the joys of dos2unix

svn path=/trunk/externals/ann/; revision=3032

2 files changed, 1229 insertions, 1229 deletions

diff --git a/src/ann_mlp.c b/src/ann_mlp.c
index c8c4cb3..326bc6d 100755
--- a/src/ann_mlp.c
+++ b/src/ann_mlp.c
@@ -1,565 +1,565 @@
-/*	nn : Neural Networks for PD

-	by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it

-	this software is simply an interface for FANN classes

-	http://fann.sourceforge.net/

-	FANN is obviously needed for compilation

-	this software is licensed under the GNU General Public License

-*/

-#include <stdio.h>

-#include <string.h>

-#include "m_pd.h"

-#include "fann.h"

-

-

-#define VERSION "0.03"


-#ifndef __DATE__ 

-#define __DATE__ ""

-#endif

-

-#define TRAIN 0

-#define RUN  1

-

-#define MAXINPUT 100

-#define MAXOUTPUT 100

-

-static t_class *ann_mlp_class;

-

-typedef struct _ann_mlp {

-	t_object  x_obj;

-	struct fann *ann;

-	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;

-	unsigned int iterations_between_reports;

-	t_outlet *l_out, *f_out;

-} t_ann_mlp;

-

-static void help(t_ann_mlp *x)

-{

-	post("");

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

-	post("ann_mlp:Davide Morelli - info@davidemorelli.it - (c)2005");

-	post("ann_mlp:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");

-	post("ann_mlp:main commands: create, filename, load, save, train-on-file, run");

-	post("ann_mlp:see help-nn.pd for details on commands and usage");

-	post("ann_mlp:this is an interface to FANN");

-

-}

-

-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;

-	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)

-	{

-		error("error creating the ann");

-	} else

-	{

-		post("created ann with:");

-		post("num_input = %i", num_input);

-		post("num_output = %i", num_output);

-		post("num_layers = %i", num_layers);

-		post("num_neurons_hidden = %i", num_neurons_hidden);

-		post("connection_rate = %f", connection_rate);

-		post("learning_rate = %f", learning_rate);

-	}

-}

-

-static void print_status(t_ann_mlp *x)

-{

-		if (x->mode == TRAIN)

-			post("nn:training");

-		else

-			post("nn:running");

-}

-

-static void train(t_ann_mlp *x)

-{

-	x->mode=TRAIN;

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-	fann_reset_MSE(x->ann);

-	print_status(x);

-}

-

-static void run(t_ann_mlp *x)

-{

-	x->mode=RUN;

-	print_status(x);

-}

-

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

-{

-	if (argc<1)

-	{

-		error("usage: setmode 0/1: 0 for training, 1 for running");

-	}

-	else	

-	{	

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

-		print_status(x);

-	}

-}

-

-

-

-static 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,

-		x->iterations_between_reports, x->desired_error);

-	

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

-}

-

-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)

-	{

-		desired_error = atom_getfloat(argv);

-		x->desired_error = desired_error;

-		post("nn:desired_error set to %f", x->desired_error);

-	} else

-	{

-		error("you must pass me a float");

-	}

-}

-

-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)

-	{

-		max_iterations = atom_getint(argv);

-		x->max_iterations = max_iterations;

-		post("nn:max_iterations set to %i", x->max_iterations);

-	} else

-	{

-		error("you must pass me an int");

-	}

-}

-

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

-{

-	

-	unsigned int iterations_between_reports = 1000;

-	if (argc>0)

-	{

-		iterations_between_reports = atom_getint(argv);

-		x->iterations_between_reports = iterations_between_reports;

-		post("nn:iterations_between_reports set to %i", x->iterations_between_reports);

-	} else

-	{

-		error("you must pass me an int");

-	}

-

-}

-

-

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

-// and send result to outlet as list of float

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

-{

-	int i=0;

-	fann_type input[MAXINPUT];	

-	fann_type *calc_out;

-	t_atom lista[MAXOUTPUT];

-	int quanti;

-	float valoreTMP;

-

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-

-	quanti = x->ann->num_output;

-

-	// fill input array with zeros

-	for (i=0; i<MAXINPUT; i++)

-	{

-		input[i]=0;

-	}

-

-	// fill output array with zeros

-	for (i=0; i<MAXOUTPUT; i++)

-	{

-		SETFLOAT(lista + i,0);

-	}

-

-	// fill input array with actual data sent to inlet

-	for (i=0;i<argc;i++)

-	{

-		input[i] = atom_getfloat(argv++);

-	}

-	

-	// run the ann

-	calc_out = fann_run(x->ann, input);

-

-	// fill the output array with result from ann

-	for (i=0;i<quanti;i++)

-	{

-		valoreTMP = calc_out[i];

-		//post("calc_out[%i]=%f", i, calc_out[i]);

-		SETFLOAT(lista+i, valoreTMP);

-	}

-

-	// send output array to outlet

-	outlet_anything(x->l_out,

-                     gensym("list") ,

-					 quanti, 

-					 lista);

-	

-}

-

-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];	

-	fann_type output[MAXOUTPUT];

-	//fann_type *calcMSE;

-	//t_atom lista[MAXOUTPUT];

-	int quantiINs;

-	int quantiOUTs;

-	float mse;

-

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-

-	quantiINs = x->ann->num_input;

-	quantiOUTs = x->ann->num_output;

-

-	if ((quantiINs + quantiOUTs)>argc)

-	{

-		error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");

-		return;

-	}

-

-	// fill input array with zeros

-	for (i=0; i<MAXINPUT; i++)

-	{

-		input[i]=0;

-	}

-		// fill input array with zeros

-	for (i=0; i<MAXOUTPUT; i++)

-	{

-		output[i]=0;

-	}

-

-	// fill input array with actual data sent to inlet

-	for (i=0;i<quantiINs;i++)

-	{

-		input[i] = atom_getfloat(argv++);

-	}

-

-	for (i=0;i<quantiOUTs;i++)

-	{

-		output[i] = atom_getfloat(argv++);

