timer work in sync header

.gitignore

@@ -0,0 +1,4 @@
+**PTHREADS-BUILT**
+**obj**
+**bin**
+.cache**

inc/genetic.h

@@ -2,29 +2,62 @@
 
 namespace genetic {
 
+template <class T> struct Array;
+template <class T> struct Stats;
+template <class T> struct Strategy;
+
+template <class T> Stats<T> run(Strategy<T>);
+
 template <class T> struct Strategy {
-  // The recommended number of threads is <= number of cores on your pc.
-  // Set this to -1 use the default value (number of cores - 1)
-  int num_threads;     // Number of worker threads that will be evaluating cell fitness
-  int num_cells;       // Size of the population pool
+  int num_threads; // Number of worker threads that will be evaluating cell
+                   // fitness.
+  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_generations; // Number of times (epochs) to run the algorithm
-  bool test_all;       // Sets whether or not every cell is tested every generation
-  float test_chance;   // Chance to test any given cell's fitness. Relevant only if test_all is false.
+  bool test_all; // Sets whether or not every cell's fitness is evaluated every
+                 // generation
+  float test_chance; // Chance to test any given cell's fitness. Relevant only
+                     // if test_all is false.
+  bool enable_crossover; // Cells that score well in the evaluation stage
+                         // produce children that replace low-scoring cells
+  bool enable_crossover_mutation;  // Mutations can occur after crossover
+  float crossover_mutation_chance; // Chance to mutate a child cell
+  int crossover_parent_num;        // Number of unique high-scoring parents in a
+                                   // crossover call.
+  int crossover_parent_stride; // Number of parents to skip over when moving to
+                               // the next set of parents. A stride of 1 would
+                               // produce maximum overlap because the set of
+                               // parents would only change by one every
+                               // crossover.
+  int crossover_children_num;  // Number of children to expect the user to
+                               // produce in the crossover function.
+  bool enable_mutation;          // Cells may be mutated
+                                 // before fitness evaluation
+  float mutation_chance; // Chance for any given cell to be mutated cells during
+                         // the mutation
+  uint64_t rand_seed;
+  bool higher_fitness_is_better; // Sets whether or not to consider higher
+                                 // fitness values better or worse. Set this to
+                                 // false if fitness is an error function.
 
   // User defined functions
   T (*make_default_cell)();
+  void (*mutate)(T &cell_to_modify);
+  void (*crossover)(const Array<T *> parents, const Array<T *> out_children);
   float (*fitness)(const T &cell);
-  void (*mutate)(const T &cell, T *out);
-  void (*crossover)(const T &a, const T &b, T *out);
-
-  float mutation_chance_per_gen;
 };
 
 template <class T> struct Stats {
   std::vector<T> best_cell;
-  std::vector<float> average_fitness;
+  std::vector<float> best_cell_fitness;
 };
 
-template <class T> Stats<T> run(Strategy<T>);
+template <class T> struct Array {
+  T *_data;
+  int len;
+
+  T &operator[](int i);
+};
 
 } // namespace genetic

inc/rand.h

@@ -0,0 +1,18 @@
+// TODO: This file needs a serious audit
+
+#include <cstdint>
+
+constexpr uint64_t half_max = UINT64_MAX / 2;
+
+// From https://en.wikipedia.org/wiki/Xorshift
+inline void xorshift64(uint64_t &state) {
+  state ^= state << 13;
+  state ^= state >> 7;
+  state ^= state << 17;
+}
+
+// returns a random value between -1 and 1. modifies seed
+inline float norm_rand(uint64_t &state) {
+  xorshift64(state);
+  return (state - half_max) / half_max;
+}

