Initial commit
This commit is contained in:
Efim Beshmenev
@@ -0,0 +1,182 @@
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,246 @@
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#include "solver.h"
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#include <algorithm>
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#include <iostream>
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||||
#define SYMMETRY make_symmetric_o4
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//#define SYMMETRY make_symmetric_o2
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//#define SYMMETRY make_symmetric_d2
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RNG eng;
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struct Sample {
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VectorXd x;
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double score;
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int time;
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bool operator<(const Sample& rhs) const {
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return this->score < rhs.score;
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}
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};
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void set_rand_seed(int seed) {
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eng.seed(seed);
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}
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void add_random_noise(const VectorXd& x, double sigma, VectorXd& result) {
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std::normal_distribution<double> rand_normal(0.0, sigma);
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for (int i = 0; i < x.size(); ++i) {
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result[i] = x[i] + rand_normal(eng);
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}
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}
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void set_random_mags(VectorXd& x, double sigma) {
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std::normal_distribution<double> rand_normal(0.0, sigma);
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for (int i = 0; i < x.size(); i += 3) {
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Eigen::Map<Vector3d> sub_x(x.data() + i);
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sub_x.normalize();
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sub_x *= rand_normal(eng);
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}
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}
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void initialize_random_noise(VectorXd& x, double sigma, size_t size) {
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std::normal_distribution<double> rand_normal(0.0, sigma);
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x.resize(size);
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for (int i = 0; i < x.size(); ++i) {
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x[i] = rand_normal(eng);
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}
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}
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void initialize_random_noise(std::vector<std::pair<double, VectorXd>>& xvec, double sigma, bool dual_problem) {
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size_t size = (dual_problem ? g_tris.size() : g_polys.size()) * 3;
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for (auto& x : xvec) {
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initialize_random_noise(x.second, sigma, size);
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}
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}
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void initialize_random_noise(std::vector<std::pair<double, VectorXd>>& xvec, const VectorXd& guess, double sigma) {
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for (auto& x : xvec) {
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initialize_random_noise(x.second, sigma, guess.size());
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x.second += guess;
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}
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}
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void initialize_random_mag(std::vector<VectorXd>& yvec, const VectorXd& n, double sigma) {
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std::normal_distribution<double> rand_normal(0.0, sigma);
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for (VectorXd& y : yvec) {
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y.resize(g_polys.size());
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for (int i = 0; i < y.size(); ++i) {
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y[i] = rand_normal(eng);
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}
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}
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}
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VectorXd shuffle_x(const VectorXd& x) {
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VectorXd result(x);
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const size_t num = x.size() / 3;
