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-rw-r--r--pro-train/mr_pro_reduce.cc34
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")) {