diff options
Diffstat (limited to 'src/ann_mlp.c')
| -rwxr-xr-x | src/ann_mlp.c | 1130 |
1 files changed, 565 insertions, 565 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")); + + +} |