Brute force optimization code, buggy yet

wip


wip


wip refactor

src/libslic3r/Optimize/Optimizer.hpp

@@ -0,0 +1,182 @@
+#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