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#include "ff_wordalign.h"
#include <string>
#include <cmath>
#include "stringlib.h"
#include "sentence_metadata.h"
#include "hg.h"
#include "fdict.h"
#include "aligner.h"
#include "tdict.h" // Blunsom hack
#include "filelib.h" // Blunsom hack
using namespace std;
RelativeSentencePosition::RelativeSentencePosition(const string& param) :
fid_(FD::Convert("RelativeSentencePosition")) {
if (!param.empty()) {
cerr << " Loading word classes from " << param << endl;
condition_on_fclass_ = true;
template_ = "RSP:FC000";
assert(!"not implemented");
} else {
condition_on_fclass_ = false;
}
}
void RelativeSentencePosition::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
// if the source word is either null or the generated word
// has no position in the reference
if (edge.i_ == -1 || edge.prev_i_ == -1)
return;
assert(smeta.GetTargetLength() > 0);
const double val = fabs(static_cast<double>(edge.i_) / smeta.GetSourceLength() -
static_cast<double>(edge.prev_i_) / smeta.GetTargetLength());
features->set_value(fid_, val);
if (condition_on_fclass_) {
assert(!"not implemented");
}
// cerr << f_len_ << " " << e_len_ << " [" << edge.i_ << "," << edge.j_ << "|" << edge.prev_i_ << "," << edge.prev_j_ << "]\t" << edge.rule_->AsString() << "\tVAL=" << val << endl;
}
MarkovJump::MarkovJump(const string& param) :
FeatureFunction(1),
fid_(FD::Convert("MarkovJump")),
individual_params_per_jumpsize_(false),
condition_on_flen_(false) {
cerr << " MarkovJump";
vector<string> argv;
int argc = SplitOnWhitespace(param, &argv);
if (argc > 0) {
if (argv[0] == "--fclasses") {
argc--;
assert(argc > 0);
const string f_class_file = argv[1];
}
if (argc != 1 || !(argv[0] == "-f" || argv[0] == "-i" || argv[0] == "-if")) {
cerr << "MarkovJump: expected parameters to be -f, -i, or -if\n";
exit(1);
}
individual_params_per_jumpsize_ = (argv[0][1] == 'i');
condition_on_flen_ = (argv[0][argv[0].size() - 1] == 'f');
if (individual_params_per_jumpsize_) {
template_ = "Jump:000";
cerr << ", individual jump parameters";
if (condition_on_flen_) {
template_ += ":F00";
cerr << " (split by f-length)";
}
}
} else {
cerr << " (Blunsom & Cohn definition)";
}
cerr << endl;
}
void MarkovJump::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
unsigned char& dpstate = *((unsigned char*)state);
if (edge.Arity() == 0) {
dpstate = static_cast<unsigned int>(edge.i_);
} else if (edge.Arity() == 1) {
dpstate = *((unsigned char*)ant_states[0]);
} else if (edge.Arity() == 2) {
int left_index = *((unsigned char*)ant_states[0]);
int right_index = *((unsigned char*)ant_states[1]);
if (right_index == -1)
dpstate = static_cast<unsigned int>(left_index);
else
dpstate = static_cast<unsigned int>(right_index);
const int jumpsize = right_index - left_index;
features->set_value(fid_, fabs(jumpsize - 1)); // Blunsom and Cohn def
if (individual_params_per_jumpsize_) {
string fname = template_;
int param = jumpsize;
if (jumpsize < 0) {
param *= -1;
fname[5]='L';
} else if (jumpsize > 0) {
fname[5]='R';
}
if (param) {
fname[6] = '0' + (param / 10);
fname[7] = '0' + (param % 10);
}
if (condition_on_flen_) {
const int flen = smeta.GetSourceLength();
fname[10] = '0' + (flen / 10);
fname[11] = '0' + (flen % 10);
