From 22b55551eaf4fe8e477fa68989e6ceaad23eea6e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?IOhannes=20m=20zm=C3=B6lnig?= Date: Thu, 19 May 2005 14:19:19 +0000 Subject: the joys of dos2unix svn path=/trunk/externals/ann/; revision=3032 --- src/ann_mlp.c | 1130 ++++++++++++++++++++++++------------------------ src/ann_td.c | 1328 ++++++++++++++++++++++++++++----------------------------- 2 files changed, 1229 insertions(+), 1229 deletions(-) diff --git a/src/ann_mlp.c b/src/ann_mlp.c index c8c4cb3..326bc6d 100755 --- a/src/ann_mlp.c +++ b/src/ann_mlp.c @@ -1,565 +1,565 @@ -/* nn : Neural Networks for PD - by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it - this software is simply an interface for FANN classes - http://fann.sourceforge.net/ - FANN is obviously needed for compilation - this software is licensed under the GNU General Public License -*/ -#include -#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; - -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 (0desired_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; iann, input); - - // fill the output array with result from ann - for (i=0;il_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; iann); - - 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 +#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; + +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 (0desired_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; iann, input); + + // fill the output array with result from ann + for (i=0;il_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; iann); + + fann_train(x->ann, input, output); + + mse = fann_get_MSE(x->ann); + + outlet_float(x->f_out, mse); + + +} + +static void manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (x->mode) + run_the_net(x, sl, argc, argv); + else + { + train_on_the_fly(x, sl, argc, argv); + } +} + +static void set_filename(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (argc>0) { + x->filename = atom_gensym(argv); + } else + { + error("you must specify the filename"); + } + post("nn:filename set to %s", x->filename->s_name); +} + +static void load_ann_from_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (argc>0) { + x->filename = atom_gensym(argv); + } + x->ann = fann_create_from_file(x->filename->s_name); + if (x->ann == 0) + error("error opening %s", x->filename->s_name); + else + post("nn:ann loaded fom file %s", x->filename->s_name); +} + +static void save_ann_to_file(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (argc>0) { + x->filename = atom_gensym(argv); + } + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_save(x->ann, x->filename->s_name); + post("nn:ann saved in file %s", x->filename->s_name); + } +} + +// functions for training algo: +static void set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL); + post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL"); + } +} +static void set_FANN_TRAIN_BATCH(t_ann_mlp *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH); + post("nn:training algorithm set to FANN_TRAIN_BATCH"); + } +} +static void set_FANN_TRAIN_RPROP(t_ann_mlp *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP); + post("nn:training algorithm set to FANN_TRAIN_RPROP"); + } +} +static void set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP); + post("nn:training algorithm set to FANN_TRAIN_QUICKPROP"); + } +} + +static void set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv) +{ + t_symbol *parametro = 0; + int funzione = 0; + + if (x->ann == 0) + { + error("ann not initialized"); + return; + } + + if (argc>0) { + parametro = atom_gensym(argv); + if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0) + funzione = FANN_THRESHOLD; + if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0) + funzione = FANN_THRESHOLD_SYMMETRIC; + if (strcmp(parametro->s_name, "FANN_LINEAR")==0) + funzione = FANN_LINEAR; + if (strcmp(parametro->s_name, "FANN_SIGMOID")==0) + funzione = FANN_SIGMOID; + if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0) + funzione = FANN_SIGMOID_STEPWISE; + if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0) + funzione = FANN_SIGMOID_SYMMETRIC; + if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0) + funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE; + fann_set_activation_function_output(x->ann, funzione); + } else + { + error("you must specify the activation function"); + } + post("nn:activation function set to %s (%i)", parametro->s_name, funzione); + +} + +static void print_ann_details(t_ann_mlp *x) +{ + if (x->ann == 0) + { + post("nn:ann is not initialized"); + } else + { + post("nn:follows a description of the current ann:"); + post("nn:num_input=%i", x->ann->num_input); + post("nn:num_output=%i", x->ann->num_output); + post("nn:learning_rate=%f", x->ann->learning_rate); + post("nn:connection_rate=%f", x->ann->connection_rate); + post("nn:total_neurons=%i", x->ann->total_neurons); + post("nn:total_connections=%i", x->ann->total_connections); + post("nn:last error=%i", x->ann->errstr); + if (x->filename == 0) + { + post("nn:filename not set"); + } else + { + post("nn:filename=%s", x->filename->s_name); + } + } +} + + +static void *nn_new(t_symbol *s, int argc, t_atom *argv) +{ + t_ann_mlp *x = (t_ann_mlp *)pd_new(ann_mlp_class); + x->l_out = outlet_new(&x->x_obj, &s_list); + x->f_out = outlet_new(&x->x_obj, &s_float); + + x->desired_error = (float)0.001; + x->max_iterations = 500000; + x->iterations_between_reports = 1000; + x->mode=RUN; + + if (argc>0) { + x->filename = atom_gensym(argv); + load_ann_from_file(x, NULL , 0, NULL); + } + return (void *)x; +} + +// free resources +static void nn_free(t_ann_mlp *x) +{ + struct fann *ann = x->ann; + fann_destroy(ann); + // TODO: free other resources! +} + +void ann_mlp_setup(void) { + + post(""); + post("ann_mlp: neural nets for PD"); + post("version: "VERSION""); + post("compiled: "__DATE__); + post("author: Davide Morelli"); + post("contact: info@davidemorelli.it www.davidemorelli.it"); + + ann_mlp_class = class_new(gensym("ann_mlp"), + (t_newmethod)nn_new, + (t_method)nn_free, sizeof(t_ann_mlp), + CLASS_DEFAULT, A_GIMME, 0); + + // general.. + class_addmethod(ann_mlp_class, (t_method)help, gensym("help"), 0); + class_addmethod(ann_mlp_class, (t_method)createFann, gensym("create"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)train, gensym("train"), 0); + class_addmethod(ann_mlp_class, (t_method)run, gensym("run"), 0); + class_addmethod(ann_mlp_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)manage_list, gensym("data"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)print_ann_details, gensym("details"), 0); + + // change training parameters + class_addmethod(ann_mlp_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0); + class_addmethod(ann_mlp_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0); + + // change training and activation algorithms + class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0); + class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0); + class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0); + class_addmethod(ann_mlp_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0); + class_addmethod(ann_mlp_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0); + + // the most important one: running the ann + class_addlist(ann_mlp_class, (t_method)manage_list); + + // help patch + class_sethelpsymbol(ann_mlp_class, gensym("help-ann_mlp")); + + +} diff --git a/src/ann_td.c b/src/ann_td.c index a519c2f..73aa364 100755 --- a/src/ann_td.c +++ b/src/ann_td.c @@ -1,664 +1,664 @@ -/* ann_td : Time Delay Neural Networks for PD - by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it - this software is simply an interface for FANN classes - http://fann.sourceforge.net/ - FANN is obviously needed for compilation - this software is licensed under the GNU General Public License -*/ -#include -#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; - -static void help(t_ann_td *x) -{ - post(""); - post("ann_td:time delay neural networks for PD"); - post("ann_td:Davide Morelli - info@davidemorelli.it - (c)2005"); - post("ann_td:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output"); - post("ann_td:main commands: create, filename, load, save, train-on-file, run"); - post("ann_td:see help-nn.pd for details on commands and usage"); - post("ann_td:this is an interface to FANN"); - -} - -static void deallocate_inputs(t_ann_td *x) -{ - if (x->inputs != 0) - { - freebytes(x->inputs, sizeof(x->inputs)); - x->inputs = 0; - } -} - -static void allocate_inputs(t_ann_td *x) -{ - unsigned int i; - deallocate_inputs(x); - // allocate space for inputs array - x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float)); - for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f; -} - -static void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - unsigned int num_input = 2; - unsigned int num_output = 1; - unsigned int num_layers = 3; - unsigned int num_neurons_hidden = 3; - float connection_rate = 1; - float learning_rate = (float)0.7; - - if (argc<3) - { - error("you must provide at least num_input, num_output amd frames number"); - return; - } - if (argc>0) - num_input = atom_getint(argv++); - - if (argc>1) - num_output = atom_getint(argv++); - - if (argc>2) - { - x->frames = atom_getint(argv++); - x->ins_frames_set=1; - } - - if (argc>3) - num_layers = atom_getint(argv++); - - if (argc>4) - num_neurons_hidden = atom_getint(argv++); - - if (argc>5) - connection_rate = atom_getfloat(argv++); - - if (argc>6) - learning_rate = atom_getfloat(argv++); - - if ((num_input * x->frames)>=MAXINPUT) - { - error("too many inputs, maximum allowed is %f", MAXINPUT/x->frames); - return; - } - - if (num_output>=MAXOUTPUT) - { - error("too many outputs, maximum allowed is MAXOUTPUT"); - return; - } - - x->ann = fann_create(connection_rate, learning_rate, num_layers, - (num_input*x->frames), num_neurons_hidden, num_output); - - fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC); - fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC); - - allocate_inputs(x); - - if (x->ann == 0) - { - error("error creating the ann"); - } else - { - post("ann_td:created ann with:"); - post("num_input = %i", num_input); - post("num_output = %i", num_output); - post("frames = %i", x->frames); - post("num_layers = %i", num_layers); - post("num_neurons_hidden = %i", num_neurons_hidden); - post("connection_rate = %f", connection_rate); - post("learning_rate = %f", learning_rate); - } -} - -static void print_status(t_ann_td *x) -{ - if (x->mode == TRAIN) - post("ann_td:training"); - else - post("ann_td:running"); -} - -static void train(t_ann_td *x) -{ - x->mode=TRAIN; - if (x->ann == 0) - { - error("ann not initialized"); - return; - } - fann_reset_MSE(x->ann); - print_status(x); -} - -static void run(t_ann_td *x) -{ - x->mode=RUN; - print_status(x); -} - -static void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - if (argc<1) - { - error("usage: setmode 0/1: 0 for training, 1 for running"); - } - else - { - x->mode = atom_getint(argv++); - print_status(x); - } -} - - - -static void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - if (x->ann == 0) - { - error("ann not initialized"); - return; - } - - if (argc<1) - { - error("you must specify the filename with training data"); - return; - } else - { - x->filenametrain = atom_gensym(argv); - } - - //post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name); - - fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations, - x->iterations_between_reports, x->desired_error); - - post("ann_td: finished training on file %s", x->filenametrain->s_name); -} - -static void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - float desired_error = (float)0.001; - if (0desired_error = desired_error; - post("ann_td:desired_error set to %f", x->desired_error); - } else - { - error("you must pass me a float"); - } -} - -static void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - unsigned int max_iterations = 500000; - if (argc>0) - { - max_iterations = atom_getint(argv); - x->max_iterations = max_iterations; - post("ann_td:max_iterations set to %i", x->max_iterations); - } else - { - error("you must pass me an int"); - } -} - -static void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - - unsigned int iterations_between_reports = 1000; - if (argc>0) - { - iterations_between_reports = atom_getint(argv); - x->iterations_between_reports = iterations_between_reports; - post("ann_td:iterations_between_reports set to %i", x->iterations_between_reports); - } else - { - error("you must pass me an int"); - } - -} - - -static void scale_inputs(t_ann_td *x) -{ - unsigned int j; - unsigned int k; - - for(j = (x->frames - 1); j>0; j--) - { - // scorro la lista all'indietro - for (k=0; k < x->num_input; k++) - { - // scalo i valori dei frames - x->inputs[(x->num_input) * j + k]=x->inputs[(x->num_input) * (j-1) + k]; - } - } -} - -// run the ann using floats in list passed to the inlet as input values -// and send result to outlet as list of float -static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - int i=0; - unsigned j=0; - //fann_type input[MAXINPUT]; - fann_type *calc_out; - t_atom lista[MAXOUTPUT]; - int quanti; - float valoreTMP; - - if (x->ann == 0) - { - error("ann not initialized"); - return; - } - - if (x->ins_frames_set==0) - { - error("num_inputs and frames not set"); - return; - } - - if (argc < (int) x->num_input) - { - error("insufficient inputs"); - return; - } - quanti = x->ann->num_output; - - scale_inputs(x); - - // fill output array with zeros - for (i=0; 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); - -} - -static void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - int i=0; - unsigned int j=0; - fann_type input_merged[MAXINPUT]; - fann_type output[MAXOUTPUT]; - //fann_type *calcMSE; - //t_atom lista[MAXOUTPUT]; - float mse; - - if (x->ann == 0) - { - error("ann not initialized"); - return; - } - - if ((x->num_input + x->ann->num_output) > (unsigned int) argc) - { - error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats"); - return; - } - - // fill input array with zeros - for (i=0; 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); - - -} - -static void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - if (x->mode) - run_the_net(x, sl, argc, argv); - else - { - train_on_the_fly(x, sl, argc, argv); - } -} - -static void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - if (argc>0) { - x->filename = atom_gensym(argv); - } else - { - error("you must specify the filename"); - } - post("nn:filename set to %s", x->filename->s_name); -} - -static void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - if (x->ins_frames_set==0) - { - error("set num_input and frames with [inputs_frames int int("); - error("I won't load without num_input and frames set"); - return; - } - if (argc>0) { - x->filename = atom_gensym(argv); - } - x->ann = fann_create_from_file(x->filename->s_name); - if (x->ann == 0) - error("error opening %s", x->filename->s_name); - else - post("nn:ann loaded fom file %s", x->filename->s_name); - - allocate_inputs(x); -} - -static void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - if (argc>0) { - x->filename = atom_gensym(argv); - } - if (x->ann == 0) - { - error("ann is not initialized"); - } else - { - fann_save(x->ann, x->filename->s_name); - post("nn:ann saved in file %s", x->filename->s_name); - } -} - -// functions for training algo: -static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x) -{ - if (x->ann == 0) - { - error("ann is not initialized"); - } else - { - fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL); - post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL"); - } -} -static void set_FANN_TRAIN_BATCH(t_ann_td *x) -{ - if (x->ann == 0) - { - error("ann is not initialized"); - } else - { - fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH); - post("nn:training algorithm set to FANN_TRAIN_BATCH"); - } -} -static void set_FANN_TRAIN_RPROP(t_ann_td *x) -{ - if (x->ann == 0) - { - error("ann is not initialized"); - } else - { - fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP); - post("nn:training algorithm set to FANN_TRAIN_RPROP"); - } -} -static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x) -{ - if (x->ann == 0) - { - error("ann is not initialized"); - } else - { - fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP); - post("nn:training algorithm set to FANN_TRAIN_QUICKPROP"); - } -} - -static void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) -{ - t_symbol *parametro = 0; - int funzione = 0; - - if (x->ann == 0) - { - error("ann not initialized"); - return; - } - - if (argc>0) { - parametro = atom_gensym(argv); - if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0) - funzione = FANN_THRESHOLD; - if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0) - funzione = FANN_THRESHOLD_SYMMETRIC; - if (strcmp(parametro->s_name, "FANN_LINEAR")==0) - funzione = FANN_LINEAR; - if (strcmp(parametro->s_name, "FANN_SIGMOID")==0) - funzione = FANN_SIGMOID; - if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0) - funzione = FANN_SIGMOID_STEPWISE; - if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0) - funzione = FANN_SIGMOID_SYMMETRIC; - if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0) - funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE; - fann_set_activation_function_output(x->ann, funzione); - } else - { - error("you must specify the activation function"); - } - post("nn:activation function set to %s (%i)", parametro->s_name, funzione); - -} - -static void print_ann_details(t_ann_td *x) -{ - if (x->ann == 0) - { - post("ann_td:ann is not initialized"); - } else - { - post("follows a description of the current ann:"); - post("num_input=%i", x->ann->num_input); - post("num_output=%i", x->ann->num_output); - post("learning_rate=%f", x->ann->learning_rate); - post("connection_rate=%f", x->ann->connection_rate); - post("total_neurons=%i", x->ann->total_neurons); - post("total_connections=%i", x->ann->total_connections); - post("last error=%i", x->ann->errstr); - if (x->filename == 0) - { - post("filename not set"); - } else - { - post("filename=%s", x->filename->s_name); - } - } -} - -static void set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames) -{ - x->num_input = ins; - x->frames = frames; - x->ins_frames_set=1; -} - -static void *nn_new(t_symbol *s, int argc, t_atom *argv) -{ - t_ann_td *x = (t_ann_td *)pd_new(ann_td_class); - x->l_out = outlet_new(&x->x_obj, &s_list); - x->f_out = outlet_new(&x->x_obj, &s_float); - - x->desired_error = (float)0.001; - x->max_iterations = 500000; - x->iterations_between_reports = 1000; - x->mode=RUN; - x->ins_frames_set=0; - - if (argc<2) - { - error("2 arguments needed: num_input and frames. filename optional"); - return (void *)x; - } - - if (argc>0) { - x->num_input = atom_getint(argv++); - } - - if (argc>1) { - x->frames = atom_getint(argv++); - x->ins_frames_set=1; - allocate_inputs(x); - } - - if (argc>2) { - x->filename = atom_gensym(argv); - load_ann_from_file(x, NULL , 0, NULL); - } - - return (void *)x; -} - -// free resources -static void nn_free(t_ann_td *x) -{ - struct fann *ann = x->ann; - fann_destroy(ann); - deallocate_inputs(x); - // TODO: free other resources! -} - -void ann_td_setup(void) { - - post(""); - post("ann_td: time delay neural nets for PD"); - post("version: "VERSION""); - post("compiled: "__DATE__); - post("author: Davide Morelli"); - post("contact: info@davidemorelli.it www.davidemorelli.it"); - - ann_td_class = class_new(gensym("ann_td"), - (t_newmethod)nn_new, - (t_method)nn_free, sizeof(t_ann_td), - CLASS_DEFAULT, A_GIMME, 0); - - // general.. - class_addmethod(ann_td_class, (t_method)help, gensym("help"), 0); - class_addmethod(ann_td_class, (t_method)createFann, gensym("create"), A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)train, gensym("train"), 0); - class_addmethod(ann_td_class, (t_method)run, gensym("run"), 0); - class_addmethod(ann_td_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)manage_list, gensym("data"), A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)print_ann_details, gensym("details"), 0); - - // change training parameters - class_addmethod(ann_td_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0); - class_addmethod(ann_td_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0); - - // change training and activation algorithms - class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0); - class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0); - class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0); - class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0); - class_addmethod(ann_td_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0); - - class_addmethod(ann_td_class, (t_method)set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0); - - // the most important one: running the ann - class_addlist(ann_td_class, (t_method)manage_list); - - // help patch - class_sethelpsymbol(ann_td_class, gensym("help-ann_td")); - - -} +/* ann_td : Time Delay Neural Networks for PD + by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it + this software is simply an interface for FANN classes + http://fann.sourceforge.net/ + FANN is obviously needed for compilation + this software is licensed under the GNU General Public License +*/ +#include +#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; + +static void help(t_ann_td *x) +{ + post(""); + post("ann_td:time delay neural networks for PD"); + post("ann_td:Davide Morelli - info@davidemorelli.it - (c)2005"); + post("ann_td:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output"); + post("ann_td:main commands: create, filename, load, save, train-on-file, run"); + post("ann_td:see help-nn.pd for details on commands and usage"); + post("ann_td:this is an interface to FANN"); + +} + +static void deallocate_inputs(t_ann_td *x) +{ + if (x->inputs != 0) + { + freebytes(x->inputs, sizeof(x->inputs)); + x->inputs = 0; + } +} + +static void allocate_inputs(t_ann_td *x) +{ + unsigned int i; + deallocate_inputs(x); + // allocate space for inputs array + x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float)); + for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f; +} + +static void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + unsigned int num_input = 2; + unsigned int num_output = 1; + unsigned int num_layers = 3; + unsigned int num_neurons_hidden = 3; + float connection_rate = 1; + float learning_rate = (float)0.7; + + if (argc<3) + { + error("you must provide at least num_input, num_output amd frames number"); + return; + } + if (argc>0) + num_input = atom_getint(argv++); + + if (argc>1) + num_output = atom_getint(argv++); + + if (argc>2) + { + x->frames = atom_getint(argv++); + x->ins_frames_set=1; + } + + if (argc>3) + num_layers = atom_getint(argv++); + + if (argc>4) + num_neurons_hidden = atom_getint(argv++); + + if (argc>5) + connection_rate = atom_getfloat(argv++); + + if (argc>6) + learning_rate = atom_getfloat(argv++); + + if ((num_input * x->frames)>=MAXINPUT) + { + error("too many inputs, maximum allowed is %f", MAXINPUT/x->frames); + return; + } + + if (num_output>=MAXOUTPUT) + { + error("too many outputs, maximum allowed is MAXOUTPUT"); + return; + } + + x->ann = fann_create(connection_rate, learning_rate, num_layers, + (num_input*x->frames), num_neurons_hidden, num_output); + + fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC); + fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC); + + allocate_inputs(x); + + if (x->ann == 0) + { + error("error creating the ann"); + } else + { + post("ann_td:created ann with:"); + post("num_input = %i", num_input); + post("num_output = %i", num_output); + post("frames = %i", x->frames); + post("num_layers = %i", num_layers); + post("num_neurons_hidden = %i", num_neurons_hidden); + post("connection_rate = %f", connection_rate); + post("learning_rate = %f", learning_rate); + } +} + +static void print_status(t_ann_td *x) +{ + if (x->mode == TRAIN) + post("ann_td:training"); + else + post("ann_td:running"); +} + +static void train(t_ann_td *x) +{ + x->mode=TRAIN; + if (x->ann == 0) + { + error("ann not initialized"); + return; + } + fann_reset_MSE(x->ann); + print_status(x); +} + +static void run(t_ann_td *x) +{ + x->mode=RUN; + print_status(x); +} + +static void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (argc<1) + { + error("usage: setmode 0/1: 0 for training, 1 for running"); + } + else + { + x->mode = atom_getint(argv++); + print_status(x); + } +} + + + +static void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (x->ann == 0) + { + error("ann not initialized"); + return; + } + + if (argc<1) + { + error("you must specify the filename with training data"); + return; + } else + { + x->filenametrain = atom_gensym(argv); + } + + //post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name); + + fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations, + x->iterations_between_reports, x->desired_error); + + post("ann_td: finished training on file %s", x->filenametrain->s_name); +} + +static void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + float desired_error = (float)0.001; + if (0desired_error = desired_error; + post("ann_td:desired_error set to %f", x->desired_error); + } else + { + error("you must pass me a float"); + } +} + +static void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + unsigned int max_iterations = 500000; + if (argc>0) + { + max_iterations = atom_getint(argv); + x->max_iterations = max_iterations; + post("ann_td:max_iterations set to %i", x->max_iterations); + } else + { + error("you