-	}

-	

-	//fann_reset_MSE(x->ann);

-

-	fann_train(x->ann, input, output);

-

-	mse = fann_get_MSE(x->ann);

-	

-	outlet_float(x->f_out, mse);

-

-

-}

-

-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);

-	else

-	{

-		train_on_the_fly(x, sl, argc, 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);

-	} else

-	{

-		error("you must specify the filename");

-	}

-	post("nn:filename set to %s", x->filename->s_name);

-}

-

-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);

-	}

-	x->ann = fann_create_from_file(x->filename->s_name);

-	if (x->ann == 0)

-		error("error opening %s", x->filename->s_name);

-	else

-		post("nn:ann loaded fom file %s", x->filename->s_name);

-}

-

-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);

-	}

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_save(x->ann, x->filename->s_name);

-		post("nn:ann saved in file %s", x->filename->s_name);

-	}

-}

-

-// functions for training algo:

-static void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);

-		post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");

-	}

-}

-static void set_FANN_TRAIN_BATCH(t_ann_mlp *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);

-		post("nn:training algorithm set to FANN_TRAIN_BATCH");

-	}

-}

-static void set_FANN_TRAIN_RPROP(t_ann_mlp *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);

-		post("nn:training algorithm set to FANN_TRAIN_RPROP");

-	}

-}

-static void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);

-		post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");

-	}

-}

-

-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;

-

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-

-	if (argc>0) {

-		parametro = atom_gensym(argv);

-		if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)

-			funzione = FANN_THRESHOLD;

-		if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)

-			funzione = FANN_THRESHOLD_SYMMETRIC;

-		if (strcmp(parametro->s_name, "FANN_LINEAR")==0)

-			funzione = FANN_LINEAR;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)

-			funzione = FANN_SIGMOID;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)

-			funzione = FANN_SIGMOID_STEPWISE;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)

-			funzione = FANN_SIGMOID_SYMMETRIC;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)

-			funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;

-		fann_set_activation_function_output(x->ann, funzione);

-	} else

-	{

-		error("you must specify the activation function");

-	}

-	post("nn:activation function set to %s (%i)", parametro->s_name, funzione);

-

-}

-

-static void print_ann_details(t_ann_mlp *x)

-{

-	if (x->ann == 0)

-	{

-		post("nn:ann is not initialized");

-	} else

-	{

-		post("nn:follows a description of the current ann:");

-		post("nn:num_input=%i", x->ann->num_input);

-		post("nn:num_output=%i", x->ann->num_output);

-		post("nn:learning_rate=%f", x->ann->learning_rate);

-		post("nn:connection_rate=%f", x->ann->connection_rate);

-		post("nn:total_neurons=%i", x->ann->total_neurons);

-		post("nn:total_connections=%i", x->ann->total_connections);

-		post("nn:last error=%i", x->ann->errstr);

-		if (x->filename == 0)

-		{

-			post("nn:filename not set");

-		} else

-		{

-			post("nn:filename=%s", x->filename->s_name);

-		}

-	}

-}

-

-

-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);

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

-

-	x->desired_error = (float)0.001;

-	x->max_iterations = 500000;

-	x->iterations_between_reports = 1000;

-	x->mode=RUN;

-

-	if (argc>0) {

-		x->filename = atom_gensym(argv);

-		load_ann_from_file(x, NULL , 0, NULL);

-	}

-	return (void *)x;

-}

-

-// free resources

-static void nn_free(t_ann_mlp *x)

-{

-  struct fann *ann = x->ann;

-  fann_destroy(ann);

-  // TODO: free other resources!

-}

-

-void ann_mlp_setup(void) {

-

-	post("");

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

-	post("version: "VERSION"");

-	post("compiled: "__DATE__);

-	post("author: Davide Morelli");

-	post("contact: info@davidemorelli.it www.davidemorelli.it");

-

-	ann_mlp_class = class_new(gensym("ann_mlp"),

-		(t_newmethod)nn_new,

-		(t_method)nn_free, sizeof(t_ann_mlp),

-		CLASS_DEFAULT, A_GIMME, 0);

-

-	// general..

-	class_addmethod(ann_mlp_class, (t_method)help, gensym("help"), 0);

-	class_addmethod(ann_mlp_class, (t_method)createFann, gensym("create"), A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)train, gensym("train"), 0);

-	class_addmethod(ann_mlp_class, (t_method)run, gensym("run"), 0);

-	class_addmethod(ann_mlp_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)manage_list, gensym("data"), A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)print_ann_details, gensym("details"), 0);

-	

-	// change training parameters

-	class_addmethod(ann_mlp_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0);

-	class_addmethod(ann_mlp_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);

-

-	// change training  and activation algorithms

-	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);

-	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);

-	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);

-	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);

-	class_addmethod(ann_mlp_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);

-	

-	// the most important one: running the ann

-	class_addlist(ann_mlp_class, (t_method)manage_list);

-

-	// help patch

-	class_sethelpsymbol(ann_mlp_class, gensym("help-ann_mlp"));

