shut down the compiler...

svn path=/trunk/externals/ann/; revision=3028

3 files changed, 137 insertions, 139 deletions

diff --git a/src/ann_mlp.c b/src/ann_mlp.c
index e83aea3..e01b203 100755
--- a/src/ann_mlp.c
+++ b/src/ann_mlp.c
@@ -30,8 +30,8 @@ typedef struct _ann_mlp {
 	int mode; // 0 = training, 1 = running

 	t_symbol *filename; // name of the file where this ann is saved

 	t_symbol *filenametrain; // name of the file with training data

-	float desired_error;
-	unsigned int max_iterations;
+	float desired_error;

+	unsigned int max_iterations;

 	unsigned int iterations_between_reports;

 	t_outlet *l_out, *f_out;

 } t_ann_mlp;

@@ -49,48 +49,48 @@ void help(t_ann_mlp *x)
 }

 

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

-{
-	unsigned int num_input = 2;
-	unsigned int num_output = 1;
-	unsigned int num_layers = 3;
-	unsigned int num_neurons_hidden = 3;
-	float connection_rate = 1;
-	float learning_rate = (float)0.7;
-
-	if (argc>0)
-		num_input = atom_getint(argv++);
-
-	if (argc>1)
-		num_output = atom_getint(argv++);
-
-	if (argc>2)
-		num_layers = atom_getint(argv++);
-
-	if (argc>3)
-		num_neurons_hidden = atom_getint(argv++);
-
-	if (argc>4)
-		connection_rate = atom_getfloat(argv++);
-
-	if (argc>5)
-		learning_rate = atom_getfloat(argv++);
-
-	if (num_input>=MAXINPUT)
-	{
-		error("too many inputs, maximum allowed is MAXINPUT");
-		return;
-	}
-
-	if (num_output>=MAXOUTPUT)
-	{
-		error("too many outputs, maximum allowed is MAXOUTPUT");
-		return;
-	}
-
-	x->ann	= fann_create(connection_rate, learning_rate, num_layers,
-		num_input, num_neurons_hidden, num_output);
-
-	fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);
+{

+	unsigned int num_input = 2;

+	unsigned int num_output = 1;

+	unsigned int num_layers = 3;

+	unsigned int num_neurons_hidden = 3;

+	float connection_rate = 1;

+	float learning_rate = (float)0.7;

+

+	if (argc>0)

+		num_input = atom_getint(argv++);

+

+	if (argc>1)

+		num_output = atom_getint(argv++);

+

+	if (argc>2)

+		num_layers = atom_getint(argv++);

+

+	if (argc>3)

+		num_neurons_hidden = atom_getint(argv++);

+

+	if (argc>4)

+		connection_rate = atom_getfloat(argv++);

+

+	if (argc>5)

+		learning_rate = atom_getfloat(argv++);

+

+	if (num_input>=MAXINPUT)

+	{

+		error("too many inputs, maximum allowed is MAXINPUT");

+		return;

+	}

+

+	if (num_output>=MAXOUTPUT)

+	{

+		error("too many outputs, maximum allowed is MAXOUTPUT");

+		return;

+	}

+

+	x->ann	= fann_create(connection_rate, learning_rate, num_layers,

+		num_input, num_neurons_hidden, num_output);

+

+	fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);

 	fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);

 

 	if (x->ann == 0)

@@ -150,25 +150,25 @@ 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)

-{
+{

 	if (x->ann == 0)

 	{

 		error("ann not initialized");

 		return;

-	}
-
-	if (argc<1)
-	{
-		error("you must specify the filename with training data");
-		return;
-	} else
-	{
-		x->filenametrain = atom_gensym(argv);
-	}

-
-	//post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name);
-
-	fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations,
+	}

+

+	if (argc<1)

+	{

+		error("you must specify the filename with training data");

+		return;

+	} else

+	{

+		x->filenametrain = atom_gensym(argv);

+	}

+

+	//post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name);

+

+	fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations,

 		x->iterations_between_reports, x->desired_error);

 	

 	post("nn: finished training on file %s", x->filenametrain->s_name);

@@ -496,8 +496,8 @@ void *nn_new(t_symbol *s, int argc, t_atom *argv)
 	x->l_out = outlet_new(&x->x_obj, &s_list);

 	x->f_out = outlet_new(&x->x_obj, &s_float);

 

-	x->desired_error = (float)0.001;
-	x->max_iterations = 500000;
+	x->desired_error = (float)0.001;

+	x->max_iterations = 500000;

 	x->iterations_between_reports = 1000;

 	x->mode=RUN;

 

@@ -562,4 +562,4 @@ void ann_mlp_setup(void) {
 	class_sethelpsymbol(ann_mlp_class, gensym("help-ann_mlp"));

 

 

-}
\ No newline at end of file
+}

diff --git a/src/ann_som.c b/src/ann_som.c
index f1da9fc..65a8682 100644
--- a/src/ann_som.c
+++ b/src/ann_som.c
@@ -329,9 +329,6 @@ static void som_dump(t_som *x, t_float nf){
   outlet_list(x->x_obj.ob_outlet, &s_list, x->num_sensors, ap);
 
   freebytes(ap, x->num_sensors*sizeof(t_atom));
-  
-
-
 }
 
 static void som_makenewsom(t_som *x, t_symbol *s, int argc, t_atom *argv)
@@ -757,6 +754,8 @@ static void som_setup(void)
   class_addmethod(som_class, (t_method)som_read, gensym("read"), A_GIMME, 0);
   class_addmethod(som_class, (t_method)som_write, gensym("write"), A_GIMME, 0);
 
