Add the full source of BambuStudio

using version 1.0.10

src/libslic3r/Optimize/BruteforceOptimizer.hpp

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+#ifndef BRUTEFORCEOPTIMIZER_HPP
+#define BRUTEFORCEOPTIMIZER_HPP
+
+#include <libslic3r/Optimize/Optimizer.hpp>
+
+namespace Slic3r { namespace opt {
+
+namespace detail {
+// Implementing a bruteforce optimizer
+
+// Return the number of iterations needed to reach a specific grid position (idx)
+template<size_t N>
+long num_iter(const std::array<size_t, N> &idx, size_t gridsz)
+{
+    long ret = 0;
+    for (size_t i = 0; i < N; ++i) ret += idx[i] * std::pow(gridsz, i);
+    return ret;
+}
+
+// Implementation of a grid search where the search interval is sampled in
+// equidistant points for each dimension. Grid size determines the number of
+// samples for one dimension so the number of function calls is gridsize ^ dimension.
+struct AlgBurteForce {
+    bool to_min;
+    StopCriteria stc;
+    size_t gridsz;
+
+    AlgBurteForce(const StopCriteria &cr, size_t gs): stc{cr}, gridsz{gs} {}
+
+    // This function is called recursively for each dimension and generates
+    // the grid values for the particular dimension. If D is less than zero,
+    // the object function input values are generated for each dimension and it
+    // can be evaluated. The current best score is compared with the newly
+    // returned score and changed appropriately.
+    template<int D, size_t N, class Fn, class Cmp>
+    bool run(std::array<size_t, N> &idx,
+             Result<N> &result,
+             const Bounds<N> &bounds,
+             Fn &&fn,
+             Cmp &&cmp)
+    {
+        if (stc.stop_condition()) return false;
+
+        if constexpr (D < 0) { // Let's evaluate fn
+            Input<N> inp;
+
+            auto max_iter = stc.max_iterations();
+            if (max_iter && num_iter(idx, gridsz) >= max_iter)
+                return false;
+
+            for (size_t d = 0; d < N; ++d) {
+                const Bound &b = bounds[d];
+                double step = (b.max() - b.min()) / (gridsz - 1);
+                inp[d] = b.min() + idx[d] * step;
+            }
+
+            auto score = fn(inp);
+            if (cmp(score, result.score)) { // Change current score to the new
+                double absdiff = std::abs(score - result.score);
+
+                result.score = score;
+                result.optimum = inp;
+
+                // Check if the required precision is reached.
+                if (absdiff < stc.abs_score_diff() ||
+                    absdiff < stc.rel_score_diff() * std::abs(score))
+                    return false;
+            }
+
+        } else {
+            for (size_t i = 0; i < gridsz; ++i) {
+                idx[D] = i; // Mark the current grid position and dig down
+                if (!run<D - 1>(idx, result, bounds, std::forward<Fn>(fn),
+                                std::forward<Cmp>(cmp)))
+                    return false;
+            }
+        }
+
+        return true;
+    }
+
+    template<class Fn, size_t N>
+    Result<N> optimize(Fn&& fn,
+                       const Input<N> &/*initvals*/,
+                       const Bounds<N>& bounds)
+    {
+        std::array<size_t, N> idx = {};
+        Result<N> result;
+
+        if (to_min) {
+            result.score = std::numeric_limits<double>::max();
+            run<int(N) - 1>(idx, result, bounds, std::forward<Fn>(fn),
+                            std::less<double>{});
+        }
+        else {
+            result.score = std::numeric_limits<double>::lowest();
+            run<int(N) - 1>(idx, result, bounds, std::forward<Fn>(fn),
+                            std::greater<double>{});
+        }
+
+        return result;
+    }
+};
+
+} // namespace detail
+
+using AlgBruteForce = detail::AlgBurteForce;
+
+template<>
+class Optimizer<AlgBruteForce> {
+    AlgBruteForce m_alg;
+
+public:
+
+    Optimizer(const StopCriteria &cr = {}, size_t gridsz = 100)
+        : m_alg{cr, gridsz}
+    {}
+
+    Optimizer& to_max() { m_alg.to_min = false; return *this; }
+    Optimizer& to_min() { m_alg.to_min = true;  return *this; }
+
+    template<class Func, size_t N>
+    Result<N> optimize(Func&& func,
+                       const Input<N> &initvals,
+                       const Bounds<N>& bounds)
+    {
+        return m_alg.optimize(std::forward<Func>(func), initvals, bounds);
+    }
+
+    Optimizer &set_criteria(const StopCriteria &cr)
+    {
+        m_alg.stc = cr; return *this;
+    }
+
+    const StopCriteria &get_criteria() const { return m_alg.stc; }
+};
+
+}} // namespace Slic3r::opt
+
+#endif // BRUTEFORCEOPTIMIZER_HPP