseDist { - CondBaseDist(unsigned max_src_size, unsigned max_trg_size, double expected_src_to_trg_ratio) : - tclm(max_src_size, max_trg_size, expected_src_to_trg_ratio) {} - - prob_t operator()(const vector<WordID>& src, unsigned sf, unsigned st, - const vector<WordID>& trg, unsigned tf, unsigned tt) const { - prob_t p = tclm(st - sf, tt - tf); // target len | source length ~ TCLM(source len) - assert(!"not impl"); - return p; - } - inline prob_t operator()(const vector<WordID>& src, const vector<WordID>& trg) const { - return (*this)(src, 0, src.size(), trg, 0, trg.size()); - } - TruncatedConditionalLengthModel tclm; -}; - -// represents transliteration phrase probabilities, e.g. -// p( a l - | A l ) , p( o | A w ) , ... -struct TransliterationChunkConditionalModel { - explicit TransliterationChunkConditionalModel(const CondBaseDist& pp0) : - d(0.0), - strength(1.0), - rp0(pp0) { - } - - void Summary() const { - std::cerr << "Number of conditioning contexts: " << r.size() << std::endl; - for (RuleModelHash::const_iterator it = r.begin(); it != r.end(); ++it) { - std::cerr << TD::GetString(it->first) << " \t(\\alpha = " << it->second.alpha() << ") --------------------------" << std::endl; - for (CCRP_NoTable<TRule>::const_iterator i2 = it->second.begin(); i2 != it->second.end(); ++i2) - std::cerr << " " << i2->second << '\t' << i2->first << std::endl; - } - } - - int DecrementRule(const TRule& rule) { - RuleModelHash::iterator it = r.find(rule.f_); - assert(it != r.end()); - int count = it->second.decrement(rule); - if (count) { - if (it->second.num_customers() == 0) r.erase(it); - } - return count; - } - - int IncrementRule(const TRule& rule) { - RuleModelHash::iterator it = r.find(rule.f_); - if (it == r.end()) { - it = r.insert(make_pair(rule.f_, CCRP_NoTable<TRule>(strength))).first; - } - int count = it->second.increment(rule); - return count; - } - - void IncrementRules(const std::vector<TRulePtr>& rules) { - for (int i = 0; i < rules.size(); ++i) - IncrementRule(*rules[i]); - } - - void DecrementRules(const std::vector<TRulePtr>& rules) { - for (int i = 0; i < rules.size(); ++i) - DecrementRule(*rules[i]); - } - - prob_t RuleProbability(const TRule& rule) const { - prob_t p; - RuleModelHash::const_iterator it = r.find(rule.f_); - if (it == r.end()) { - p = rp0(rule.f_, rule.e_); - } else { - p = it->second.prob(rule, rp0(rule.f_, rule.e_)); - } - return p; - } - - double LogLikelihood(const double& dd, const double& aa) const { - if (aa <= -dd) return -std::numeric_limits<double>::infinity(); - //double llh = Md::log_beta_density(dd, 10, 3) + Md::log_gamma_density(aa, 1, 1); - double llh = //Md::log_beta_density(dd, 1, 1) + - Md::log_gamma_density(dd + aa, 1, 1); - std::tr1::unordered_map<std::vector<WordID>, CCRP_NoTable<TRule>, boost::hash<std::vector<WordID> > >::const_iterator it; - for (it = r.begin(); it != r.end(); ++it) - llh += it->second.log_crp_prob(aa); - return llh; - } - - struct AlphaResampler { - AlphaResampler(const TransliterationChunkConditionalModel& m) : m_(m) {} - const TransliterationChunkConditionalModel& m_; - double operator()(const double& proposed_strength) const { - return m_.LogLikelihood(m_.d, proposed_strength); - } - }; - - void ResampleHyperparameters(MT19937* rng) { - std::tr1::unordered_map<std::vector<WordID>, CCRP_NoTable<TRule>, boost::hash<std::vector<WordID> > >::iterator it; - //const unsigned nloop = 5; - const unsigned niterations = 10; - //DiscountResampler dr(*this); - AlphaResampler ar(*this); -#if 0 - for (int iter = 0; iter < nloop; ++iter) { - strength = slice_sampler1d(ar, strength, *rng, -d + std::numeric_limits<double>::min(), - std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); - double min_discount = std::numeric_limits<double>::min(); - if (strength < 0.0) min_discount -= strength; - d = slice_sampler1d(dr, d, *rng, min_discount, - 1.0, 0.0, niterations, 100*niterations); - } -#endif - strength = slice_sampler1d(ar, strength, *rng, -d, - std::numeric_limits<double>::infinity(), 0.0, niterations, 100*niterations); - std::cerr << "CTMModel(alpha=" << strength << ") = " << LogLikelihood(d, strength) << std::endl; - for (it = r.begin(); it != r.end(); ++it) { -#if 0 - it->second.set_discount(d); -#endif - it->second.set_alpha(strength); - } - } - - prob_t Likelihood() const { - prob_t p; p.logeq(LogLikelihood(d, strength)); - return p; - } - - const CondBaseDist& rp0; - typedef std::tr1::unordered_map<std::vector<WordID>, - CCRP_NoTable<TRule>, - boost::hash<std::vector<WordID> > > RuleModelHash; - RuleModelHash r; - double d, strength; -}; - -struct GraphStructure { - GraphStructure() : r() {} - // leak memory - these are basically static - const Reachability* r; - bool IsReachable() const { return r->nodes > 0; } -}; - -struct ProbabilityEstimates { - ProbabilityEstimates() : gs(), backward() {} - explicit ProbabilityEstimates(const