must pass me an int"); + } +} + +static void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + + unsigned int iterations_between_reports = 1000; + if (argc>0) + { + iterations_between_reports = atom_getint(argv); + x->iterations_between_reports = iterations_between_reports; + post("ann_td:iterations_between_reports set to %i", x->iterations_between_reports); + } else + { + error("you must pass me an int"); + } + +} + + +static void scale_inputs(t_ann_td *x) +{ + unsigned int j; + unsigned int k; + + for(j = (x->frames - 1); j>0; j--) + { + // scorro la lista all'indietro + for (k=0; k < x->num_input; k++) + { + // scalo i valori dei frames + x->inputs[(x->num_input) * j + k]=x->inputs[(x->num_input) * (j-1) + k]; + } + } +} + +// run the ann using floats in list passed to the inlet as input values +// and send result to outlet as list of float +static void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + int i=0; + unsigned j=0; + //fann_type input[MAXINPUT]; + fann_type *calc_out; + t_atom lista[MAXOUTPUT]; + int quanti; + float valoreTMP; + + if (x->ann == 0) + { + error("ann not initialized"); + return; + } + + if (x->ins_frames_set==0) + { + error("num_inputs and frames not set"); + return; + } + + if (argc < (int) x->num_input) + { + error("insufficient inputs"); + return; + } + quanti = x->ann->num_output; + + scale_inputs(x); + + // fill output array with zeros + for (i=0; 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); + +} + +static void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + int i=0; + unsigned int j=0; + fann_type input_merged[MAXINPUT]; + fann_type output[MAXOUTPUT]; + //fann_type *calcMSE; + //t_atom lista[MAXOUTPUT]; + float mse; + + if (x->ann == 0) + { + error("ann not initialized"); + return; + } + + if ((x->num_input + x->ann->num_output) > (unsigned int) argc) + { + error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats"); + return; + } + + // fill input array with zeros + for (i=0; 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); + + +} + +static void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (x->mode) + run_the_net(x, sl, argc, argv); + else + { + train_on_the_fly(x, sl, argc, argv); + } +} + +static void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (argc>0) { + x->filename = atom_gensym(argv); + } else + { + error("you must specify the filename"); + } + post("nn:filename set to %s", x->filename->s_name); +} + +static void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (x->ins_frames_set==0) + { + error("set num_input and frames with [inputs_frames int int("); + error("I won't load without num_input and frames set"); + return; + } + if (argc>0) { + x->filename = atom_gensym(argv); + } + x->ann = fann_create_from_file(x->filename->s_name); + if (x->ann == 0) + error("error opening %s", x->filename->s_name); + else + post("nn:ann loaded fom file %s", x->filename->s_name); + + allocate_inputs(x); +} + +static void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + if (argc>0) { + x->filename = atom_gensym(argv); + } + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_save(x->ann, x->filename->s_name); + post("nn:ann saved in file %s", x->filename->s_name); + } +} + +// functions for training algo: +static void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL); + post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL"); + } +} +static void set_FANN_TRAIN_BATCH(t_ann_td *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH); + post("nn:training algorithm set to FANN_TRAIN_BATCH"); + } +} +static void set_FANN_TRAIN_RPROP(t_ann_td *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP); + post("nn:training algorithm set to FANN_TRAIN_RPROP"); + } +} +static void set_FANN_TRAIN_QUICKPROP(t_ann_td *x) +{ + if (x->ann == 0) + { + error("ann is not initialized"); + } else + { + fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP); + post("nn:training algorithm set to FANN_TRAIN_QUICKPROP"); + } +} + +static void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv) +{ + t_symbol *parametro = 0; + int funzione = 0; + + if (x->ann == 0) + { + error("ann not initialized"); + return; + } + + if (argc>0) { + parametro = atom_gensym(argv); + if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0) + funzione = FANN_THRESHOLD; + if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0) + funzione = FANN_THRESHOLD_SYMMETRIC; + if (strcmp(parametro->s_name, "FANN_LINEAR")==0) + funzione = FANN_LINEAR; + if (strcmp(parametro->s_name, "FANN_SIGMOID")==0) + funzione = FANN_SIGMOID; + if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0) + funzione = FANN_SIGMOID_STEPWISE; + if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0) + funzione = FANN_SIGMOID_SYMMETRIC; + if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0) + funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE; + fann_set_activation_function_output(x->ann, funzione); + } else + { + error("you must specify the activation function"); + } + post("nn:activation function set to %s (%i)", parametro->s_name, funzione); + +} + +static void print_ann_details(t_ann_td *x) +{ + if (x->ann == 0) + { + post("ann_td:ann is not initialized"); + } else + { + post("follows a description of the current ann:"); + post("num_input=%i", x->ann->num_input); + post("num_output=%i", x->ann->num_output); + post("learning_rate=%f", x->ann->learning_rate); + post("connection_rate=%f", x->ann->connection_rate); + post("total_neurons=%i", x->ann->total_neurons); + post("total_connections=%i", x->ann->total_connections); + post("last error=%i", x->ann->errstr); + if (x->filename == 0) + { + post("filename not set"); + } else + { + post("filename=%s", x->filename->s_name); + } + } +} + +static void set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames) +{ + x->num_input = ins; + x->frames = frames; + x->ins_frames_set=1; +} + +static void *nn_new(t_symbol *s, int argc, t_atom *argv) +{ + t_ann_td *x = (t_ann_td *)pd_new(ann_td_class); + x->l_out = outlet_new(&x->x_obj, &s_list); + x->f_out = outlet_new(&x->x_obj, &s_float); + + x->desired_error = (float)0.001; + x->max_iterations = 500000; + x->iterations_between_reports = 1000; + x->mode=RUN; + x->ins_frames_set=0; + + if (argc<2) + { + error("2 arguments needed: num_input and frames. filename optional"); + return (void *)x; + } + + if (argc>0) { + x->num_input = atom_getint(argv++); + } + + if (argc>1) { + x->frames = atom_getint(argv++); + x->ins_frames_set=1; + allocate_inputs(x); + } + + if (argc>2) { + x->filename = atom_gensym(argv); + load_ann_from_file(x, NULL , 0, NULL); + } + + return (void *)x; +} + +// free resources +static void nn_free(t_ann_td *x) +{ + struct fann *ann = x->ann; + fann_destroy(ann); + deallocate_inputs(x); + // TODO: free other resources! +} + +void ann_td_setup(void) { + + post(""); + post("ann_td: time delay neural nets for PD"); + post("version: "VERSION""); + post("compiled: "__DATE__); + post("author: Davide Morelli"); + post("contact: info@davidemorelli.it www.davidemorelli.it"); + + ann_td_class = class_new(gensym("ann_td"), + (t_newmethod)nn_new, + (t_method)nn_free, sizeof(t_ann_td), + CLASS_DEFAULT, A_GIMME, 0); + + // general.. + class_addmethod(ann_td_class, (t_method)help, gensym("help"), 0); + class_addmethod(ann_td_class, (t_method)createFann, gensym("create"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)train, gensym("train"), 0); + class_addmethod(ann_td_class, (t_method)run, gensym("run"), 0); + class_addmethod(ann_td_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)manage_list, gensym("data"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)print_ann_details, gensym("details"), 0); + + // change training parameters + class_addmethod(ann_td_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0); + class_addmethod(ann_td_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0); + + // change training and activation algorithms + class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0); + class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0); + class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0); + class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0); + class_addmethod(ann_td_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0); + + class_addmethod(ann_td_class, (t_method)set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0); + + // the most important one: running the ann + class_addlist(ann_td_class, (t_method)manage_list); + + // help patch + class_sethelpsymbol(ann_td_class, gensym("help-ann_td")); + + +} -- cgit v1.2.1