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#ifndef _BASE_MEASURES_H_
#define _BASE_MEASURES_H_
#include <vector>
#include <map>
#include <string>
#include <cmath>
#include <iostream>
#include <cassert>
#include "unigrams.h"
#include "trule.h"
#include "prob.h"
#include "tdict.h"
#include "sampler.h"
#include "m.h"
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 PoissonUniformUninformativeBase {
explicit PoissonUniformUninformativeBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
prob_t operator()(const TRule& r) const {
prob_t p; p.logeq(Md::log_poisson(r.e_.size(), 1.0));
prob_t q = kUNIFORM; q.poweq(r.e_.size());
p *= q;
return p;
}
void Summary() const {}
void ResampleHyperparameters(MT19937*) {}
void Increment(const TRule&) {}
void Decrement(const TRule&) {}
prob_t Likelihood() const { return prob_t::One(); }
const prob_t kUNIFORM;
};
struct CompletelyUniformBase {
explicit CompletelyUniformBase(const unsigned ves) : kUNIFORM(1.0 / ves) {}
prob_t operator()(const TRule&) const {
return kUNIFORM;
}
void Summary() const {}
void ResampleHyperparameters(MT19937*) {}
void Increment(const TRule&) {}
void Decrement(const TRule&) {}
prob_t Likelihood() const { return prob_t::One(); }
const prob_t kUNIFORM;
};
struct UnigramWordBase {
explicit UnigramWordBase(const std::string& fname) : un(fname) {}
prob_t operator()(const TRule& r) const {
return un(r.e_);
}
const UnigramWordModel un;
};
struct RuleHasher {
size_t operator()(const TRule& r) const {
return hash_value(r);
}
};
struct TableLookupBase {
TableLookupBase(const std::string& fname);
prob_t operator()(const TRule& rule) const {
const std::tr1::unordered_map<TRule,prob_t>::const_iterator it = table.find(rule);
if (it == table.end()) {
std::cerr << rule << " not found\n";
abort();
}
return it->second;
}
void ResampleHyperparameters(MT19937*) {}
void Increment(const TRule&) {}
void Decrement(const TRule&) {}
prob_t Likelihood() const { return prob_t::One(); }
void Summary() const {}
std::tr1::unordered_map<TRule,prob_t,RuleHasher> table;
};
struct PhraseConditionalUninformativeBase {
explicit PhraseConditionalUninformativeBase(const unsigned vocab_e_size) :
kUNIFORM_TARGET(1.0 / vocab_e_size) {
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;
void Summary() const {}
void ResampleHyperparameters(MT19937*) {}
void Increment(const TRule&) {}
void Decrement(const TRule&) {}
prob_t Likelihood() const { return prob_t::One(); }
const prob_t kUNIFORM_TARGET;
};
struct PhraseConditionalUninformativeUnigramBase {
explicit PhraseConditionalUninformativeUnigramBase(const std::string& file, const unsigned vocab_e_size) : u(file, vocab_e_size) {}
// 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 UnigramModel u;
};
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;
};
struct PhraseJointBase_BiDir {
explicit PhraseJointBase_BiDir(const Model1& m1,
const Model1& im1,
const double m1mixture,
const unsigned vocab_e_size,
const unsigned vocab_f_size) :
model1(m1),
invmodel1(im1),
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 Model1& invmodel1;
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
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