-

-

-}

+/*	nn : Neural Networks for PD
+	by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it
+	this software is simply an interface for FANN classes
+	http://fann.sourceforge.net/
+	FANN is obviously needed for compilation
+	this software is licensed under the GNU General Public License
+*/
+#include <stdio.h>
+#include <string.h>
+#include "m_pd.h"
+#include "fann.h"
+
+
+#define VERSION "0.03"
+#ifndef __DATE__ 
+#define __DATE__ ""
+#endif
+
+#define TRAIN 0
+#define RUN  1
+
+#define MAXINPUT 100
+#define MAXOUTPUT 100
+
+static t_class *ann_mlp_class;
+
+typedef struct _ann_mlp {
+	t_object  x_obj;
+	struct fann *ann;
+	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;
+	unsigned int iterations_between_reports;
+	t_outlet *l_out, *f_out;
+} t_ann_mlp;
+
+static void help(t_ann_mlp *x)
+{
+	post("");
+	post("ann_mlp: neural nets for PD");
+	post("ann_mlp:Davide Morelli - info@davidemorelli.it - (c)2005");
+	post("ann_mlp:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");
+	post("ann_mlp:main commands: create, filename, load, save, train-on-file, run");
+	post("ann_mlp:see help-nn.pd for details on commands and usage");
+	post("ann_mlp:this is an interface to FANN");
+
+}
+
+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;
+	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)
+	{
+		error("error creating the ann");
+	} else
+	{
+		post("created ann with:");
+		post("num_input = %i", num_input);
+		post("num_output = %i", num_output);
+		post("num_layers = %i", num_layers);
+		post("num_neurons_hidden = %i", num_neurons_hidden);
+		post("connection_rate = %f", connection_rate);
+		post("learning_rate = %f", learning_rate);
+	}
+}
+
+static void print_status(t_ann_mlp *x)
+{
+		if (x->mode == TRAIN)
+			post("nn:training");
+		else
+			post("nn:running");
+}
+
+static void train(t_ann_mlp *x)
+{
+	x->mode=TRAIN;
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+	fann_reset_MSE(x->ann);
+	print_status(x);
+}
+
+static void run(t_ann_mlp *x)
+{
+	x->mode=RUN;
+	print_status(x);
+}
+
+static void set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	if (argc<1)
+	{
+		error("usage: setmode 0/1: 0 for training, 1 for running");
+	}
+	else	
+	{	
+		x->mode = atom_getint(argv++);
+		print_status(x);
+	}
+}
+
+
+
+static 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,
+		x->iterations_between_reports, x->desired_error);
+	
+	post("nn: finished training on file %s", x->filenametrain->s_name);
+}
+
+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)
+	{
+		desired_error = atom_getfloat(argv);
+		x->desired_error = desired_error;
+		post("nn:desired_error set to %f", x->desired_error);
+	} else
+	{
+		error("you must pass me a float");
+	}
+}
+
+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)
+	{
+		max_iterations = atom_getint(argv);
+		x->max_iterations = max_iterations;
+		post("nn:max_iterations set to %i", x->max_iterations);
+	} else
+	{
+		error("you must pass me an int");
+	}
+}
+
+static void set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	
+	unsigned int iterations_between_reports = 1000;
+	if (argc>0)
+	{
+		iterations_between_reports = atom_getint(argv);
+		x->iterations_between_reports = iterations_between_reports;
+		post("nn:iterations_between_reports set to %i", x->iterations_between_reports);
+	} else
+	{
+		error("you must pass me an int");
+	}
+
+}
+
+
+// run the ann using floats in list passed to the inlet as input values
+// and send result to outlet as list of float
+static void run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	int i=0;
+	fann_type input[MAXINPUT];	
+	fann_type *calc_out;
+	t_atom lista[MAXOUTPUT];
+	int quanti;
+	float valoreTMP;
+
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+
+	quanti = x->ann->num_output;
+
+	// fill input array with zeros
+	for (i=0; i<MAXINPUT; i++)
+	{
+		input[i]=0;
+	}
+
+	// fill output array with zeros
+	for (i=0; i<MAXOUTPUT; i++)
+	{
+		SETFLOAT(lista + i,0);
+	}
+
+	// fill input array with actual data sent to inlet
+	for (i=0;i<argc;i++)
+	{
+		input[i] = atom_getfloat(argv++);
+	}
+	
+	// run the ann
+	calc_out = fann_run(x->ann, input);
+
+	// fill the output array with result from ann
+	for (i=0;i<quanti;i++)
+	{
+		valoreTMP = calc_out[i];
+		//post("calc_out[%i]=%f", i, calc_out[i]);
+		SETFLOAT(lista+i, valoreTMP);
+	}
+
+	// send output array to outlet
+	outlet_anything(x->l_out,
+                     gensym("list") ,
+					 quanti, 
+					 lista);
+	
+}
+
+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];	
+	fann_type output[MAXOUTPUT];
+	//fann_type *calcMSE;
+	//t_atom lista[MAXOUTPUT];
+	int quantiINs;
+	int quantiOUTs;
+	float mse;
+
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+
+	quantiINs = x->ann->num_input;
+	quantiOUTs = x->ann->num_output;
+
+	if ((quantiINs + quantiOUTs)>argc)
+	{
+		error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");
+		return;
+	}
+
+	// fill input array with zeros
+	for (i=0; i<MAXINPUT; i++)
+	{
+		input[i]=0;
+	}
+		// fill input array with zeros
+	for (i=0; i<MAXOUTPUT; i++)
+	{
+		output[i]=0;
+	}
+
+	// fill input array with actual data sent to inlet
+	for (i=0;i<quantiINs;i++)
+	{
+		input[i] = atom_getfloat(argv++);
+	}
+
+	for (i=0;i<quantiOUTs;i++)
+	{
+		output[i] = atom_getfloat(argv++);
+	}
+	
+	//fann_reset_MSE(x->ann);
+
+	fann_train(x->ann, input, output);
+
+	mse = fann_get_MSE(x->ann);
+	
+	outlet_float(x->f_out, mse);
+
+
+}
+
+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);
+	else
+	{
+		train_on_the_fly(x, sl, argc, 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);
+	} else
+	{
+		error("you must specify the filename");
+	}
+	post("nn:filename set to %s", x->filename->s_name);
+}
+
+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);
+	}
+	x->ann = fann_create_from_file(x->filename->s_name);
+	if (x->ann == 0)
+		error("error opening %s", x->filename->s_name);
+	else
+		post("nn:ann loaded fom file %s", x->filename->s_name);
+}
+
+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);
+	}
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_save(x->ann, x->filename->s_name);
+		post("nn:ann saved in file %s", x->filename->s_name);
+	}
+}
+
+// functions for training algo:
+static void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);
+		post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");
+	}
+}
+static void set_FANN_TRAIN_BATCH(t_ann_mlp *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);
+		post("nn:training algorithm set to FANN_TRAIN_BATCH");
+	}
+}
+static void set_FANN_TRAIN_RPROP(t_ann_mlp *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);
+		post("nn:training algorithm set to FANN_TRAIN_RPROP");
+	}
+}
+static void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);
+		post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");
+	}
+}
+
+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;
+
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+
+	if (argc>0) {
+		parametro = atom_gensym(argv);
+		if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
+			funzione = FANN_THRESHOLD;
+		if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
+			funzione = FANN_THRESHOLD_SYMMETRIC;
+		if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
+			funzione = FANN_LINEAR;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
+			funzione = FANN_SIGMOID;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
+			funzione = FANN_SIGMOID_STEPWISE;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
+			funzione = FANN_SIGMOID_SYMMETRIC;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
+			funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
+		fann_set_activation_function_output(x->ann, funzione);
+	} else
+	{
+		error("you must specify the activation function");
+	}
+	post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
+
+}
+
+static void print_ann_details(t_ann_mlp *x)
+{
+	if (x->ann == 0)
+	{
+		post("nn:ann is not initialized");
+	} else
+	{
+		post("nn:follows a description of the current ann:");
+		post("nn:num_input=%i", x->ann->num_input);
+		post("nn:num_output=%i", x->ann->num_output);
+		post("nn:learning_rate=%f", x->ann->learning_rate);
+		post("nn:connection_rate=%f", x->ann->connection_rate);
+		post("nn:total_neurons=%i", x->ann->total_neurons);
+		post("nn:total_connections=%i", x->ann->total_connections);
+		post("nn:last error=%i", x->ann->errstr);
+		if (x->filename == 0)
+		{
+			post("nn:filename not set");
+		} else
+		{
+			post("nn:filename=%s", x->filename->s_name);
+		}
+	}
+}
+
+
+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);
+	x->f_out = outlet_new(&x->x_obj, &s_float);
+
+	x->desired_error = (float)0.001;
+	x->max_iterations = 500000;
+	x->iterations_between_reports = 1000;
+	x->mode=RUN;
+
+	if (argc>0) {
+		x->filename = atom_gensym(argv);
+		load_ann_from_file(x, NULL , 0, NULL);
+	}
+	return (void *)x;
+}
+
+// free resources
+static void nn_free(t_ann_mlp *x)
+{
+  struct fann *ann = x->ann;
+  fann_destroy(ann);
+  // TODO: free other resources!
+}
+
+void ann_mlp_setup(void) {
+
+	post("");
+	post("ann_mlp: neural nets for PD");
+	post("version: "VERSION"");
+	post("compiled: "__DATE__);
+	post("author: Davide Morelli");
+	post("contact: info@davidemorelli.it www.davidemorelli.it");
+
+	ann_mlp_class = class_new(gensym("ann_mlp"),
+		(t_newmethod)nn_new,
+		(t_method)nn_free, sizeof(t_ann_mlp),
+		CLASS_DEFAULT, A_GIMME, 0);
+
+	// general..
+	class_addmethod(ann_mlp_class, (t_method)help, gensym("help"), 0);
+	class_addmethod(ann_mlp_class, (t_method)createFann, gensym("create"), A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)train, gensym("train"), 0);
+	class_addmethod(ann_mlp_class, (t_method)run, gensym("run"), 0);
+	class_addmethod(ann_mlp_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)manage_list, gensym("data"), A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)print_ann_details, gensym("details"), 0);
+	
+	// change training parameters
+	class_addmethod(ann_mlp_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
+	class_addmethod(ann_mlp_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
+
+	// change training  and activation algorithms
+	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
+	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
+	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
+	class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
+	class_addmethod(ann_mlp_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
+	
+	// the most important one: running the ann
+	class_addlist(ann_mlp_class, (t_method)manage_list);
+
+	// help patch
+	class_sethelpsymbol(ann_mlp_class, gensym("help-ann_mlp"));
+
+
+}
diff --git a/src/ann_td.c b/src/ann_td.c
index a519c2f..73aa364 100755
--- a/src/ann_td.c
+++ b/src/ann_td.c
@@ -1,664 +1,664 @@
-/*	ann_td : Time Delay Neural Networks for PD

