本文整理汇总了C++中Chromosome::create_random_gene方法的典型用法代码示例。如果您正苦于以下问题：C++ Chromosome::create_random_gene方法的具体用法？C++ Chromosome::create_random_gene怎么用？C++ Chromosome::create_random_gene使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Chromosome的用法示例。
在下文中一共展示了Chromosome::create_random_gene方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: mutate_chromosome
void Generation::mutate_chromosome(Chromosome& chromosome,
double const& prob_mutation,
std::vector<bound> const& param_bounds,
std::mt19937& urng) const
{
std::uniform_real_distribution<> uniform_distr(0.0, 1.0);
for (size_t i = 0; i < chromosome.size; ++i) {
if (uniform_distr(urng) <= prob_mutation) {
chromosome.genes[i] = chromosome.create_random_gene(param_bounds[i].lower, param_bounds[i].upper, urng);
}
}
}示例2: record_error_plot
void GeneticAlgorithm::record_error_plot(PeldorCalculator const& peldor_calc,
Chromosome const& chromosome,
std::vector<exp_signal> const& signals_exp,
Spin const& spinA, Spin const& spinB,
std::vector<int> const& param_numbers,
std::vector<int> const& param_modes,
std::vector<bound> const& param_bounds,
genetic_parameters const& genetic_param,
output_parameters const& output_param) const
{
const size_t n_plots = output_param.error_plot_variables.size();
const size_t plot_size = output_param.error_plot_size;
size_t n_vars(0);
// Initialize random generator
std::random_device rd;
std::array<unsigned int, std::mt19937::state_size> seed_data;
std::generate_n(seed_data.begin(), seed_data.size(), std::ref(rd));
std::seed_seq seq(std::begin(seed_data), std::end(seed_data));
std::mt19937 engine(seq);
for (size_t i = 0; i < n_plots; ++i) { // iterate over the error plots
n_vars = output_param.error_plot_variables[i].size();
// Check if the selected variables of the error plot were optimized
int indexVariable(0);
bool inappropriateVariables(0);
for (size_t j = 0; j < n_vars; ++j) {
indexVariable = output_param.error_plot_variables[i][j] - 1;
if (param_numbers[indexVariable] == -1) {
std::cout << " Inappropriate variable (it was not optimized): " << output_param.error_plot_variables[i][j] << std::endl;
inappropriateVariables = 1;
break;
}
}
if (inappropriateVariables) continue;
// Create a generation with different values of the parameters to be varied
genetic_parameters genetic_param_ep(genetic_param);
genetic_param_ep.size_generation = output_param.error_plot_size;
Generation* generation_ep = new Generation(genetic_param_ep);
for (size_t k = 0; k < plot_size; ++k) generation_ep->chromosomes.push_back(chromosome); // initialize all chromosomes with the optimized chromosome
int indexGene(0);
for (size_t j = 0; j < n_vars; ++j) { // vary the genes corresponding to the defined variables
indexGene = param_numbers[output_param.error_plot_variables[i][j] - 1];
for (size_t k = 0; k < plot_size; ++k) {
generation_ep->chromosomes[k].genes[indexGene] = chromosome.create_random_gene(param_bounds[indexGene].lower, param_bounds[indexGene].upper, engine);
}
}
// Score the generation
generation_ep->score_chromosomes(peldor_calc, signals_exp, spinA, spinB, param_numbers, param_modes, genetic_param);
generation_ep->sort_chromosomes();
//// Modified! Save fits
//output_parameters output_param_ep(output_param);
//for (size_t j = 0; j < n_vars; ++j) {
// std::stringstream nPlot;
// nPlot << j;
// output_param_ep.directory = output_param.directory + nPlot.str();
// record_fit(peldor_calc, generation_ep->chromosomes[j], signals_exp, spinA, spinB, param_numbers, param_modes, output_param_ep);
//}
// Create & open a file
std::ostringstream filename;
filename << output_param.directory << "error_plot";
for (size_t j = 0; j < n_vars; ++j) filename << "_" << output_param.error_plot_variables[i][j];
filename << ".dat";
std::fstream file;
file.open(filename.str().c_str(), std::ios::out);
// Write the names of columns
size_t const col_width = 15;
std::ostringstream col_name;
for (size_t j = 0; j < n_vars; ++j) {
col_name << "Parameter" << (j + 1);
file << std::left << std::setw(col_width) << col_name.str();
col_name.str(std::string()); col_name.clear();
}
if (genetic_param.merit_function == 1) file << std::left << std::setw(col_width) << "RMSD" << std::endl;
else if (genetic_param.merit_function == 2) file << std::left << std::setw(col_width) << "RMSD/PCC" << std::endl;
else if (genetic_param.merit_function == 3) file << std::left << std::setw(col_width) << "PCC" << std::endl;
// Write the error plot
for (size_t k = 0; k < plot_size; ++k) {
for (size_t j = 0; j < n_vars; ++j) {
indexGene = param_numbers[output_param.error_plot_variables[i][j] - 1];
if (std::count(angular_indices, angular_indices + 20, indexGene)) {
file << std::left << std::setw(col_width) << std::fixed << std::setprecision(0) << generation_ep->chromosomes[k].genes[indexGene] * rad2deg;
}
else {
file << std::left << std::setw(col_width) << std::fixed << std::setprecision(2) << generation_ep->chromosomes[k].genes[indexGene];
}
}
file << std::left << std::setw(col_width) << std::fixed << std::setprecision(5) << generation_ep->chromosomes[k].fitness;
file << std::endl;
}
file.close();
// Clean up
delete generation_ep;
}
}