// individuals, epochs, number of recombined individuals, mutation range, mutation probability, abort fitness

Ea_settings ea_settings {2000, 100, 1000, 2.0, 0.2, 1.0};

// input neurons, hidden neurons, output neurons, initial weight range

// if output neurons is set to 0 the hidden neurons become the output neurons

Ann_settings ann_settings {2, 1, 0, 2.0, Sigmoid};

std::string file_descriptor = "data";

std::vector<struct data_point> set = parse_data_file(file_descriptor);

Evolution evo = Evolution(ea_settings, ann_settings, fitness_classification);

std::tuple<Network, Genome> winner = evo.eval(set);

Network winner_net = get<0>(winner);

Genome winner_genome = get<1>(winner);

winner_net.set_input_weights(winner_genome.input_weights);

winner_net.set_hidden_weights(winner_genome.hidden_weights);

FILE * fp = fopen("classification_results_data1", "w");

std::cout << "Best classification: " << winner_genome.fitness << std::endl;

std::cout << "X Y Classification Prediction" << std::endl;

for(auto e: set){

int output = (int) std::round(winner_net.evaluate(e.input)[0]);

fprintf(fp, "%f %f %i %i \n", e.input[0], e.input[1], e.classification, output);

std::cout << e.input[0] << " " << e.input[1] << " " << e.classification << " " << output << std::endl;

}

}