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Diffstat (limited to 'src/ann_td.c')
| -rwxr-xr-x | src/ann_td.c | 665 |
1 files changed, 665 insertions, 0 deletions
diff --git a/src/ann_td.c b/src/ann_td.c new file mode 100755 index 0000000..c2cf6b5 --- /dev/null +++ b/src/ann_td.c @@ -0,0 +1,665 @@ +/* ann_td : Time Delay Neural Networks for PD
+ by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it
+ this software is simply an interface for FANN classes
+ http://fann.sourceforge.net/
+ FANN is obviously needed for compilation
+ this software is licensed under the GNU General Public License
+*/
+#include <stdio.h>
+#include <string.h>
+#include "m_pd.h"
+#include "fann.h"
+
+
+#define VERSION "0.01"
+#ifndef __DATE__
+#define __DATE__ ""
+#endif
+
+#define TRAIN 0
+#define RUN 1
+
+#define MAXINPUT 100
+#define MAXOUTPUT 100
+
+static t_class *ann_td_class;
+
+typedef struct _ann_td {
+ t_object x_obj;
+ struct fann *ann;
+ int mode; // 0 = training, 1 = running
+ t_symbol *filename; // name of the file where this ann is saved
+ t_symbol *filenametrain; // name of the file with training data
+ float desired_error; + unsigned int max_iterations; + unsigned int iterations_between_reports;
+ unsigned int frames;
+ unsigned int num_input;
+ t_float *inputs;
+ unsigned int ins_frames_set;
+ t_outlet *l_out, *f_out;
+} t_ann_td;
+
+void help(t_ann_td *x)
+{
+ post("");
+ post("ann_td:time delay neural networks for PD");
+ post("ann_td:Davide Morelli - info@davidemorelli.it - (c)2005");
+ post("ann_td:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");
+ post("ann_td:main commands: create, filename, load, save, train-on-file, run");
+ post("ann_td:see help-nn.pd for details on commands and usage");
+ post("ann_td:this is an interface to FANN");
+
+}
+
+void deallocate_inputs(t_ann_td *x)
+{
+ if (x->inputs != 0)
+ {
+ freebytes(x->inputs, sizeof(x->inputs));
+ x->inputs = 0;
+ }
+}
+
+void allocate_inputs(t_ann_td *x)
+{
+ unsigned int i;
+ deallocate_inputs(x);
+ // allocate space for inputs array
+ x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float));
+ for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f;
+}
+
+void createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{ + unsigned int num_input = 2; + unsigned int num_output = 1; + unsigned int num_layers = 3; + unsigned int num_neurons_hidden = 3; + float connection_rate = 1; + float learning_rate = (float)0.7; + + if (argc<3) + { + error("you must provide at least num_input, num_output amd frames number"); + return; + } + if (argc>0) + num_input = atom_getint(argv++); + + if (argc>1) + num_output = atom_getint(argv++); + + if (argc>2) + { + x->frames = atom_getint(argv++); + x->ins_frames_set=1; + } + + if (argc>3) + num_layers = atom_getint(argv++); + + if (argc>4) + num_neurons_hidden = atom_getint(argv++); + + if (argc>5) + connection_rate = atom_getfloat(argv++); + + if (argc>6) + learning_rate = atom_getfloat(argv++); + + if ((num_input * x->frames)>=MAXINPUT) + { + error("too many inputs, maximum allowed is %f", MAXINPUT/x->frames); + return; + } + + if (num_output>=MAXOUTPUT) + { + error("too many outputs, maximum allowed is MAXOUTPUT"); + return; + } + + x->ann = fann_create(connection_rate, learning_rate, num_layers, + (num_input*x->frames), num_neurons_hidden, num_output); + + fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC); + fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);
+
+ allocate_inputs(x);
+
+ if (x->ann == 0)
+ {
+ error("error creating the ann");
+ } else
+ {
+ post("ann_td:created ann with:");
+ post("num_input = %i", num_input);
+ post("num_output = %i", num_output);
+ post("frames = %i", x->frames);
+ post("num_layers = %i", num_layers);
+ post("num_neurons_hidden = %i", num_neurons_hidden);
+ post("connection_rate = %f", connection_rate);
+ post("learning_rate = %f", learning_rate);
+ }
+}
+
+void print_status(t_ann_td *x)
+{
+ if (x->mode == TRAIN)
+ post("ann_td:training");
+ else
+ post("ann_td:running");
+}
+
+void train(t_ann_td *x)
+{
+ x->mode=TRAIN;
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+ fann_reset_MSE(x->ann);