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for (size_t i = 0; i < num; ++i) {
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const size_t ix = std::uniform_int_distribution<size_t>(i, num - 1)(eng);
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std::swap(result[ix*3 + 0], result[i*3 + 0]);
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std::swap(result[ix*3 + 1], result[i*3 + 1]);
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std::swap(result[ix*3 + 2], result[i*3 + 2]);
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}
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return result;
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}
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VectorXd random_plane_shift(const VectorXd& x) {
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std::uniform_real_distribution<double> rand_uniform(0.8, 1.2);
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VectorXd result(x);
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const size_t num = x.size() / 3;
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||||
const size_t ix = std::uniform_int_distribution<size_t>(0, num - 1)(eng);
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const double scale = rand_uniform(eng);
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result[ix * 3 + 0] *= scale;
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||||
result[ix * 3 + 1] *= scale;
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result[ix * 3 + 2] *= scale;
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return result;
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||||
}
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||||
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||||
void z_symmetric(VectorXd& x, size_t i, size_t j) {
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x[j*3 + 0] = -x[i*3 + 0];
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x[j*3 + 1] = -x[i*3 + 1];
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x[j*3 + 2] = x[i*3 + 2];
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||||
}
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||||
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||||
void z_rotate_inv(VectorXd& x, size_t i, size_t j) {
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||||
x[j * 3 + 0] = x[i * 3 + 1];
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||||
x[j * 3 + 1] = -x[i * 3 + 0];
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||||
x[j * 3 + 2] = -x[i * 3 + 2];
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||||
}
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||||
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||||
void make_symmetric_d2(VectorXd& x) {
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||||
z_symmetric(x, 0, 1);
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||||
z_symmetric(x, 2, 3);
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||||
z_symmetric(x, 4, 5);
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||||
z_symmetric(x, 6, 7);
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||||
z_symmetric(x, 8, 9);
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||||
z_symmetric(x, 10, 11);
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||||
}
|
||||
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||||
void make_symmetric_o2(VectorXd& x) {
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||||
z_symmetric(x, 0, 2);
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||||
z_symmetric(x, 5, 7);
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||||
z_symmetric(x, 8, 10);
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||||
z_symmetric(x, 1, 3);
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||||
z_symmetric(x, 4, 6);
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||||
z_symmetric(x, 9, 11);
|
||||
}
|
||||
|
||||
void make_symmetric_o4(VectorXd& x) {
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||||
z_symmetric(x, 0, 2);
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||||
z_symmetric(x, 5, 7);
|
||||
z_symmetric(x, 8, 10);
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||||
z_rotate_inv(x, 5, 4);
|
||||
z_rotate_inv(x, 7, 6);
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||||
z_rotate_inv(x, 0, 3);
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||||
z_rotate_inv(x, 2, 1);
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||||
z_rotate_inv(x, 8, 11);
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||||
z_rotate_inv(x, 10, 9);
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||||
}
|
||||
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||||
bool v_pred(const std::pair<double, VectorXd>& left, const std::pair<double, VectorXd>& right) {
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||||
return left.first < right.first;
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||||
}
|
||||
|
||||
double my_optimizer(double (*objective_function)(const VectorXd&), VectorXd& result, int max_iters,
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||||
double sigma, double beta, int clusters, bool use_symmetry, bool dual_problem) {
|
||||
static const int extra_tries = 10;
