From 4c962acc9f2b6e275f02ab7660a8471453c2f393 Mon Sep 17 00:00:00 2001 From: Davide Morelli Date: Wed, 18 May 2005 15:53:29 +0000 Subject: added ann_mlp and ann_td svn path=/trunk/externals/ann/; revision=3011 --- src/ann_mlp.c | 565 ++++++++++++++++++++++++++++++++++++++++++++++ src/ann_td.c | 665 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ src/makefile.msvc | 48 ++++ 3 files changed, 1278 insertions(+) create mode 100755 src/ann_mlp.c create mode 100755 src/ann_td.c create mode 100755 src/makefile.msvc (limited to 'src') 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 +#include +#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 (0desired_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; iann, input); + + // fill the output array with result from ann + for (i=0;il_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; iann); + + 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 +#include +#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 (0desired_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; inum_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;il_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; inum_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 + -- cgit v1.2.1