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#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;
// cerr << "---- s=" << sco << "\n";
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";
}
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