more renaming, random pair sampler uses boost rng

1 files changed, 0 insertions, 74 deletions

diff --git a/dtrain/sample_hg.cc b/dtrain/sample_hg.cc
deleted file mode 100644
index 33872fb8..00000000
--- a/dtrain/sample_hg.cc
+++ /dev/null
@@ -1,74 +0,0 @@
-#include "sample_hg.h"
-
-#include <queue>
-
-#include "viterbi.h"
-#include "inside_outside.h"
-
-using namespace std;
-
-struct SampledDerivationWeightFunction {
-  typedef double Weight;
-  explicit SampledDerivationWeightFunction(const vector<bool>& sampled) : sampled_edges(sampled) {}
-  double operator()(const Hypergraph::Edge& e) const {
-    return static_cast<double>(sampled_edges[e.id_]);
-  }
-  const vector<bool>& sampled_edges;
-};
-
-void HypergraphSampler::sample_hypotheses(const Hypergraph& hg,
-                                          unsigned n,
-                                          MT19937* rng,
-                                          vector<Hypothesis>* hypos) {
-  hypos->clear();
-  hypos->resize(n);
-
-  // compute inside probabilities
-  vector<prob_t> node_probs;
-  Inside<prob_t, EdgeProb>(hg, &node_probs, EdgeProb());
-
-  vector<bool> sampled_edges(hg.edges_.size());
-  queue<unsigned> q;
-  SampleSet<prob_t> ss;
-  for (unsigned i = 0; i < n; ++i) {
-    fill(sampled_edges.begin(), sampled_edges.end(), false);
-    // sample derivation top down
-    assert(q.empty());
-    Hypothesis& hyp = (*hypos)[i];
-    SparseVector<double>& deriv_features = hyp.fmap;
-    q.push(hg.nodes_.size() - 1);
-    prob_t& model_score = hyp.model_score;
-    model_score = prob_t::One();
-    while(!q.empty()) {
-      unsigned cur_node_id = q.front();
-      q.pop();
-      const Hypergraph::Node& node = hg.nodes_[cur_node_id];
-      const unsigned num_in_edges = node.in_edges_.size();
-      unsigned sampled_edge_idx = 0;
-      if (num_in_edges == 1) {
-        sampled_edge_idx = node.in_edges_[0];
-      } else {
-        assert(num_in_edges > 1);
-        ss.clear();
-        for (unsigned j = 0; j < num_in_edges; ++j) {
-          const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-          prob_t p = edge.edge_prob_;   // edge weight
-          for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
-            p *= node_probs[edge.tail_nodes_[k]];  // tail node inside weight
-          ss.add(p);
-        }
-        sampled_edge_idx = node.in_edges_[rng->SelectSample(ss)];
-      }
-      sampled_edges[sampled_edge_idx] = true;
-      const Hypergraph::Edge& sampled_edge = hg.edges_[sampled_edge_idx];
-      deriv_features += sampled_edge.feature_values_;
-      model_score *= sampled_edge.edge_prob_;
-      //sampled_deriv->push_back(sampled_edge_idx);
-      for (unsigned j = 0; j < sampled_edge.tail_nodes_.size(); ++j) {
-        q.push(sampled_edge.tail_nodes_[j]);
-      }
-    }
-    Viterbi(hg, &hyp.words, ESentenceTraversal(), SampledDerivationWeightFunction(sampled_edges));
-  }
-}
-