+ print_status(x);
+}
+
+void run(t_ann_td *x)
+{
+ x->mode=RUN;
+ print_status(x);
+}
+
+void set_mode(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (argc<1)
+ {
+ error("usage: setmode 0/1: 0 for training, 1 for running");
+ }
+ else
+ {
+ x->mode = atom_getint(argv++);
+ print_status(x);
+ }
+}
+
+
+
+void train_on_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{ + if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ } + + if (argc<1) + { + error("you must specify the filename with training data"); + return; + } else + { + x->filenametrain = atom_gensym(argv); + }
+ + //post("nn: starting training on file %s, please be patient and wait for my next message (it could take severeal minutes to complete training)", x->filenametrain->s_name); + + fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations, + x->iterations_between_reports, x->desired_error);
+
+ post("ann_td: finished training on file %s", x->filenametrain->s_name);
+}
+
+void set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ float desired_error = (float)0.001;
+ if (0<argc)
+ {
+ desired_error = atom_getfloat(argv);
+ x->desired_error = desired_error;
+ post("ann_td:desired_error set to %f", x->desired_error);
+ } else
+ {
+ error("you must pass me a float");
+ }
+}
+
+void set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ unsigned int max_iterations = 500000;
+ if (argc>0)
+ {
+ max_iterations = atom_getint(argv);
+ x->max_iterations = max_iterations;
+ post("ann_td:max_iterations set to %i", x->max_iterations);
+ } else
+ {
+ error("you must pass me an int");
+ }
+}
+
+void set_iterations_between_reports(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+
+ unsigned int iterations_between_reports = 1000;
+ if (argc>0)
+ {
+ iterations_between_reports = atom_getint(argv);
+ x->iterations_between_reports = iterations_between_reports;
+ post("ann_td:iterations_between_reports set to %i", x->iterations_between_reports);
+ } else
+ {
+ error("you must pass me an int");
+ }
+
+}
+
+
+void scale_inputs(t_ann_td *x)
+{
+ unsigned int j;
+ unsigned int k;
+
+ for(j = (x->frames - 1); j>0; j--)
+ {
+ // scorro la lista all'indietro
+ for (k=0; k < x->num_input; k++)
+ {
+ // scalo i valori dei frames
+ x->inputs[(x->num_input) * j + k]=x->inputs[(x->num_input) * (j-1) + k];
+ }
+ }
+}
+
+// run the ann using floats in list passed to the inlet as input values
+// and send result to outlet as list of float
+void run_the_net(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ int i=0;
+ unsigned j=0;
+ unsigned k=0;
+ //fann_type input[MAXINPUT];
+ fann_type *calc_out;
+ t_atom lista[MAXOUTPUT];
+ int quanti;
+ float valoreTMP;
+
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+
+ if (x->ins_frames_set==0)
+ {
+ error("num_inputs and frames not set");
+ return;
+ }
+
+ if (argc < (int) x->num_input)
+ {
+ error("insufficient inputs");
+ return;
+ }
+ quanti = x->ann->num_output;
+
+ scale_inputs(x);
+
+ // fill output array with zeros
+ for (i=0; i<MAXOUTPUT; i++)
+ {
+ SETFLOAT(lista + i,0);
+ }
+
+ // fill input array with actual data sent to inlet
+ for (j=0; j < x->num_input ;j++)
+ {
+ //input[j] = atom_getfloat(argv++);
+ x->inputs[j] = atom_getfloat(argv++);
+ }
+
+ // run the ann
+ //calc_out = fann_run(x->ann, input);
+ calc_out = fann_run(x->ann, x->inputs);
+
+ // fill the output array with result from ann
+ for (i=0;i<quanti;i++)
+ {
+ valoreTMP = calc_out[i];
+ //post("calc_out[%i]=%f", i, calc_out[i]);
+ SETFLOAT(lista+i, valoreTMP);
+ }
+
+ // send output array to outlet
+ outlet_anything(x->l_out,
+ gensym("list") ,
+ quanti,
+ lista);
+
+}
+
+void train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ int i=0;
+ unsigned int j=0;
+ fann_type input_merged[MAXINPUT];
+ fann_type output[MAXOUTPUT];
+ //fann_type *calcMSE;
+ //t_atom lista[MAXOUTPUT];
+ float mse;
+
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+
+ if ((x->num_input + x->ann->num_output) > (unsigned int) argc)
+ {
+ error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");
+ return;
+ }
+
+ // fill input array with zeros