-	by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it

-	this software is simply an interface for FANN classes

-	http://fann.sourceforge.net/

-	FANN is obviously needed for compilation

-	this software is licensed under the GNU General Public License

-*/

-#include <stdio.h>

-#include <string.h>

-#include "m_pd.h"

-#include "fann.h"

-

-

-#define VERSION "0.01"


-#ifndef __DATE__ 

-#define __DATE__ ""

-#endif

-

-#define TRAIN 0

-#define RUN  1

-

-#define MAXINPUT 100

-#define MAXOUTPUT 100

-

-static t_class *ann_td_class;

-

-typedef struct _ann_td {

-	t_object  x_obj;

-	struct fann *ann;

-	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;

-	unsigned int iterations_between_reports;

-	unsigned int frames;

-	unsigned int num_input;

-	t_float *inputs;

-	unsigned int ins_frames_set;

-	t_outlet *l_out, *f_out;

-} t_ann_td;

-

-static void help(t_ann_td *x)

-{

-	post("");

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

-	post("ann_td:Davide Morelli - info@davidemorelli.it - (c)2005");

-	post("ann_td:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");

-	post("ann_td:main commands: create, filename, load, save, train-on-file, run");

-	post("ann_td:see help-nn.pd for details on commands and usage");

-	post("ann_td:this is an interface to FANN");

-

-}

-

-static void deallocate_inputs(t_ann_td *x)

-{

-	if (x->inputs != 0)

-	{

-		freebytes(x->inputs, sizeof(x->inputs));

-		x->inputs = 0;

-	}

-}

-

-static void allocate_inputs(t_ann_td *x)

-{

-	unsigned int i;

-	deallocate_inputs(x);

-	// allocate space for inputs array

-	x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float));

-	for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f;

-}

-

-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;

-	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);

-

-	if (x->ann == 0)

-	{

-		error("error creating the ann");

-	} else

-	{

-		post("ann_td:created ann with:");

-		post("num_input = %i", num_input);

-		post("num_output = %i", num_output);