GraphStructure& g) : - gs(&g), backward() { - if (g.r->nodes > 0) - backward = new float[g.r->nodes]; - } - // leak memory, these are static - - // returns an estimate of the marginal probability - double MarginalEstimate() const { - if (!backward) return 0; - return backward[0]; - } - - // returns an backward estimate - double Backward(int src_covered, int trg_covered) const { - if (!backward) return 0; - int ind = gs->r->node_addresses[src_covered][trg_covered]; - if (ind < 0) return 0; - return backward[ind]; - } - - prob_t estp; - float* backward; - private: - const GraphStructure* gs; -}; - -struct TransliterationsImpl { - TransliterationsImpl(int max_src, int max_trg, double sr, const BackwardEstimator& b) : - cp0(max_src, max_trg, sr), - tccm(cp0), - be(b), - kMAX_SRC_CHUNK(max_src), - kMAX_TRG_CHUNK(max_trg), - kS2T_RATIO(sr), - tot_pairs(), tot_mem() { - } - const CondBaseDist cp0; - TransliterationChunkConditionalModel tccm; - const BackwardEstimator& be; - - void Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) { - const size_t src_len = src_lets.size(); - const size_t trg_len = trg_lets.size(); - - // init graph structure - if (src_len >= graphs.size()) graphs.resize(src_len + 1); - if (trg_len >= graphs[src_len].size()) graphs[src_len].resize(trg_len + 1); - GraphStructure& gs = graphs[src_len][trg_len]; - if (!gs.r) { - double rat = exp(fabs(log(trg_len / (src_len * kS2T_RATIO)))); - if (rat > 1.5 || (rat > 2.4 && src_len < 6)) { - cerr << " ** Forbidding transliterations of size " << src_len << "," << trg_len << ": " << rat << endl; - gs.r = new Reachability(src_len, trg_len, 0, 0); - } else { - gs.r = new Reachability(src_len, trg_len, kMAX_SRC_CHUNK, kMAX_TRG_CHUNK); - } - } - - const Reachability& r = *gs.r; - - // init backward estimates - if (src >= ests.size()) ests.resize(src + 1); - unordered_map<WordID, ProbabilityEstimates>::iterator it = ests[src].find(trg); - if (it != ests[src].end()) return; // already initialized - - it = ests[src].insert(make_pair(trg, ProbabilityEstimates(gs))).first; - ProbabilityEstimates& est = it->second; - if (!gs.r->nodes) return; // not derivable subject to length constraints - - be.InitializeGrid(src_lets, trg_lets, r, kS2T_RATIO, est.backward); - cerr << TD::GetString(src_lets) << " ||| " << TD::GetString(trg_lets) << " ||| " << (est.backward[0] / trg_lets.size()) << endl; - tot_pairs++; - tot_mem += sizeof(float) * gs.r->nodes; - } - - void Forbid(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) { - const size_t src_len = src_lets.size(); - const size_t trg_len = trg_lets.size(); - // TODO - } - - prob_t EstimateProbability(WordID s, const vector<WordID>& src, WordID t, const vector<WordID>& trg) const { - assert(src.size() < graphs.size()); - const vector<GraphStructure>& tv = graphs[src.size()]; - assert(trg.size() < tv.size()); - const GraphStructure& gs = tv[trg.size()]; - if (gs.r->nodes == 0) - return prob_t::Zero(); - const unordered_map<WordID, ProbabilityEstimates>::const_iterator it = ests[s].find(t); - assert(it != ests[s].end()); - return it->second.estp; - } - - void GraphSummary() const { - double to = 0; - double tn = 0; - double tt = 0; - for (int i = 0; i < graphs.size(); ++i) { - const vector<GraphStructure>& vt = graphs[i]; - for (int j = 0; j < vt.size(); ++j) { - const GraphStructure& gs = vt[j]; - if (!gs.r) continue; - tt++; - for (int k = 0; k < i; ++k) { - for (int l = 0; l < j; ++l) { - size_t c = gs.r->valid_deltas[k][l].size(); - if (c) { - tn += 1; - to += c; - } - } - } - } - } - cerr << " Average nodes = " << (tn / tt) << endl; - cerr << "Average out-degree = " << (to / tn) << endl; - cerr << " Unique structures = " << tt << endl; - cerr << " Unique pairs = " << tot_pairs << endl; - cerr << " BEs size = " << (tot_mem / (1024.0*1024.0)) << " MB" << endl; - } - - const int kMAX_SRC_CHUNK; - const int kMAX_TRG_CHUNK; - const double kS2T_RATIO; - unsigned tot_pairs; - size_t tot_mem; - vector<vector<GraphStructure> > graphs; // graphs[src_len][trg_len] - vector<unordered_map<WordID, ProbabilityEstimates> > ests; // ests[src][trg] -}; - -Transliterations::Transliterations(int max_src, int max_trg, double sr, const BackwardEstimator& be) : - pimpl_(new TransliterationsImpl(max_src, max_trg, sr, be)) {} -Transliterations::~Transliterations() { delete pimpl_; } - -void Transliterations::Initialize(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) { - pimpl_->Initialize(src, src_lets, trg, trg_lets); -} - -prob_t Transliterations::EstimateProbability(WordID s, const vector<WordID>& src, WordID t, const vector<WordID>& trg) const { - return pimpl_->EstimateProbability(s, src,t, trg); -} - -void Transliterations::Forbid(WordID src, const vector<WordID>& src_lets, WordID trg, const vector<WordID>& trg_lets) { - pimpl_->Forbid(src, src_lets, trg, trg_lets); -} - -void Transliterations::GraphSummary() const { - pimpl_->GraphSummary(); -} - |