146 lines
4.3 KiB
C++
146 lines
4.3 KiB
C++
/********************************************************
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* ██████╗ ██████╗████████╗██╗
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* ██╔════╝ ██╔════╝╚══██╔══╝██║
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* ██║ ███╗██║ ██║ ██║
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* ██║ ██║██║ ██║ ██║
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* ╚██████╔╝╚██████╗ ██║ ███████╗
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* ╚═════╝ ╚═════╝ ╚═╝ ╚══════╝
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* Geophysical Computational Tools & Library (GCTL)
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*
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* Copyright (c) 2022 Yi Zhang (yizhang-geo@zju.edu.cn)
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*
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* GCTL is distributed under a dual licensing scheme. You can redistribute
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* it and/or modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation, either version 2
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* of the License, or (at your option) any later version. You should have
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* received a copy of the GNU Lesser General Public License along with this
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* program. If not, see <http://www.gnu.org/licenses/>.
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*
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* If the terms and conditions of the LGPL v.2. would prevent you from using
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* the GCTL, please consider the option to obtain a commercial license for a
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* fee. These licenses are offered by the GCTL's original author. As a rule,
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* licenses are provided "as-is", unlimited in time for a one time fee. Please
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* send corresponding requests to: yizhang-geo@zju.edu.cn. Please do not forget
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* to include some description of your company and the realm of its activities.
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* Also add information on how to contact you by electronic and paper mail.
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******************************************************/
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#include "../lib/optimization.h"
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using namespace gctl;
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class cfg : public lgd_solver
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{
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private:
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std::vector<gaussian_para2d> gs1_, gs2_;
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array<double> g1_, g2_;
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common_gradient cmg_;
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array<double> x_;
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array<double> xm_;
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array<double> xs_;
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public:
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cfg(/* args */){}
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~cfg(){}
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virtual double LGD_Evaluate(const array<double> &x, array<double> &g);
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void read_gaussians(std::string file, std::vector<gaussian_para2d> &gs);
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void routine(int argc, char *argv[]);
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};
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double cfg::LGD_Evaluate(const array<double> &x, array<double> &g)
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{
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double f, fx = 0.0;
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g1_.assign(0.0);
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g2_.assign(0.0);
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f = 0.0;
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for (int i = 0; i < gs1_.size(); i++)
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{
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f += -1e+3*gctl::gaussian_dist2d(x[0], x[1], gs1_[i]);
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g1_[0] += -1e+3*gctl::gaussian_dist2d(x[0], x[1], gs1_[i], gctl::Dx);
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g1_[1] += -1e+3*gctl::gaussian_dist2d(x[0], x[1], gs1_[i], gctl::Dy);
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}
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cmg_.fill_model_gradient(0, f, g1_);
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fx += f;
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f = 0.0;
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for (int i = 0; i < gs2_.size(); i++)
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{
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f += -1e+3*gctl::gaussian_dist2d(x[0], x[1], gs2_[i]);
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g2_[0] += -1e+3*gctl::gaussian_dist2d(x[0], x[1], gs2_[i], gctl::Dx);
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g2_[1] += -1e+3*gctl::gaussian_dist2d(x[0], x[1], gs2_[i], gctl::Dy);
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}
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cmg_.fill_model_gradient(1, f, g2_);
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fx += f;
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fx += 0.666027; // 目标值
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g = cmg_.get_common_gradient();
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return fx;
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}
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void cfg::read_gaussians(std::string file, std::vector<gaussian_para2d> &gs)
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{
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gaussian_para2d tmp_p;
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std::string tmp_str;
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std::stringstream tmp_ss;
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std::ifstream infile;
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gctl::open_infile(infile, file, ".txt");
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while(getline(infile, tmp_str))
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{
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if (tmp_str[0] == '#') continue;
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else
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{
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gctl::str2ss(tmp_str, tmp_ss);
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tmp_ss >> tmp_p.mu_x >> tmp_p.mu_y >> tmp_p.sigma_x >> tmp_p.sigma_y >> tmp_p.rho;
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gs.push_back(tmp_p);
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}
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}
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infile.close();
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return;
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}
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void cfg::routine(int argc, char *argv[])
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{
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cmg_.init(2, 2);
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g1_.resize(2);
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g2_.resize(2);
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read_gaussians("example/data/gauss_model", gs1_);
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read_gaussians("example/data/gauss_model2", gs2_);
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x_.resize(2, 20.0);
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xm_.resize(2, 0.0);
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xs_.resize(2, 0.0);
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array<double> low(2, 0.0);
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array<double> high(2, 100.0);
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lgd_para para = default_lgd_para();
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para.alpha = 0.01;
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para.flight_times = 1000;
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set_lgd_para(para);
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set_lgd_record_trace();
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LGD_Minimize(x_, xm_, xs_, low, high);
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save_lgd_trace("trace.txt");
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std::cout << "x = (" << x_[0] << ", " << x_[1] << ")\n";
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return;
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}
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int main(int argc, char *argv[]) try
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{
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cfg c;
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c.routine(argc, argv);
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return 0;
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}
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catch (const std::exception& e)
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{
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GCTL_ShowWhatError(e.what(), GCTL_ERROR_ERROR, 0, 0, 0);
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} |