mirror of
https://github.com/bkaradzic/bgfx.git
synced 2026-02-17 20:52:36 +01:00
588 lines
12 KiB
C++
588 lines
12 KiB
C++
#include "../src/meshoptimizer.h"
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#include "objparser.h"
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#include <algorithm>
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#include <functional>
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#include <vector>
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#include <cmath>
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#include <cstdint>
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#include <cstdio>
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#include <cstring>
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const int kCacheSizeMax = 16;
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const int kValenceMax = 8;
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namespace meshopt
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{
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extern thread_local float kVertexScoreTableCache[1 + kCacheSizeMax];
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extern thread_local float kVertexScoreTableLive[1 + kValenceMax];
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} // namespace meshopt
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struct { int cache, warp, triangle; } profiles[] =
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{
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{14, 64, 128}, // AMD GCN
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{32, 32, 32}, // NVidia Pascal
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// { 16, 32, 32 }, // NVidia Kepler, Maxwell
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// { 128, 0, 0 }, // Intel
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};
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const int Profile_Count = sizeof(profiles) / sizeof(profiles[0]);
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struct pcg32_random_t
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{
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uint64_t state;
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uint64_t inc;
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};
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#define PCG32_INITIALIZER { 0x853c49e6748fea9bULL, 0xda3e39cb94b95bdbULL }
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uint32_t pcg32_random_r(pcg32_random_t* rng)
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{
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uint64_t oldstate = rng->state;
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// Advance internal state
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rng->state = oldstate * 6364136223846793005ULL + (rng->inc | 1);
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// Calculate output function (XSH RR), uses old state for max ILP
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uint32_t xorshifted = ((oldstate >> 18u) ^ oldstate) >> 27u;
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uint32_t rot = oldstate >> 59u;
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return (xorshifted >> rot) | (xorshifted << ((-rot) & 31));
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}
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pcg32_random_t rngstate = PCG32_INITIALIZER;
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float rand01()
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{
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return pcg32_random_r(&rngstate) / float(1ull << 32);
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}
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uint32_t rand32()
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{
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return pcg32_random_r(&rngstate);
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}
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struct State
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{
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float cache[kCacheSizeMax];
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float live[kValenceMax];
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};
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struct Mesh
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{
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size_t vertex_count;
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std::vector<unsigned int> indices;
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float atvr_base[Profile_Count];
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};
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Mesh gridmesh(unsigned int N)
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{
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Mesh result;
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result.vertex_count = (N + 1) * (N + 1);
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result.indices.reserve(N * N * 6);
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for (unsigned int y = 0; y < N; ++y)
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for (unsigned int x = 0; x < N; ++x)
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{
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result.indices.push_back((y + 0) * (N + 1) + (x + 0));
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result.indices.push_back((y + 0) * (N + 1) + (x + 1));
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result.indices.push_back((y + 1) * (N + 1) + (x + 0));
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result.indices.push_back((y + 1) * (N + 1) + (x + 0));
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result.indices.push_back((y + 0) * (N + 1) + (x + 1));
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result.indices.push_back((y + 1) * (N + 1) + (x + 1));
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}
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return result;
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}
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Mesh objmesh(const char* path)
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{
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ObjFile file;
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if (!objParseFile(file, path))
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{
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printf("Error loading %s: file not found\n", path);
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return Mesh();
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}
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if (!objValidate(file))
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{
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printf("Error loading %s: invalid file data\n", path);
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return Mesh();
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}
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size_t total_indices = file.f_size / 3;
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struct Vertex
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{
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float px, py, pz;
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float nx, ny, nz;
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float tx, ty;
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};
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std::vector<Vertex> vertices(total_indices);
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for (size_t i = 0; i < total_indices; ++i)
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{
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int vi = file.f[i * 3 + 0];
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int vti = file.f[i * 3 + 1];
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int vni = file.f[i * 3 + 2];
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Vertex v =
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{
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file.v[vi * 3 + 0],
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file.v[vi * 3 + 1],
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file.v[vi * 3 + 2],
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vni >= 0 ? file.vn[vni * 3 + 0] : 0,
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vni >= 0 ? file.vn[vni * 3 + 1] : 0,
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vni >= 0 ? file.vn[vni * 3 + 2] : 0,