inc/sync.h

@@ -0,0 +1,155 @@
+#pragma once 
+
+#ifdef _WIN32
+#include "windows.h"
+#endif
+
+namespace sync {
+
+#ifdef _WIN32
+typedef CRITICAL_SECTION Mutex;
+typedef CONDITION_VARIABLE ConditionVar; 
+typedef HANDLE Semaphore;
+typedef HANDLE Thread;
+typedef DWORD TimeSpan; 
+typedef DWORD WINAPI (*ThreadFunc)(_In_ LPVOID lpParameter);
+typedef LPVOID ThreadArg
+const TimeSpan infinite_ts = INFINITE;
+#endif
+
+Thread make_thread(ThreadFunc t);
+void join(Thread t);
+
+Mutex make_mutex();
+void lock(Mutex &m);
+bool trylock(Mutex &m);
+void unlock(Mutex &m);
+void dispose(Mutex &m);
+
+ConditionVar make_condition_var();
+void wait(ConditionVar &c, Mutex &m, TimeSpan ts);
+void wake_one(ConditionVar &c);
+void wake_all(ConditionVar &c);
+void dispose(ConditionVar &c);
+
+Semaphore make_semaphore(int initial, int max);
+void wait(Semaphore &s);
+void post(Semaphore &s);
+void dispose(Semaphore &s);
+
+TimeSpan from_ms(double milliseconds);
+TimeSpan from_s(double seconds);
+TimeSpan from_min(double minutes);
+TimeSpan from_hours(double hours);
+
+double to_ms(TimeSpan &sp);
+double to_s(TimeSpan &sp);
+double to_min(TimeSpan &sp);
+double to_hours(TimeSpan &sp);
+
+#ifdef _WIN32
+
+Thread make_thread(ThreadFunc f, ThreadArg a) {
+    DWORD tid;
+    return CreateThread(NULL, 0, t, a, 0, &tid);
+}
+
+void join(Thread t) {
+    WaitForSingleObject(t, infinite_ts);
+}
+
+Mutex make_mutex() {
+    Mutex m;
+    InitializeCriticalSection(&m);
+    return m;
+}
+
+void lock(Mutex &m) {
+    EnterCriticalSection(&m);
+}
+
+bool trylock(Mutex &m) {
+    return TryEnterCriticalSection(&m);
+}
+
+void unlock(Mutex &m) {
+    LeaveCriticalSection(&m);
+}
+
+void dispose(Mutex &m) {
+    DeleteCriticalSection(&m);
+}
+
+ConditionVar make_condition_var() {
+    ConditionVar c;
+    InitializeConditionVariable(&c);
+    return c;
+}
+
+void wait(ConditionVar &c, Mutex &m, TimeSpan ts) {
+    SleepConditionVariable(&c, &m, ts);
+}
+
+void wake_one(ConditionVar &c) {
+    WakeConditionVariable(&c);
+}
+
+void wake_all(ConditionVar &c) {
+    WakeAllConditionVariable(&c);
+}
+
+void dispose(ConditionVar &c) {
+    return; // Windows doesn't have a delete condition variable func
+}
+
+Semaphore make_semaphore(int initial, int max) {
+    return CreateSemaphoreA(NULL, (long)initial, (long)max, NULL);
+}
+
+void wait(Semaphore &s) {
+    WaitForSingleObject(s, infinite_ts);
+}
+
+void post(Semaphore &s) {
+    ReleaseSemaphore(s);
+}
+
+void dispose(Semaphore &s) {
+    CloseHandle(s);
+}
+
+TimeSpan from_ms(double milliseconds) {
+    return static_cast<TimeSpan>(milliseconds);
+}
+
+TimeSpan from_s(double seconds) {
+    return static_cast<TimeSpan>(seconds*1000.0);
+}
+
+TimeSpan from_min(double minutes) {
+    return static_cast<TimeSpan>(minutes*60.0*1000.0);
+}
+
+TimeSpan from_hours(double hours) {
+    return static_cast<TimeSpan>(hours*60.0*60.0*1000.0);
+}
+
+double to_ms(TimeSpan &sp) {
+    return static_cast<double>(sp);
+}
+
+double to_s(TimeSpan &sp) {
+    return static_cast<double>(sp)/1000.0;
+}
+
+double to_min(TimeSpan &sp) {
+    return static_cast<double>(sp)/(1000.0*60.0);
+}
+
+double to_hours(TimeSpan &sp) {
+    return static_cast<double>(sp)/(1000.0*60.0*60.0);
+}
+ 
+#endif
+
+} // namespace sync