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||||
std::vector<std::pair<double, VectorXd>> xv(clusters * extra_tries);
|
||||
VectorXd new_pt;
|
||||
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||||
initialize_random_noise(xv, 1.0, dual_problem);
|
||||
for (auto& x : xv) {
|
||||
if (use_symmetry) { SYMMETRY(x.second); }
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||||
x.first = objective_function(x.second);
|
||||
}
|
||||
std::sort(xv.begin(), xv.end(), v_pred);
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||||
xv.resize(clusters);
|
||||
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||||
int iter = 0;
|
||||
double best_cost = 99999;
|
||||
const size_t x_size = xv[0].second.size();
|
||||
new_pt.resize(x_size);
|
||||
while (true) {
|
||||
const size_t min_ix = std::min_element(xv.begin(), xv.end(), v_pred) - xv.begin();
|
||||
const double min_cost = xv[min_ix].first;
|
||||
iter += 1;
|
||||
if (min_cost < best_cost) {
|
||||
std::cout << min_cost << " " << iter << std::endl;
|
||||
best_cost = min_cost;
|
||||
result = xv[min_ix].second;
|
||||
}
|
||||
|
||||
#if 0
|
||||
if (best_cost > 500 && iter > max_iters) { break; }
|
||||
if (best_cost > 300 && iter > max_iters * 2) { break; }
|
||||
if (best_cost > 200 && iter > max_iters * 3) { break; }
|
||||
if (best_cost > 100 && iter > max_iters * 4) { break; }
|
||||
#elif 0
|
||||
if (best_cost > 22 && iter > max_iters) { break; }
|
||||
if (best_cost > 18 && iter > max_iters * 2) { break; }
|
||||
if (best_cost > 15 && iter > max_iters * 3) { break; }
|
||||
if (best_cost > 12 && iter > max_iters * 4) { break; }
|
||||
#else
|
||||
if (best_cost > 11 && iter > max_iters) { break; }
|
||||
if (best_cost > 9 && iter > max_iters * 2) { break; }
|
||||
if (best_cost > 7 && iter > max_iters * 3) { break; }
|
||||
if (best_cost > 5 && iter > max_iters * 4) { break; }
|
||||
#endif
|
||||
if (iter > max_iters * 10) { break; }
|
||||
if (best_cost == 0.0) { break; }
|
||||
|
||||
for (size_t i = 0; i < clusters; ++i) {
|
||||
size_t ix = i;
|
||||
if (xv[i].first > best_cost * 1.25) {
|
||||
ix = std::uniform_int_distribution<size_t>(0, clusters-1)(eng);
|
||||
}
|
||||
add_random_noise(xv[ix].second, sigma, new_pt);
|
||||
if (use_symmetry) { SYMMETRY(new_pt); }
|
||||
new_pt *= beta;
|
||||
const double new_cost = objective_function(new_pt);
|
||||
const double cost_mult = (i == min_ix ? 1.0 : 1.25);
|
||||
if (new_cost <= best_cost * cost_mult || new_cost < xv[i].first) {
|
||||
xv[i].first = new_cost;
|
||||
xv[i].second = new_pt;
|
||||
}
|
||||
}
|
||||
}
|
||||
return best_cost;
|
||||
}
|
||||
|
||||
void study_sample(double (*objective_function)(const VectorXd&), VectorXd& result, int max_iters, int clusters, double sigma, double beta, bool use_symmetry) {
|
||||
const size_t x_size = result.size();
|
||||
if (use_symmetry) { SYMMETRY(result); }
|
||||
double min_score = objective_function(result);
|
||||
std::vector<VectorXd> xv(clusters, result);
|
||||
std::vector<double> scores(clusters, min_score);
|
||||
VectorXd new_x(x_size);
|
||||
|
||||
for(int iter = 0; iter < max_iters; ++iter) {
|
||||
int num_updated = 0;
|
||||
for (int i = 0; i < clusters; ++i) {
|
||||
add_random_noise(xv[i], sigma, new_x);
|
||||
if (use_symmetry) { SYMMETRY(new_x); }
|
||||
new_x.normalize();
|
||||
const double new_cost = objective_function(new_x);
|
||||
if (new_cost < scores[i]) {
|
||||
if (new_cost < min_score) {
|
||||
std::cout << "==== New Best! ==== (" << new_cost << ")" << std::endl;
|
||||
result = new_x;
|
||||
if (int(new_cost) < int(min_score)) {
|
||||
std::fill(xv.begin(), xv.end(), new_x);
|
||||
}
|
||||
min_score = new_cost;
|
||||
}
|
||||
xv[i] = new_x;
|
||||
scores[i] = new_cost;
|
||||
num_updated += 1;
|
||||
}
|
||||
}
|
||||
std::cout << "Updated: " << num_updated << "/" << clusters << " " << sigma << std::endl;
|
||||
if (num_updated < clusters / 100) {
|
||||
sigma *= beta;
|
||||
} else if (num_updated > clusters / 10) {
|
||||
sigma *= 1.01;
|
||||
}
|
||||
if (sigma < 1e-7) { break; }
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,21 @@
|
||||
#pragma once
|
||||
#include "util.h"
|
||||
#include <random>
|
||||
|
||||
//using RNG = std::mt19937;
|
||||
using RNG = std::minstd_rand;
|
||||
extern RNG eng;
|
||||
|
||||
void set_rand_seed(int seed);
|
||||
void add_random_noise(const VectorXd& x, double sigma, VectorXd& result);
|
||||
void set_random_mags(VectorXd& x, double sigma);
|
||||
void initialize_random_noise(VectorXd& x, double sigma, size_t size);
|
||||
void initialize_random_noise(std::vector<std::pair<double, VectorXd>>& xvec, double sigma, bool dual_problem);
|
||||
void initialize_random_noise(std::vector<std::pair<double, VectorXd>>& xvec, const VectorXd& guess, double sigma);
|
||||
VectorXd shuffle_x(const VectorXd& x);
|
||||
void make_symmetric_o4(VectorXd& x);
|
||||
|
||||
double my_optimizer(double (*objective_function)(const VectorXd&), VectorXd& result, int max_iters,
|
||||
double sigma, double beta, int clusters, bool use_symmetry, bool dual_problem);
|
||||
void study_sample(double (*objective_function)(const VectorXd&), VectorXd& result, int max_iters,
|
||||
int clusters, double sigma, double beta, bool use_symmetry);
|
||||
Reference in New Issue
Block a user