include/neural.h

@@ -27,7 +27,6 @@ typedef struct _neural_data {
 
 Neural_Network *neural_new(size_t, size_t, size_t);
 void neural_free(Neural_Network *);
-void neural_populate_sequential(Neural_Network *);
 void neural_randomize(Neural_Network *);
 float *neural_loadData(Neural_Network *, const char *);
 float *neural_process(Neural_Network *, float *);

src/cx.c

@@ -183,7 +183,7 @@ cx_nninit(Neural_Network **nn) {
     }
 
     // Populate the neural network with sensible values.
-    neural_populate_sequential(*nn);
+    neural_randomize(*nn);
 
     return 0;
 }
@@ -306,8 +306,6 @@ cx_run(CX_Context *ctx) {
 
     pthread_join(tg[1]->group_manager->thread, &neural_xml);
 
-    printf("%s\n", neural_xml);
-
     ctx->gl_ctx->master_lock = 0;
 
     neural_getMesh(ctx->nn_ctx->nn, ctx->gl_ctx->mr);

src/neural.c

@@ -67,22 +67,6 @@ neural_free(Neural_Network *self) {
     free(self);
 }
 
-void
-neural_populate_sequential(Neural_Network *self) {
-    Neural_Layer *nl;
-
-    for (int i = 0; i < self->layer_count; i++) {
-        nl = self->layers[i];
-        int populator = 0;
-        for (int j = 0; j < nl->layer_size; j++) {
-            for (int k = 0; k < nl->layer_size_next; k++) {
-                nl->neurons[j].synapses[k] = (float)populator;
-                populator++;
-            }
-        }
-    }
-}
-
 void
 neural_randomize(Neural_Network *self) {
     FILE *f;
@@ -238,10 +222,6 @@ neural_data_new(int layer_size, int layer_size_next) {
                                         * sizeof(float));
         self->mat_len = layer_size_next;
     }
-    else {
-        self->synapse_matrix = NULL;
-        self->mat_len = 0;
-    }
     return self;
 }
 
@@ -250,13 +230,20 @@ neural_getData(Neural_Network *self, size_t layer) {
     Neural_Layer *nl;
     Neural_Data *retval;
 
+
     nl = self->layers[layer];
 
     retval = neural_data_new(nl->layer_size, nl->layer_size_next);
-    if (retval->mat_len) {
+
+    retval->vect_len = nl->layer_size;
+    if (!nl->layer_size_next) {
+        retval->synapse_matrix = NULL;
+        retval->mat_len = 0;
+    }
+    else {
         for (int i = 0; i < nl->layer_size; i++) {
             for (int j = 0; j < nl->layer_size_next; j++) {
-                retval->synapse_matrix[i+(nl->layer_size*j)] = nl->neurons[i].synapses[j];
+                retval->synapse_matrix[i*j+i] = nl->neurons[i].synapses[j];
             }
         }
     }
@@ -394,7 +381,7 @@ neural_getXML(Neural_Network *nn) {
             line_prep = strcat(line_prep, "[ ");
             for (int k = 0; k < nd->vect_len; k++) {
 
-                strfromf(number_buffer, 32, "%.4f ", nd->synapse_matrix[k+(j*nd->vect_len)]);
+                strfromf(number_buffer, 32, "%.2f ", nd->synapse_matrix[k+j*nd->mat_len]);
                 line_prep = strcat(line_prep, number_buffer);
                 if (k < nd->vect_len - 1) {
                     line_prep = strcat(line_prep, ", ");