本文整理汇总了C++中command_line_parser::print_options方法的典型用法代码示例。如果您正苦于以下问题：C++ command_line_parser::print_options方法的具体用法？C++ command_line_parser::print_options怎么用？C++ command_line_parser::print_options使用的例子？那么恭喜您, 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command_line_parser的用法示例。
在下文中一共展示了command_line_parser::print_options方法的1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: kernel
static void
parse_args (command_line_parser& parser, int argc, char* argv[])
{
try {
// Algorithm-independent options
parser.add_option ("a",
"Choose the learning algorithm: {krls,krr,mlp,svr}.",1);
parser.add_option ("h","Display this help message.");
parser.add_option ("help","Display this help message.");
parser.add_option ("k",
"Learning kernel (for krls,krr,svr methods): {lin,rbk}.",1);
parser.add_option ("in","A libsvm-formatted file to test.",1);
parser.add_option ("normalize",
"Normalize the sample inputs to zero-mean unit variance.");
parser.add_option ("train-best",
"Train and save a network using best parameters", 1);
parser.set_group_name("Algorithm Specific Options");
// Algorithm-specific options
parser.add_option ("rbk-gamma",
"Width of radial basis kernels: {float}.",1);
parser.add_option ("krls-tolerance",
"Numerical tolerance of krls linear dependency test: {float}.",1);
parser.add_option ("mlp-hidden-units",
"Number of hidden units in mlp: {integer}.",1);
parser.add_option ("mlp-num-iterations",
"Number of epochs to train the mlp: {integer}.",1);
parser.add_option ("svr-c",
"SVR regularization parameter \"C\": "
"{float}.",1);
parser.add_option ("svr-epsilon-insensitivity",
"SVR fitting tolerance parameter: "
"{float}.",1);
parser.add_option ("verbose", "Use verbose trainers");
// Parse the command line arguments
parser.parse(argc,argv);
// Check that options aren't given multiple times
const char* one_time_opts[] = {"a", "h", "help", "in"};
parser.check_one_time_options(one_time_opts);
const char* valid_kernels[] = {"lin", "rbk"};
const char* valid_algs[] = {"krls", "krr", "mlp", "svr"};
parser.check_option_arg_range("a", valid_algs);
parser.check_option_arg_range("k", valid_kernels);
parser.check_option_arg_range("rbk-gamma", 1e-200, 1e200);
parser.check_option_arg_range("krls-tolerance", 1e-200, 1e200);
parser.check_option_arg_range("mlp-hidden-units", 1, 10000000);
parser.check_option_arg_range("mlp-num-iterations", 1, 10000000);
parser.check_option_arg_range("svr-c", 1e-200, 1e200);
parser.check_option_arg_range("svr-epsilon-insensitivity", 1e-200, 1e200);
// Check if the -h option was given
if (parser.option("h") || parser.option("help")) {
std::cout << "Usage: mltool [-a algorithm] --in input_file\n";
parser.print_options(std::cout);
std::cout << std::endl;
exit (0);
}
// Check that an input file was given
if (!parser.option("in")) {
std::cout
<< "Error in command line:\n"
<< "You must specify an input file with the --in option.\n"
<< "\nTry the -h option for more information\n";
exit (0);
}
}
catch (std::exception& e) {
// Catch cmd_line_parse_error exceptions and print usage message.
std::cout << e.what() << std::endl;
exit (1);
}
catch (...) {
std::cout << "Some error occurred" << std::endl;
}
}