Improve neural randomization

Start implementing neural training algorithm.

include/cx.h

@@ -8,6 +8,7 @@
 #include <math.h>
 #include <time.h>
 #include <unistd.h>
+#include <stdint.h>
 
 // Include GLEW
 #include <GL/glew.h>

src/neural.c

@@ -54,13 +54,20 @@ void
 neural_randomize(Neural_Network *self) {
     FILE *f;
     Neural_Layer *nl;
+    uint64_t *rand_vals;
+
 
     f = fopen("/dev/urandom", "r");
 
     for (int i = 0; i < self->layer_count; i++) {
         nl = self->layers[i];
+        rand_vals = malloc(nl->layer_size_next * sizeof(uint64_t));
+        fread(rand_vals, sizeof(uint64_t),
+              nl->layer_size_next, f);
         for (int j = 0; j < nl->layer_size; j++) {
-            fread(nl->neurons[j].synapses, sizeof(float), nl->layer_size_next, f);
+            for (int k = 0; k < nl->layer_size_next; k++) {
+                nl->neurons[j].synapses[k] = UINT64_MAX / rand_vals[k]
+            }
         }
     }
 }
@@ -137,11 +144,25 @@ neural_process(Neural_Network *self, float *input) {
     return retval;
 }
 
+// These two will be merged into one once I have
+// enough patience to create more dynamic objects.
+static void *
+neural_backprop_up(Neural_Network *self, size_t neuron, size_t layer) {
+    return NULL;
+}
+
+static void *
+neural_backprop_down(Neural_Network *self, size_t neuron, size_t layer) {
+    return NULL;
+}
+
 int
 neural_train(Neural_Network *self,
              const char *testdata,
              const float *testresult) {
-    // Insert algorithm you lazy fuck.
+    float *testdata_converted;
+    testdata_converted = malloc(self->layers[self->layer_count-1]->layer_size * sizeof(float));
+
     return 0;
 }