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-rw-r--r--pro-train/mr_pro_map.cc42
-rw-r--r--pro-train/mr_pro_reduce.cc34
2 files changed, 38 insertions, 38 deletions
diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
index bc59285b..0a9b75d7 100644
--- a/pro-train/mr_pro_map.cc
+++ b/pro-train/mr_pro_map.cc
@@ -27,7 +27,7 @@ namespace po = boost::program_options;
struct ApproxVectorHasher {
static const size_t MASK = 0xFFFFFFFFull;
union UType {
- double f;
+ double f; // leave as double
size_t i;
};
static inline double round(const double x) {
@@ -40,9 +40,9 @@ struct ApproxVectorHasher {
t.i &= (1ull - MASK);
return t.f;
}
- size_t operator()(const SparseVector<double>& x) const {
+ size_t operator()(const SparseVector<weight_t>& x) const {
size_t h = 0x573915839;
- for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) {
+ for (SparseVector<weight_t>::const_iterator it = x.begin(); it != x.end(); ++it) {
UType t;
t.f = it->second;
if (t.f) {
@@ -56,9 +56,9 @@ struct ApproxVectorHasher {
};
struct ApproxVectorEquals {
- bool operator()(const SparseVector<double>& a, const SparseVector<double>& b) const {
- SparseVector<double>::const_iterator bit = b.begin();
- for (SparseVector<double>::const_iterator ait = a.begin(); ait != a.end(); ++ait) {
+ bool operator()(const SparseVector<weight_t>& a, const SparseVector<weight_t>& b) const {
+ SparseVector<weight_t>::const_iterator bit = b.begin();
+ for (SparseVector<weight_t>::const_iterator ait = a.begin(); ait != a.end(); ++ait) {
if (bit == b.end() ||
ait->first != bit->first ||
ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second))
@@ -105,18 +105,18 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
struct HypInfo {
- HypInfo() : g_(-100.0) {}
- HypInfo(const vector<WordID>& h, const SparseVector<double>& feats) : hyp(h), g_(-100.0), x(feats) {}
+ HypInfo() : g_(-100.0f) {}
+ HypInfo(const vector<WordID>& h, const SparseVector<weight_t>& feats) : hyp(h), g_(-100.0f), x(feats) {}
// lazy evaluation
double g(const SentenceScorer& scorer) const {
- if (g_ == -100.0)
+ if (g_ == -100.0f)
g_ = scorer.ScoreCandidate(hyp)->ComputeScore();
return g_;
}
vector<WordID> hyp;
- mutable double g_;
- SparseVector<double> x;
+ mutable float g_;
+ SparseVector<weight_t> x;
};
struct HypInfoCompare {
@@ -146,8 +146,8 @@ void WriteKBest(const string& file, const vector<HypInfo>& kbest) {
}
}
-void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
- SparseVector<double>& x = *out;
+void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) {
+ SparseVector<weight_t>& x = *out;
size_t last_start = cur;
size_t last_comma = string::npos;
while(cur <= line.size()) {
@@ -211,15 +211,15 @@ struct ThresholdAlpha {
};
struct TrainingInstance {
- TrainingInstance(const SparseVector<double>& feats, bool positive, double diff) : x(feats), y(positive), gdiff(diff) {}
- SparseVector<double> x;
+ TrainingInstance(const SparseVector<weight_t>& feats, bool positive, float diff) : x(feats), y(positive), gdiff(diff) {}
+ SparseVector<weight_t> x;
#undef DEBUGGING_PRO
#ifdef DEBUGGING_PRO
vector<WordID> a;
vector<WordID> b;
#endif
bool y;
- double gdiff;
+ float gdiff;
};
#ifdef DEBUGGING_PRO
ostream& operator<<(ostream& os, const TrainingInstance& d) {
@@ -235,19 +235,19 @@ struct DiffOrder {
void Sample(const unsigned gamma, const unsigned xi, const vector<HypInfo>& J_i, const SentenceScorer& scorer, const bool invert_score, vector<TrainingInstance>* pv) {
vector<TrainingInstance> v1, v2;
- double avg_diff = 0;
+ float avg_diff = 0;
for (unsigned i = 0; i < gamma; ++i) {
const size_t a = rng->inclusive(0, J_i.size() - 1)();
const size_t b = rng->inclusive(0, J_i.size() - 1)();
if (a == b) continue;
- double ga = J_i[a].g(scorer);
- double gb = J_i[b].g(scorer);
+ float ga = J_i[a].g(scorer);
+ float gb = J_i[b].g(scorer);
bool positive = gb < ga;
if (invert_score) positive = !positive;
- const double gdiff = fabs(ga - gb);
+ const float gdiff = fabs(ga - gb);