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vti >= 0 ? file.vt[vti * 3 + 0] : 0,
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vti >= 0 ? file.vt[vti * 3 + 1] : 0,
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};
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vertices[i] = v;
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}
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Mesh result;
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std::vector<unsigned int> remap(total_indices);
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size_t total_vertices = meshopt_generateVertexRemap(&remap[0], NULL, total_indices, &vertices[0], total_indices, sizeof(Vertex));
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result.indices.resize(total_indices);
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meshopt_remapIndexBuffer(&result.indices[0], NULL, total_indices, &remap[0]);
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result.vertex_count = total_vertices;
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return result;
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}
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void compute_atvr(const State& state, const Mesh& mesh, float result[Profile_Count])
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{
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memcpy(meshopt::kVertexScoreTableCache + 1, state.cache, kCacheSizeMax * sizeof(float));
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memcpy(meshopt::kVertexScoreTableLive + 1, state.live, kValenceMax * sizeof(float));
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std::vector<unsigned int> indices(mesh.indices.size());
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meshopt_optimizeVertexCache(&indices[0], &mesh.indices[0], mesh.indices.size(), mesh.vertex_count);
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for (int profile = 0; profile < Profile_Count; ++profile)
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result[profile] = meshopt_analyzeVertexCache(&indices[0], indices.size(), mesh.vertex_count, profiles[profile].cache, profiles[profile].warp, profiles[profile].triangle).atvr;
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}
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float fitness_score(const State& state, const std::vector<Mesh>& meshes)
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{
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float result = 0;
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float count = 0;
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for (auto& mesh : meshes)
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{
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float atvr[Profile_Count];
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compute_atvr(state, mesh, atvr);
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for (int profile = 0; profile < Profile_Count; ++profile)
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{
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result += mesh.atvr_base[profile] / atvr[profile];
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count += 1;
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}
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}
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return result / count;
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}
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float rndcache()
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{
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return rand01();
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}
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float rndlive()
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{
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return rand01();
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}
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std::vector<State> gen0(size_t count)
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{
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std::vector<State> result;
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for (size_t i = 0; i < count; ++i)
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{
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State state = {};
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for (int j = 0; j < kCacheSizeMax; ++j)
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state.cache[j] = rndcache();
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for (int j = 0; j < kValenceMax; ++j)
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state.live[j] = rndlive();
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result.push_back(state);
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}
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return result;
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}
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size_t rndindex(const std::vector<float>& prob)
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{
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float r = rand01();
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for (size_t i = 0; i < prob.size(); ++i)
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{
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r -= prob[i];
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if (r <= 0)
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return i;
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}
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return prob.size() - 1;
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}
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State mutate(const State& state)
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{
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State result = state;
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if (rand01() < 0.7f)
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{
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size_t idxcache = std::min(int(rand01() * kCacheSizeMax + 0.5f), int(kCacheSizeMax - 1));
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result.cache[idxcache] = rndcache();
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}
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if (rand01() < 0.7f)
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{
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size_t idxlive = std::min(int(rand01() * kValenceMax + 0.5f), int(kValenceMax - 1));
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result.live[idxlive] = rndlive();
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}
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if (rand01() < 0.2f)
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{
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uint32_t mask = rand32();
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for (size_t i = 0; i < kCacheSizeMax; ++i)
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if (mask & (1 << i))
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result.cache[i] *= 0.9f + 0.2f * rand01();
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}
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if (rand01() < 0.2f)
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{
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uint32_t mask = rand32();
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for (size_t i = 0; i < kValenceMax; ++i)
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if (mask & (1 << i))
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result.live[i] *= 0.9f + 0.2f * rand01();
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}
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if (rand01() < 0.05f)
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{
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uint32_t mask = rand32();
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for (size_t i = 0; i < kCacheSizeMax; ++i)
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if (mask & (1 << i))
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result.cache[i] = rndcache();
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}
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if (rand01() < 0.05f)
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{
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uint32_t mask = rand32();
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for (size_t i = 0; i < kValenceMax; ++i)
