diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/model1.cc | 29 |
1 files changed, 17 insertions, 12 deletions
diff --git a/training/model1.cc b/training/model1.cc index a87d388f..73104304 100644 --- a/training/model1.cc +++ b/training/model1.cc @@ -4,6 +4,7 @@ #include <boost/program_options.hpp> #include <boost/program_options/variables_map.hpp> +#include "m.h" #include "lattice.h" #include "stringlib.h" #include "filelib.h" @@ -13,11 +14,6 @@ namespace po = boost::program_options; using namespace std; -inline double log_poisson(unsigned x, const double& lambda) { - assert(lambda > 0.0); - return log(lambda) * x - lgamma(x + 1) - lambda; -} - bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { po::options_description opts("Configuration options"); opts.add_options() @@ -82,6 +78,7 @@ int main(int argc, char** argv) { TTable::Word2Word2Double was_viterbi; double tot_len_ratio = 0; double mean_srclen_multiplier = 0; + vector<double> unnormed_a_i; for (int iter = 0; iter < ITERATIONS; ++iter) { const bool final_iteration = (iter == (ITERATIONS - 1)); cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl; @@ -108,6 +105,8 @@ int main(int argc, char** argv) { assert(src.size() > 0); assert(trg.size() > 0); } + if (src.size() > unnormed_a_i.size()) + unnormed_a_i.resize(src.size()); if (iter == 0) tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size()); denom += trg.size(); @@ -125,13 +124,15 @@ int main(int argc, char** argv) { } double az = 0; if (favor_diagonal) { - for (int ta = 0; ta < src.size(); ++ta) - az += exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + for (int ta = 0; ta < src.size(); ++ta) { + unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + az += unnormed_a_i[ta]; + } az /= prob_align_not_null; } for (int i = 1; i <= src.size(); ++i) { if (favor_diagonal) - prob_a_i = exp(-fabs(double(i) / src.size() - j_over_ts) * diagonal_tension) / az; + prob_a_i = unnormed_a_i[i-1] / az; probs[i] = tt.prob(src[i-1][0].label, f_j) * prob_a_i; sum += probs[i]; } @@ -202,7 +203,9 @@ int main(int argc, char** argv) { Lattice src, trg; LatticeTools::ConvertTextToLattice(ssrc, &src); LatticeTools::ConvertTextToLattice(strg, &trg); - double log_prob = log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier); + 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) { @@ -216,13 +219,15 @@ int main(int argc, char** argv) { } double az = 0; if (favor_diagonal) { - for (int ta = 0; ta < src.size(); ++ta) - az += exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + for (int ta = 0; ta < src.size(); ++ta) { + unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + az += unnormed_a_i[ta]; + } az /= prob_align_not_null; } for (int i = 1; i <= src.size(); ++i) { if (favor_diagonal) - prob_a_i = exp(-fabs(double(i) / src.size() - j_over_ts) * diagonal_tension) / az; + prob_a_i = unnormed_a_i[i-1] / az; sum += tt.prob(src[i-1][0].label, f_j) * prob_a_i; } log_prob += log(sum); |