made all local functions static (in order to not interfere with each other)

svn path=/trunk/externals/ann/; revision=3029

2 files changed, 50 insertions, 50 deletions

diff --git a/src/ann_mlp.c b/src/ann_mlp.c
index e01b203..c8c4cb3 100755
--- a/src/ann_mlp.c
+++ b/src/ann_mlp.c
@@ -36,7 +36,7 @@ typedef struct _ann_mlp {
 	t_outlet *l_out, *f_out;

 } t_ann_mlp;

 

-void help(t_ann_mlp *x)

+static void help(t_ann_mlp *x)

 {

 	post("");

 	post("ann_mlp: neural nets for PD");

@@ -48,7 +48,7 @@ void help(t_ann_mlp *x)
 

 }

 

-void createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	unsigned int num_input = 2;

 	unsigned int num_output = 1;

@@ -108,7 +108,7 @@ void createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void print_status(t_ann_mlp *x)

+static void print_status(t_ann_mlp *x)

 {

 		if (x->mode == TRAIN)

 			post("nn:training");

@@ -116,7 +116,7 @@ void print_status(t_ann_mlp *x)
 			post("nn:running");

 }

 

-void train(t_ann_mlp *x)

+static void train(t_ann_mlp *x)

 {

 	x->mode=TRAIN;

 	if (x->ann == 0)

@@ -128,13 +128,13 @@ void train(t_ann_mlp *x)
 	print_status(x);

 }

 

-void run(t_ann_mlp *x)

+static void run(t_ann_mlp *x)

 {

 	x->mode=RUN;

 	print_status(x);

 }

 

-void set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc<1)

 	{

@@ -149,7 +149,7 @@ void set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 

 

 

-void train_on_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void train_on_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (x->ann == 0)

 	{

@@ -174,7 +174,7 @@ void train_on_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	post("nn: finished training on file %s", x->filenametrain->s_name);

 }

 

-void set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	float desired_error = (float)0.001;

 	if (0<argc)

@@ -188,7 +188,7 @@ void set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	unsigned int max_iterations = 500000;

 	if (argc>0)

@@ -202,7 +202,7 @@ void set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	

 	unsigned int iterations_between_reports = 1000;

@@ -221,7 +221,7 @@ void set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom
 

 // run the ann using floats in list passed to the inlet as input values

 // and send result to outlet as list of float

-void run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	int i=0;

 	fann_type input[MAXINPUT];	

@@ -275,7 +275,7 @@ void run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	

 }

 

-void train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	int i=0;

 	fann_type input[MAXINPUT];	

@@ -334,7 +334,7 @@ void train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 

 }

 

-void manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (x->mode)

 		run_the_net(x, sl, argc, argv);

@@ -344,7 +344,7 @@ void manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc>0) {

     x->filename = atom_gensym(argv);

@@ -355,7 +355,7 @@ void set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 	post("nn:filename set to %s", x->filename->s_name);

 }

 

-void load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc>0) {

     x->filename = atom_gensym(argv);

@@ -367,7 +367,7 @@ void load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 		post("nn:ann loaded fom file %s", x->filename->s_name);

 }

 

-void save_ann_to_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void save_ann_to_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc>0) {

     x->filename = atom_gensym(argv);

@@ -383,7 +383,7 @@ void save_ann_to_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
 }

 

 // functions for training algo:

-void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)

+static void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)

 {

 	if (x->ann == 0)

 	{

@@ -394,7 +394,7 @@ void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)
 		post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");

 	}

 }

-void set_FANN_TRAIN_BATCH(t_ann_mlp *x)

+static void set_FANN_TRAIN_BATCH(t_ann_mlp *x)

 {

 	if (x->ann == 0)

 	{

@@ -405,7 +405,7 @@ void set_FANN_TRAIN_BATCH(t_ann_mlp *x)
 		post("nn:training algorithm set to FANN_TRAIN_BATCH");

 	}

 }

-void set_FANN_TRAIN_RPROP(t_ann_mlp *x)

+static void set_FANN_TRAIN_RPROP(t_ann_mlp *x)

 {

 	if (x->ann == 0)

 	{

@@ -416,7 +416,7 @@ void set_FANN_TRAIN_RPROP(t_ann_mlp *x)
 		post("nn:training algorithm set to FANN_TRAIN_RPROP");

 	}

 }

-void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)

+static void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)

 {

 	if (x->ann == 0)

 	{

@@ -428,7 +428,7 @@ void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)
 	}

 }

 

-void set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	t_symbol *parametro = 0;

 	int funzione = 0;

@@ -464,7 +464,7 @@ void set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom
 

 }

 

-void print_ann_details(t_ann_mlp *x)

+static void print_ann_details(t_ann_mlp *x)

 {

 	if (x->ann == 0)

 	{

@@ -490,7 +490,7 @@ void print_ann_details(t_ann_mlp *x)
 }

 

 

-void *nn_new(t_symbol *s, int argc, t_atom *argv)

+static void *nn_new(t_symbol *s, int argc, t_atom *argv)

 {

 	t_ann_mlp *x = (t_ann_mlp *)pd_new(ann_mlp_class);

 	x->l_out = outlet_new(&x->x_obj, &s_list);

diff --git a/src/ann_td.c b/src/ann_td.c
index 0184139..a519c2f 100755
--- a/src/ann_td.c
+++ b/src/ann_td.c
@@ -40,7 +40,7 @@ typedef struct _ann_td {
 	t_outlet *l_out, *f_out;

 } t_ann_td;

 

-void help(t_ann_td *x)

