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diff --git a/ml/dlib/dlib/global_optimization/upper_bound_function.h b/ml/dlib/dlib/global_optimization/upper_bound_function.h
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-// Copyright (C) 2017 Davis E. King (davis@dlib.net)
-// License: Boost Software License See LICENSE.txt for the full license.
-#ifndef DLIB_UPPER_bOUND_FUNCTION_Hh_
-#define DLIB_UPPER_bOUND_FUNCTION_Hh_
-
-#include "upper_bound_function_abstract.h"
-#include "../svm/svm_c_linear_dcd_trainer.h"
-#include "../statistics.h"
-#include <limits>
-#include <utility>
-
-namespace dlib
-{
-
-// ----------------------------------------------------------------------------------------
-
- struct function_evaluation
- {
- function_evaluation() = default;
- function_evaluation(const matrix<double,0,1>& x, double y) :x(x), y(y) {}
-
- matrix<double,0,1> x;
- double y = std::numeric_limits<double>::quiet_NaN();
- };
-
-// ----------------------------------------------------------------------------------------
-
- class upper_bound_function
- {
-
- public:
-
- upper_bound_function(
- ) = default;
-
- upper_bound_function(
- const double relative_noise_magnitude,
- const double solver_eps
- ) : relative_noise_magnitude(relative_noise_magnitude), solver_eps(solver_eps)
- {
- DLIB_CASSERT(relative_noise_magnitude >= 0);
- DLIB_CASSERT(solver_eps > 0);
- }
-
- explicit upper_bound_function(
- const std::vector<function_evaluation>& _points,
- const double relative_noise_magnitude = 0.001,
- const double solver_eps = 0.0001
- ) : relative_noise_magnitude(relative_noise_magnitude), solver_eps(solver_eps), points(_points)
- {
- DLIB_CASSERT(relative_noise_magnitude >= 0);
- DLIB_CASSERT(solver_eps > 0);
-
- if (points.size() > 1)
- {
- DLIB_CASSERT(points[0].x.size() > 0, "The vectors can't be empty.");
-
- const long dims = points[0].x.size();
- for (auto& p : points)
- DLIB_CASSERT(p.x.size() == dims, "All the vectors given to upper_bound_function must have the same dimensionality.");
-
- learn_params();
- }
-
- }
-
- void add (
- const function_evaluation& point
- )
- {
- DLIB_CASSERT(point.x.size() != 0, "The vectors can't be empty.");
- if (points.size() == 0)
- {
- points.push_back(point);
- return;
- }
-
- DLIB_CASSERT(point.x.size() == dimensionality(), "All the vectors given to upper_bound_function must have the same dimensionality.");
-
- if (points.size() < 4)
- {
- points.push_back(point);
- *this = upper_bound_function(points, relative_noise_magnitude, solver_eps);
- return;
- }
-
- points.push_back(point);
- // add constraints between the new point and the old points
- for (size_t i = 0; i < points.size()-1; ++i)
- active_constraints.push_back(std::make_pair(i,points.size()-1));
-
- learn_params();
- }
-
- long num_points(
- ) const
- {
- return points.size();
- }
-
- long dimensionality(
- ) const
- {
- if (points.size() == 0)
- return 0;
- else
- return points[0].x.size();
- }
-
- const std::vector<function_evaluation>& get_points(
- ) const
- {
- return points;
- }
-
- double operator() (
- const matrix<double,0,1>& x
- ) const
- {
- DLIB_CASSERT(num_points() > 0);
- DLIB_CASSERT(x.size() == dimensionality());
-
-
-
- double upper_bound = std::numeric_limits<double>::infinity();
-
- for (size_t i = 0; i < points.size(); ++i)
- {
- const double local_bound = points[i].y + std::sqrt(offsets[i] + dot(slopes, squared(x-points[i].x)));
- upper_bound = std::min(upper_bound, local_bound);
- }
-
- return upper_bound;
- }
-
- private:
-
- void learn_params (
- )
- {
- const long dims = points[0].x.size();
-
- using sample_type = std::vector<std::pair<size_t,double>>;
- using kernel_type = sparse_linear_kernel<sample_type>;
- std::vector<sample_type> x;
- std::vector<double> y;
-
- // We are going to normalize the data so the values aren't extreme. First, we
- // collect statistics on our data.
