added ann_mlp and ann_td

svn path=/trunk/externals/ann/; revision=3011

3 files changed, 1278 insertions, 0 deletions

diff --git a/src/ann_mlp.c b/src/ann_mlp.c
new file mode 100755
index 0000000..e83aea3
--- /dev/null
+++ b/src/ann_mlp.c
@@ -0,0 +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;

+

+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");

+

+}

+

+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);

+	}

+}

+

+void print_status(t_ann_mlp *x)

+{

+		if (x->mode == TRAIN)

+			post("nn:training");

+		else

+			post("nn:running");

+}

+

+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);

+}

+

+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)

+{

+	if (argc<1)

+	{

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

+	}

+	else	

+	{	

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

+		print_status(x);

+	}

+}

+

+

+

+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);

+}

+

+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");

+	}

+}

+

+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");

+	}

+}

+

+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

+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);

+	

+}

+

+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);

+

+

+}

+

+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);

+	}

+}

+

+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);

+}

+

+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);

+}

+

+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:

+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");

+	}

+}

+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");

+	}

+}

+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");

+	}

+}

+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");

+	}

+}

+

+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);

+

+}

+

+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);

+		}

+	}

+}

+

+

+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"));

+

+

+}
\ No newline at end of file
diff --git a/src/ann_td.c b/src/ann_td.c
new file mode 100755
index 0000000..c2cf6b5
--- /dev/null
+++ b/src/ann_td.c
@@ -0,0 +1,665 @@
+/*	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;

+

+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");

+

+}

+

+void deallocate_inputs(t_ann_td *x)

+{

+	if (x->inputs != 0)

+	{

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

+		x->inputs = 0;

+	}

+}

+

+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;

+}

+

+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);

+	}

+}

+

+void print_status(t_ann_td *x)

+{

+		if (x->mode == TRAIN)

+			post("ann_td:training");

+		else

+			post("ann_td:running");

+}

+

+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);

+}

+

+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)

+{

+	if (argc<1)

+	{

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

+	}

+	else	

+	{	

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

+		print_status(x);

+	}

+}

+

+

+

+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);

+}

+

+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");

+	}

+}

+

+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");

+	}

+}

+

+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");

+	}

+

+}

+

+

+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

+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];

+	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);

+	

+}

+

+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);

+

+

+}

+

+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);

+	}

+}

+

+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);

+}

+

+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);

+}

+

+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:

+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");

+	}

+}

+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");

+	}

+}

+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");

+	}

+}

+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");

+	}

+}

+

+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);

+

+}

+

+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);

+		}

+	}

+}

+

+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)

+{

+	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"));

+

+

+}
\ No newline at end of file
diff --git a/src/makefile.msvc b/src/makefile.msvc
new file mode 100755
index 0000000..5a32386
--- /dev/null
+++ b/src/makefile.msvc
@@ -0,0 +1,48 @@
+# to compile ann fann libs are needed

+# go to http://fann.sourceforge.net/

+# current is http://prdownloads.sourceforge.net/fann/fann-1.2.0.zip?download

+# go to MSVC++ folder and open all.dsw

+# compile everything

+

+# customize here !

+VC="C:\Programmi\Microsoft Visual Studio .NET\Vc7" 

+PDPATH="H:\PureData\pd-0.38-3.msw\pd"

+FANNSRC="H:\PureData\FANN\fann-1.2.0\fann-1.2.0\src\include"

+FANNLIB="H:\PureData\FANN\fann-1.2.0\fann-1.2.0\MSVC++\Release"

+

+

+current: clean pd_nt

+

+pd_nt: ann.dll ann_som.dll ann_mlp.dll ann_td.dll

+

+.SUFFIXES: .dll

+

+PDNTCFLAGS = /W3 /WX /O2 /G6 /DNT /DPD /nologo

+

+

+PDNTINCLUDE = /I. /I$(PDPATH)\tcl\include /I$(PDPATH)\src /I$(PDPATH)\flext /I$(VC)\include /I$(FANNSRC) /Iinclude

+

+PDNTLDIR = $(VC)\Lib

+PDNTLIB = $(PDNTLDIR)\libc.lib \

+	$(PDNTLDIR)\oldnames.lib \

+	$(PDNTLDIR)\kernel32.lib \

+	$(PDPATH)\bin\pd.lib \

+	$(FANNLIB)\libfann.lib

+

+.c.dll:

+	cl $(PDNTCFLAGS) $(PDNTINCLUDE) /c $*.c

+	link /dll /export:$*_setup $*.obj $(PDNTLIB)

+	-del *.obj

+	-del *.lib

+	-del *.exp

+

+#install:

+#	copy help-*.pd $(PDPATH)/doc/5.reference/

+

+clean:

+	-del link.stamp

+	-del *.obj

+	-del *.lib

+	-del *.exp

+	-del *.dll

+