+static void help(t_ann_td *x)

 {

 	post("");

 	post("ann_td:time delay neural networks for PD");

@@ -52,7 +52,7 @@ void help(t_ann_td *x)
 

 }

 

-void deallocate_inputs(t_ann_td *x)

+static void deallocate_inputs(t_ann_td *x)

 {

 	if (x->inputs != 0)

 	{

@@ -61,7 +61,7 @@ void deallocate_inputs(t_ann_td *x)
 	}

 }

 

-void allocate_inputs(t_ann_td *x)

+static void allocate_inputs(t_ann_td *x)

 {

 	unsigned int i;

 	deallocate_inputs(x);

@@ -70,7 +70,7 @@ void allocate_inputs(t_ann_td *x)
 	for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f;

 }

 

-void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	unsigned int num_input = 2;

 	unsigned int num_output = 1;

@@ -144,7 +144,7 @@ void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void print_status(t_ann_td *x)

+static void print_status(t_ann_td *x)

 {

 		if (x->mode == TRAIN)

 			post("ann_td:training");

@@ -152,7 +152,7 @@ void print_status(t_ann_td *x)
 			post("ann_td:running");

 }

 

-void train(t_ann_td *x)

+static void train(t_ann_td *x)

 {

 	x->mode=TRAIN;

 	if (x->ann == 0)

@@ -164,13 +164,13 @@ void train(t_ann_td *x)
 	print_status(x);

 }

 

-void run(t_ann_td *x)

+static void run(t_ann_td *x)

 {

 	x->mode=RUN;

 	print_status(x);

 }

 

-void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc<1)

 	{

@@ -185,7 +185,7 @@ void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 

 

 

-void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (x->ann == 0)

 	{

@@ -210,7 +210,7 @@ void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	post("ann_td: finished training on file %s", x->filenametrain->s_name);

 }

 

-void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	float desired_error = (float)0.001;

 	if (0<argc)

@@ -224,7 +224,7 @@ void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	unsigned int max_iterations = 500000;

 	if (argc>0)

@@ -238,7 +238,7 @@ void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	

 	unsigned int iterations_between_reports = 1000;

@@ -255,7 +255,7 @@ void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom
 }

 

 

-void scale_inputs(t_ann_td *x)

+static void scale_inputs(t_ann_td *x)

 {

 	unsigned int j;

 	unsigned int k;

@@ -273,7 +273,7 @@ void scale_inputs(t_ann_td *x)
 

 // run the ann using floats in list passed to the inlet as input values

 // and send result to outlet as list of float

-void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	int i=0;

 	unsigned j=0;

@@ -337,7 +337,7 @@ void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	

 }

 

-void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	int i=0;

 	unsigned int j=0;

@@ -398,7 +398,7 @@ void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 

 }

 

-void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (x->mode)

 		run_the_net(x, sl, argc, argv);

@@ -408,7 +408,7 @@ void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	}

 }

 

-void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc>0) {

     x->filename = atom_gensym(argv);

@@ -419,7 +419,7 @@ void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	post("nn:filename set to %s", x->filename->s_name);

 }

 

-void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (x->ins_frames_set==0)

 	{

@@ -439,7 +439,7 @@ void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 	allocate_inputs(x);

 }

 

-void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	if (argc>0) {

     x->filename = atom_gensym(argv);

@@ -455,7 +455,7 @@ void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 }

 

 // functions for training algo:

-void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x)

+static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x)

 {

 	if (x->ann == 0)

 	{

@@ -466,7 +466,7 @@ void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x)
 		post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");

 	}

 }

-void set_FANN_TRAIN_BATCH(t_ann_td *x)

+static void set_FANN_TRAIN_BATCH(t_ann_td *x)

 {

 	if (x->ann == 0)

 	{

@@ -477,7 +477,7 @@ void set_FANN_TRAIN_BATCH(t_ann_td *x)
 		post("nn:training algorithm set to FANN_TRAIN_BATCH");

 	}

 }

-void set_FANN_TRAIN_RPROP(t_ann_td *x)

+static void set_FANN_TRAIN_RPROP(t_ann_td *x)

 {

 	if (x->ann == 0)

 	{

@@ -488,7 +488,7 @@ void set_FANN_TRAIN_RPROP(t_ann_td *x)
 		post("nn:training algorithm set to FANN_TRAIN_RPROP");

 	}

 }

-void set_FANN_TRAIN_QUICKPROP(t_ann_td *x)

+static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x)

 {

 	if (x->ann == 0)

 	{

@@ -500,7 +500,7 @@ void set_FANN_TRAIN_QUICKPROP(t_ann_td *x)
 	}

 }

 

-void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

+static void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)

 {

 	t_symbol *parametro = 0;

 	int funzione = 0;

@@ -536,7 +536,7 @@ void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom
 

 }

 

-void print_ann_details(t_ann_td *x)

+static void print_ann_details(t_ann_td *x)

 {

 	if (x->ann == 0)

 	{

@@ -561,14 +561,14 @@ void print_ann_details(t_ann_td *x)
 	}

 }

 

-void set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames)

+static void set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames)

 {

 	x->num_input = ins;

 	x->frames = frames;

 	x->ins_frames_set=1;

 }

 

-void *nn_new(t_symbol *s, int argc, t_atom *argv)

+static void *nn_new(t_symbol *s, int argc, t_atom *argv)

 {

 	t_ann_td *x = (t_ann_td *)pd_new(ann_td_class);

 	x->l_out = outlet_new(&x->x_obj, &s_list);