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-rw-r--r--extools/featurize_grammar.cc60
-rw-r--r--extools/filter_grammar.cc8
-rw-r--r--extools/filter_score_grammar.cc56
-rw-r--r--extools/mr_stripe_rule_reduce.cc2
4 files changed, 63 insertions, 63 deletions
diff --git a/extools/featurize_grammar.cc b/extools/featurize_grammar.cc
index 2fc53ff9..0c3418eb 100644
--- a/extools/featurize_grammar.cc
+++ b/extools/featurize_grammar.cc
@@ -36,12 +36,12 @@ static const size_t MAX_LINE_LENGTH = 64000000;
// Data structures for indexing and counting rules
//typedef boost::tuple< WordID, vector<WordID>, vector<WordID> > RuleTuple;
struct RuleTuple {
- RuleTuple(const WordID& lhs, const vector<WordID>& s, const vector<WordID>& t)
+ RuleTuple(const WordID& lhs, const vector<WordID>& s, const vector<WordID>& t)
: m_lhs(lhs), m_source(s), m_target(t) {
hash_value();
m_dirty = false;
}
-
+
size_t hash_value() const {
// if (m_dirty) {
size_t hash = 0;
@@ -99,17 +99,17 @@ struct FreqCount {
Counts counts;
int inc(const Key& r, int c=1) {
- pair<typename Counts::iterator,bool> itb
+ pair<typename Counts::iterator,bool> itb
= counts.insert(make_pair(r,c));
- if (!itb.second)
- itb.first->second += c;
+ if (!itb.second)
+ itb.first->second += c;
return itb.first->second;
}
int inc_if_exists(const Key& r, int c=1) {
typename Counts::iterator it = counts.find(r);
- if (it != counts.end())
- it->second += c;
+ if (it != counts.end())
+ it->second += c;
return it->second;
}
@@ -284,9 +284,9 @@ struct LogRuleCount : public FeatureExtractor {
const RuleStatistics& info,
SparseVector<float>* result) const {
(void) lhs; (void) src; (void) trg;
- //result->set_value(fid_, log(info.counts.value(kCFE)));
- result->set_value(fid_, log(info.counts.value(kCFE)));
- if (IsZero(info.counts.value(kCFE)))
+ //result->set_value(fid_, log(info.counts.get(kCFE)));
+ result->set_value(fid_, log(info.counts.get(kCFE)));
+ if (IsZero(info.counts.get(kCFE)))
result->set_value(sfid_, 1);
}
const int fid_;
@@ -300,14 +300,14 @@ struct RulePenalty : public FeatureExtractor {
const vector<WordID>& /*src*/,
const vector<WordID>& /*trg*/,
const RuleStatistics& /*info*/,
- SparseVector<float>* result) const
+ SparseVector<float>* result) const
{ result->set_value(fid_, 1); }
const int fid_;
};
-// The negative log of the condition rule probs
-// ignoring the identities of the non-terminals.
+// The negative log of the condition rule probs
+// ignoring the identities of the non-terminals.
// i.e. the prob Hiero would assign.
// Also extracts Labelled features.
struct XFeatures: public FeatureExtractor {
@@ -335,7 +335,7 @@ struct XFeatures: public FeatureExtractor {
const RuleStatistics& info) {
RuleTuple r(-1, src, trg);
map_rule(r);
- const int count = info.counts.value(kCFE);
+ const int count = info.counts.get(kCFE);
assert(count > 0);
rule_counts.inc_if_exists(r, count);
source_counts.inc_if_exists(r.source(), count);
@@ -354,14 +354,14 @@ struct XFeatures: public FeatureExtractor {
const int t_c = target_counts(r.target());
assert(t_c > 0);
result->set_value(fid_xfe, log(t_c) - l_r_freq);
- result->set_value(fid_labelledfe, log(t_c) - log(info.counts.value(kCFE)));
+ result->set_value(fid_labelledfe, log(t_c) - log(info.counts.get(kCFE)));
// if (t_c == 1)
// result->set_value(fid_xesingleton, 1.0);
const int s_c = source_counts(r.source());
assert(s_c > 0);
result->set_value(fid_xef, log(s_c) - l_r_freq);
- result->set_value(fid_labelledef, log(s_c) - log(info.counts.value(kCFE)));
+ result->set_value(fid_labelledef, log(s_c) - log(info.counts.get(kCFE)));
// if (s_c == 1)
// result->set_value(fid_xfsingleton, 1.0);
}
@@ -407,10 +407,10 @@ struct LabelledRuleConditionals: public FeatureExtractor {
const vector<WordID>& trg,
const RuleStatistics& info) {
RuleTuple r(lhs, src, trg);
- rule_counts.inc_if_exists(r, info.counts.value(kCFE));