-		post("frames = %i", x->frames);

-		post("num_layers = %i", num_layers);

-		post("num_neurons_hidden = %i", num_neurons_hidden);

-		post("connection_rate = %f", connection_rate);

-		post("learning_rate = %f", learning_rate);

-	}

-}

-

-static void print_status(t_ann_td *x)

-{

-		if (x->mode == TRAIN)

-			post("ann_td:training");

-		else

-			post("ann_td:running");

-}

-

-static void train(t_ann_td *x)

-{

-	x->mode=TRAIN;

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-	fann_reset_MSE(x->ann);

-	print_status(x);

-}

-

-static void run(t_ann_td *x)

-{

-	x->mode=RUN;

-	print_status(x);

-}

-

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

-{

-	if (argc<1)

-	{

-		error("usage: setmode 0/1: 0 for training, 1 for running");

-	}

-	else	

-	{	

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

-		print_status(x);

-	}

-}

-

-

-

-static 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,

-		x->iterations_between_reports, x->desired_error);

-	

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

-}

-

-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)

-	{

-		desired_error = atom_getfloat(argv);

-		x->desired_error = desired_error;

-		post("ann_td:desired_error set to %f", x->desired_error);

-	} else

-	{

-		error("you must pass me a float");

-	}

-}

-

-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)

-	{

-		max_iterations = atom_getint(argv);

-		x->max_iterations = max_iterations;

-		post("ann_td:max_iterations set to %i", x->max_iterations);

-	} else

-	{

-		error("you must pass me an int");

-	}

-}

-

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

-{

-	

-	unsigned int iterations_between_reports = 1000;

-	if (argc>0)

-	{

-		iterations_between_reports = atom_getint(argv);

-		x->iterations_between_reports = iterations_between_reports;

-		post("ann_td:iterations_between_reports set to %i", x->iterations_between_reports);

-	} else

-	{

-		error("you must pass me an int");

-	}

-

-}

-

-

-static void scale_inputs(t_ann_td *x)

-{

-	unsigned int j;

-	unsigned int k;

-

-	for(j = (x->frames - 1); j>0; j--)

-	{

-		// scorro la lista all'indietro

-		for (k=0; k < x->num_input; k++)

-		{

-			// scalo i valori dei frames

-			x->inputs[(x->num_input) * j + k]=x->inputs[(x->num_input) * (j-1) + k];

-		}

-	}

-}

-

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

-// and send result to outlet as list of float

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

-{

-	int i=0;

-	unsigned j=0;

-	//fann_type input[MAXINPUT];	

-	fann_type *calc_out;

-	t_atom lista[MAXOUTPUT];

-	int quanti;

-	float valoreTMP;

-

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-

-	if (x->ins_frames_set==0)

-	{

-		error("num_inputs and frames not set");

-		return;

-	}

-

-	if (argc < (int) x->num_input)

-	{

-		error("insufficient inputs");

-		return;

-	}

-	quanti = x->ann->num_output;

-

-	scale_inputs(x);

-

-	// fill output array with zeros

-	for (i=0; i<MAXOUTPUT; i++)

-	{

-		SETFLOAT(lista + i,0);

-	}

-

-	// fill input array with actual data sent to inlet

-	for (j=0; j < x->num_input ;j++)

-	{

-		//input[j] = atom_getfloat(argv++);

-		x->inputs[j] = atom_getfloat(argv++);

-	}

-	

-	// run the ann

-	//calc_out = fann_run(x->ann, input);

-	calc_out = fann_run(x->ann, x->inputs);

-

-	// fill the output array with result from ann

-	for (i=0;i<quanti;i++)

-	{

-		valoreTMP = calc_out[i];

-		//post("calc_out[%i]=%f", i, calc_out[i]);

-		SETFLOAT(lista+i, valoreTMP);

-	}

-

-	// send output array to outlet

-	outlet_anything(x->l_out,

-                     gensym("list") ,

-					 quanti, 

-					 lista);

-	

-}

-

-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;

-	fann_type input_merged[MAXINPUT];	

-	fann_type output[MAXOUTPUT];

-	//fann_type *calcMSE;

-	//t_atom lista[MAXOUTPUT];

-	float mse;

-

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-

-	if ((x->num_input + x->ann->num_output) > (unsigned int) argc)

-	{

-		error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");

-		return;

-	}

-

-	// fill input array with zeros

-	for (i=0; i<MAXINPUT; i++)

-	{

-		input_merged[i]=0;

-	}

-	// fill input array with zeros

-	for (i=0; i<MAXOUTPUT; i++)

-	{

-		output[i]=0;

-	}

-

-	scale_inputs(x);

-

-	// fill input array with actual data sent to inlet

-	for (j = 0; j < x->num_input; j++)

-	{

-		input_merged[j] = atom_getfloat(argv++);

-	}

-	for (j = x->num_input; j < (x->num_input * x->frames); j++)

-	{

-		input_merged[j] = x->inputs[j];

-	}

-

-	for (j = 0; j < (x->ann->num_output);j++)

-	{

-		output[j] = atom_getfloat(argv++);

-	}

-	

-	//fann_reset_MSE(x->ann);

-

-	fann_train(x->ann, input_merged, output);

-

-	mse = fann_get_MSE(x->ann);

-	

-	outlet_float(x->f_out, mse);

-

-

-}

-

-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);

-	else

-	{

-		train_on_the_fly(x, sl, argc, 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);

-	} else

-	{

-		error("you must specify the filename");

-	}

-	post("nn:filename set to %s", x->filename->s_name);

-}

-

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

-{

-	if (x->ins_frames_set==0)

-	{

-		error("set num_input and frames with [inputs_frames int int(");

-		error("I won't load without num_input and frames set");

-		return;

-	}

-	if (argc>0) {

-    x->filename = atom_gensym(argv);

-	}

-	x->ann = fann_create_from_file(x->filename->s_name);

-	if (x->ann == 0)

-		error("error opening %s", x->filename->s_name);

-	else

-		post("nn:ann loaded fom file %s", x->filename->s_name);