makefile

@@ -1,13 +1,61 @@
-src_files = $(find src -iname "*.cpp")
-obj_files = $(src_files:%.cpp=%.o)
+src_files = $(shell find src -iname "*.cpp")
+obj_files = $(src_files:src/%.cpp=obj/%.o)
+
+
+ifeq ($(OS),Windows_NT)
+    CCFLAGS += -D WIN32 -Iext/PTHREADS-BUILT/include -std=c++20
+    PTHREADLIB = ext/PTHREADS-BUILT/lib/pthreadVCE3.lib
+    ifeq ($(PROCESSOR_ARCHITEW6432),AMD64)
+        CCFLAGS += -D AMD64
+    else
+        ifeq ($(PROCESSOR_ARCHITECTURE),AMD64)
+            CCFLAGS += -D AMD64
+        endif
+        ifeq ($(PROCESSOR_ARCHITECTURE),x86)
+            CCFLAGS += -D IA32
+        endif
+    endif
+else
+    UNAME_S := $(shell uname -s)
+    ifeq ($(UNAME_S),Linux)
+        CCFLAGS += -D LINUX
+    endif
+    ifeq ($(UNAME_S),Darwin)
+        CCFLAGS += -D OSX
+    endif
+    UNAME_P := $(shell uname -p)
+    ifeq ($(UNAME_P),x86_64)
+        CCFLAGS += -D AMD64
+    endif
+    ifneq ($(filter %86,$(UNAME_P)),)
+        CCFLAGS += -D IA32
+    endif
+    ifneq ($(filter arm%,$(UNAME_P)),)
+        CCFLAGS += -D ARM
+    endif
+endif
+
+debug: OPTIMIZATION_FLAG = -g
+release: OPTIMIZATION_FLAG = -O3
+
+release: all export_comp_db
+
+debug: all export_comp_db
 
 all: $(obj_files)
-	echo $(obj_files)
-	echo $(src_files)
-	g++ -o main $^
+	@ mkdir -p bin
+	g++ -I inc/ $^ $(PTHREADLIB) -o bin/main $(OPTIMIZATION_FLAG) $(CCFLAGS)
 
-%.o: %.cpp
-	g++ -o $@ $<
+obj/%.o: src/%.cpp
+	@ mkdir -p obj
+	g++ -I inc/ -c $< -o $@ $(OPTIMIZATION_FLAG) $(CCFLAGS)
+
+export_comp_db:
+	echo [ > compile_commands.json
+	make debug -B --dry-run > temp
+	awk '/g\+\+.*\.cpp/ { f="compile_commands.json"; printf "\t\{\n\t\t\"directory\": \"%s\",\n\t\t\"command\": \"%s\",\n\t\t\"file\": \"%s\"\n\t\},\n", ENVIRON["PWD"], $$0, $$5 >> f }' temp
+	echo ] >> compile_commands.json
+	rm temp
 
 clean:
-	rm -f *.o *.exe
+	rm -f obj/*.o bin/*.exe

src/genetic.cpp

@@ -1,42 +1,279 @@
-#include "genetic.h"
-#include <queue>
+#include <algorithm>
+#include <cstdint>
+#include <cstdlib>
+#include <optional>
+#include <variant>
 #include <vector>
-#include <pthread.h>
+
+#include "genetic.h"
+#include "pthread.h"
+#include "rand.h"
+
+#define NUM_QUEUE_RETRIES 10
+
+using namespace std;
+
+// std::visit/std::variant overload pattern
+// See:
+// https://www.modernescpp.com/index.php/visiting-a-std-variant-with-the-overload-pattern/
+// You don't have to understand this, just use it :)
+template <typename... Ts> struct overload : Ts... {
+  using Ts::operator()...;
+};
+template <class... Ts> overload(Ts...) -> overload<Ts...>;
 
 namespace genetic {
 
-template <class T> struct CellEntry {
+template <class T> struct cell_entry {
   float score;
-  T cell;
+  T *cell;
   bool stale;
 };
 
-template <class T> struct WorkEntry {
-  const std::vector<CellEntry<T>> &cur;
-  std::vector<CellEntry<T>> &next;
-  int cur_i;
+template <class T> struct crossover_job {
+  Array<cell_entry<T> *> &parents;
+  Array<cell_entry<T> *> &children_out;
 };
 
-// Definitions
-template <class T> Stats<T> run(Strategy<T> strat) {
-  Stats<T> stats;
+template <class T> struct fitness_job {
+  cell_entry<T> *cell_entry;
+};
 
