merge with upstream

1 files changed, 89 insertions, 0 deletions

diff --git a/gi/pf/backward.cc b/gi/pf/backward.cc
new file mode 100644
index 00000000..b92629fd
--- /dev/null
+++ b/gi/pf/backward.cc
@@ -0,0 +1,89 @@
+#include "backward.h"
+
+#include <queue>
+#include <utility>
+
+#include "array2d.h"
+#include "reachability.h"
+#include "base_distributions.h"
+
+using namespace std;
+
+BackwardEstimator::BackwardEstimator(const string& s2t,
+                    const string& t2s) : m1(new Model1(s2t)), m1inv(new Model1(t2s)) {}
+
+BackwardEstimator::~BackwardEstimator() {
+  delete m1; m1 = NULL;
+  delete m1inv; m1inv = NULL;
+}
+
+float BackwardEstimator::ComputeBackwardProb(const std::vector<WordID>& src,
+                                             const std::vector<WordID>& trg,
+                                             unsigned src_covered,
+                                             unsigned trg_covered,
+                                             double s2t_ratio) const {
+  if (src_covered == src.size() || trg_covered == trg.size()) {
+    assert(src_covered == src.size());
+    assert(trg_covered == trg.size());
+    return 0;
+  }
+  static const WordID kNULL = TD::Convert("<eps>");
+  const prob_t uniform_alignment(1.0 / (src.size() - src_covered + 1));
+  // TODO factor in expected length ratio
+  prob_t e; e.logeq(Md::log_poisson(trg.size() - trg_covered, (src.size() - src_covered) * s2t_ratio)); // p(trg len remaining | src len remaining)
+  for (unsigned j = trg_covered; j < trg.size(); ++j) {
+    prob_t p = (*m1)(kNULL, trg[j]) + prob_t(1e-12);
+    for (unsigned i = src_covered; i < src.size(); ++i)
+      p += (*m1)(src[i], trg[j]);
+    if (p.is_0()) {
+      cerr << "ERROR: p(" << TD::Convert(trg[j]) << " | " << TD::GetString(src) << ") = 0!\n";
+      assert(!"failed");
+    }
+    p *= uniform_alignment;
+    e *= p;
+  }
+  // TODO factor in expected length ratio
+  const prob_t inv_uniform(1.0 / (trg.size() - trg_covered + 1.0));
+  prob_t inv;
+  inv.logeq(Md::log_poisson(src.size() - src_covered, (trg.size() - trg_covered) / s2t_ratio));
+  for (unsigned i = src_covered; i < src.size(); ++i) {
+    prob_t p = (*m1inv)(kNULL, src[i]) + prob_t(1e-12);
+    for (unsigned j = trg_covered; j < trg.size(); ++j)
+      p += (*m1inv)(trg[j], src[i]);
+    if (p.is_0()) {
+      cerr << "ERROR: p_inv(" << TD::Convert(src[i]) << " | " << TD::GetString(trg) << ") = 0!\n";
+      assert(!"failed");
+    }
+    p *= inv_uniform;
+    inv *= p;
+  }
+  return (log(e) + log(inv)) / 2;
+}
+
+void BackwardEstimator::InitializeGrid(const vector<WordID>& src,
+                      const vector<WordID>& trg,
+                      const Reachability& r,
+                      double s2t_ratio,
+                      float* grid) const {
+  queue<pair<int,int> > q;
+  q.push(make_pair(0,0));
+  Array2D<bool> done(src.size()+1, trg.size()+1, false);
+  //cerr << TD::GetString(src) << " ||| " << TD::GetString(trg) << endl;
+  while(!q.empty()) {
+    const pair<int,int> n = q.front();
+    q.pop();
+    if (done(n.first,n.second)) continue;
+    done(n.first,n.second) = true;
+
+    float lp = ComputeBackwardProb(src, trg, n.first, n.second, s2t_ratio);
+    if (n.first == 0 && n.second == 0) grid[0] = lp;
+    //cerr << "  " << n.first << "," << n.second << "\t" << lp << endl;
+
+    if (n.first == src.size() || n.second == trg.size()) continue;
+    const vector<pair<short,short> >& edges = r.valid_deltas[n.first][n.second];
+    for (int i = 0; i < edges.size(); ++i)
+      q.push(make_pair(n.first + edges[i].first, n.second + edges[i].second));
+  }
+  //static int cc = 0; ++cc; if (cc == 80) exit(1);
+}
+