major restructure of the training code

1 files changed, 0 insertions, 114 deletions

diff --git a/dpmert/line_optimizer.cc b/dpmert/line_optimizer.cc
deleted file mode 100644
index 9cf33502..00000000
--- a/dpmert/line_optimizer.cc
+++ /dev/null
@@ -1,114 +0,0 @@
-#include "line_optimizer.h"
-
-#include <limits>
-#include <algorithm>
-
-#include "sparse_vector.h"
-#include "ns.h"
-
-using namespace std;
-
-typedef ErrorSurface::const_iterator ErrorIter;
-
-// sort by increasing x-ints
-struct IntervalComp {
-  bool operator() (const ErrorIter& a, const ErrorIter& b) const {
-    return a->x < b->x;
-  }
-};
-
-double LineOptimizer::LineOptimize(
-    const EvaluationMetric* metric,
-    const vector<ErrorSurface>& surfaces,
-    const LineOptimizer::ScoreType type,
-    float* best_score,
-    const double epsilon) {
-  // cerr << "MIN=" << MINIMIZE_SCORE << " MAX=" << MAXIMIZE_SCORE << "  MINE=" << type << endl;
-  vector<ErrorIter> all_ints;
-  for (vector<ErrorSurface>::const_iterator i = surfaces.begin();
-       i != surfaces.end(); ++i) {
-    const ErrorSurface& surface = *i;
-    for (ErrorIter j = surface.begin(); j != surface.end(); ++j)
-      all_ints.push_back(j);
-  }
-  sort(all_ints.begin(), all_ints.end(), IntervalComp());
-  double last_boundary = all_ints.front()->x;
-  SufficientStats acc;
-  float& cur_best_score = *best_score;
-  cur_best_score = (type == MAXIMIZE_SCORE ?
-    -numeric_limits<float>::max() : numeric_limits<float>::max());
-  bool left_edge = true;
-  double pos = numeric_limits<double>::quiet_NaN();
-  for (vector<ErrorIter>::iterator i = all_ints.begin();
-       i != all_ints.end(); ++i) {
-    const ErrorSegment& seg = **i;
-    if (seg.x - last_boundary > epsilon) {
-      float sco = metric->ComputeScore(acc);
-      if ((type == MAXIMIZE_SCORE && sco > cur_best_score) ||
-          (type == MINIMIZE_SCORE && sco < cur_best_score) ) {
-        cur_best_score = sco;
-	if (left_edge) {
-	  pos = seg.x - 0.1;
-	  left_edge = false;
-	} else {
-	  pos = last_boundary + (seg.x - last_boundary) / 2;
-	}
-	//cerr << "NEW BEST: " << pos << "  (score=" << cur_best_score << ")\n";
-      }
-      // string xx = metric->DetailedScore(acc); cerr << "---- " << xx;
-#undef SHOW_ERROR_SURFACES
-#ifdef SHOW_ERROR_SURFACES
-      cerr << "x=" << seg.x << "\ts=" << sco << "\n";
-#endif
-      last_boundary = seg.x;
-    }
-    // cerr << "x-boundary=" << seg.x << "\n";
-    //string x2; acc.Encode(&x2); cerr << "   ACC: " << x2 << endl;
-    //string x1; seg.delta.Encode(&x1); cerr << " DELTA: " << x1 << endl;
-    acc += seg.delta;
-  }
-  float sco = metric->ComputeScore(acc);
-  if ((type == MAXIMIZE_SCORE && sco > cur_best_score) ||
-      (type == MINIMIZE_SCORE && sco < cur_best_score) ) {
-    cur_best_score = sco;
-    if (left_edge) {
-      pos = 0;
-    } else {
-      pos = last_boundary + 1000.0;
-    }
-  }
-  return pos;
-}
-
-void LineOptimizer::RandomUnitVector(const vector<int>& features_to_optimize,
-                                     SparseVector<double>* axis,
-                                     RandomNumberGenerator<boost::mt19937>* rng) {
-  axis->clear();
-  for (int i = 0; i < features_to_optimize.size(); ++i)
-    axis->set_value(features_to_optimize[i], rng->NextNormal(0.0,1.0));
-  (*axis) /= axis->l2norm();
-}
-
-void LineOptimizer::CreateOptimizationDirections(
-     const vector<int>& features_to_optimize,
-     int additional_random_directions,
-     RandomNumberGenerator<boost::mt19937>* rng,
-     vector<SparseVector<double> >* dirs
-     , bool include_orthogonal
-  ) {
-  dirs->clear();
-  typedef SparseVector<double> Dir;
-  vector<Dir> &out=*dirs;
-  int i=0;
-  if (include_orthogonal)
-    for (;i<features_to_optimize.size();++i) {
-      Dir d;
-      d.set_value(features_to_optimize[i],1.);
-      out.push_back(d);
-    }
-  out.resize(i+additional_random_directions);
-  for (;i<out.size();++i)
-     RandomUnitVector(features_to_optimize, &out[i], rng);
-  cerr << "Generated " << out.size() << " total axes to optimize along.\n";
-}
-