1 files changed, 63 insertions, 6 deletions

diff --git a/training/lbl_model.cc b/training/lbl_model.cc
index a114bba7..2af848b5 100644
--- a/training/lbl_model.cc
+++ b/training/lbl_model.cc
@@ -28,7 +28,7 @@
 namespace po = boost::program_options;
 using namespace std;
 
-#define kDIMENSIONS 110
+#define kDIMENSIONS 100
 typedef Eigen::Matrix<float, kDIMENSIONS, 1> RVector;
 typedef Eigen::Matrix<float, 1, kDIMENSIONS> RTVector;
 typedef Eigen::Matrix<float, kDIMENSIONS, kDIMENSIONS> TMatrix;
@@ -40,7 +40,9 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
         ("input,i",po::value<string>(),"Input file")
         ("iterations,I",po::value<unsigned>()->default_value(1000),"Number of iterations of training")
         ("regularization_strength,C",po::value<float>()->default_value(0.1),"L2 regularization strength (0 for no regularization)")
-        ("eta,e", po::value<float>()->default_value(0.1f), "Eta for SGD")
+        ("eta", po::value<float>()->default_value(0.1f), "Eta for SGD")
+        ("source_embeddings,f", po::value<string>(), "File containing source embeddings (if unset, random vectors will be used)")
+        ("target_embeddings,e", po::value<string>(), "File containing target embeddings (if unset, random vectors will be used)")
         ("random_seed,s", po::value<unsigned>(), "Random seed")
         ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (0 = uniform, >0 sharpens)")
         ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model");
@@ -106,6 +108,59 @@ double ApplyRegularization(const double C,
   return reg;
 }
 
+void LoadEmbeddings(const string& filename, vector<RVector>* pv) {
+  vector<RVector>& v = *pv;
+  cerr << "Reading embeddings from " << filename << " ...\n";
+  ReadFile rf(filename);
+  istream& in = *rf.stream();
+  string line;
+  unsigned lc = 0;
+  while(getline(in, line)) {
+    ++lc;
+    size_t cur = line.find(' ');
+    if (cur == string::npos || cur == 0) {
+      cerr << "Parse error reading line " << lc << ":\n" << line << endl;
+      abort();
+    }
+    WordID w = TD::Convert(line.substr(0, cur));
+    if (w >= v.size()) continue;
+    RVector& curv = v[w];
+    line[cur] = 0;
+    size_t start = cur + 1;
+    cur = start + 1;
+    size_t c = 0;
+    while(cur < line.size()) {
+      if (line[cur] == ' ') {
+        line[cur] = 0;
+        curv[c++] = strtod(&line[start], NULL);
+        start = cur + 1;
+        cur = start;
+        if (c == kDIMENSIONS) break;
+      }
+      ++cur;
+    }
+    if (c < kDIMENSIONS && cur != start) {
+      if (cur < line.size()) line[cur] = 0;
+      curv[c++] = strtod(&line[start], NULL);
+    }
+    if (c != kDIMENSIONS) {
+      static bool first = true;
+      if (first) {
+        cerr << " read " << c << " dimensions from embedding file, but built with " << kDIMENSIONS << " (filling in with random values)\n";
+        first = false;
+      }
+      for (; c < kDIMENSIONS; ++c) curv[c] = rand();
+    }
+    if (c == kDIMENSIONS && cur != line.size()) {
+      static bool first = true;
+      if (first) {
+        cerr << " embedding file contains more dimensions than configured with, truncating.\n";
+        first = false;
+      }
+    }
+  }
+}
+
 int main(int argc, char** argv) {
   po::variables_map conf;
   if (!InitCommandLine(argc, argv, &conf)) return 1;
@@ -175,11 +230,11 @@ int main(int argc, char** argv) {
   for (unsigned i = 1; i < r_trg.size(); ++i) {
     r_trg[i] = RVector::Random();
     r_src[i] = RVector::Random();
-    r_trg[i][i % kDIMENSIONS] = 0.5;
-    r_src[i][(i-1) % kDIMENSIONS] = 0.5;
-    Normalize(&r_trg[i]);
-    Normalize(&r_src[i]);
   }
+  if (conf.count("source_embeddings"))
+    LoadEmbeddings(conf["source_embeddings"].as<string>(), &r_src);
+  if (conf.count("target_embeddings"))
+    LoadEmbeddings(conf["target_embeddings"].as<string>(), &r_trg);
   vector<set<unsigned> > trg_pos(TD::NumWords() + 1);
 
   // do optimization
@@ -242,6 +297,8 @@ int main(int argc, char** argv) {
 
       // model expectations for a single target generation with
       // uniform alignment prior
+      // TODO: when using a non-uniform alignment, m_exp will be
+      // a function of j (below)
       double m_z = 0;
       TMatrix m_exp = TMatrix::Zero();
       for (unsigned i = 0; i < src.size(); ++i) {