-  std::queue<WorkEntry<T>> fitness_queue;
-  std::vector<CellEntry<T>> cells_a, cells_b;
-  for (int i = 0; i < strat.num_cells; i++) {
-    T cell = strat.make_default_cell();
-    cells_a.push_back({0, cell, true});
-    cells_b.push_back({0, cell, true});
-  }
+template <class T> struct mutate_job {
+  cell_entry<T> *cell_entry;
+};
 
-  std::vector<CellEntry<T>> &cur_cells = cells_a;
-  std::vector<CellEntry<T>> &next_cells = cells_b;
+template <class T> struct work_queue {
+  variant<crossover_job<T>, fitness_job<T>, mutate_job<T>> *jobs;
+  int len;
+  int read_i;
+  int write_i;
+  bool done_writing;
 
-  for (int i = 0; i < strat.num_generations; i++) {
+  pthread_mutex_t data_mutex;
+  pthread_mutex_t gen_complete_mutex;
+  pthread_mutex_t jobs_available_mutex;
 
-    cur_cells = cur_cells == cells_a ? cells_b : cells_a;
-    next_cells = cur_cells == cells_a ? cells_b : cells_a;
+  pthread_cond_t gen_complete_cond;
+  pthread_cond_t jobs_available_cond;
+};
+
+template <class T> work_queue<T> make_work_queue(int len) {
+  return {.jobs = (variant<fitness_job<T>, crossover_job<T>> *)malloc(
+              sizeof(variant<fitness_job<T>, crossover_job<T>>) * len),
+          .len = len,
+          .read_i = 0,
+          .write_i = 0,
+          .done_writing = false,
+          .data_mutex = PTHREAD_MUTEX_INITIALIZER,
+          .gen_complete_mutex = PTHREAD_MUTEX_INITIALIZER,
+          .jobs_available_mutex = PTHREAD_MUTEX_INITIALIZER,
+          .gen_complete_cond = PTHREAD_COND_INITIALIZER,
+          .jobs_available_cond = PTHREAD_COND_INITIALIZER};
+}
+
+template <class T> struct job_batch {
+  Array<variant<crossover_job<T>, fitness_job<T>>> jobs;
+  bool gen_complete;
+};
+
+template <class T>
+optional<job_batch<T>> get_job_batch(work_queue<T> &queue, int batch_size,
+                                     bool *stop_flag) {
+  while (true) {
+    for (int i = 0; i < NUM_QUEUE_RETRIES; i++) {
+      if (queue.read_i < queue.write_i &&
+          pthread_mutex_trylock(&queue.data_mutex)) {
+        job_batch<T> res;
+        res.jobs._data = &queue._jobs[queue.read_i];
+        int span_size = min(batch_size, queue.write_i - queue.read_i);
+        res.jobs.len = span_size;
+
+        queue.read_i += span_size;
+        res.gen_complete = queue.done_writing && queue.read_i == queue.write_i;
+
+        pthread_mutex_unlock(&queue.data_mutex);
+        return res;
+      }
+    }
+    pthread_mutex_lock(&queue.jobs_available_mutex);
+    pthread_cond_wait(queue.jobs_available_cond, &queue.jobs_available_mutex);
+    if (stop_flag)
+      return {};
   }
 }
 
