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#ifndef OPTIMIZER_HPP
#define OPTIMIZER_HPP
#include <utility>
#include <tuple>
#include <array>
#include <cmath>
#include <functional>
#include <limits>
#include <cassert>
namespace Slic3r { namespace opt {
// A type to hold the complete result of the optimization.
template<size_t N> struct Result {
int resultcode; // Method dependent
std::array<double, N> optimum;
double score;
};
// An interval of possible input values for optimization
class Bound {
double m_min, m_max;
public:
Bound(double min = std::numeric_limits<double>::min(),
double max = std::numeric_limits<double>::max())
: m_min(min), m_max(max)
{}
double min() const noexcept { return m_min; }
double max() const noexcept { return m_max; }
};
// Helper types for optimization function input and bounds
template<size_t N> using Input = std::array<double, N>;
template<size_t N> using Bounds = std::array<Bound, N>;
// A type for specifying the stop criteria. Setter methods can be concatenated
class StopCriteria {
// If the absolute value difference between two scores.
double m_abs_score_diff = std::nan("");
// If the relative value difference between two scores.
double m_rel_score_diff = std::nan("");
// Stop if this value or better is found.
double m_stop_score = std::nan("");
// A predicate that if evaluates to true, the optimization should terminate
// and the best result found prior to termination should be returned.
std::function<bool()> m_stop_condition = [] { return false; };
// The max allowed number of iterations.
unsigned m_max_iterations = 0;
public:
StopCriteria & abs_score_diff(double val)
{
m_abs_score_diff = val; return *this;
}
double abs_score_diff() const { return m_abs_score_diff; }
StopCriteria & rel_score_diff(double val)
{
m_rel_score_diff = val; return *this;
}
double rel_score_diff() const { return m_rel_score_diff; }
StopCriteria & stop_score(double val)
{
m_stop_score = val; return *this;
}
double stop_score() const { return m_stop_score; }
StopCriteria & max_iterations(double val)
{
m_max_iterations = val; return *this;
}
double max_iterations() const { return m_max_iterations; }
template<class Fn> StopCriteria & stop_condition(Fn &&cond)
{
m_stop_condition = cond; return *this;
}
bool stop_condition() { return m_stop_condition(); }
};
// Helper class to use optimization methods involving gradient.
template<size_t N> struct ScoreGradient {
double score;
std::optional<std::array<double, N>> gradient;
ScoreGradient(double s, const std::array<double, N> &grad)
: score{s}, gradient{grad}
{}
};
// Helper to be used in static_assert.
template<class T> struct always_false { enum { value = false }; };
// Basic interface to optimizer object
template<class Method, class Enable = void> class Optimizer {
public:
Optimizer(const StopCriteria &)
{
static_assert (always_false<Method>::value,
"Optimizer unimplemented for given method!");
}
// Switch optimization towards function minimum
Optimizer &to_min() { return *this; }
// Switch optimization towards function maximum
Optimizer &to_max() { return *this; }
// Set criteria for successive optimizations
Optimizer &set_criteria(const StopCriteria &) { return *this; }
// Get current criteria
StopCriteria get_criteria() const { return {}; };
// Find function minimum or maximum for Func which has has signature:
// double(const Input<N> &input) and input with dimension N
//
// Initial starting point can be given as the second parameter.
//
// For each dimension an interval (Bound) has to be given marking the bounds
// for that dimension.
//
// initvals have to be within the specified bounds, otherwise its undefined
// behavior.
//
// Func can return a score of type double or optionally a ScoreGradient
// class to indicate the function gradient for a optimization methods that
// make use of the gradient.
template<class Func, size_t N>
Result<N> optimize(Func&& /*func*/,
const Input<N> &/*initvals*/,
const Bounds<N>& /*bounds*/) { return {}; }
// optional for randomized methods:
void seed(long /*s*/) {}
};
namespace detail {
// Helper to convert C style array to std::array. The copy should be optimized
// away with modern compilers.
template<size_t N, class T> auto to_arr(const T *a)
{
std::array<T, N> r;
std::copy(a, a + N, std::begin(r));
return r;
}
template<size_t N, class T> auto to_arr(const T (&a) [N])
{
return to_arr<N>(static_cast<const T *>(a));
}
} // namespace detail
// Helper functions to create bounds, initial value
template<size_t N> Bounds<N> bounds(const Bound (&b) [N]) { return detail::to_arr(b); }
template<size_t N> Input<N> initvals(const double (&a) [N]) { return detail::to_arr(a); }
template<size_t N> auto score_gradient(double s, const double (&grad)[N])
{
return ScoreGradient<N>(s, detail::to_arr(grad));
}
}} // namespace Slic3r::opt
#endif // OPTIMIZER_HPP
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