diff options
Diffstat (limited to 'src')
| -rwxr-xr-x | src/ann_mlp.c | 151 | ||||
| -rwxr-xr-x | src/ann_td.c | 146 | ||||
| -rwxr-xr-x | src/makefile.msvc | 21 |
3 files changed, 165 insertions, 153 deletions
diff --git a/src/ann_mlp.c b/src/ann_mlp.c index 6db3238..64ce75f 100755 --- a/src/ann_mlp.c +++ b/src/ann_mlp.c @@ -10,8 +10,10 @@ #include "m_pd.h" #include "fann.h" +#ifndef VERSION +#define VERSION "0.2" +#endif -#define VERSION "0.03" #ifndef __DATE__ #define __DATE__ "" #endif @@ -36,7 +38,7 @@ typedef struct _ann_mlp { t_outlet *l_out, *f_out; } t_ann_mlp; -static void help(t_ann_mlp *x) +static void ann_mlp_help(t_ann_mlp *x) { post(""); post("ann_mlp: neural nets for PD"); @@ -48,7 +50,7 @@ static void help(t_ann_mlp *x) } -static void createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { unsigned int num_input = 2; unsigned int num_output = 1; @@ -77,13 +79,13 @@ static void createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) if (num_input>MAXINPUT) { - error("too many inputs, maximum allowed is %d",MAXINPUT); + error("too many inputs, maximum allowed is MAXINPUT"); return; } if (num_output>MAXOUTPUT) { - error("too many outputs, maximum allowed is %d", MAXOUTPUT); + error("too many outputs, maximum allowed is MAXOUTPUT"); return; } @@ -108,7 +110,7 @@ static void createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) } } -static void print_status(t_ann_mlp *x) +static void ann_mlp_print_status(t_ann_mlp *x) { if (x->mode == TRAIN) post("nn:training"); @@ -116,7 +118,7 @@ static void print_status(t_ann_mlp *x) post("nn:running"); } -static void train(t_ann_mlp *x) +static void ann_mlp_train(t_ann_mlp *x) { x->mode=TRAIN; if (x->ann == 0) @@ -125,16 +127,16 @@ static void train(t_ann_mlp *x) return; } fann_reset_MSE(x->ann); - print_status(x); + ann_mlp_print_status(x); } -static void run(t_ann_mlp *x) +static void ann_mlp_run(t_ann_mlp *x) { x->mode=RUN; - print_status(x); + ann_mlp_print_status(x); } -static void set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { if (argc<1) { @@ -143,13 +145,13 @@ static void set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) else { x->mode = atom_getint(argv++); - print_status(x); + ann_mlp_print_status(x); } } -static void train_on_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_train_on_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { if (x->ann == 0) { @@ -171,10 +173,10 @@ static void train_on_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) 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); + post("ann_mlp: 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) +static void ann_mlp_set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { float desired_error = (float)0.001; if (0<argc) @@ -188,7 +190,7 @@ static void set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv } } -static void set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { unsigned int max_iterations = 500000; if (argc>0) @@ -202,7 +204,7 @@ static void set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *arg } } -static void set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { unsigned int iterations_between_reports = 1000; @@ -221,7 +223,7 @@ static void set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, // 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) +static void ann_mlp_run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { int i=0; fann_type input[MAXINPUT]; @@ -275,7 +277,7 @@ static void run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) } -static void train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { int i=0; fann_type input[MAXINPUT]; @@ -334,17 +336,17 @@ static void train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) } -static void manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { if (x->mode) - run_the_net(x, sl, argc, argv); + ann_mlp_run_the_net(x, sl, argc, argv); else { - train_on_the_fly(x, sl, argc, argv); + ann_mlp_train_on_the_fly(x, sl, argc, argv); } } -static void set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { if (argc>0) { x->filename = atom_gensym(argv); @@ -355,7 +357,7 @@ static void set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) post("nn:filename set to %s", x->filename->s_name); } -static void load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { if (argc>0) { x->filename = atom_gensym(argv); @@ -367,7 +369,7 @@ static void load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *arg 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) +static void ann_mlp_save_ann_to_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { if (argc>0) { x->filename = atom_gensym(argv); @@ -383,7 +385,7 @@ static void save_ann_to_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) } // functions