diff options
author | Patrick Simianer <p@simianer.de> | 2011-10-20 02:31:25 +0200 |
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committer | Patrick Simianer <p@simianer.de> | 2011-10-20 02:31:25 +0200 |
commit | a5a92ebe23c5819ed104313426012011e32539da (patch) | |
tree | 3416818c758d5ece4e71fe522c571e75ea04f100 /gi/pf/base_measures.h | |
parent | b88332caac2cbe737c99b8098813f868ca876d8b (diff) | |
parent | 78baccbb4231bb84a456702d4f574f8e601a8182 (diff) |
finalized merge
Diffstat (limited to 'gi/pf/base_measures.h')
-rw-r--r-- | gi/pf/base_measures.h | 116 |
1 files changed, 116 insertions, 0 deletions
diff --git a/gi/pf/base_measures.h b/gi/pf/base_measures.h new file mode 100644 index 00000000..df17aa62 --- /dev/null +++ b/gi/pf/base_measures.h @@ -0,0 +1,116 @@ +#ifndef _BASE_MEASURES_H_ +#define _BASE_MEASURES_H_ + +#include <vector> +#include <map> +#include <string> +#include <cmath> +#include <iostream> + +#include "trule.h" +#include "prob.h" +#include "tdict.h" + +inline double log_poisson(unsigned x, const double& lambda) { + assert(lambda > 0.0); + return log(lambda) * x - lgamma(x + 1) - lambda; +} + +inline std::ostream& operator<<(std::ostream& os, const std::vector<WordID>& p) { + os << '['; + for (int i = 0; i < p.size(); ++i) + os << (i==0 ? "" : " ") << TD::Convert(p[i]); + return os << ']'; +} + +struct Model1 { + explicit Model1(const std::string& fname) : + kNULL(TD::Convert("<eps>")), + kZERO() { + LoadModel1(fname); + } + + void LoadModel1(const std::string& fname); + + // returns prob 0 if src or trg is not found + const prob_t& operator()(WordID src, WordID trg) const { + if (src == 0) src = kNULL; + if (src < ttable.size()) { + const std::map<WordID, prob_t>& cpd = ttable[src]; + const std::map<WordID, prob_t>::const_iterator it = cpd.find(trg); + if (it != cpd.end()) + return it->second; + } + return kZERO; + } + + const WordID kNULL; + const prob_t kZERO; + std::vector<std::map<WordID, prob_t> > ttable; +}; + +struct PhraseConditionalBase { + explicit PhraseConditionalBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size) : + model1(m1), + kM1MIXTURE(m1mixture), + kUNIFORM_MIXTURE(1.0 - m1mixture), + kUNIFORM_TARGET(1.0 / vocab_e_size) { + assert(m1mixture >= 0.0 && m1mixture <= 1.0); + assert(vocab_e_size > 0); + } + + // return p0 of rule.e_ | rule.f_ + prob_t operator()(const TRule& rule) const { + return p0(rule.f_, rule.e_, 0, 0); + } + + prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const; + + const Model1& model1; + const prob_t kM1MIXTURE; // Model 1 mixture component + const prob_t kUNIFORM_MIXTURE; // uniform mixture component + const prob_t kUNIFORM_TARGET; +}; + +struct PhraseJointBase { + explicit PhraseJointBase(const Model1& m1, const double m1mixture, const unsigned vocab_e_size, const unsigned vocab_f_size) : + model1(m1), + kM1MIXTURE(m1mixture), + kUNIFORM_MIXTURE(1.0 - m1mixture), + kUNIFORM_SOURCE(1.0 / vocab_f_size), + kUNIFORM_TARGET(1.0 / vocab_e_size) { + assert(m1mixture >= 0.0 && m1mixture <= 1.0); + assert(vocab_e_size > 0); + } + + // return p0 of rule.e_ | rule.f_ + prob_t operator()(const TRule& rule) const { + return p0(rule.f_, rule.e_, 0, 0); + } + + prob_t p0(const std::vector<WordID>& vsrc, const std::vector<WordID>& vtrg, int start_src, int start_trg) const; + + const Model1& model1; + const prob_t kM1MIXTURE; // Model 1 mixture component + const prob_t kUNIFORM_MIXTURE; // uniform mixture component + const prob_t kUNIFORM_SOURCE; + const prob_t kUNIFORM_TARGET; +}; + +// base distribution for jump size multinomials +// basically p(0) = 0 and then, p(1) is max, and then +// you drop as you move to the max jump distance +struct JumpBase { + JumpBase(); + + const prob_t& operator()(int jump, unsigned src_len) const { + assert(jump != 0); + const std::map<int, prob_t>::const_iterator it = p[src_len].find(jump); + assert(it != p[src_len].end()); + return it->second; + } + std::vector<std::map<int, prob_t> > p; +}; + + +#endif |