146 lines
5.1 KiB
C++
146 lines
5.1 KiB
C++
#pragma once
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#include <algorithm>
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#include <cstdlib>
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#include <vector>
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#include "sync.h"
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#include "rand.h"
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namespace genetic {
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template <class T> struct Array;
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template <class T> struct Stats;
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template <class T> struct Strategy;
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struct CellTracker;
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template <class T> Stats<T> run(Strategy<T>);
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template <class T> struct Strategy {
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// Number of worker threads that will be evaluating cell fitness
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int num_threads;
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int batch_size; // Number of cells a worker thread tries to work on in a row
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// before accessing/locking the work queue again.
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int num_cells; // Size of the population pool
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int num_generations; // Number of times (epochs) to run the algorithm
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bool test_all; // Sets whether or not every cell's fitness is evaluated every
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// generation
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float test_chance; // Chance to test any given cell's fitness. Relevant only
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// if test_all is false.
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bool enable_crossover; // Cells that score well in the evaluation stage
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// produce children that replace low-scoring cells
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int crossover_parent_num; // Number of unique high-scoring parents in a
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// crossover call.
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int crossover_parent_stride; // Number of parents to skip over when moving to
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// the next set of parents. A stride of 1 would
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// produce maximum overlap because the set of
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// parents would only change by one every
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// crossover.
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int crossover_children_num; // Number of children to expect the user to
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// produce in the crossover function.
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bool enable_mutation; // Cells may be mutated
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// before fitness evaluation
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float mutation_chance; // Chance for any given cell to be mutated cells during
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// the mutation
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uint64_t rand_seed;
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bool higher_fitness_is_better; // Sets whether or not to consider higher
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// fitness values better or worse. Set this to
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// false if fitness is an error function.
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// User defined functions
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T (*make_default_cell)();
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void (*mutate)(T &cell_to_modify);
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void (*crossover)(const Array<T *> parents, const Array<T *> out_children);
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float (*fitness)(const T &cell);
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};
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template<class T> struct Stats {
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std::vector<T> best_cell;
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std::vector<float> best_cell_fitness;
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};
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struct CellTracker {
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float score;
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int cellid;
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};
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template <class T> struct Array {
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T *data;
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int len;
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T &operator[](int i) { return data[i]; }
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};
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template <class T> Array<T> make_array(int len) {
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return {
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.data = (T*)malloc(sizeof(T)*len),
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.len = len
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};
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}
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template <class T> Stats<T> run(Strategy<T> strat) {
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// Create cells
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Array<T> cells = make_array<T>(strat.num_cells);
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for (int i = 0; i < cells.len; i++) cells[i] = strat.make_default_cell();
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// Create cell trackers
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Array<CellTracker> trackers = make_array<CellTracker>(strat.num_cells);
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for (int i = 0; i < trackers.len; i++) trackers[i] = { .score=0, .cellid=i };
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// Init stat tracker
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Stats<T> stats;
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// Run the algorithm
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for (int gen = 0; gen < strat.num_generations; gen++) {
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// 1. mutate
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for (int i = 0; i < trackers.len; i++) {
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if (abs(norm_rand(strat.rand_seed)) < strat.mutation_chance) {
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strat.mutate(cells[trackers[i].cellid]);
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}
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}
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// 2. crossover
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if (strat.enable_crossover) {
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int parent_end = strat.crossover_parent_num;
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int child_begin = trackers.len-strat.crossover_children_num;
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while (parent_end <= child_begin) {
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// Get pointers to all the parent cells
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Array<T*> parents = make_array<T*>(strat.crossover_parent_num);
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for (int i = parent_end-strat.crossover_parent_num; i < parent_end; i++) {
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parents[i] = &cells[trackers[i].cellid];
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}
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// Get pointers to all the child cells (these will be overwritten)
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Array<T*> children = make_array<T*>(strat.crossover_children_num);
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for (int i = child_begin; i < child_begin+strat.crossover_children_num; i++) {
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children[i] = &cells[trackers[i].cellid];
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}
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strat.crossover(parents, children);
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parent_end += strat.crossover_parent_stride;
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child_begin -= strat.crossover_children_num;
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}
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}
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// 3. evaluate
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if (strat.test_all) {
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for (int i = 0; i < trackers.len; i++) {
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trackers[i].score = strat.fitness(cells[trackers[i].cellid]);
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}
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} else {
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for (int i = 0; i < trackers.len; i++) {
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if (abs(norm_rand(strat.rand_seed)) < strat.test_chance) {
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trackers[i].score = strat.fitness(cells[trackers[i].cellid]);
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}
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}
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}
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// 4. sort
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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; });
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printf("Gen: %d, Best Score: %f\n", gen, trackers[0].score);
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stats.best_cell.push_back(cells[trackers[0].cellid]);
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stats.best_cell_fitness.push_back(trackers[0].score);
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}
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return stats;
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}
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} // namespace genetic
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