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authorPatrick Simianer <p@simianer.de>2011-10-20 02:31:25 +0200
committerPatrick Simianer <p@simianer.de>2011-10-20 02:31:25 +0200
commita5a92ebe23c5819ed104313426012011e32539da (patch)
tree3416818c758d5ece4e71fe522c571e75ea04f100 /gi/pf/base_measures.h
parentb88332caac2cbe737c99b8098813f868ca876d8b (diff)
parent78baccbb4231bb84a456702d4f574f8e601a8182 (diff)
finalized merge
Diffstat (limited to 'gi/pf/base_measures.h')
-rw-r--r--gi/pf/base_measures.h116
1 files changed, 116 insertions, 0 deletions
diff --git a/gi/pf/base_measures.h b/gi/pf/base_measures.h
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+#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