+template <class T> struct worker_thread_args {
+  Strategy<T> &strat;
+  work_queue<T> &queue;
+  bool *stop_flag;
+};
+
+template <class T> void *worker(void *args) {
+  worker_thread_args<T> *work_args = (worker_thread_args<T> *)args;
+  Strategy<T> &strat = work_args->strat;
+  work_queue<T> &queue = work_args->queue;
+  bool *stop_flag = work_args->stop_flag;
+
+  auto job_dispatcher = overload{
+      [strat](mutate_job<T> mj) {
+        strat.mutate(*mj.cell_entry->cell);
+        mj.cell_entry->stale = true;
+      },
+      [strat](fitness_job<T> fj) {
+        fj.cell_entry->score = strat.fitness(*fj.cell_entry->cell);
+        fj.cell_entry->stale = false;
+      },
+      [strat](crossover_job<T> cj) {
+        Array<T *> parent_cells, child_cells;
+        parent_cells = {(T **)malloc(sizeof(T *) * cj.parents.len),
+                        cj.parents.len};
+        child_cells = {(T **)malloc(sizeof(T *) * cj.children_out.len),
+                       cj.children_out.len};
+        for (int i = 0; i < cj.parents.len; i++) {
+          parent_cells[i] = cj.parents[i].cell;
+        }
+        for (int i = 0; i < cj.children_out.len; i++) {
+          child_cells[i] = cj.children_out[i].cell;
+          cj.children_out[i].stale = true;
+        }
+        strat.crossover(parent_cells, child_cells);
+      },
+  };
+
+  while (true) {
+    auto batch = get_job_batch(queue, strat.batch_size, stop_flag);
+    if (!batch || *stop_flag)
+      return NULL;
+
+    // Do the actual work
+    for (int i = 0; i < batch->jobs.len; i++) {
+      visit(job_dispatcher, batch->jobs[i]);
+    }
+
+    if (batch->gen_complete) {
+      pthread_cond_signal(&queue.gen_complete_cond, &queue.gen_complete_mutex);
+    }
+  }
+}
+
+template <class T> Stats<T> run(Strategy<T> strat) {
+  Stats<T> stats;
+
+  // The work queue is what all the worker threads will checking
+  // for jobs
+  work_queue<T> queue = make_work_queue<T>(strat.num_cells);
+
+  // The actual cells. Woo!
+  T cells[strat.num_cells];
+
+  // Using a vector so I can use the make_heap, push_heap, etc.
+  vector<cell_entry<T>> cell_queue;
+  for (int i = 0; i < strat.num_cells; i++) {
+    cells[i] = strat.make_default_cell();
+    cell_queue.push_back({0, &cells[i], true});
+  }
+
+  bool stop_flag = false;
+  worker_thread_args<T> args = {
+      .strat = strat, .queue = queue, .stop_flag = &stop_flag};
+
+  // spawn worker threads
+  pthread_t threads[strat.num_threads];
+  for (int i = 0; i < strat.num_threads; i++) {
+    pthread_create(&threads[i], NULL, worker<T>, (void *)args);
+  }
+
+  uint64_t rand_state = strat.rand_seed;
+
+  for (int i = 0; i < strat.num_generations; i++) {
+    // Mutate some random cells in the population
+    for (int i = 0; i < cell_queue.size(); i++) {
+      if (abs(norm_rand(rand_state)) < strat.mutation_chance) {
+        queue.jobs[queue.write_i] = mutate_job<T>{&cell_queue[i]};
+        queue.write_i++;
+      }
+    }
+    pthread_cond_broadcast(&queue.jobs_available_cond);
+
+    // Potential issue here where mutations aren't done computing and fitness
+    // jobs begin. maybe need to gate this.
+
+    // Generate fitness jobs
+    for (int i = 0; i < cell_queue.size(); i++) {
+      if (cell_queue[i].stale &&
+          (strat.test_all || abs(norm_rand(rand_state)) < strat.test_chance)) {
+        queue.jobs[queue.write_i] = fitness_job<T>{&cell_queue[i]};
+        queue.write_i++;
+      }
+      pthread_cond_broadcast(&queue.jobs_available_cond);
+    }
+    queue.done_writing = true;
+
+    // wait for fitness jobs to complete
+    pthread_mutex_lock(&queue.gen_complete_mutex);
+
+    // Before going to sleep, do a quick check to see if the fitness jobs are
+    // already complete.
+    pthread_mutex_lock(&queue.data_mutex);
+    bool already_complete = queue.read_i != queue.write_i;
+    pthread_mutex_unlock(&queue.data_mutex);
+    if (already_complete) {
+      pthread_mutex_unlock(&queue.gen_complete_mutex);
+    } else {
+      pthread_cond_wait(&queue.gen_complete_cond, &queue.gen_complete_mutex);
+    }
+
+    // Sort cells on performance
+    std::sort(cell_queue.begin(), cell_queue.end(),
+              [strat](cell_entry<T> a, cell_entry<T> b) {
+                return strat.higher_fitness_is_better ? a > b : a < b;
+              });
+
+    printf("Top Score: %f\n", cell_queue[0].score);
+
+    if (!strat.enable_crossover)
+      continue;
+
+    // generate crossover jobs
+    // dear god. forgive me father
+    queue.write_i = 0;
+    queue.read_i = 0;
+    int count = 0;
+    int n_par = strat.crossover_parent_num;
+    int n_child = strat.crossover_children_num;
+    int child_i = cell_queue.size() - 1;
+    int par_i = 0;
+    while (child_i - par_i <= n_par + n_child) {
+      Array<cell_entry<T> *> parents = {
+          (cell_entry<T> **)malloc(sizeof(cell_entry<T> *) * n_par), n_par};
+      Array<cell_entry<T> *> children = {
+          (cell_entry<T> **)malloc(sizeof(cell_entry<T> *) * n_child), n_child};
+
+      for (; par_i < par_i + n_par; par_i++) {
+        parents[i] = cell_queue[par_i];
+      }
+
+      for (; child_i > child_i - n_child; child_i--) {
+        children[i] = cell_queue[child_i];
+      }
+
+      queue.jobs[queue.write_i] = crossover_job<T>{parents, children};
+      par_i += strat.crossover_parent_stride;
+      child_i += strat.crossover_children_stride;
+    }
+  }
+
+  // stop worker threads
+  stop_flag = true;
+  pthread_cond_broadcast(&queue.jobs_available_cond);
+  for (int i = 0; i < strat.num_threads; i++) {
+    pthread_join(threads[i], NULL);
+  }
+}
+
+template <class T> T &Array<T>::operator[](int i) {
+  return _data[i];
+}
+
 } // namespace genetic

