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#include <sstream>
#include <iostream>
#include <vector>
#include <cassert>
#include <cmath>
#include "config.h"
#include <boost/shared_ptr.hpp>
#include <boost/algorithm/string.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "sentence_metadata.h"
#include "scorer.h"
#include "verbose.h"
#include "viterbi.h"
#include "hg.h"
#include "prob.h"
#include "kbest.h"
#include "ff_register.h"
#include "decoder.h"
#include "filelib.h"
#include "fdict.h"
#include "weights.h"
#include "sparse_vector.h"
#include "sampler.h"
using namespace std;
namespace po = boost::program_options;
struct ScorePair
{
ScorePair(double modelscore, double score) : modelscore_(modelscore), score_(score) {}
double modelscore_, score_;
double GetModelScore() { return modelscore_; }
double GetScore() { return score_; }
};
typedef vector<ScorePair> Scores;
/*
* KBestGetter
*
*/
struct KBestList {
vector<SparseVector<double> > feats;
vector<vector<WordID> > sents;
vector<double> scores;
};
struct KBestGetter : public DecoderObserver
{
KBestGetter( const size_t k ) : k_(k) {}
const size_t k_;
KBestList kb;
virtual void
NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
{
GetKBest(smeta.GetSentenceID(), *hg);
}
KBestList* GetKBest() { return &kb; }
void
GetKBest(int sent_id, const Hypergraph& forest)
{
kb.scores.clear();
kb.sents.clear();
kb.feats.clear();
KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest( forest, k_ );
for ( size_t i = 0; i < k_; ++i ) {
const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
kbest.LazyKthBest( forest.nodes_.size() - 1, i );
if (!d) break;
kb.sents.push_back( d->yield);
kb.feats.push_back( d->feature_values );
kb.scores.push_back( d->score );
}
}
};
/*
* NgramCounts
*
*/
struct NgramCounts
{
NgramCounts( const size_t N ) : N_( N ) {
reset();
}
size_t N_;
map<size_t, size_t> clipped;
map<size_t, size_t> sum;
void
operator+=( const NgramCounts& rhs )
{
assert( N_ == rhs.N_ );
for ( size_t i = 0; i < N_; i++ ) {
this->clipped[i] += rhs.clipped.find(i)->second;
this->sum[i] += rhs.sum.find(i)->second;
}
}
void
add( size_t count, size_t ref_count, size_t i )
{
assert( i < N_ );
if ( count > ref_count ) {
clipped[i] += ref_count;
sum[i] += count;
} else {
clipped[i] += count;
sum[i] += count;
}
}
void
reset()
{
size_t i;
for ( i = 0; i < N_; i++ ) {
clipped[i] = 0;
sum[i] = 0;
}
}
void
print()
{
for ( size_t i = 0; i < N_; i++ ) {
cout << i+1 << "grams (clipped):\t" << clipped[i] << endl;
cout << i+1 << "grams:\t\t\t" << sum[i] << endl;
}
}
};
typedef map<vector<WordID>, size_t> Ngrams;
Ngrams make_ngrams( vector<WordID>& s, size_t N );
NgramCounts make_ngram_counts( vector<WordID> hyp, vector<WordID> ref, size_t N );
double brevity_penaly( const size_t hyp_len, const size_t ref_len );
double bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, size_t N, vector<float> weights = vector<float>() );
double stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, size_t N, vector<float> weights = vector<float>() );
double smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, const size_t N, vector<float> weights = vector<float>() );
double approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, const size_t N, vector<float> weights = vector<float>() );
void register_and_convert(const vector<string>& strs, vector<WordID>& ids);
void print_FD();
void run_tests();
void test_SetWeights();
#include <boost/assign/std/vector.hpp>
#include <iomanip>
void test_metrics();
double approx_equal( double x, double y );
void test_ngrams();
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