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-rw-r--r--gi/pf/dpnaive.cc95
1 files changed, 26 insertions, 69 deletions
diff --git a/gi/pf/dpnaive.cc b/gi/pf/dpnaive.cc
index 608f73d5..c926487b 100644
--- a/gi/pf/dpnaive.cc
+++ b/gi/pf/dpnaive.cc
@@ -7,12 +7,14 @@
#include <boost/program_options/variables_map.hpp>
#include "base_measures.h"
+#include "monotonic_pseg.h"
#include "trule.h"
#include "tdict.h"
#include "filelib.h"
#include "dict.h"
#include "sampler.h"
#include "ccrp_nt.h"
+#include "corpus.h"
using namespace std;
using namespace std::tr1;
@@ -52,57 +54,12 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-void ReadParallelCorpus(const string& filename,
- vector<vector<WordID> >* f,
- vector<vector<int> >* e,
- set<int>* vocab_e,
- set<int>* vocab_f) {
- f->clear();
- e->clear();
- vocab_f->clear();
- vocab_e->clear();
- istream* in;
- if (filename == "-")
- in = &cin;
- else
- in = new ifstream(filename.c_str());
- assert(*in);
- string line;
- const WordID kDIV = TD::Convert("|||");
- vector<WordID> tmp;
- while(*in) {
- getline(*in, line);
- if (line.empty() && !*in) break;
- e->push_back(vector<int>());
- f->push_back(vector<int>());
- vector<int>& le = e->back();
- vector<int>& lf = f->back();
- tmp.clear();
- TD::ConvertSentence(line, &tmp);
- bool isf = true;
- for (unsigned i = 0; i < tmp.size(); ++i) {
- const int cur = tmp[i];
- if (isf) {
- if (kDIV == cur) { isf = false; } else {
- lf.push_back(cur);
- vocab_f->insert(cur);
- }
- } else {
- assert(cur != kDIV);
- le.push_back(cur);
- vocab_e->insert(cur);
- }
- }
- assert(isf == false);
- }
- if (in != &cin) delete in;
-}
-
shared_ptr<MT19937> prng;
template <typename Base>
struct ModelAndData {
- explicit ModelAndData(const Base& b, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
+ explicit ModelAndData(MonotonicParallelSegementationModel& m, const Base& b, const vector<vector<int> >& ce, const vector<vector<int> >& cf, const set<int>& ve, const set<int>& vf) :
+ model(m),
rng(&*prng),
p0(b),
baseprob(prob_t::One()),
@@ -110,14 +67,12 @@ struct ModelAndData {
corpusf(cf),
vocabe(ve),
vocabf(vf),
- rules(1,1),
mh_samples(),
mh_rejects(),
kX(-TD::Convert("X")),
derivations(corpuse.size()) {}
void ResampleHyperparameters() {
- rules.resample_hyperparameters(&*prng);
}
void InstantiateRule(const pair<short,short>& from,
@@ -139,12 +94,10 @@ struct ModelAndData {
TRule x;
for (int i = 1; i < d.size(); ++i) {
InstantiateRule(d[i], d[i-1], sentf, sente, &x);
- //cerr << "REMOVE: " << x.AsString() << endl;
- if (rules.decrement(x)) {
- baseprob /= p0(x);
- //cerr << " (REMOVED ONLY INSTANCE)\n";
- }
+ model.DecrementRule(x);
+ model.DecrementContinue();
}
+ model.DecrementStop();
}
void PrintDerivation(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) {
@@ -161,39 +114,38 @@ struct ModelAndData {
TRule x;
for (int i = 1; i < d.size(); ++i) {
InstantiateRule(d[i], d[i-1], sentf, sente, &x);
- if (rules.increment(x)) {
- baseprob *= p0(x);
- }
+ model.IncrementRule(x);
+ model.IncrementContinue();
}
+ model.IncrementStop();
}
prob_t Likelihood() const {
- prob_t p;
- p.logeq(rules.log_crp_prob());
- return p * baseprob;
+ return model.Likelihood();
}
prob_t DerivationProposalProbability(const vector<pair<short,short> >& d, const vector<int>& sentf, const vector<int>& sente) const {
- prob_t p = prob_t::One();
+ prob_t p = model.StopProbability();
if (d.size() < 2) return p;
TRule x;
+ const prob_t p_cont = model.ContinueProbability();
for (int i = 1; i < d.size(); ++i) {
InstantiateRule(d[i], d[i-1], sentf, sente, &x);
- prob_t rp; rp.logeq(rules.logprob(x, log(p0(x))));
- p *= rp;
+ p *= p_cont;
+ p *= model.RuleProbability(x);
}
return p;
}
void Sample();
+ MonotonicParallelSegementationModel& model;
MT19937* rng;
const Base& p0;
prob_t baseprob; // cached value of generating the table table labels from p0
// this can't be used if we go to a hierarchical prior!