src/main.cpp

@@ -1,133 +1,81 @@
-#include <algorithm>
 #include <cassert>
+#include <cstdint>
 #include <cstdlib>
-#include <iostream>
-#include <vector>
+#include "genetic.h"
+#include "rand.h"
 
-#define MUTATION_CHANCE 1.0
+using namespace genetic;
 
-float norm_rand() { return (float)rand() / RAND_MAX; }
+const int len = 10;
+const float max_float = 9999.9f;
+static uint64_t seed = 12;
+static float num_mutate_chance = 0.5;
+static int num_parents = 2;
+static int num_children = 2;
 
-enum class ConstraintType {
-  PRODUCT = 0,
-  SUM = 1,
-  INDEX_EQ = 2,
-};
 
-struct Constraint {
-  ConstraintType type;
-  int optional_i;
-  float value;
-};
-static std::vector<Constraint> constraints;
+static int target_sum = 200;
+static int target_product = 300;
 
-struct Cell {
-  int n;
-  float *params;
-};
-
-Cell make_cell(int num_params) {
-  Cell res = {num_params, (float *)malloc(num_params * sizeof(float))};
-  for (int i = 0; i < num_params; i++) {
-    res.params[i] = 0;
-  }
-  return res;
+Array<float> make_new_arr() {
+    Array<float> arr = { (float*)malloc(sizeof(float)*len), len };
+    for (int i = 0; i < arr.len; i++) {
+        arr[i] = norm_rand(seed) * max_float;
+    }
+    return arr;
 }
 
-float get_cell_err(const Cell &a) {
-  float total_diff = 0;
-  for (auto c : constraints) {
-    switch (c.type) {
-    case ConstraintType::SUM: {
-      float sum = 0;
-      for (int i = 0; i < a.n; i++) {
-        sum += a.params[i];
-      }
-      total_diff += abs(c.value - sum);
-      break;
+void mutate(Array<float> &arr_to_mutate) {
+    for (int i = 0; i < len; i++) {
+        if (norm_rand(seed) < num_mutate_chance) {
+            arr_to_mutate[i] = norm_rand(seed) * max_float;
+        }
     }
-    case ConstraintType::PRODUCT: {
-      float prod = 1;
-      for (int i = 0; i < a.n; i++) {
-        prod *= a.params[i];
-      }
-      total_diff += abs(c.value - prod);
-      break;
-    }
-    case ConstraintType::INDEX_EQ: {
-      assert(c.optional_i < a.n);
-      total_diff += abs(c.value - a.params[c.optional_i]);
-      break;
-    }
-    }
-  }
-  return total_diff;
 }
 