-	

-	allocate_inputs(x);

-}

-

-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);

-	}

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_save(x->ann, x->filename->s_name);

-		post("nn:ann saved in file %s", x->filename->s_name);

-	}

-}

-

-// functions for training algo:

-static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);

-		post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");

-	}

-}

-static void set_FANN_TRAIN_BATCH(t_ann_td *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);

-		post("nn:training algorithm set to FANN_TRAIN_BATCH");

-	}

-}

-static void set_FANN_TRAIN_RPROP(t_ann_td *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);

-		post("nn:training algorithm set to FANN_TRAIN_RPROP");

-	}

-}

-static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x)

-{

-	if (x->ann == 0)

-	{

-		error("ann is not initialized");

-	} else

-	{

-		fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);

-		post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");

-	}

-}

-

-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;

-

-	if (x->ann == 0)

-	{

-		error("ann not initialized");

-		return;

-	}

-

-	if (argc>0) {

-		parametro = atom_gensym(argv);

-		if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)

-			funzione = FANN_THRESHOLD;

-		if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)

-			funzione = FANN_THRESHOLD_SYMMETRIC;

-		if (strcmp(parametro->s_name, "FANN_LINEAR")==0)

-			funzione = FANN_LINEAR;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)

-			funzione = FANN_SIGMOID;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)

-			funzione = FANN_SIGMOID_STEPWISE;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)

-			funzione = FANN_SIGMOID_SYMMETRIC;

-		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)

-			funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;

-		fann_set_activation_function_output(x->ann, funzione);

-	} else

-	{

-		error("you must specify the activation function");

-	}

-	post("nn:activation function set to %s (%i)", parametro->s_name, funzione);

-

-}

-

-static void print_ann_details(t_ann_td *x)

-{

-	if (x->ann == 0)

-	{

-		post("ann_td:ann is not initialized");

-	} else

-	{

-		post("follows a description of the current ann:");

-		post("num_input=%i", x->ann->num_input);

-		post("num_output=%i", x->ann->num_output);

-		post("learning_rate=%f", x->ann->learning_rate);

-		post("connection_rate=%f", x->ann->connection_rate);

-		post("total_neurons=%i", x->ann->total_neurons);

-		post("total_connections=%i", x->ann->total_connections);

-		post("last error=%i", x->ann->errstr);

-		if (x->filename == 0)

-		{

-			post("filename not set");

-		} else

-		{

-			post("filename=%s", x->filename->s_name);

-		}

-	}

-}

-

-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;

-}

-

-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);

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

-

-	x->desired_error = (float)0.001;

-	x->max_iterations = 500000;

-	x->iterations_between_reports = 1000;

-	x->mode=RUN;

-	x->ins_frames_set=0;

-

-	if (argc<2)

-	{

-		error("2 arguments needed: num_input and frames. filename optional");

-		return (void *)x;

-	}

-

-	if (argc>0) {

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

-	}

-

-	if (argc>1) {

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

-		x->ins_frames_set=1;

-		allocate_inputs(x);

-	}

-

-	if (argc>2) {

-		x->filename = atom_gensym(argv);

-		load_ann_from_file(x, NULL , 0, NULL);

-	}

-

-	return (void *)x;

-}

-

-// free resources

-static void nn_free(t_ann_td *x)

-{

-  struct fann *ann = x->ann;

-  fann_destroy(ann);

-  deallocate_inputs(x);

-  // TODO: free other resources!

-}

-

-void ann_td_setup(void) {

-

-	post("");

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

-	post("version: "VERSION"");

-	post("compiled: "__DATE__);

-	post("author: Davide Morelli");

-	post("contact: info@davidemorelli.it www.davidemorelli.it");

-

-	ann_td_class = class_new(gensym("ann_td"),

-		(t_newmethod)nn_new,

-		(t_method)nn_free, sizeof(t_ann_td),

-		CLASS_DEFAULT, A_GIMME, 0);

-

-	// general..

-	class_addmethod(ann_td_class, (t_method)help, gensym("help"), 0);

-	class_addmethod(ann_td_class, (t_method)createFann, gensym("create"), A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)train, gensym("train"), 0);

-	class_addmethod(ann_td_class, (t_method)run, gensym("run"), 0);

-	class_addmethod(ann_td_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)manage_list, gensym("data"), A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)print_ann_details, gensym("details"), 0);

-	

-	// change training parameters

-	class_addmethod(ann_td_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0);

-	class_addmethod(ann_td_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);

-

-	// change training  and activation algorithms

-	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);

-	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);

-	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);

-	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);

-	class_addmethod(ann_td_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);

-	

-	class_addmethod(ann_td_class, (t_method)set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0);

-	

-	// the most important one: running the ann

-	class_addlist(ann_td_class, (t_method)manage_list);

-

-	// help patch

-	class_sethelpsymbol(ann_td_class, gensym("help-ann_td"));