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if (mask & (1 << i))
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result.live[i] = rndlive();
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}
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return result;
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}
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bool accept(float fitnew, float fitold, float temp)
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{
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if (fitnew >= fitold)
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return true;
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if (temp == 0)
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return false;
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float prob = exp2((fitnew - fitold) / temp);
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return rand01() < prob;
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}
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std::pair<State, float> genN_SA(std::vector<State>& seed, const std::vector<Mesh>& meshes, size_t steps)
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{
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std::vector<State> result;
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result.reserve(seed.size() * (1 + steps));
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// perform several parallel steps of mutation for each temperature
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for (size_t i = 0; i < seed.size(); ++i)
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{
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result.push_back(seed[i]);
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for (size_t s = 0; s < steps; ++s)
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result.push_back(mutate(seed[i]));
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}
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// compute fitness for all temperatures & mutations in parallel
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std::vector<float> resultfit(result.size());
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#pragma omp parallel for
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for (size_t i = 0; i < result.size(); ++i)
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{
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resultfit[i] = fitness_score(result[i], meshes);
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}
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// perform annealing for each temperature
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std::vector<float> seedfit(seed.size());
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for (size_t i = 0; i < seed.size(); ++i)
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{
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size_t offset = i * (1 + steps);
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seedfit[i] = resultfit[offset];
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float temp = (float(i) / float(seed.size() - 1)) / 0.1f;
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for (size_t s = 0; s < steps; ++s)
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{
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if (accept(resultfit[offset + s + 1], seedfit[i], temp))
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{
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seedfit[i] = resultfit[offset + s + 1];
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seed[i] = result[offset + s + 1];
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}
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}
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}
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// perform promotion from each temperature to the next one
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for (size_t i = seed.size() - 1; i > 0; --i)
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{
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if (seedfit[i] > seedfit[i - 1])
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{
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seedfit[i - 1] = seedfit[i];
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seed[i - 1] = seed[i];
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}
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}
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return std::make_pair(seed[0], seedfit[0]);
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}
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std::pair<State, float> genN_GA(std::vector<State>& seed, const std::vector<Mesh>& meshes, float crossover, float mutate)
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{
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std::vector<State> result;
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result.reserve(seed.size());
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std::vector<float> seedprob(seed.size());
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#pragma omp parallel for
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for (size_t i = 0; i < seed.size(); ++i)
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{
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seedprob[i] = fitness_score(seed[i], meshes);
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}
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State best = {};
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float bestfit = 0;
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float probsum = 0;
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for (size_t i = 0; i < seed.size(); ++i)
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{
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float score = seedprob[i];
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probsum += score;
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if (score > bestfit)
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{
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best = seed[i];
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bestfit = score;
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}
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}
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for (auto& prob : seedprob)
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{
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prob /= probsum;
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}
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std::vector<unsigned int> seedidx;
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seedidx.reserve(seed.size());
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for (size_t i = 0; i < seed.size(); ++i)
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seedidx.push_back(i);
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std::sort(seedidx.begin(), seedidx.end(), [&](size_t l, size_t r) { return seedprob[l] < seedprob[r]; });
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while (result.size() < seed.size() / 4)
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{
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size_t idx = seedidx.back();
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seedidx.pop_back();
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result.push_back(seed[idx]);
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}
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while (result.size() < seed.size())
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{
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State s0 = seed[rndindex(seedprob)];
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State s1 = seed[rndindex(seedprob)];
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State state = s0;
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// crossover
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if (rand01() < crossover)
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{
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size_t idxcache = std::min(int(rand01() * kCacheSizeMax + 0.5f), 15);
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memcpy(state.cache + idxcache, s1.cache + idxcache, (kCacheSizeMax - idxcache) * sizeof(float));
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}
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if (rand01() < crossover)
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{
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size_t idxlive = std::min(int(rand01() * kValenceMax + 0.5f), 7);
