83 lines
2.1 KiB
C++
83 lines
2.1 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 "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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using current_decider = neural_decider<24, 12, 12, 32, 16>;
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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.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;
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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(1) >> input_file;
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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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//
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// Engines
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//
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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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input.open(input_file);
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for (int i = 0; i < iterations; i++) {
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input.clear();
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input.seekg(0);
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train.test(input);
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}
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input.clear();
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input.seekg(0);
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train.see_best(input);
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}
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