small api cleanup

Author: mseminatore

README.md

@@ -138,7 +138,7 @@ ann_confusion_matrix | compute binary confusion matrix and MCC
 ann_print_confusion_matrix | print formatted confusion matrix
 ann_class_prediction | determine predicted class from output activations
 ann_print_props | print network properties and configuration
-print_outputs | print output layer activations (debug)
+ann_print_outputs | print output layer activations (debug)
 ann_strerror | convert error code to human-readable message
 ann_set_error_log_callback | install error logging callback
 ann_get_error_log_callback | get current error callback
@@ -150,22 +150,22 @@ Built-in schedulers adjust the learning rate during training:
 
 | Scheduler | Function | Description |
 |-----------|----------|-------------|
-| Step decay | `lr_scheduler_step` | Multiply LR by gamma every N epochs |
-| Exponential | `lr_scheduler_exponential` | Multiply LR by gamma each epoch |
-| Cosine | `lr_scheduler_cosine` | Smooth decay from base LR to min LR |
+| Step decay | `ann_lr_scheduler_step` | Multiply LR by gamma every N epochs |
+| Exponential | `ann_lr_scheduler_exponential` | Multiply LR by gamma each epoch |
+| Cosine | `ann_lr_scheduler_cosine` | Smooth decay from base LR to min LR |
 
 ```c
 // Step decay: halve LR every 10 epochs
 LRStepParams step_params = { .step_size = 10, .gamma = 0.5f };
-ann_set_lr_scheduler(net, lr_scheduler_step, &step_params);
+ann_set_lr_scheduler(net, ann_lr_scheduler_step, &step_params);
 
 // Exponential decay: 5% reduction per epoch
 LRExponentialParams exp_params = { .gamma = 0.95f };
-ann_set_lr_scheduler(net, lr_scheduler_exponential, &exp_params);
+ann_set_lr_scheduler(net, ann_lr_scheduler_exponential, &exp_params);
 
 // Cosine annealing: decay to 0.0001 over 100 epochs
 LRCosineParams cos_params = { .T_max = 100, .min_lr = 0.0001f };
-ann_set_lr_scheduler(net, lr_scheduler_cosine, &cos_params);
+ann_set_lr_scheduler(net, ann_lr_scheduler_cosine, &cos_params);
 
 // Custom scheduler
 real my_scheduler(unsigned epoch, real base_lr, void *data) {
@@ -681,7 +681,7 @@ int main(int argc, char *argv[])
     real outputs[1];
     ann_predict(pnet, &data[0], outputs);
 
-    print_outputs(pnet);
+    ann_print_outputs(pnet);
 
     // free resources
     ann_free_network(pnet);

ann.c

@@ -435,7 +435,7 @@ static void init_weights(PNetwork pnet)
 //--------------------------------
 // print nodes in the output layer
 //--------------------------------
-void print_outputs(const PNetwork pnet)
+void ann_print_outputs(const PNetwork pnet)
 {
 	if (!pnet)
 		return;
@@ -2481,7 +2481,7 @@ void ann_set_lr_scheduler(PNetwork pnet, LRSchedulerFunc scheduler, void *user_d
 // Step decay LR scheduler
 // LR = base_lr * (gamma ^ (epoch / step_size))
 //------------------------------
-real lr_scheduler_step(unsigned epoch, real base_lr, void *user_data)
+real ann_lr_scheduler_step(unsigned epoch, real base_lr, void *user_data)
 {
 	if (!user_data)
 		return base_lr;
@@ -2500,7 +2500,7 @@ real lr_scheduler_step(unsigned epoch, real base_lr, void *user_data)
 // Exponential decay LR scheduler
 // LR = base_lr * (gamma ^ epoch)
 //------------------------------
-real lr_scheduler_exponential(unsigned epoch, real base_lr, void *user_data)
+real ann_lr_scheduler_exponential(unsigned epoch, real base_lr, void *user_data)
 {
 	if (!user_data)
 		return base_lr;
@@ -2513,7 +2513,7 @@ real lr_scheduler_exponential(unsigned epoch, real base_lr, void *user_data)
 // Cosine annealing LR scheduler
 // LR = min_lr + (base_lr - min_lr) * (1 + cos(pi * epoch / T_max)) / 2
 //------------------------------
-real lr_scheduler_cosine(unsigned epoch, real base_lr, void *user_data)
+real ann_lr_scheduler_cosine(unsigned epoch, real base_lr, void *user_data)
 {
 	if (!user_data)
 		return base_lr;

ann.h

@@ -670,7 +670,7 @@ void ann_set_epoch_limit(PNetwork pnet, unsigned limit);
  * @param scheduler Scheduler callback function, or NULL to disable
  * @param user_data User-provided data passed to scheduler (e.g., parameters)
  * 
- * @see lr_scheduler_step, lr_scheduler_exponential, lr_scheduler_cosine
+ * @see ann_lr_scheduler_step, ann_lr_scheduler_exponential, ann_lr_scheduler_cosine
  */
 void ann_set_lr_scheduler(PNetwork pnet, LRSchedulerFunc scheduler, void *user_data);
 
@@ -709,7 +709,7 @@ typedef struct {
  * @param user_data Pointer to LRStepParams
  * @return Scheduled learning rate
  */
-real lr_scheduler_step(unsigned epoch, real base_lr, void *user_data);
+real ann_lr_scheduler_step(unsigned epoch, real base_lr, void *user_data);
 
 /**
  * Exponential decay scheduler: multiplies LR by gamma each epoch.
@@ -720,7 +720,7 @@ real lr_scheduler_step(unsigned epoch, real base_lr, void *user_data);
  * @param user_data Pointer to LRExponentialParams
  * @return Scheduled learning rate
  */
-real lr_scheduler_exponential(unsigned epoch, real base_lr, void *user_data);
+real ann_lr_scheduler_exponential(unsigned epoch, real base_lr, void *user_data);
 
 /**
  * Cosine annealing scheduler: smooth decay to min_lr.
@@ -731,7 +731,7 @@ real lr_scheduler_exponential(unsigned epoch, real base_lr, void *user_data);
  * @param user_data Pointer to LRCosineParams
  * @return Scheduled learning rate
  */
-real lr_scheduler_cosine(unsigned epoch, real base_lr, void *user_data);
+real ann_lr_scheduler_cosine(unsigned epoch, real base_lr, void *user_data);
 
 // ============================================================================
 // DROPOUT REGULARIZATION
@@ -826,7 +826,7 @@ void ann_print_props(const PNetwork pnet);
  * 
  * @param pnet Network whose outputs to print (must not be NULL)
  */
-void print_outputs(const PNetwork pnet);
+void ann_print_outputs(const PNetwork pnet);
 
 // ============================================================================
 // ERROR HANDLING

logic.c

@@ -74,7 +74,7 @@ int main(int argc, char *argv[])
 	real outputs[1];
 	CHECK_OK(ann_predict(pnet, &data[0], outputs));
 
-	print_outputs(pnet);
+	ann_print_outputs(pnet);
 
 	ann_free_network(pnet);