108 lines
3.2 KiB
C++
108 lines
3.2 KiB
C++
#include <iostream>
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#include <functional>
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#include <math.h>
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#include <typeinfo>
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#include <random>
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#include <ctime>
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#include <algorithm>
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#include <fstream>
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#include <vector>
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#include <map>
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#include "argh.h"
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#include "matrix.h"
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#include "network.h"
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#include "decider.h"
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#include "trainer.h"
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#include "common.h"
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int main(int argc, char* argv[])
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{
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argh::parser args;
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args.add_params({"-m", "--money"});
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args.add_params({"-s", "--stock"});
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args.add_params({"-p", "--population"});
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args.add_params({"-n", "--iterations"});
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args.add_params({"-o", "--output-dir"});
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args.parse(argc, argv);
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double money;
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unsigned stock, population, iterations;
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std::string input_file, output_dir;
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args({ "m", "money" }, 1000.) >> money;
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args({ "s", "stock" }, 1000) >> stock;
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args({ "p", "population" }, 25) >> population;
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args({ "n", "iterations" }, 4) >> iterations;
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args({ "o", "output-dir" }, "") >> output_dir;
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std::uniform_real_distribution<double> distribution(-2.0, 2.0);
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std::uniform_real_distribution<double> bias_distribution(-16.0, 16.0);
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std::random_device rd;
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std::mt19937 random_engine(rd());
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std::function<double(const int&, const int&)> randomizer = [&](const int&, const int&) -> double {
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return distribution(random_engine);
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};
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std::function<double(const int&, const int&)> bias_randomizer = [&](const int&, const int&) -> double {
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return distribution(random_engine);
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};
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std::function<double(const double&)> normalizer = [](const double& result) -> double { return erf(result); };
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std::function<current_decider ()> factory = [&]() -> current_decider {
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current_decider decider(normalizer);
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decider.network.fill(randomizer, bias_randomizer);
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return decider;
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};
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trainer<current_decider> train(money, stock, population, factory);
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std::fstream input;
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std::map<std::string, trainer<current_decider>::dataset> datasets;
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for (auto it = args.begin() + 1; it != args.end(); it++) {
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std::string filename = *it;
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std::ifstream file;
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file.open(filename);
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if (!file.is_open()) continue;
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trainer<current_decider>::dataset set;
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double price;
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while (file >> price) set.push_back(price);
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datasets[filename] = set;
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file.close();
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std::cout << "Zaladowano zestaw testowy " << filename << " z " << set.size() << " wartosciami." << std::endl;
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}
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if (datasets.size() == 0) {
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std::cout << "Brak poprawnie zaladowanych zestawow testowych." << std::endl;
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return -1;
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}
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while (train.generation <= iterations) {
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for (auto pair : datasets) {
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train.train(pair.second, pair.first);
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}
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train.evolve();
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}
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if (!output_dir.empty()) {
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for (auto trained : train.population()) {
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std::stringstream stream;
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stream << output_dir << trained->id << ".net";
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std::string filename = stream.str();
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std::cout << "Zapisuje siec #" << trained->id << " do pliku " << filename << std::endl;
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std::ofstream file(filename, std::ios::out | std::ios::binary);
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trained->decider.save(file);
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file.close();
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}
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}
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}
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