- std::vector<running_stats<double>> x_rs(dims);
- running_stats<double> y_rs;
- for (auto& v : points)
- {
- for (long i = 0; i < v.x.size(); ++i)
- x_rs[i].add(v.x(i));
- y_rs.add(v.y);
- }
-
-
- // compute normalization vectors for the data. The only reason we do this is
- // to make the optimization well conditioned. In particular, scaling the y
- // values will prevent numerical errors in the 1-diff*diff computation below that
- // would otherwise result when diff is really big. Also, scaling the xvalues
- // to be about 1 will similarly make the optimization more stable and it also
- // has the added benefit of keeping the relative_noise_magnitude's scale
- // constant regardless of the size of x values.
- const double yscale = 1.0/y_rs.stddev();
- std::vector<double> xscale(dims);
- for (size_t i = 0; i < xscale.size(); ++i)
- xscale[i] = 1.0/(x_rs[i].stddev()*yscale); // make it so that xscale[i]*yscale == 1/x_rs[i].stddev()
-
- sample_type samp;
- auto add_constraint = [&](long i, long j) {
- samp.clear();
- for (long k = 0; k < dims; ++k)
- {
- double temp = (points[i].x(k) - points[j].x(k))*xscale[k]*yscale;
- samp.push_back(std::make_pair(k, temp*temp));
- }
-
- if (points[i].y > points[j].y)
- samp.push_back(std::make_pair(dims + j, relative_noise_magnitude));
- else
- samp.push_back(std::make_pair(dims + i, relative_noise_magnitude));
-
- const double diff = (points[i].y - points[j].y)*yscale;
- samp.push_back(std::make_pair(dims + points.size(), 1-diff*diff));
-
- x.push_back(samp);
- y.push_back(1);
- };
-
- if (active_constraints.size() == 0)
- {
- x.reserve(points.size()*(points.size()-1)/2);
- y.reserve(points.size()*(points.size()-1)/2);
- for (size_t i = 0; i < points.size(); ++i)
- {
- for (size_t j = i+1; j < points.size(); ++j)
- {
- add_constraint(i,j);
- }
- }
- }
- else
- {
- for (auto& p : active_constraints)
- add_constraint(p.first, p.second);
- }
-
-
-
-
- svm_c_linear_dcd_trainer<kernel_type> trainer;
- trainer.set_c(std::numeric_limits<double>::infinity());
- //trainer.be_verbose();
- trainer.force_last_weight_to_1(true);
- trainer.set_epsilon(solver_eps);
-
- svm_c_linear_dcd_trainer<kernel_type>::optimizer_state state;
- auto df = trainer.train(x,y, state);
-
- // save the active constraints for later so we can use them inside add() to add
- // new points efficiently.
- if (active_constraints.size() == 0)
- {
- long k = 0;
- for (size_t i = 0; i < points.size(); ++i)
- {
- for (size_t j = i+1; j < points.size(); ++j)
- {
- if (state.get_alpha()[k++] != 0)
- active_constraints.push_back(std::make_pair(i,j));
- }
- }
- }
- else
- {
- DLIB_CASSERT(state.get_alpha().size() == active_constraints.size());
- new_active_constraints.clear();
- for (size_t i = 0; i < state.get_alpha().size(); ++i)
- {
- if (state.get_alpha()[i] != 0)
- new_active_constraints.push_back(active_constraints[i]);
- }
- active_constraints.swap(new_active_constraints);
- }
-
- //std::cout << "points.size(): " << points.size() << std::endl;
- //std::cout << "active_constraints.size(): " << active_constraints.size() << std::endl;
-
-
- const auto& bv = df.basis_vectors(0);
- slopes.set_size(dims);
- for (long i = 0; i < dims; ++i)
- slopes(i) = bv[i].second*xscale[i]*xscale[i];
-
- //std::cout << "slopes:" << trans(slopes);
-
- offsets.assign(points.size(),0);
-
-
- for (size_t i = 0; i < points.size(); ++i)
- {
- offsets[i] += bv[slopes.size()+i].second*relative_noise_magnitude;
- }
- }
-
-
-
- double relative_noise_magnitude = 0.001;
- double solver_eps = 0.0001;
- std::vector<std::pair<size_t,size_t>> active_constraints, new_active_constraints;
-
- std::vector<function_evaluation> points;
- std::vector<double> offsets; // offsets.size() == points.size()
- matrix<double,0,1> slopes; // slopes.size() == points[0].first.size()
- };
-
-// ----------------------------------------------------------------------------------------
-
-}
-
-#endif // DLIB_UPPER_bOUND_FUNCTION_Hh_
-
-
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