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draft complete. debugging

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This commit is contained in:
Seth Hamilton
2025-09-09 09:39:53 -05:00
parent bd9820dd68
commit 1b8801519e
4 changed files with 226 additions and 65 deletions
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215
inc/genetic.h
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@@ -16,15 +16,17 @@ template <class T> struct Stats;
template <class T> struct Strategy;
struct CellTracker;
template <class T> Stats<T> run(Strategy<T>);
template <class T> T run(Strategy<T>);
template <class T> struct Strategy {
// Number of worker threads that will be evaluating cell fitness
int num_threads;
float stats_print_period;
int batch_size; // Number of cells a worker thread tries to work on in a row
// before accessing/locking the work queue again.
int num_cells; // Size of the population pool
int num_cells_per_thread; // Size of the population pool per sim thread
int num_generations; // Number of times (epochs) to run the algorithm
bool test_all; // Sets whether or not every cell's fitness is evaluated every
// generation
@@ -60,8 +62,23 @@ template <class T> struct Strategy {
template<class T> struct Stats {
std::vector<T> best_cell;
std::vector<float> best_cell_fitness;
TimeSpan setup_time;
TimeSpan run_time;
int gen;
bool done;
TimeSpan start, end;
TimeSpan total_crossover_time;
int total_crossovers;
TimeSpan total_mutate_time;
int total_mutates;
TimeSpan total_fitness_time;
int total_evaluations;
TimeSpan total_sorting_time;
int total_sorts;
Mutex m;
};
struct CellTracker {
@@ -83,58 +100,75 @@ template <class T> Array<T> make_array(int len) {
};
}
template <class T> Stats<T> run(Strategy<T> strat) {
Stats<T> stats;
template<class T>
struct WorkerThreadArgs {
Strategy<T> strat;
Array<T> cells;
Array<CellTracker> trackers;
Stats<T> &stats;
};
// ************* SETUP **************
TimeSpan start_setup = now();
template<class T> T* _cellp(Array<T> cells, CellTracker tracker) { return &cells[tracker.cellid]; }
// Create cells
Array<T> cells = make_array<T>(strat.num_cells);
for (int i = 0; i < cells.len; i++) cells[i] = strat.make_default_cell();
template <class T> DWORD worker(LPVOID args) {
// Unpack everything...
WorkerThreadArgs<T>* worker_args = static_cast<WorkerThreadArgs<T>*>(args);
Strategy<T> strat = worker_args->strat;
Array<T> cells = worker_args->cells;
Array<CellTracker> trackers = worker_args->trackers;
Stats<T> &stats = worker_args->stats;
// Create cell trackers
Array<CellTracker> trackers = make_array<CellTracker>(strat.num_cells);
for (int i = 0; i < trackers.len; i++) trackers[i] = { .score=0, .cellid=i };
// Prepare crossover operations as these will be the same every time except
// for the exact cell pointers
int npar = strat.crossover_parent_num;
int nchild = strat.crossover_children_num;
Array<T*> parents = make_array<T*>(npar);
Array<T*> children = make_array<T*>(nchild);
stats.setup_time = now() - start_setup;
// *********** ALGORITHM ************
TimeSpan start_algo = now();
for (int gen = 0; gen < strat.num_generations; gen++) {
// 1. mutate
for (int i = 0; i < trackers.len; i++) {
if (abs(norm_rand(strat.rand_seed)) < strat.mutation_chance) {
strat.mutate(cells[trackers[i].cellid]);
}
}
// 2. crossover
if (strat.enable_crossover) {
int npar = strat.crossover_parent_num;
int nchild = strat.crossover_children_num;
TimeSpan start;
while(stats.gen < strat.num_generations) {
// 1. crossover
start = now();
if (strat.enable_crossover) {
int parent_end = npar;
int child_begin = trackers.len-nchild;
Array<T*> parents = make_array<T*>(npar);
Array<T*> children = make_array<T*>(nchild);
while (parent_end <= child_begin) {
// Get pointers to all the parent cells
for (int i = parent_end-npar; i < parent_end; i++) {
parents[i - (parent_end-npar)] = &cells[trackers[i].cellid];
parents[i - (parent_end-npar)] = _cellp(cells, trackers[i]);
}
// Get pointers to all the child cells (these will be overwritten)
for (int i = child_begin; i < child_begin+nchild; i++) {
children[i-child_begin] = &cells[trackers[i].cellid];
children[i-child_begin] = _cellp(cells, trackers[i]);
}
strat.crossover(parents, children);
parent_end += strat.crossover_parent_stride;
child_begin -= nchild;
}
free(parents.data);
free(children.data);
}
lock(stats.m);
stats.total_crossover_time = stats.total_crossover_time + (now() - start);
stats.total_crossovers++;
unlock(stats.m);
// 2. mutate
start = now();
for (int i = 0; i < trackers.len; i++) {
