diff options
Diffstat (limited to 'pro-train/mr_pro_reduce.cc')
| -rw-r--r-- | pro-train/mr_pro_reduce.cc | 34 |
1 files changed, 17 insertions, 17 deletions
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")) { |