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-rw-r--r--training/model1.cc61
1 files changed, 33 insertions, 28 deletions
diff --git a/training/model1.cc b/training/model1.cc
index 73104304..19692b9a 100644
--- a/training/model1.cc
+++ b/training/model1.cc
@@ -5,7 +5,7 @@
#include <boost/program_options/variables_map.hpp>
#include "m.h"
-#include "lattice.h"
+#include "corpus_tools.h"
#include "stringlib.h"
#include "filelib.h"
#include "ttables.h"
@@ -19,6 +19,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
opts.add_options()
("iterations,i",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
("beam_threshold,t",po::value<double>()->default_value(-4),"log_10 of beam threshold (-10000 to include everything, 0 max)")
+ ("bidir,b", "Run bidirectional alignment")
("no_null_word,N","Do not generate from the null token")
("write_alignments,A", "Write alignments instead of parameters")
("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal")
@@ -51,6 +52,15 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
return true;
}
+// src and trg are source and target strings, respectively (not really lattices)
+double PosteriorInference(const vector<WordID>& src, const vector<WordID>& trg) {
+ double llh = 0;
+ static vector<double> unnormed_a_i;
+ if (src.size() > unnormed_a_i.size())
+ unnormed_a_i.resize(src.size());
+ return llh;
+}
+
int main(int argc, char** argv) {
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
@@ -74,8 +84,8 @@ int main(int argc, char** argv) {
return 1;
}
- TTable tt;
- TTable::Word2Word2Double was_viterbi;
+ TTable s2t, t2s;
+ TTable::Word2Word2Double s2t_viterbi;
double tot_len_ratio = 0;
double mean_srclen_multiplier = 0;
vector<double> unnormed_a_i;
@@ -96,14 +106,11 @@ int main(int argc, char** argv) {
++lc;
if (lc % 1000 == 0) { cerr << '.'; flag = true; }
if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
- ParseTranslatorInput(line, &ssrc, &strg);
- Lattice src, trg;
- LatticeTools::ConvertTextToLattice(ssrc, &src);
- LatticeTools::ConvertTextToLattice(strg, &trg);
+ vector<WordID> src, trg;
+ CorpusTools::ReadLine(line, &src, &trg);
if (src.size() == 0 || trg.size() == 0) {
cerr << "Error: " << lc << "\n" << line << endl;
- assert(src.size() > 0);
- assert(trg.size() > 0);
+ return 1;
}
if (src.size() > unnormed_a_i.size())
unnormed_a_i.resize(src.size());
@@ -113,13 +120,13 @@ int main(int argc, char** argv) {
vector<double> probs(src.size() + 1);
bool first_al = true; // used for write_alignments
for (int j = 0; j < trg.size(); ++j) {
- const WordID& f_j = trg[j][0].label;
+ const WordID& f_j = trg[j];
double sum = 0;
const double j_over_ts = double(j) / trg.size();
double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1)
if (use_null) {
if (favor_diagonal) prob_a_i = prob_align_null;
- probs[0] = tt.prob(kNULL, f_j) * prob_a_i;
+ probs[0] = s2t.prob(kNULL, f_j) * prob_a_i;
sum += probs[0];
}
double az = 0;
@@ -133,7 +140,7 @@ int main(int argc, char** argv) {
for (int i = 1; i <= src.size(); ++i) {
if (favor_diagonal)
prob_a_i = unnormed_a_i[i-1] / az;
- probs[i] = tt.prob(src[i-1][0].label, f_j) * prob_a_i;
+ probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i;
sum += probs[i];
}
if (final_iteration) {
@@ -150,7 +157,7 @@ int main(int argc, char** argv) {
if (probs[i] > max_p) {
max_index = i;
max_p = probs[i];
- max_i = src[i-1][0].label;
+ max_i = src[i-1];
}
}
if (write_alignments) {
@@ -159,13 +166,13 @@ int main(int argc, char** argv) {
cout << (max_index - 1) << "-" << j;
}
}
- was_viterbi[max_i][f_j] = 1.0;
+ s2t_viterbi[max_i][f_j] = 1.0;
}
} else {
if (use_null)
- tt.Increment(kNULL, f_j, probs[0] / sum);
+ s2t.Increment(kNULL, f_j, probs[0] / sum);
for (int i = 1; i <= src.size(); ++i)
- tt.Increment(src[i-1][0].label, f_j, probs[i] / sum);
+ s2t.Increment(src[i-1], f_j, probs[i] / sum);
}
likelihood += log(sum);
}
@@ -186,9 +193,9 @@ int main(int argc, char** argv) {
cerr << " perplexity: " << pow(2.0, -base2_likelihood / denom) << endl;
if (!final_iteration) {
if (variational_bayes)
- tt.NormalizeVB(alpha);
+ s2t.NormalizeVB(alpha);
else
- tt.Normalize();
+ s2t.Normalize();
}
}
if (testset.size()) {
@@ -199,23 +206,21 @@ int main(int argc, char** argv) {
string ssrc, strg, line;
while (getline(in, line)) {
++lc;
- ParseTranslatorInput(line, &ssrc, &strg);
- Lattice src, trg;
- LatticeTools::ConvertTextToLattice(ssrc, &src);
- LatticeTools::ConvertTextToLattice(strg, &trg);
+ vector<WordID> src, trg;
+ CorpusTools::ReadLine(line, &src, &trg);
double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
if (src.size() > unnormed_a_i.size())
unnormed_a_i.resize(src.size());
// compute likelihood
for (int j = 0; j < trg.size(); ++j) {
- const WordID& f_j = trg[j][0].label;
+ const WordID& f_j = trg[j];
double sum = 0;
const double j_over_ts = double(j) / trg.size();
double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1)
if (use_null) {
if (favor_diagonal) prob_a_i = prob_align_null;
- sum += tt.prob(kNULL, f_j) * prob_a_i;
+ sum += s2t.prob(kNULL, f_j) * prob_a_i;
}
double az = 0;
if (favor_diagonal) {
@@ -228,7 +233,7 @@ int main(int argc, char** argv) {
for (int i = 1; i <= src.size(); ++i) {
if (favor_diagonal)
prob_a_i = unnormed_a_i[i-1] / az;
- sum += tt.prob(src[i-1][0].label, f_j) * prob_a_i;
+ sum += s2t.prob(src[i-1], f_j) * prob_a_i;
}
log_prob += log(sum);
}
@@ -240,16 +245,16 @@ int main(int argc, char** argv) {
if (write_alignments) return 0;
- for (TTable::Word2Word2Double::iterator ei = tt.ttable.begin(); ei != tt.ttable.end(); ++ei) {
+ for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) {
const TTable::Word2Double& cpd = ei->second;
- const TTable::Word2Double& vit = was_viterbi[ei->first];
+ const TTable::Word2Double& vit = s2t_viterbi[ei->first];
const string& esym = TD::Convert(ei->first);
double max_p = -1;
for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi)
if (fi->second > max_p) max_p = fi->second;
const double threshold = max_p * BEAM_THRESHOLD;
for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) {
- if (fi->second > threshold || (vit.count(fi->first) > 0)) {
+ if (fi->second > threshold || (vit.find(fi->first) != vit.end())) {
cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl;
}
}
generated by cgit v1.2.3 (git 2.46.0) at 2025-10-31 05:32:58 +0000