-

-

-}

+/*	ann_td : Time Delay Neural Networks for PD
+	by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it
+	this software is simply an interface for FANN classes
+	http://fann.sourceforge.net/
+	FANN is obviously needed for compilation
+	this software is licensed under the GNU General Public License
+*/
+#include <stdio.h>
+#include <string.h>
+#include "m_pd.h"
+#include "fann.h"
+
+
+#define VERSION "0.01"
+#ifndef __DATE__ 
+#define __DATE__ ""
+#endif
+
+#define TRAIN 0
+#define RUN  1
+
+#define MAXINPUT 100
+#define MAXOUTPUT 100
+
+static t_class *ann_td_class;
+
+typedef struct _ann_td {
+	t_object  x_obj;
+	struct fann *ann;
+	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;
+	unsigned int iterations_between_reports;
+	unsigned int frames;
+	unsigned int num_input;
+	t_float *inputs;
+	unsigned int ins_frames_set;
+	t_outlet *l_out, *f_out;
+} t_ann_td;
+
+static void help(t_ann_td *x)
+{
+	post("");
+	post("ann_td:time delay neural networks for PD");
+	post("ann_td:Davide Morelli - info@davidemorelli.it - (c)2005");
+	post("ann_td:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");
+	post("ann_td:main commands: create, filename, load, save, train-on-file, run");
+	post("ann_td:see help-nn.pd for details on commands and usage");
+	post("ann_td:this is an interface to FANN");
+
+}
+
+static void deallocate_inputs(t_ann_td *x)
+{
+	if (x->inputs != 0)
+	{
+		freebytes(x->inputs, sizeof(x->inputs));
+		x->inputs = 0;
+	}
+}
+
+static void allocate_inputs(t_ann_td *x)
+{
+	unsigned int i;
+	deallocate_inputs(x);
+	// allocate space for inputs array
+	x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float));
+	for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f;
+}
+
+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;
+	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);
+
+	if (x->ann == 0)
+	{
+		error("error creating the ann");
+	} else
+	{
+		post("ann_td:created ann with:");
+		post("num_input = %i", num_input);
+		post("num_output = %i", num_output);
+		post("frames = %i", x->frames);
+		post("num_layers = %i", num_layers);
+		post("num_neurons_hidden = %i", num_neurons_hidden);
+		post("connection_rate = %f", connection_rate);
+		post("learning_rate = %f", learning_rate);
+	}
+}
+
+static void print_status(t_ann_td *x)
+{
+		if (x->mode == TRAIN)
+			post("ann_td:training");
+		else
+			post("ann_td:running");
+}
+
+static void train(t_ann_td *x)
+{
+	x->mode=TRAIN;
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+	fann_reset_MSE(x->ann);
+	print_status(x);
+}
+
+static void run(t_ann_td *x)
+{
+	x->mode=RUN;
+	print_status(x);
+}
+
+static void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	if (argc<1)
+	{
+		error("usage: setmode 0/1: 0 for training, 1 for running");
+	}
+	else	
+	{	
+		x->mode = atom_getint(argv++);
+		print_status(x);
+	}
+}
+
+
+
+static 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,
+		x->iterations_between_reports, x->desired_error);
+	
+	post("ann_td: finished training on file %s", x->filenametrain->s_name);
+}
+
+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)
+	{
+		desired_error = atom_getfloat(argv);
+		x->desired_error = desired_error;
+		post("ann_td:desired_error set to %f", x->desired_error);
+	} else
+	{
+		error("you must pass me a float");
+	}
+}
+
+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)
+	{
+		max_iterations = atom_getint(argv);
+		x->max_iterations = max_iterations;
+		post("ann_td:max_iterations set to %i", x->max_iterations);
+	} else
+	{
+		error("you must pass me an int");
+	}
+}
+
+static void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	
+	unsigned int iterations_between_reports = 1000;
+	if (argc>0)
+	{
+		iterations_between_reports = atom_getint(argv);
+		x->iterations_between_reports = iterations_between_reports;
+		post("ann_td:iterations_between_reports set to %i", x->iterations_between_reports);
+	} else
+	{
+		error("you must pass me an int");
+	}
+
+}
+
+
+static void scale_inputs(t_ann_td *x)
+{
+	unsigned int j;
+	unsigned int k;
+
+	for(j = (x->frames - 1); j>0; j--)
+	{
+		// scorro la lista all'indietro
+		for (k=0; k < x->num_input; k++)
+		{
+			// scalo i valori dei frames
+			x->inputs[(x->num_input) * j + k]=x->inputs[(x->num_input) * (j-1) + k];
+		}
+	}
+}
+
+// run the ann using floats in list passed to the inlet as input values
+// and send result to outlet as list of float
+static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	int i=0;
+	unsigned j=0;
+	//fann_type input[MAXINPUT];	
+	fann_type *calc_out;
+	t_atom lista[MAXOUTPUT];
+	int quanti;
+	float valoreTMP;
+
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+
+	if (x->ins_frames_set==0)
+	{
+		error("num_inputs and frames not set");
+		return;
+	}
+
+	if (argc < (int) x->num_input)
+	{
+		error("insufficient inputs");
+		return;
+	}
+	quanti = x->ann->num_output;
+
+	scale_inputs(x);
+
+	// fill output array with zeros
+	for (i=0; i<MAXOUTPUT; i++)
+	{
+		SETFLOAT(lista + i,0);
+	}
+
+	// fill input array with actual data sent to inlet
+	for (j=0; j < x->num_input ;j++)
+	{
+		//input[j] = atom_getfloat(argv++);
+		x->inputs[j] = atom_getfloat(argv++);
+	}
+	
+	// run the ann
+	//calc_out = fann_run(x->ann, input);
+	calc_out = fann_run(x->ann, x->inputs);
+
+	// fill the output array with result from ann
+	for (i=0;i<quanti;i++)
+	{
+		valoreTMP = calc_out[i];
+		//post("calc_out[%i]=%f", i, calc_out[i]);
+		SETFLOAT(lista+i, valoreTMP);
+	}
+
+	// send output array to outlet
+	outlet_anything(x->l_out,
+                     gensym("list") ,
+					 quanti, 
+					 lista);
+	
+}
+
+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;
+	fann_type input_merged[MAXINPUT];	
+	fann_type output[MAXOUTPUT];
+	//fann_type *calcMSE;
+	//t_atom lista[MAXOUTPUT];
+	float mse;
+
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+
+	if ((x->num_input + x->ann->num_output) > (unsigned int) argc)
+	{
+		error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");
+		return;
+	}
+
+	// fill input array with zeros
+	for (i=0; i<MAXINPUT; i++)
+	{
+		input_merged[i]=0;
+	}
+	// fill input array with zeros
+	for (i=0; i<MAXOUTPUT; i++)
+	{
+		output[i]=0;
+	}
+
+	scale_inputs(x);
+
+	// fill input array with actual data sent to inlet
+	for (j = 0; j < x->num_input; j++)
+	{
+		input_merged[j] = atom_getfloat(argv++);
+	}
+	for (j = x->num_input; j < (x->num_input * x->frames); j++)