+  class_addmethod(som_class, (t_method)som_dump, gensym("dump"), A_FLOAT, 0);
+
   class_addmethod(som_class, (t_method)som_print, gensym("print"), 0);
   class_addmethod(som_class, (t_method)som_help, gensym("help"), 0);
   class_sethelpsymbol(som_class, gensym("som"));
diff --git a/src/ann_td.c b/src/ann_td.c
index c2cf6b5..0184139 100755
--- a/src/ann_td.c
+++ b/src/ann_td.c
@@ -30,8 +30,8 @@ typedef struct _ann_td {
 	int mode; // 0 = training, 1 = running

 	t_symbol *filename; // name of the file where this ann is saved

 	t_symbol *filenametrain; // name of the file with training data

-	float desired_error;
-	unsigned int max_iterations;
+	float desired_error;

+	unsigned int max_iterations;

 	unsigned int iterations_between_reports;

 	unsigned int frames;

 	unsigned int num_input;

@@ -71,59 +71,59 @@ void allocate_inputs(t_ann_td *x)
 }

 

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

-{
-	unsigned int num_input = 2;
-	unsigned int num_output = 1;
-	unsigned int num_layers = 3;
-	unsigned int num_neurons_hidden = 3;
-	float connection_rate = 1;
-	float learning_rate = (float)0.7;
-	
-	if (argc<3)
-	{
-		error("you must provide at least num_input, num_output amd frames number");
-		return;
-	}
-	if (argc>0)
-		num_input = atom_getint(argv++);
-
-	if (argc>1)
-		num_output = atom_getint(argv++);
-	
-	if (argc>2)
-	{
-		x->frames = atom_getint(argv++);
-		x->ins_frames_set=1;
-	}
-
-	if (argc>3)
-		num_layers = atom_getint(argv++);
-
-	if (argc>4)
-		num_neurons_hidden = atom_getint(argv++);
-
-	if (argc>5)
-		connection_rate = atom_getfloat(argv++);
-
-	if (argc>6)
-		learning_rate = atom_getfloat(argv++);
-
-	if ((num_input * x->frames)>=MAXINPUT)
-	{
-		error("too many inputs, maximum allowed is %f", MAXINPUT/x->frames);
-		return;
-	}
-
-	if (num_output>=MAXOUTPUT)
-	{
-		error("too many outputs, maximum allowed is MAXOUTPUT");
-		return;
-	}
-
-	x->ann	= fann_create(connection_rate, learning_rate, num_layers,
-		(num_input*x->frames), num_neurons_hidden, num_output);
-
-	fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);
+{

+	unsigned int num_input = 2;

+	unsigned int num_output = 1;

+	unsigned int num_layers = 3;

+	unsigned int num_neurons_hidden = 3;

+	float connection_rate = 1;

+	float learning_rate = (float)0.7;

+	

+	if (argc<3)

+	{

+		error("you must provide at least num_input, num_output amd frames number");

+		return;

+	}

+	if (argc>0)

+		num_input = atom_getint(argv++);

+

+	if (argc>1)

+		num_output = atom_getint(argv++);

+	

+	if (argc>2)

+	{

+		x->frames = atom_getint(argv++);

+		x->ins_frames_set=1;

+	}

+

+	if (argc>3)

+		num_layers = atom_getint(argv++);

+

+	if (argc>4)

+		num_neurons_hidden = atom_getint(argv++);

+

+	if (argc>5)

+		connection_rate = atom_getfloat(argv++);

+

+	if (argc>6)

+		learning_rate = atom_getfloat(argv++);

+

+	if ((num_input * x->frames)>=MAXINPUT)

+	{

+		error("too many inputs, maximum allowed is %f", MAXINPUT/x->frames);

+		return;

+	}

+

+	if (num_output>=MAXOUTPUT)

+	{

+		error("too many outputs, maximum allowed is MAXOUTPUT");

+		return;

+	}

+

+	x->ann	= fann_create(connection_rate, learning_rate, num_layers,

+		(num_input*x->frames), num_neurons_hidden, num_output);

+

+	fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);

 	fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);

 

 	allocate_inputs(x);

@@ -186,25 +186,25 @@ 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)

-{
+{

 	if (x->ann == 0)

 	{

 		error("ann not initialized");

 		return;

-	}
-
-	if (argc<1)
-	{
-		error("you must specify the filename with training data");
-		return;
-	} else
-	{
-		x->filenametrain = atom_gensym(argv);
-	}

-
-	//post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name);
-
-	fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations,
+	}

+

+	if (argc<1)

+	{

+		error("you must specify the filename with training data");

+		return;

+	} else

+	{

+		x->filenametrain = atom_gensym(argv);

+	}

+

+	//post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name);

+

+	fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations,

 		x->iterations_between_reports, x->desired_error);

 	

 	post("ann_td: finished training on file %s", x->filenametrain->s_name);

@@ -277,7 +277,6 @@ void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
 {

 	int i=0;

 	unsigned j=0;

-	unsigned k=0;

 	//fann_type input[MAXINPUT];	

 	fann_type *calc_out;

 	t_atom lista[MAXOUTPUT];

@@ -575,8 +574,8 @@ void *nn_new(t_symbol *s, int argc, t_atom *argv)
 	x->l_out = outlet_new(&x->x_obj, &s_list);

 	x->f_out = outlet_new(&x->x_obj, &s_float);

 

-	x->desired_error = (float)0.001;
-	x->max_iterations = 500000;
+	x->desired_error = (float)0.001;

+	x->max_iterations = 500000;

 	x->iterations_between_reports = 1000;

 	x->mode=RUN;

 	x->ins_frames_set=0;

@@ -662,4 +661,4 @@ void ann_td_setup(void) {
 	class_sethelpsymbol(ann_td_class, gensym("help-ann_td"));

 

 

-}
\ No newline at end of file
+}