- source_counts.inc_if_exists(r.source(), info.counts.value(kCFE));
+ rule_counts.inc_if_exists(r, info.counts.get(kCFE));
+ source_counts.inc_if_exists(r.source(), info.counts.get(kCFE));
- target_counts.inc_if_exists(r.target(), info.counts.value(kCFE));
+ target_counts.inc_if_exists(r.target(), info.counts.get(kCFE));
}
virtual void ExtractFeatures(const WordID lhs,
@@ -436,10 +436,10 @@ struct LHSProb: public FeatureExtractor {
virtual void ObserveUnfilteredRule(const WordID lhs,
const vector<WordID>& /*src*/,
const vector<WordID>& /*trg*/,
- const RuleStatistics& info) {
- int count = info.counts.value(kCFE);
+ const RuleStatistics& info) {
+ int count = info.counts.get(kCFE);
total_count += count;
- lhs_counts.inc(lhs, count);
+ lhs_counts.inc(lhs, count);
}
virtual void ExtractFeatures(const WordID lhs,
@@ -459,22 +459,22 @@ struct LHSProb: public FeatureExtractor {
// Proper rule generative probability: p( s,t | lhs)
struct GenerativeProb: public FeatureExtractor {
- GenerativeProb() :
+ GenerativeProb() :
fid_(FD::Convert("GenerativeProb")),
kCFE(FD::Convert("CFE")) {}
virtual void ObserveUnfilteredRule(const WordID lhs,
const vector<WordID>& /*src*/,
const vector<WordID>& /*trg*/,
- const RuleStatistics& info)
- { lhs_counts.inc(lhs, info.counts.value(kCFE)); }
+ const RuleStatistics& info)
+ { lhs_counts.inc(lhs, info.counts.get(kCFE)); }
virtual void ExtractFeatures(const WordID lhs,
const vector<WordID>& /*src*/,
const vector<WordID>& /*trg*/,
const RuleStatistics& info,
SparseVector<float>* result) const {
- double log_prob = log(lhs_counts(lhs)) - log(info.counts.value(kCFE));
+ double log_prob = log(lhs_counts(lhs)) - log(info.counts.get(kCFE));
result->set_value(fid_, log_prob);
}
@@ -502,8 +502,8 @@ struct LabellingShape: public FeatureExtractor {
const RuleStatistics& info) {
RuleTuple r(-1, src, trg);
map_rule(r);
- rule_counts.inc_if_exists(r, info.counts.value(kCFE));
- source_counts.inc_if_exists(r.source(), info.counts.value(kCFE));
+ rule_counts.inc_if_exists(r, info.counts.get(kCFE));
+ source_counts.inc_if_exists(r.source(), info.counts.get(kCFE));
}
virtual void ExtractFeatures(const WordID /*lhs*/,
@@ -519,9 +519,9 @@ struct LabellingShape: public FeatureExtractor {
// Replace all terminals with generic -1
void map_rule(RuleTuple& r) const {
- for (vector<WordID>::iterator it = r.target().begin(); it != r.target().end(); ++it)
+ for (vector<WordID>::iterator it = r.target().begin(); it != r.target().end(); ++it)
if (*it <= 0) *it = -1;
- for (vector<WordID>::iterator it = r.source().begin(); it != r.source().end(); ++it)
+ for (vector<WordID>::iterator it = r.source().begin(); it != r.source().end(); ++it)
if (*it <= 0) *it = -1;
}
diff --git a/extools/filter_grammar.cc b/extools/filter_grammar.cc
index ca329de1..cafcc923 100644
--- a/extools/filter_grammar.cc
+++ b/extools/filter_grammar.cc
@@ -37,7 +37,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description clo("Command line options");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
-
+
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
po::notify(*conf);
@@ -46,7 +46,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
cerr << dcmdline_options << endl;
exit(1);
}
-}
+}
struct SourceFilter {
// return true to keep the rule, otherwise false
@@ -95,7 +95,7 @@ struct DumbSuffixTreeFilter : SourceFilter {
};
boost::shared_ptr<SourceFilter> filter;
-multimap<float, ID2RuleStatistics::const_iterator> options;
+multimap<float, ID2RuleStatistics::const_iterator> options;
int kCOUNT;
int max_options;
@@ -103,7 +103,7 @@ void cb(WordID lhs, const vector<WordID>& src_rhs, const ID2RuleStatistics& rule
options.clear();
if (!filter || filter->Matches(src_rhs)) {
for (ID2RuleStatistics::const_iterator it = rules.begin(); it != rules.end(); ++it) {
- options.insert(make_pair(-it->second.counts.value(kCOUNT), it));