if (abs(norm_rand(strat.rand_seed)) < strat.mutation_chance) {
strat.mutate(cells[trackers[i].cellid]);
}
}
lock(stats.m);
stats.total_mutate_time = stats.total_mutate_time + (now() - start);
stats.total_mutates++;
unlock(stats.m);
// 3. evaluate
start = now();
if (strat.test_all) {
for (int i = 0; i < trackers.len; i++) {
trackers[i].score = strat.fitness(cells[trackers[i].cellid]);
@@ -146,15 +180,118 @@ template <class T> Stats<T> run(Strategy<T> strat) {
}
}
}
// 4. sort
std::sort(&trackers[0], &trackers[trackers.len-1], [strat](CellTracker &a, CellTracker &b){ return strat.higher_fitness_is_better ? a.score > b.score : a.score < b.score; });
lock(stats.m);
stats.total_fitness_time = stats.total_fitness_time + (now() - start);
stats.total_evaluations++;
unlock(stats.m);
// 4. sort
start = now();
std::sort(&trackers[0], &trackers[trackers.len-1], [strat](CellTracker &a, CellTracker &b){ return strat.higher_fitness_is_better ? a.score > b.score : a.score < b.score; });
lock(stats.m);
stats.total_sorting_time = stats.total_sorting_time + (now() - start);
stats.total_sorts++;
printf("Gen: %d, Best Score: %f\n", gen, trackers[0].score);
stats.best_cell.push_back(cells[trackers[0].cellid]);
stats.best_cell_fitness.push_back(trackers[0].score);
stats.gen++;
unlock(stats.m);
}
stats.run_time = now() - start_algo;
return stats;
stats.done = true;
stats.end = now();
return 0;
}
template <class T> T run(Strategy<T> strat) {
Array<Stats<T>> stats = make_array<Stats<T>>(strat.num_threads);
Array<Thread> threads = make_array<Thread>(strat.num_threads);
Array<T> cells = make_array<T>(strat.num_threads*strat.num_cells_per_thread);
Array<CellTracker> trackers = make_array<CellTracker>(cells.len);
Array<WorkerThreadArgs<T>> args = make_array<WorkerThreadArgs<T>>(strat.num_threads);
for (int i = 0; i < cells.len; i++) {
cells[i] = strat.make_default_cell();
trackers[i] = {0, i};
}
for (int i = 0; i < strat.num_threads; i++) {
stats[i] = {
.gen=0,
.m=make_mutex()
};
Array<T> tcells = { &cells[i*strat.num_cells_per_thread], strat.num_cells_per_thread };
Array<CellTracker> ttrackers = { &trackers[i*strat.num_cells_per_thread], strat.num_cells_per_thread };
args[i].strat=strat;
args[i].cells=tcells;
args[i].trackers=ttrackers;
args[i].stats=stats[i];
threads[i] = make_thread(worker<T>, &args[i]);
}
// We are the stats thread
bool complete = false;
while (!complete) {
sleep(from_s(strat.stats_print_period));
printf("**********************\n");
float g_avg_crossover_time = 0;
float g_avg_mutate_time = 0;
float g_avg_fitness_time = 0;
float g_avg_sorting_time = 0;
float g_progress_per = 0;
complete = true;
for (int i = 0; i < stats.len; i++) {
lock(stats[i].m);
complete &= stats[i].done;
float avg_crossover_time = to_s(stats[i].total_crossover_time) / static_cast<float>(stats[i].total_crossovers);
float avg_mutate_time = to_s(stats[i].total_mutate_time) / static_cast<float>(stats[i].total_mutates);
float avg_fitness_time = to_s(stats[i].total_fitness_time) / static_cast<float>(stats[i].total_evaluations);
float avg_sorting_time = to_s(stats[i].total_sorting_time) / static_cast<float>(stats[i].total_sorts);
float progress_per = static_cast<float>(stats[i].gen) / static_cast<float>(strat.num_generations) * 100;
g_avg_crossover_time += avg_crossover_time;
g_avg_mutate_time += avg_mutate_time;
g_avg_fitness_time += avg_fitness_time;
g_avg_sorting_time += avg_sorting_time;
g_progress_per += progress_per;
printf("THREAD %d, Progress %.1f%, Average Crossover Time/Cell %.5f (s), Average Mutate Time/Cell: %.5f (s), Average Fitness Time/Cell: %.5f (s), Average Sorting Time: %.5f (s)\n", i, progress_per, avg_crossover_time, avg_mutate_time, avg_fitness_time, avg_sorting_time);
unlock(stats[i].m);
}
g_avg_crossover_time /= stats.len;
g_avg_mutate_time /= stats.len;
g_avg_fitness_time /= stats.len;
g_avg_sorting_time /= stats.len;
g_progress_per /= stats.len;
printf("OVERALL, Progress %.1f%, Average Crossover Time/Cell %.5f (s), Average Mutate Time/Cell: %.5f (s), Average Fitness Time/Cell: %.5f (s), Average Sorting Time: %.5f (s)\n", g_progress_per, g_avg_crossover_time, g_avg_mutate_time, g_avg_fitness_time, g_avg_sorting_time);
if (complete) break;
}
T best_cell;
// TODO: bad
float best_score = strat.higher_fitness_is_better ? 999999999999999999.9 : 0.0;
for (int i = 0; i < stats.len; i++) {
float score = stats[i].best_cell_fitness.back();
if (strat.higher_fitness_is_better ? score > best_score : score < best_score) {
best_cell = stats[i].best_cell.back();
best_score = score;
}
}
return best_cell;
}
} // namespace genetic