From 1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 18 Nov 2012 13:35:42 -0500 Subject: major restructure of the training code --- training/dpmert/line_optimizer.cc | 114 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 training/dpmert/line_optimizer.cc (limited to 'training/dpmert/line_optimizer.cc') diff --git a/training/dpmert/line_optimizer.cc b/training/dpmert/line_optimizer.cc new file mode 100644 index 00000000..9cf33502 --- /dev/null +++ b/training/dpmert/line_optimizer.cc @@ -0,0 +1,114 @@ +#include "line_optimizer.h" + +#include +#include + +#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& surfaces, + const LineOptimizer::ScoreType type, + float* best_score, + const double epsilon) { + // cerr << "MIN=" << MINIMIZE_SCORE << " MAX=" << MAXIMIZE_SCORE << " MINE=" << type << endl; + vector all_ints; + for (vector::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::max() : numeric_limits::max()); + bool left_edge = true; + double pos = numeric_limits::quiet_NaN(); + for (vector::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& features_to_optimize, + SparseVector* axis, + RandomNumberGenerator* 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& features_to_optimize, + int additional_random_directions, + RandomNumberGenerator* rng, + vector >* dirs + , bool include_orthogonal + ) { + dirs->clear(); + typedef SparseVector Dir; + vector &out=*dirs; + int i=0; + if (include_orthogonal) + for (;i