+ options.insert(make_pair(-it->second.counts.get(kCOUNT), it));
}
int ocount = 0;
cout << '[' << TD::Convert(-lhs) << ']' << " ||| ";
diff --git a/extools/filter_score_grammar.cc b/extools/filter_score_grammar.cc
index 24f5fd1c..d1a20b63 100644
--- a/extools/filter_score_grammar.cc
+++ b/extools/filter_score_grammar.cc
@@ -45,7 +45,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description clo("Command line options");
po::options_description dcmdline_options;
dcmdline_options.add(opts);
-
+
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
po::notify(*conf);
@@ -54,7 +54,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
cerr << dcmdline_options << endl;
exit(1);
}
-}
+}
namespace {
inline bool IsWhitespace(char c) { return c == ' ' || c == '\t'; }
inline bool IsBracket(char c){return c == '[' || c == ']';}
@@ -143,9 +143,9 @@ void ParseLine(const char* buf, vector<WordID>* cur_key, ID2RuleStatistics* coun
void LexTranslationTable::createTTable(const char* buf){
AnnotatedParallelSentence sent;
sent.ParseInputLine(buf);
-
+
//iterate over the alignment to compute aligned words
-
+
for(int i =0;i<sent.aligned.width();i++)
{
for (int j=0;j<sent.aligned.height();j++)
@@ -162,7 +162,7 @@ void LexTranslationTable::createTTable(const char* buf){
if (DEBUG) cerr << endl;
}
if (DEBUG) cerr << endl;
-
+
const WordID NULL_ = TD::Convert("NULL");
//handle unaligned words - align them to null
for (int j =0; j < sent.e_len; j++) {
@@ -171,7 +171,7 @@ void LexTranslationTable::createTTable(const char* buf){
++total_foreign[NULL_];
++total_english[sent.e[j]];
}
-
+
for (int i =0; i < sent.f_len; i++) {
if (sent.f_aligned[i]) continue;
++word_translation[pair<WordID,WordID> (sent.f[i], NULL_)];
@@ -261,8 +261,8 @@ struct LogRuleCount : public FeatureExtractor {
const RuleStatistics& info,
SparseVector<float>* result) const {
(void) lhs_src; (void) trg;
- result->set_value(fid_, log(info.counts.value(kCFE)));
- if (IsZero(info.counts.value(kCFE)))
+ result->set_value(fid_, log(info.counts.get(kCFE)));
+ if (IsZero(info.counts.get(kCFE)))
result->set_value(sfid_, 1);
}
const int fid_;
@@ -280,9 +280,9 @@ struct LogECount : public FeatureExtractor {
const RuleStatistics& info,
SparseVector<float>* result) const {
(void) lhs_src; (void) trg;
- assert(info.counts.value(kCE) > 0);
- result->set_value(fid_, log(info.counts.value(kCE)));
- if (IsZero(info.counts.value(kCE)))
+ assert(info.counts.get(kCE) > 0);
+ result->set_value(fid_, log(info.counts.get(kCE)));
+ if (IsZero(info.counts.get(kCE)))
result->set_value(sfid_, 1);
}
const int sfid_;
@@ -300,9 +300,9 @@ struct LogFCount : public FeatureExtractor {
const RuleStatistics& info,
SparseVector<float>* result) const {
(void) lhs_src; (void) trg;
- assert(info.counts.value(kCF) > 0);
- result->set_value(fid_, log(info.counts.value(kCF)));
- if (IsZero(info.counts.value(kCF)))
+ assert(info.counts.get(kCF) > 0);
+ result->set_value(fid_, log(info.counts.get(kCF)));
+ if (IsZero(info.counts.get(kCF)))
result->set_value(sfid_, 1);
}
const int sfid_;
@@ -319,8 +319,8 @@ struct EGivenFExtractor : public FeatureExtractor {
const RuleStatistics& info,
SparseVector<float>* result) const {
(void) lhs_src; (void) trg;
- assert(info.counts.value(kCF) > 0.0f);
- result->set_value(fid_, safenlog(info.counts.value(kCFE) / info.counts.value(kCF)));
+ assert(info.counts.get(kCF) > 0.0f);
+ result->set_value(fid_, safenlog(info.counts.get(kCFE) / info.counts.get(kCF)));
}
const int fid_, kCF, kCFE;
};
@@ -334,8 +334,8 @@ struct FGivenEExtractor : public FeatureExtractor {
const RuleStatistics& info,
SparseVector<float>* result) const {
(void) lhs_src; (void) trg;
- assert(info.counts.value(kCE) > 0.0f);
- result->set_value(fid_, safenlog(info.counts.value(kCFE) / info.counts.value(kCE)));
+ assert(info.counts.get(kCE) > 0.0f);
