// Determine the minimum useful population size
int bisection(Random& rand, Configuration& config, evaluation::pointer problem,
optimize::pointer solver) {
int runs = config.get<int>("runs");
float good_enough = config.get<int>("fitness_limit");
vector<Record> records;
// initial bounds
int min = 0;
int max = 1;
size_t failed_on = 0;
bool success;
// Double the maximum size until a successful run is found
do {
// double the bounds each time
min = max;
max *= 2;
config.set("pop_size", max);
std::cout << "Pop size: " << max << std::endl;
success = true;
// Perform multiple runs, stopping as soon as one of the runs failed to find
// the global optimum
for (int i = 0; success and i < runs; i++) {
int problem_number = ((i + failed_on) % runs);
std::cout << "\tTrying problem: " << problem_number << std::endl;
auto results = single_run(rand, config, problem, solver, problem_number);
if (results.best().first < good_enough) {
failed_on = problem_number;
success = false;
}
}
} while (!success);
// Bisect between the min (unsuccessful) and max (successful) until they meet
int guess;
while (min + 1 < max) {
guess = (max + min) / 2;
std::cout << "Pop size: " << guess << std::endl;
config.set("pop_size", guess);
success = true;
// Perform multiple runs, stopping as soon as one of the runs failed to find
// the global optimum
for (int i = 0; success and i < runs; i++) {
int problem_number = ((i + failed_on) % runs);
std::cout << "\tTrying problem: " << problem_number << std::endl;
auto results = single_run(rand, config, problem, solver, problem_number);
if (results.best().first < good_enough) {
failed_on = problem_number;
success = false;
}
}
// update the bound
if (success) {
max = guess;
} else {
min = guess;
}
}
return max;
}
// Recursively drills down from a given size until either it finds the correct
// population size, or you discover that max isn't big enough
int recurse(Random& rand, Configuration& config, evaluation::pointer problem,
optimize::pointer solver, int min, int max) {
int runs = config.get<int>("runs");
float good_enough = config.get<float>("fitness_limit");
vector<Record> records;
int result;
for (int i = 0; i < runs; i++) {
config.set("pop_size", max);
std::cout << "Pop size: " << max << " Trying problem: " << i << std::endl;
auto results = single_run(rand, config, problem, solver, i);
if (results.best().first < good_enough) {
// fail this size, you have to get bigger
std::cout << "\tfailed" << std::endl;
return -1;
} else {
int guess = (max + min) / 2;
if (min != guess) {
result = recurse(rand, config, problem, solver, min, guess);
// If you found the correct population size
if (result != -1) {
return result;
} else {
// You failed, increase the minimum
min = guess;
}
}
}
}
return max;
}
int
main (int argc, char ** argv)
{ double freq ;
memset (input, 0, sizeof (input)) ;
freq = 0.01 ;
gen_windowed_sines (1, &freq, 1.0, input, BUFFER_LEN) ;
if (argc == 1)
single_run () ;
else if (argc == 3 && strcmp (argv [1], "--best-of") == 0)
{ int run_count = atoi (argv [2]) ;
if (run_count < 1 || run_count > 20)
{ printf ("Please be sensible. Run count should be in range (1, 10].\n") ;
exit (1) ;
} ;
multi_run (run_count) ;
}
else
usage_exit (argv [0]) ;
puts (
" Duration is in seconds.\n"
" Throughput is in frames/sec (more is better).\n"
) ;
return 0 ;
} /* main */
void do_single_crack(struct db_main *db)
{
struct rpp_context ctx;
single_db = db;
rule_ctx = &ctx;
single_init();
single_run();
single_done();
}
void do_single_crack(struct db_main *db)
{
struct rpp_context ctx;
single_db = db;
rule_ctx = &ctx;
single_init();
single_run();
single_done();
rule_ctx = NULL; /* Just for good measure */
}
int main(int argc, char *argv[])
{
errval_t err;
// initialization
vfs_init();
bench_init();
// mount nfs
err = vfs_mkdir("/nfs");
assert(err_is_ok(err));
err = vfs_mount("/nfs", "nfs://10.110.4.4/local/nfs");
assert(err_is_ok(err));
// argument processing
if (argc == 3) {
printf("Started vfs_bench in command-line mode\n");
int32_t chunksize = atol(argv[1]);
int32_t repetitions = atol(argv[2]);
single_run(chunksize, repetitions);
} else {
printf("Started vfs_bench.\n");
for (int32_t i = 1; i < 20; i++) {
single_run(4096, i * 2000);
}
}
//err = vfs_unmount("/nfs"); // unmount is NYI
//assert(err_is_ok(err));
err = vfs_rmdir("/nfs");
assert(err_is_ok(err));
return 0;
}
// Performs multiple runs of optimization
vector<Record> multirun(Random& rand, Configuration& config,
evaluation::pointer problem, optimize::pointer solver) {
int runs = config.get<int>("runs");
int verbosity = config.get<int>("verbosity");
vector<Record> records;
// perform the desired number of runs
for (int run = 0; run < runs; run++) {
records.push_back(single_run(rand, config, problem, solver, run));
// output partial summaries
if (verbosity > 0) {
auto summary = Record::summarize(records, config);
std::cout << "Run: " << run << " Evals: "
<< records[records.size() - 1].best().second << " MES: "
<< summary[MES] << " MAD: " << summary[MAD] << " FAILURES: "
<< summary[FAILURES] << std::endl;
}
}
return records;
}