for training algo: -static void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x) +static void ann_mlp_set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x) { if (x->ann == 0) { @@ -394,7 +396,7 @@ static void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x) post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL"); } } -static void set_FANN_TRAIN_BATCH(t_ann_mlp *x) +static void ann_mlp_set_FANN_TRAIN_BATCH(t_ann_mlp *x) { if (x->ann == 0) { @@ -405,7 +407,7 @@ static void set_FANN_TRAIN_BATCH(t_ann_mlp *x) post("nn:training algorithm set to FANN_TRAIN_BATCH"); } } -static void set_FANN_TRAIN_RPROP(t_ann_mlp *x) +static void ann_mlp_set_FANN_TRAIN_RPROP(t_ann_mlp *x) { if (x->ann == 0) { @@ -416,7 +418,7 @@ static void set_FANN_TRAIN_RPROP(t_ann_mlp *x) post("nn:training algorithm set to FANN_TRAIN_RPROP"); } } -static void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x) +static void ann_mlp_set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x) { if (x->ann == 0) { @@ -428,7 +430,7 @@ static void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x) } } -static void set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_mlp_set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) { t_symbol *parametro = 0; int funzione = 0; @@ -464,33 +466,33 @@ static void set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, } -static void print_ann_details(t_ann_mlp *x) +static void ann_mlp_print_ann_details(t_ann_mlp *x) { if (x->ann == 0) { - post("nn:ann is not initialized"); + post("ann_mlp: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); + 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("nn:filename not set"); + post("ann_mlp:filename not set"); } else { - post("nn:filename=%s", x->filename->s_name); + post("filename=%s", x->filename->s_name); } } } -static void *nn_new(t_symbol *s, int argc, t_atom *argv) +static void *ann_mlp_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); @@ -503,13 +505,21 @@ static void *nn_new(t_symbol *s, int argc, t_atom *argv) if (argc>0) { x->filename = atom_gensym(argv); - load_ann_from_file(x, NULL , 0, NULL); + ann_mlp_load_ann_from_file(x, NULL , 0, NULL); } + + 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"); + return (void *)x; } // free resources -static void nn_free(t_ann_mlp *x) +static void ann_mlp_free(t_ann_mlp *x) { struct fann *ann = x->ann; fann_destroy(ann); @@ -518,45 +528,38 @@ static void nn_free(t_ann_mlp *x) 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), + (t_newmethod)ann_mlp_new, + (t_method)ann_mlp_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); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_help, gensym("help"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_createFann, gensym("create"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_train, gensym("train"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_run, gensym("run"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_mode, gensym("setmode"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_train_on_file, gensym("train-on-file"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_manage_list, gensym("data"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_filename, gensym("filename"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_load_ann_from_file, gensym("load"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_save_ann_to_file, gensym("save"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_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); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_desired_error, gensym("desired_error"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_max_iterations, gensym("max_iterations"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_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); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0); + class_addmethod(ann_mlp_class, (t_method)ann_mlp_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); + class_addlist(ann_mlp_class, (t_method)ann_mlp_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 d7dcf3e..8712e42 100755 --- a/src/ann_td.c +++ b/src/ann_td.c @@ -10,8 +10,10 @@ #include "m_pd.h" #include "fann.h" +#ifndef VERSION +#define VERSION "0.2" +#endif -#define VERSION "0.01" #ifndef __DATE__ #define __DATE__ "" #endif @@ -40,7 +42,7 @@ typedef struct _ann_td { t_outlet *l_out, *f_out; } t_ann_td; -static void help(t_ann_td *x) +static void ann_td_help(t_ann_td *x) { post(""); post("ann_td:time delay neural networks for PD"); @@ -52,7 +54,7 @@ static void help(t_ann_td *x) } -static void deallocate_inputs(t_ann_td *x) +static void ann_td_deallocate_inputs(t_ann_td *x) { if (x->inputs != 0) { @@ -61,16 +63,16 @@ static void deallocate_inputs(t_ann_td *x) } } -static void allocate_inputs(t_ann_td *x) +static void ann_td_allocate_inputs(t_ann_td *x) { unsigned int i; - deallocate_inputs(x); + ann_td_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) +static void ann_td_createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { unsigned int num_input = 2; unsigned int num_output = 1; @@ -126,7 +128,7 @@ static void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC); fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC); - allocate_inputs(x); + ann_td_allocate_inputs(x); if (x->ann == 0) { @@ -144,7 +146,7 @@ static void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) } } -static void print_status(t_ann_td *x) +static void ann_td_print_status(t_ann_td *x) { if (x->mode == TRAIN) post("ann_td:training"); @@ -152,7 +154,7 @@ static void print_status(t_ann_td *x) post("ann_td:running"); } -static void train(t_ann_td *x) +static void ann_td_train(t_ann_td *x) { x->mode=TRAIN; if (x->ann == 0) @@ -161,16 +163,16 @@ static void train(t_ann_td *x) return; } fann_reset_MSE(x->ann); - print_status(x); + ann_td_print_status(x); } -static void run(t_ann_td *x) +static void ann_td_run(t_ann_td *x) { x->mode=RUN; - print_status(x); + ann_td_print_status(x); } -static void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { if (argc<1) { @@ -179,13 +181,13 @@ static void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) else { x->mode = atom_getint(argv++); - print_status(x); + ann_td_print_status(x); } } -static void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { if (x->ann == 0) { @@ -210,7 +212,7 @@ static void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) post("ann_td: finished training on file %s", x->filenametrain->s_name); } -static void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { float desired_error = (float)0.001; if (0<argc) @@ -224,7 +226,7 @@ static void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) } } -static void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { unsigned int max_iterations = 500000; if (argc>0) @@ -238,7 +240,7 @@ static void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv } } -static void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { unsigned int iterations_between_reports = 1000; @@ -255,7 +257,7 @@ static void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, } -static void scale_inputs(t_ann_td *x) +static void ann_td_scale_inputs(t_ann_td *x) { unsigned int j; unsigned int k; @@ -273,7 +275,7 @@ static void scale_inputs(t_ann_td *x) // run the ann using floats in list passed to the inlet as input values // and send result to outlet as list of float -static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { int i=0; unsigned j=0; @@ -302,7 +304,7 @@ static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) } quanti = x->ann->num_output; - scale_inputs(x); + ann_td_scale_inputs(x); // fill output array with zeros for (i=0; i<MAXOUTPUT; i++) @@ -337,7 +339,7 @@ static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) } -static void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { int i=0; unsigned int j=0; @@ -370,7 +372,7 @@ static void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) output[i]=0; } - scale_inputs(x); + ann_td_scale_inputs(x); // fill input array with actual data sent to inlet for (j = 0; j < x->num_input; j++) @@ -398,17 +400,17 @@ static void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) } -static void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { if (x->mode) - run_the_net(x, sl, argc, argv); + ann_td_run_the_net(x, sl, argc, argv); else { - train_on_the_fly(x, sl, argc, argv); + ann_td_train_on_the_fly(x, sl, argc, argv); } } -static void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { if (argc>0) { x->filename = atom_gensym(argv); @@ -419,7 +421,7 @@ static void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) post("nn:filename set to %s", x->filename->s_name); } -static void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { if (x->ins_frames_set==0) { @@ -436,10 +438,10 @@ static void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv else post("nn:ann loaded fom file %s", x->filename->s_name); - allocate_inputs(x); + ann_td_allocate_inputs(x); } -static void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { if (argc>0) { x->filename = atom_gensym(argv); @@ -455,7 +457,7 @@ static void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) } // functions for training algo: -static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x) +static void ann_td_set_FANN_TRAIN_INCREMENTAL(t_ann_td *x) { if (x->ann == 0) { @@ -466,7 +468,7 @@ static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x) post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL"); } } -static