const vector<vector<int> >& corpuse, corpusf;
const set<int>& vocabe, vocabf;
- CCRP_NoTable<TRule> rules;
unsigned mh_samples, mh_rejects;
const int kX;
vector<vector<pair<short, short> > > derivations;
@@ -201,8 +153,8 @@ struct ModelAndData {
template <typename Base>
void ModelAndData<Base>::Sample() {
- unsigned MAXK = 4;
- unsigned MAXL = 4;
+ unsigned MAXK = kMAX_SRC_PHRASE;
+ unsigned MAXL = kMAX_TRG_PHRASE;
TRule x;
x.lhs_ = -TD::Convert("X");
for (int samples = 0; samples < 1000; ++samples) {
@@ -228,6 +180,8 @@ void ModelAndData<Base>::Sample() {
boost::multi_array<prob_t, 2> a(boost::extents[sentf.size() + 1][sente.size() + 1]);
boost::multi_array<prob_t, 4> trans(boost::extents[sentf.size() + 1][sente.size() + 1][MAXK][MAXL]);
a[0][0] = prob_t::One();
+ const prob_t q_stop = model.StopProbability();
+ const prob_t q_cont = model.ContinueProbability();
for (int i = 0; i < sentf.size(); ++i) {
for (int j = 0; j < sente.size(); ++j) {
const prob_t src_a = a[i][j];
@@ -239,7 +193,9 @@ void ModelAndData<Base>::Sample() {
for (int l = 1; l <= MAXL; ++l) {
if (j + l > sente.size()) break;
x.e_.push_back(sente[j + l - 1]);
- trans[i][j][k - 1][l - 1].logeq(rules.logprob(x, log(p0(x))));
+ const bool stop_now = ((j + l) == sente.size()) && ((i + k) == sentf.size());
+ const prob_t& cp = stop_now ? q_stop : q_cont;
+ trans[i][j][k - 1][l - 1] = model.RuleProbability(x) * cp;
a[i + k][j + l] += src_a * trans[i][j][k - 1][l - 1];
}
}
@@ -319,7 +275,7 @@ int main(int argc, char** argv) {
vector<vector<int> > corpuse, corpusf;
set<int> vocabe, vocabf;
- ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
+ corpus::ReadParallelCorpus(conf["input"].as<string>(), &corpusf, &corpuse, &vocabf, &vocabe);
cerr << "f-Corpus size: " << corpusf.size() << " sentences\n";
cerr << "f-Vocabulary size: " << vocabf.size() << " types\n";
cerr << "f-Corpus size: " << corpuse.size() << " sentences\n";
@@ -328,8 +284,9 @@ int main(int argc, char** argv) {
Model1 m1(conf["model1"].as<string>());
PhraseJointBase lp0(m1, conf["model1_interpolation_weight"].as<double>(), vocabe.size(), vocabf.size());
+ MonotonicParallelSegementationModel m(lp0);
- ModelAndData<PhraseJointBase> posterior(lp0, corpuse, corpusf, vocabe, vocabf);
+ ModelAndData<PhraseJointBase> posterior(m, lp0, corpuse, corpusf, vocabe, vocabf);
posterior.Sample();
return 0;
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 21:36:42 +0000