-bool operator<(const Cell &a, const Cell &b) {
-  assert(a.n == b.n);
-  return get_cell_err(a) < get_cell_err(b);
+void crossover(const Array<Array<float>*> parents, const Array<Array<float> *> out_children) {
+    for (int i = 0; i < len; i++) {
+        (*out_children._data[0])[i] = i < len/2 ? (*parents._data[0])[i] : (*parents._data[1])[i];
+        (*out_children._data[1])[i] = i < len/2 ? (*parents._data[1])[i] : (*parents._data[0])[i];
+    }
 }
 
-void combine_cells(const Cell &a, const Cell &b, Cell *child) {
-  bool a_first = norm_rand() > 0.5f;
-  for (int i = 0; i < a.n; i++) {
-    float offset = norm_rand() * 10;
-    float roll = norm_rand();
-    if (a_first) {
-      child->params[i] = (i < a.n / 2 ? a.params[i] : b.params[i]) +
-                         (roll > 0.5 ? offset : -offset);
-    } else {
-      child->params[i] = (i < a.n / 2 ? b.params[i] : a.params[i]) +
-                         (roll > 0.5 ? offset : -offset);
+// norm_rand can go negative. fix in genetic.cpp
+// child stride doesn't make sense. Should always skip over child num
+
+float fitness(const Array<float> &cell) {
+    float sum = 0;
+    float product = 1;
+    for (int i = 0; i < cell.len; i++) {
+        sum += cell._data[i];
+        product *= cell._data[i];
     }
-  }
-  float r = norm_rand();
-  child->params[(int)r * (a.n - 1)] = r * 100.0;
+    return abs(sum - target_sum) + abs(product - target_product);
 }
 
 int main(int argc, char **argv) {
-  int num_params, num_cells, num_generations, num_constraints = 0;
-  std::cin >> num_params >> num_cells >> num_generations >> num_constraints;
+    Strategy<Array<float>> strat {
+        .num_threads = 1,
+        .batch_size  = 1,
+        .num_cells   = 10,
+        .num_generations = 10,
+        .test_all = true,
+        .test_chance = 0.0, // doesn't matter
+        .enable_crossover = true,
+        .enable_crossover_mutation = true,
+        .crossover_mutation_chance = 0.6f,
+        .crossover_parent_num = 2,
+        .crossover_parent_stride = 1,
+        .crossover_children_num = 2,
+        .enable_mutation = true,
+        .mutation_chance = 0.8,
+        .rand_seed = seed,
+        .higher_fitness_is_better = false,
+        .make_default_cell=make_new_arr,
+        .mutate=mutate,
+        .crossover=crossover,
+        .fitness=fitness
+    };
 
-  std::cout << num_params << " " << num_cells << " " << num_generations << " "
-            << num_constraints << std::endl;
-
-  for (int i = 0; i < num_constraints; i++) {
-    int type_in, optional_i = 0;
-    float value;
-    std::cin >> type_in >> value;
-    ConstraintType type = static_cast<ConstraintType>(type_in);
-    if (type == ConstraintType::INDEX_EQ) {
-      std::cin >> optional_i;
-    }
-    constraints.push_back({type, optional_i, value});
-  }
-
-  std::vector<Cell> cells;
-  for (int i = 0; i < num_cells; i++) {
-    cells.push_back(make_cell(num_params));
-  }
-
-  for (int i = 0; i < num_generations; i++) {
-    std::sort(cells.begin(), cells.end());
-    for (int j = 0; j < num_cells / 2; j++) {
-      combine_cells(cells[j], cells[j + 1], &cells[num_cells / 2 + j]);
-    }
-    if (i % 1000 == 0) {
-      std::cout << i << "\t" << get_cell_err(cells[0]) << std::endl;
-    }
-  }
-  std::cout << "Final Answer: ";
-  float sum = 0;
-  float product = 1;
-  for (int i = 0; i < cells[0].n; i++) {
-    std::cout << cells[0].params[i] << " ";
-    sum += cells[0].params[i];
-    product *= cells[0].params[i];
-  }
-  std::cout << std::endl;
-
-  std::cout << "Sum: " << sum << std::endl;
-  std::cout << "Product: " << product << std::endl;
+    auto res = run(strat);
 }