if (!gdiff) continue;
avg_diff += gdiff;
- SparseVector<double> xdiff = (J_i[a].x - J_i[b].x).erase_zeros();
+ SparseVector<weight_t> xdiff = (J_i[a].x - J_i[b].x).erase_zeros();
if (xdiff.empty()) {
cerr << "Empty diff:\n " << TD::GetString(J_i[a].hyp) << endl << "x=" << J_i[a].x << endl;
cerr << " " << TD::GetString(J_i[b].hyp) << endl << "x=" << J_i[b].x << endl;
diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
index 9caaa1d1..239649c1 100644
--- a/pro-train/mr_pro_reduce.cc
+++ b/pro-train/mr_pro_reduce.cc
@@ -40,8 +40,8 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
- SparseVector<double>& x = *out;
+void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) {
+ SparseVector<weight_t>& x = *out;
size_t last_start = cur;
size_t last_comma = string::npos;
while(cur <= line.size()) {
@@ -52,7 +52,7 @@ void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
}
const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
if (cur < line.size()) line[cur] = 0;
- const double val = strtod(&line[last_comma + 1], NULL);
+ const weight_t val = strtod(&line[last_comma + 1], NULL);
x.set_value(fid, val);
last_comma = string::npos;
@@ -65,13 +65,13 @@ void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
}
}
-void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<double> > >* corpus) {
+void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<weight_t> > >* corpus) {
istream& in = *pin;
corpus->clear();
bool flag = false;
int lc = 0;
string line;
- SparseVector<double> x;
+ SparseVector<weight_t> x;
while(getline(in, line)) {
++lc;
if (lc % 1000 == 0) { cerr << '.'; flag = true; }
@@ -88,16 +88,16 @@ void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<double> > >* corpus
if (flag) cerr << endl;
}
-void GradAdd(const SparseVector<double>& v, const double scale, vector<double>* acc) {
- for (SparseVector<double>::const_iterator it = v.begin();
+void GradAdd(const SparseVector<weight_t>& v, const double scale, vector<weight_t>* acc) {
+ for (SparseVector<weight_t>::const_iterator it = v.begin();
it != v.end(); ++it) {
(*acc)[it->first] += it->second * scale;
}
}
-double TrainingInference(const vector<double>& x,
- const vector<pair<bool, SparseVector<double> > >& corpus,
- vector<double>* g = NULL) {
+double TrainingInference(const vector<weight_t>& x,
+ const vector<pair<bool, SparseVector<weight_t> > >& corpus,
+ vector<weight_t>* g = NULL) {
double cll = 0;
for (int i = 0; i < corpus.size(); ++i) {
const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias
@@ -132,13 +132,13 @@ double TrainingInference(const vector<double>& x,
}
// return held-out log likelihood
-double LearnParameters(const vector<pair<bool, SparseVector<double> > >& training,
- const vector<pair<bool, SparseVector<double> > >& testing,
+double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training,
+ const vector<pair<bool, SparseVector<weight_t> > >& testing,
const double sigsq,
const unsigned memory_buffers,
- vector<double>* px) {
- vector<double>& x = *px;
- vector<double> vg(FD::NumFeats(), 0.0);
+ vector<weight_t>* px) {
+ vector<weight_t>& x = *px;
+ vector<weight_t> vg(FD::NumFeats(), 0.0);
bool converged = false;
LBFGSOptimizer opt(FD::NumFeats(), memory_buffers);
double tppl = 0.0;
@@ -172,7 +172,7 @@ double LearnParameters(const vector<pair<bool, SparseVector<double> > >& trainin
cll += reg;
cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t";
try {
- vector<double> old_x = x;
+ vector<weight_t> old_x = x;
do {
opt.Optimize(cll, vg, &x);
converged = opt.HasConverged();
@@ -193,7 +193,7 @@ int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
string line;
- vector<pair<bool, SparseVector<double> > > training, testing;
+ vector<pair<bool, SparseVector<weight_t> > > training, testing;
SparseVector<weight_t> old_weights;
const bool tune_regularizer = conf.count("tune_regularizer");
if (tune_regularizer && !conf.count("testset")) {
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-22 18:32:39 +0000