1 files changed, 91 insertions, 0 deletions
diff --git a/example/genetic_ann.c b/example/genetic_ann.c
new file mode 100755
index 0000000..071ccf5
--- /dev/null
+++ b/example/genetic_ann.c
@@ -0,0 +1,91 @@
+#include <stdint.h>
+#include <stdio.h>
+#include <string.h>
+#include <time.h>
+
+#define ANN_IMPLEMENTATION
+#include "../include/ann.h"
+
+#define POPULATION_N 100
+#define RATE_SELECTION 0.2
+#define RATE_MUTATON 10
+#define MAGNITUDE_MUTATION 0.01
+#define ERROR 0.00001
+
+typedef ann_t *ga_individual_t;
+
+#define GENETIC_IMPLEMENTATION
+#include "../include/genetic.h"
+
+double fitness(ann_t *const x, void const *c) {
+  fp_t *i = *((fp_t **)c);
+  fp_t *t = *((fp_t **)c + 1);
+  fp_t o[4];
+
+  fp_t e = 0, sum = 0;
+  for (uint_t b = 0; b < 4; b++) {
+    ann_propagation_forward(x, &i[2 * b], &o[b]);
+    e = ann_error_total(&o[b], &t[b], x->layer_neuron_n[x->layer_n - 1]);
+    sum += e;
+  }
+
+  return sum;
+}
+
+void crossover(ann_t **y, ann_t *x0, ann_t *x1) {
+  uint32_t i_crossover = rand() % x0->weight_n;
+  memcpy((*y)->weight, x0->weight, i_crossover * sizeof(fp_t));
+  memcpy((*y)->weight + i_crossover, x1->weight + i_crossover,
+         (x0->weight_n - i_crossover) * sizeof(fp_t));
+}
+
+void mutate(ann_t *x) {
+  uint32_t i_mutation = rand() % x->weight_n;
+  fp_t r =
+      (2 * (MAGNITUDE_MUTATION * (fp_t)rand() / RAND_MAX)) - MAGNITUDE_MUTATION;
+  x->weight[i_mutation] += r;
+}
+
+int main(void) {
+  srand(time(0));
+
+  fp_t input[12] = {0, 0, 0, 1, 1, 0, 1, 1};
+  fp_t output;
+  fp_t target[4] = {0, 1, 1, 0};
+
+  ann_t **networks = malloc(sizeof(ann_t *) * POPULATION_N);
+
+  for (uint_t i = 0; i < POPULATION_N; i++) {
+    networks[i] = ann_init(3, (uint_t[]){2, 2, 1});
+    ann_set_activation(networks[i], SIGMOID, IDENTITY);
+    ann_random(networks[i]);
+  }
+
+  fp_t e = ERROR + 1;
+  uint32_t n = 0, i = 0;
+  while (e > ERROR) {
+    e = ga_generation(networks, POPULATION_N, RATE_SELECTION, RATE_MUTATON,
+                      fitness, (fp_t *[2]){input, target}, crossover, mutate);
+
+    n++;
+    i++;
+
+    if (i > 1000) {
+      printf("\rERROR: %10.5lf", e);
+      i = 0;
+    }
+  }
+
+  printf("\33[2K\r");
+  printf("ERROR: %lf", e);
+
+  for (int i = 0; i < 1; i++) {
+    for (int j = 0; j < 4; j++) {
+      ann_propagation_forward(networks[i], (fp_t const *const)&input[2 * j],
+                              &output);
+      ann_print_neuron(networks[i], &input[2 * j], &output);
+    }
+  }
+
+  printf("\nITERATIONS: %u\n", n);
+}