void set_FANN_TRAIN_BATCH(t_ann_td *x) +static void ann_td_set_FANN_TRAIN_BATCH(t_ann_td *x) { if (x->ann == 0) { @@ -477,7 +479,7 @@ static void set_FANN_TRAIN_BATCH(t_ann_td *x) post("nn:training algorithm set to FANN_TRAIN_BATCH"); } } -static void set_FANN_TRAIN_RPROP(t_ann_td *x) +static void ann_td_set_FANN_TRAIN_RPROP(t_ann_td *x) { if (x->ann == 0) { @@ -488,7 +490,7 @@ static void set_FANN_TRAIN_RPROP(t_ann_td *x) post("nn:training algorithm set to FANN_TRAIN_RPROP"); } } -static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x) +static void ann_td_set_FANN_TRAIN_QUICKPROP(t_ann_td *x) { if (x->ann == 0) { @@ -500,7 +502,7 @@ static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x) } } -static void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +static void ann_td_set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) { t_symbol *parametro = 0; int funzione = 0; @@ -536,7 +538,7 @@ static void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, } -static void print_ann_details(t_ann_td *x) +static void ann_td_print_ann_details(t_ann_td *x) { if (x->ann == 0) { @@ -561,14 +563,14 @@ static void print_ann_details(t_ann_td *x) } } -static void set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames) +static void ann_td_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) +static void *ann_td_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); @@ -593,69 +595,69 @@ static void *nn_new(t_symbol *s, int argc, t_atom *argv) if (argc>1) { x->frames = atom_getint(argv++); x->ins_frames_set=1; - allocate_inputs(x); + ann_td_allocate_inputs(x); } if (argc>2) { x->filename = atom_gensym(argv); - load_ann_from_file(x, NULL , 0, NULL); + ann_td_load_ann_from_file(x, NULL , 0, NULL); } + 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"); + return (void *)x; } // free resources -static void nn_free(t_ann_td *x) +static void ann_td_free(t_ann_td *x) { struct fann *ann = x->ann; fann_destroy(ann); - deallocate_inputs(x); + ann_td_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), + (t_newmethod)ann_td_new, + (t_method)ann_td_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); + class_addmethod(ann_td_class, (t_method)ann_td_help, gensym("help"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_createFann, gensym("create"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_train, gensym("train"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_run, gensym("run"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_set_mode, gensym("setmode"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_train_on_file, gensym("train-on-file"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_manage_list, gensym("data"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_set_filename, gensym("filename"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_load_ann_from_file, gensym("load"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_save_ann_to_file, gensym("save"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_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); + class_addmethod(ann_td_class, (t_method)ann_td_set_desired_error, gensym("desired_error"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_set_max_iterations, gensym("max_iterations"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)ann_td_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)ann_td_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0); + class_addmethod(ann_td_class, (t_method)ann_td_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); + class_addmethod(ann_td_class, (t_method)ann_td_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); + class_addlist(ann_td_class, (t_method)ann_td_manage_list); // help patch class_sethelpsymbol(ann_td_class, gensym("help-ann_td")); diff --git a/src/makefile.msvc b/src/makefile.msvc index ebe696f..130d726 100755 --- a/src/makefile.msvc +++ b/src/makefile.msvc @@ -11,9 +11,11 @@ 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
+current: clean pd_nt distclean
+
+pd_nt: ann_som.dll ann_mlp.dll ann_td.dll ann.dll
+#pd_nt: ann.dll
-pd_nt: ann_som.dll ann_mlp.dll ann_td.dll
.SUFFIXES: .dll
@@ -27,14 +29,15 @@ PDNTLIB = $(PDNTLDIR)\libc.lib \ $(PDNTLDIR)\oldnames.lib \
$(PDNTLDIR)\kernel32.lib \
$(PDPATH)\bin\pd.lib \
- $(FANNLIB)\libfann.lib
+ $(FANNLIB)\libfann.lib
+# ann_mlp.lib ann_som.lib ann_td.lib
.c.dll:
cl $(PDNTCFLAGS) $(PDNTINCLUDE) /c $*.c
- link /dll /export:$*_setup $*.obj $(PDNTLIB)
- -del *.obj
- -del *.lib
- -del *.exp
+ link /dll /export:$*_setup $*.obj $(PDNTLIB) *.lib
+# -del *.obj
+# -del *.lib
+# -del *.exp
#install:
# copy help-*.pd $(PDPATH)/doc/5.reference/
@@ -46,3 +49,7 @@ clean: -del *.exp
-del *.dll
+distclean:
+ -del *.obj
+ -del *.lib
+ -del *.exp
|