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memcpy(state.live + idxlive, s1.live + idxlive, (kValenceMax - idxlive) * sizeof(float));
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}
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// mutate
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if (rand01() < mutate)
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{
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size_t idxcache = std::min(int(rand01() * kCacheSizeMax + 0.5f), 15);
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state.cache[idxcache] = rndcache();
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}
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if (rand01() < mutate)
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{
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size_t idxlive = std::min(int(rand01() * kValenceMax + 0.5f), 7);
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state.live[idxlive] = rndlive();
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}
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result.push_back(state);
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}
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seed.swap(result);
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return std::make_pair(best, bestfit);
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}
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bool load_state(const char* path, std::vector<State>& result)
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{
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FILE* file = fopen(path, "rb");
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if (!file)
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return false;
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State state;
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result.clear();
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while (fread(&state, sizeof(State), 1, file) == 1)
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result.push_back(state);
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fclose(file);
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return true;
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}
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bool save_state(const char* path, const std::vector<State>& result)
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{
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FILE* file = fopen(path, "wb");
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if (!file)
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return false;
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for (auto& state : result)
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{
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if (fwrite(&state, sizeof(State), 1, file) != 1)
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{
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fclose(file);
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return false;
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}
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}
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return fclose(file) == 0;
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}
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void dump_state(const State& state)
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{
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printf("cache:");
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for (int i = 0; i < kCacheSizeMax; ++i)
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{
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printf(" %.3f", state.cache[i]);
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}
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printf("\n");
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printf("live:");
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for (int i = 0; i < kValenceMax; ++i)
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{
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printf(" %.3f", state.live[i]);
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}
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printf("\n");
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}
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int main(int argc, char** argv)
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{
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bool annealing = false;
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State baseline;
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memcpy(baseline.cache, meshopt::kVertexScoreTableCache + 1, kCacheSizeMax * sizeof(float));
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memcpy(baseline.live, meshopt::kVertexScoreTableLive + 1, kValenceMax * sizeof(float));
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std::vector<Mesh> meshes;
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meshes.push_back(gridmesh(50));
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for (int i = 1; i < argc; ++i)
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meshes.push_back(objmesh(argv[i]));
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size_t total_triangles = 0;
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for (auto& mesh : meshes)
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{
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compute_atvr(baseline, mesh, mesh.atvr_base);
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total_triangles += mesh.indices.size() / 3;
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}
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std::vector<State> pop;
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size_t gen = 0;
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if (load_state("mutator.state", pop))
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{
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printf("Loaded %d state vectors\n", int(pop.size()));
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}
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else
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{
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pop = gen0(annealing ? 32 : 1000);
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}
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printf("%d meshes, %.1fM triangles\n", int(meshes.size()), double(total_triangles) / 1e6);
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float atvr_0[Profile_Count];
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float atvr_N[Profile_Count];
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compute_atvr(baseline, meshes[0], atvr_0);
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compute_atvr(baseline, meshes.back(), atvr_N);
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printf("baseline: grid %f %f %s %f %f\n", atvr_0[0], atvr_0[1], argv[argc - 1], atvr_N[0], atvr_N[1]);
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for (;;)
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{
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auto best = annealing ? genN_SA(pop, meshes, 31) : genN_GA(pop, meshes, 0.7f, 0.3f);
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gen++;
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|
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compute_atvr(best.first, meshes[0], atvr_0);
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compute_atvr(best.first, meshes.back(), atvr_N);
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|
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printf("%d: fitness %f; grid %f %f %s %f %f\n", int(gen), best.second, atvr_0[0], atvr_0[1], argv[argc - 1], atvr_N[0], atvr_N[1]);
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|
|
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if (gen % 100 == 0)
|
|
{
|
|
char buf[128];
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sprintf(buf, "gcloud logging write vcache-log \"fitness %f; grid %f %f %s %f %f\"", best.second, atvr_0[0], atvr_0[1], argv[argc - 1], atvr_N[0], atvr_N[1]);
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|
system(buf);
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|
}
|
|
|
|
dump_state(best.first);
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|
|
|
if (save_state("mutator.state-temp", pop) && rename("mutator.state-temp", "mutator.state") == 0)
|
|
{
|
|
}
|
|
else
|
|
{
|
|
printf("ERROR: Can't save state\n");
|
|
}
|
|
}
|
|
}
|