+	{
+		input_merged[j] = x->inputs[j];
+	}
+
+	for (j = 0; j < (x->ann->num_output);j++)
+	{
+		output[j] = atom_getfloat(argv++);
+	}
+	
+	//fann_reset_MSE(x->ann);
+
+	fann_train(x->ann, input_merged, output);
+
+	mse = fann_get_MSE(x->ann);
+	
+	outlet_float(x->f_out, mse);
+
+
+}
+
+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);
+	else
+	{
+		train_on_the_fly(x, sl, argc, 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);
+	} else
+	{
+		error("you must specify the filename");
+	}
+	post("nn:filename set to %s", x->filename->s_name);
+}
+
+static void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+	if (x->ins_frames_set==0)
+	{
+		error("set num_input and frames with [inputs_frames int int(");
+		error("I won't load without num_input and frames set");
+		return;
+	}
+	if (argc>0) {
+    x->filename = atom_gensym(argv);
+	}
+	x->ann = fann_create_from_file(x->filename->s_name);
+	if (x->ann == 0)
+		error("error opening %s", x->filename->s_name);
+	else
+		post("nn:ann loaded fom file %s", x->filename->s_name);
+	
+	allocate_inputs(x);
+}
+
+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);
+	}
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_save(x->ann, x->filename->s_name);
+		post("nn:ann saved in file %s", x->filename->s_name);
+	}
+}
+
+// functions for training algo:
+static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);
+		post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");
+	}
+}
+static void set_FANN_TRAIN_BATCH(t_ann_td *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);
+		post("nn:training algorithm set to FANN_TRAIN_BATCH");
+	}
+}
+static void set_FANN_TRAIN_RPROP(t_ann_td *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);
+		post("nn:training algorithm set to FANN_TRAIN_RPROP");
+	}
+}
+static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x)
+{
+	if (x->ann == 0)
+	{
+		error("ann is not initialized");
+	} else
+	{
+		fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);
+		post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");
+	}
+}
+
+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;
+
+	if (x->ann == 0)
+	{
+		error("ann not initialized");
+		return;
+	}
+
+	if (argc>0) {
+		parametro = atom_gensym(argv);
+		if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
+			funzione = FANN_THRESHOLD;
+		if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
+			funzione = FANN_THRESHOLD_SYMMETRIC;
+		if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
+			funzione = FANN_LINEAR;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
+			funzione = FANN_SIGMOID;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
+			funzione = FANN_SIGMOID_STEPWISE;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
+			funzione = FANN_SIGMOID_SYMMETRIC;
+		if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
+			funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
+		fann_set_activation_function_output(x->ann, funzione);
+	} else
+	{
+		error("you must specify the activation function");
+	}
+	post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
+
+}
+
+static void print_ann_details(t_ann_td *x)
+{
+	if (x->ann == 0)
+	{
+		post("ann_td:ann is not initialized");
+	} else
+	{
+		post("follows a description of the current ann:");
+		post("num_input=%i", x->ann->num_input);
+		post("num_output=%i", x->ann->num_output);
+		post("learning_rate=%f", x->ann->learning_rate);
+		post("connection_rate=%f", x->ann->connection_rate);
+		post("total_neurons=%i", x->ann->total_neurons);
+		post("total_connections=%i", x->ann->total_connections);
+		post("last error=%i", x->ann->errstr);
+		if (x->filename == 0)
+		{
+			post("filename not set");
+		} else
+		{
+			post("filename=%s", x->filename->s_name);
+		}
+	}
+}
+
+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;
+}
+
+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);
+	x->f_out = outlet_new(&x->x_obj, &s_float);
+
+	x->desired_error = (float)0.001;
+	x->max_iterations = 500000;
+	x->iterations_between_reports = 1000;
+	x->mode=RUN;
+	x->ins_frames_set=0;
+
+	if (argc<2)
+	{
+		error("2 arguments needed: num_input and frames. filename optional");
+		return (void *)x;
+	}
+
+	if (argc>0) {
+		x->num_input = atom_getint(argv++);
+	}
+
+	if (argc>1) {
+		x->frames = atom_getint(argv++);
+		x->ins_frames_set=1;
+		allocate_inputs(x);
+	}
+
+	if (argc>2) {
+		x->filename = atom_gensym(argv);
+		load_ann_from_file(x, NULL , 0, NULL);
+	}
+
+	return (void *)x;
+}
+
+// free resources
+static void nn_free(t_ann_td *x)
+{
+  struct fann *ann = x->ann;
+  fann_destroy(ann);
+  deallocate_inputs(x);
+  // TODO: free other resources!
+}
+
+void ann_td_setup(void) {
+
+	post("");
+	post("ann_td: time delay neural nets for PD");
+	post("version: "VERSION"");
+	post("compiled: "__DATE__);
+	post("author: Davide Morelli");
+	post("contact: info@davidemorelli.it www.davidemorelli.it");
+
+	ann_td_class = class_new(gensym("ann_td"),
+		(t_newmethod)nn_new,
+		(t_method)nn_free, sizeof(t_ann_td),
+		CLASS_DEFAULT, A_GIMME, 0);
+
+	// general..
+	class_addmethod(ann_td_class, (t_method)help, gensym("help"), 0);
+	class_addmethod(ann_td_class, (t_method)createFann, gensym("create"), A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)train, gensym("train"), 0);
+	class_addmethod(ann_td_class, (t_method)run, gensym("run"), 0);
+	class_addmethod(ann_td_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)manage_list, gensym("data"), A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)print_ann_details, gensym("details"), 0);
+	
+	// change training parameters
+	class_addmethod(ann_td_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
+	class_addmethod(ann_td_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
+
+	// change training  and activation algorithms
+	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
+	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
+	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
+	class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
+	class_addmethod(ann_td_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
+	
+	class_addmethod(ann_td_class, (t_method)set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0);
+	
+	// the most important one: running the ann
+	class_addlist(ann_td_class, (t_method)manage_list);
+
+	// help patch
+	class_sethelpsymbol(ann_td_class, gensym("help-ann_td"));
+
+
+}