}
features->set_value(FD::Convert(fname), 1.0);
}
} else {
assert(!"something really unexpected is happening");
}
}
AlignerResults::AlignerResults(const std::string& param) :
cur_sent_(-1),
cur_grid_(NULL) {
vector<string> argv;
int argc = SplitOnWhitespace(param, &argv);
if (argc != 2) {
cerr << "Required format: AlignerResults [FeatureName] [file.pharaoh]\n";
exit(1);
}
cerr << " feature: " << argv[0] << "\talignments: " << argv[1] << endl;
fid_ = FD::Convert(argv[0]);
ReadFile rf(argv[1]);
istream& in = *rf.stream(); int lc = 0;
while(in) {
string line;
getline(in, line);
if (!in) break;
++lc;
is_aligned_.push_back(AlignerTools::ReadPharaohAlignmentGrid(line));
}
cerr << " Loaded " << lc << " refs\n";
}
void AlignerResults::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
if (edge.i_ == -1 || edge.prev_i_ == -1)
return;
if (cur_sent_ != smeta.GetSentenceID()) {
assert(smeta.HasReference());
cur_sent_ = smeta.GetSentenceID();
assert(cur_sent_ < is_aligned_.size());
cur_grid_ = is_aligned_[cur_sent_].get();
}
//cerr << edge.rule_->AsString() << endl;
int j = edge.i_; // source side (f)
int i = edge.prev_i_; // target side (e)
if (j < cur_grid_->height() && i < cur_grid_->width() && (*cur_grid_)(i, j)) {
// if (edge.rule_->e_[0] == smeta.GetReference()[i][0].label) {
features->set_value(fid_, 1.0);
// cerr << edge.rule_->AsString() << " (" << i << "," << j << ")\n";
// }
}
}
BlunsomSynchronousParseHack::BlunsomSynchronousParseHack(const string& param) :
FeatureFunction((100 / 8) + 1), fid_(FD::Convert("NotRef")), cur_sent_(-1) {
ReadFile rf(param);
istream& in = *rf.stream(); int lc = 0;
while(in) {
string line;
getline(in, line);
if (!in) break;
++lc;
refs_.push_back(vector<WordID>());
TD::ConvertSentence(line, &refs_.back());
}
cerr << " Loaded " << lc << " refs\n";
}
void BlunsomSynchronousParseHack::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_states,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* state) const {
if (cur_sent_ != smeta.GetSentenceID()) {
// assert(smeta.HasReference());
cur_sent_ = smeta.GetSentenceID();
assert(cur_sent_ < refs_.size());
cur_ref_ = &refs_[cur_sent_];
cur_map_.clear();
for (int i = 0; i < cur_ref_->size(); ++i) {
vector<WordID> phrase;
for (int j = i; j < cur_ref_->size(); ++j) {
phrase.push_back((*cur_ref_)[j]);
cur_map_[phrase] = i;
}
}
}
//cerr << edge.rule_->AsString() << endl;
for (int i = 0; i < ant_states.size(); ++i) {
if (DoesNotBelong(ant_states[i])) {
//cerr << " ant " << i << " does not belong\n";
return;
}
}
vector<vector<WordID> > ants(ant_states.size());
vector<const vector<WordID>* > pants(ant_states.size());
for (int i = 0; i < ant_states.size(); ++i) {
AppendAntecedentString(ant_states[i], &ants[i]);
//cerr << " ant[" << i << "]: " << ((int)*(static_cast<const unsigned char*>(ant_states[i]))) << " " << TD::GetString(ants[i]) << endl;
pants[i] = &ants[i];
}
vector<WordID> yield;
edge.rule_->ESubstitute(pants, &yield);
//cerr << "YIELD: " << TD::GetString(yield) << endl;
Vec2Int::iterator it = cur_map_.find(yield);
if (it == cur_map_.end()) {
features->set_value(fid_, 1);
//cerr << " BAD!\n";
return;
}
SetStateMask(it->second, it->second + yield.size(), state);
}
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