coco
118 lines
4.1 KiB
C++
118 lines
4.1 KiB
C++
// Copyright (C) 2016 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_fIND_OPTIMAL_PARAMETERS_Hh_
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#define DLIB_fIND_OPTIMAL_PARAMETERS_Hh_
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#include "../matrix.h"
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#include "find_optimal_parameters_abstract.h"
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#include "optimization_bobyqa.h"
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#include "optimization_line_search.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename funct
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>
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double find_optimal_parameters (
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double initial_search_radius,
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double eps,
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const unsigned int max_f_evals,
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matrix<double,0,1>& x,
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const matrix<double,0,1>& x_lower,
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const matrix<double,0,1>& x_upper,
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const funct& f
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)
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{
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DLIB_CASSERT(x.size() == x_lower.size() && x_lower.size() == x_upper.size() && x.size() > 0,
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"\t double find_optimal_parameters()"
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<< "\n\t x.size(): " << x.size()
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<< "\n\t x_lower.size(): " << x_lower.size()
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<< "\n\t x_upper.size(): " << x_upper.size()
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);
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// check the requirements. Also split the assert up so that the error message isn't huge.
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DLIB_CASSERT(max_f_evals > 1 && eps > 0 && initial_search_radius > eps,
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"\t double find_optimal_parameters()"
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<< "\n\t Invalid arguments have been given to this function"
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<< "\n\t initial_search_radius: " << initial_search_radius
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<< "\n\t eps: " << eps
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<< "\n\t max_f_evals: " << max_f_evals
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);
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DLIB_CASSERT( min(x_upper - x_lower) > 0 &&
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min(x - x_lower) >= 0 && min(x_upper - x) >= 0,
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"\t double find_optimal_parameters()"
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<< "\n\t The bounds constraints have to make sense and also contain the starting point."
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<< "\n\t min(x_upper - x_lower): " << min(x_upper - x_lower)
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<< "\n\t min(x - x_lower) >= 0 && min(x_upper - x) >= 0: " << (min(x - x_lower) >= 0 && min(x_upper - x) >= 0)
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);
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// if the search radius is too big then shrink it so it fits inside the bounds.
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if (initial_search_radius*2 >= min(x_upper-x_lower))
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initial_search_radius = 0.5*min(x_upper-x_lower)*0.99;
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double objective_val = std::numeric_limits<double>::infinity();
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size_t num_iter_used = 0;
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if (x.size() == 1)
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{
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// BOBYQA requires x to have at least 2 variables in it. So we can't call it in
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// this case. Instead we call find_min_single_variable().
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matrix<double,0,1> temp(1);
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auto ff = [&](const double& xx)
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{
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temp = xx;
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double obj = f(temp);
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++num_iter_used;
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// keep track of the best x.
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if (obj < objective_val)
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{
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objective_val = obj;
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x = temp;
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}
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return obj;
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};
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try
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{
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double dx = x(0);
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find_min_single_variable(ff, dx, x_lower(0), x_upper(0), eps, max_f_evals, initial_search_radius);
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} catch (optimize_single_variable_failure& )
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{
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}
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}
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else
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{
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auto ff = [&](const matrix<double,0,1>& xx)
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{
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double obj = f(xx);
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++num_iter_used;
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// keep track of the best x.
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if (obj < objective_val)
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{
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objective_val = obj;
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x = xx;
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}
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return obj;
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};
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try
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{
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matrix<double,0,1> start_x = x;
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find_min_bobyqa(ff, start_x, 2*x.size()+1, x_lower, x_upper, initial_search_radius, eps, max_f_evals);
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} catch (bobyqa_failure& )
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{
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}
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}
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return objective_val;
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}
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_fIND_OPTIMAL_PARAMETERS_Hh_
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