+ result->set_value(fid_, safenlog(info.counts.get(kCFE) / info.counts.get(kCE)));
}
const int fid_, kCE, kCFE;
};
@@ -353,7 +353,7 @@ struct LexProbExtractor : public FeatureExtractor {
while(alignment) {
alignment.getline(buf, MAX_LINE_LENGTH);
if (buf[0] == 0) continue;
- table.createTTable(buf);
+ table.createTTable(buf);
}
delete[] buf;
#if 0
@@ -363,7 +363,7 @@ struct LexProbExtractor : public FeatureExtractor {
trans_table.open("lex_trans_table.out");
for(map < pair<WordID,WordID>,int >::iterator it = table.word_translation.begin(); it != table.word_translation.end(); ++it) {
trans_table << TD::Convert(trg.first) << "|||" << TD::Convert(trg.second) << "==" << it->second << "//" << table.total_foreign[trg.first] << "//" << table.total_english[trg.second] << endl;
- }
+ }
trans_table.close();
}
#endif
@@ -393,7 +393,7 @@ struct LexProbExtractor : public FeatureExtractor {
if ( table.total_english[trg[ita->second]] !=0 )
e2f = (float) temp / table.total_english[trg[ita->second]];
if (DEBUG) printf (" %d %E %E\n", temp, f2e, e2f);
-
+
//local counts to keep track of which things haven't been aligned, to later compute their null alignment
if (foreign_aligned.count(lhs_src[ita->first+2])) {
foreign_aligned[ lhs_src[ita->first+2] ].first++;
@@ -401,7 +401,7 @@ struct LexProbExtractor : public FeatureExtractor {
} else {
foreign_aligned[ lhs_src[ita->first+2] ] = pair<int,float> (1,e2f);
}
-
+
if (english_aligned.count( trg[ ita->second] )) {
english_aligned[ trg[ ita->second] ].first++;
english_aligned[ trg[ ita->second] ].second += f2e;
@@ -416,8 +416,8 @@ struct LexProbExtractor : public FeatureExtractor {
//compute lexical weight P(F|E) and include unaligned foreign words
for(int i=0;i<lhs_src.size(); i++) {
if (!table.total_foreign.count(lhs_src[i])) continue; //if we dont have it in the translation table, we won't know its lexical weight
-
- if (foreign_aligned.count(lhs_src[i]))
+
+ if (foreign_aligned.count(lhs_src[i]))
{
pair<int, float> temp_lex_prob = foreign_aligned[lhs_src[i]];
final_lex_e2f *= temp_lex_prob.second / temp_lex_prob.first;
@@ -427,14 +427,14 @@ struct LexProbExtractor : public FeatureExtractor {
int temp_count = table.word_translation[pair<WordID,WordID> (lhs_src[i],NULL_)];
float temp_e2f = (float) temp_count / table.total_english[NULL_];
final_lex_e2f *= temp_e2f;
- }
+ }
}
//compute P(E|F) unaligned english words
for(int j=0; j< trg.size(); j++) {
if (!table.total_english.count(trg[j])) continue;
-
+
if (english_aligned.count(trg[j]))
{
pair<int, float> temp_lex_prob = english_aligned[trg[j]];
@@ -490,7 +490,7 @@ int main(int argc, char** argv){
int line = 0;
const int kLogRuleCount = FD::Convert("LogRuleCount");
- multimap<float, string> options;
+ multimap<float, string> options;
while(!unscored_grammar.eof())
{
++line;
@@ -510,7 +510,7 @@ int main(int argc, char** argv){
os << TD::GetString(cur_key)
<< ' ' << TD::GetString(it->first) << " ||| ";
feats.Write(false, &os);
- options.insert(make_pair(-feats.value(kLogRuleCount), os.str()));
+ options.insert(make_pair(-feats.get(kLogRuleCount), os.str()));
}
int ocount = 0;
for (multimap<float,string>::iterator it = options.begin(); it != options.end(); ++it) {
diff --git a/extools/mr_stripe_rule_reduce.cc b/extools/mr_stripe_rule_reduce.cc
index 0be1834d..c9b2eb2a 100644
--- a/extools/mr_stripe_rule_reduce.cc
+++ b/extools/mr_stripe_rule_reduce.cc
@@ -82,7 +82,7 @@ void DoPhraseMarginals(const vector<WordID>& key, const bool bidir, ID2RuleStati
}
double tot = 0;
for (ID2RuleStatistics::iterator it = val->begin(); it != val->end(); ++it)
- tot += it->second.counts.value(kCFE);
+ tot += it->second.counts.get(kCFE);
for (ID2RuleStatistics::iterator it = val->begin(); it != val->end(); ++it) {
it->second.counts.set_value(cur_marginal_id, tot);
generated by cgit v1.2.3 (git 2.46.0) at 2025-10-31 00:09:25 +0000