+ for (i=0; i<MAXINPUT; i++)
+ {
+ input_merged[i]=0;
+ }
+ // fill input array with zeros
+ for (i=0; i<MAXOUTPUT; i++)
+ {
+ output[i]=0;
+ }
+
+ scale_inputs(x);
+
+ // fill input array with actual data sent to inlet
+ for (j = 0; j < x->num_input; j++)
+ {
+ input_merged[j] = atom_getfloat(argv++);
+ }
+ for (j = x->num_input; j < (x->num_input * x->frames); j++)
+ {
+ input_merged[j] = x->inputs[j];
+ }
+
+ for (j = 0; j < (x->ann->num_output);j++)
+ {
+ output[j] = atom_getfloat(argv++);
+ }
+
+ //fann_reset_MSE(x->ann);
+
+ fann_train(x->ann, input_merged, output);
+
+ mse = fann_get_MSE(x->ann);
+
+ outlet_float(x->f_out, mse);
+
+
+}
+
+void manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (x->mode)
+ run_the_net(x, sl, argc, argv);
+ else
+ {
+ train_on_the_fly(x, sl, argc, argv);
+ }
+}
+
+void set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (argc>0) {
+ x->filename = atom_gensym(argv);
+ } else
+ {
+ error("you must specify the filename");
+ }
+ post("nn:filename set to %s", x->filename->s_name);
+}
+
+void load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (x->ins_frames_set==0)
+ {
+ error("set num_input and frames with [inputs_frames int int(");
+ error("I won't load without num_input and frames set");
+ return;
+ }
+ if (argc>0) {
+ x->filename = atom_gensym(argv);
+ }
+ x->ann = fann_create_from_file(x->filename->s_name);
+ if (x->ann == 0)
+ error("error opening %s", x->filename->s_name);
+ else
+ post("nn:ann loaded fom file %s", x->filename->s_name);
+
+ allocate_inputs(x);
+}
+
+void save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (argc>0) {
+ x->filename = atom_gensym(argv);
+ }
+ if (x->ann == 0)
+ {
+ error("ann is not initialized");
+ } else
+ {
+ fann_save(x->ann, x->filename->s_name);
+ post("nn:ann saved in file %s", x->filename->s_name);
+ }
+}
+
+// functions for training algo:
+void set_FANN_TRAIN_INCREMENTAL(t_ann_td *x)
+{
+ if (x->ann == 0)
+ {
+ error("ann is not initialized");
+ } else
+ {
+ fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);
+ post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");
+ }
+}
+void set_FANN_TRAIN_BATCH(t_ann_td *x)
+{
+ if (x->ann == 0)
+ {
+ error("ann is not initialized");
+ } else
+ {
+ fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);
+ post("nn:training algorithm set to FANN_TRAIN_BATCH");
+ }
+}
+void set_FANN_TRAIN_RPROP(t_ann_td *x)
+{
+ if (x->ann == 0)
+ {
+ error("ann is not initialized");
+ } else
+ {
+ fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);
+ post("nn:training algorithm set to FANN_TRAIN_RPROP");
+ }
+}
+void set_FANN_TRAIN_QUICKPROP(t_ann_td *x)
+{
+ if (x->ann == 0)
+ {
+ error("ann is not initialized");
+ } else
+ {
+ fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);
+ post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");
+ }
+}
+
+void set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ t_symbol *parametro = 0;
+ int funzione = 0;
+
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+
+ if (argc>0) {
+ parametro = atom_gensym(argv);
+ if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
+ funzione = FANN_THRESHOLD;
+ if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
+ funzione = FANN_THRESHOLD_SYMMETRIC;
+ if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
+ funzione = FANN_LINEAR;
+ if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
+ funzione = FANN_SIGMOID;
+ if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
+ funzione = FANN_SIGMOID_STEPWISE;
+ if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
+ funzione = FANN_SIGMOID_SYMMETRIC;
+ if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
+ funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
+ fann_set_activation_function_output(x->ann, funzione);
+ } else
+ {
+ error("you must specify the activation function");
+ }
+ post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
+
+}
+
+void print_ann_details(t_ann_td *x)
+{
+ if (x->ann == 0)
+ {
+ post("ann_td:ann is not initialized");
+ } else
+ {
+ post("follows a description of the current ann:");
+ post("num_input=%i", x->ann->num_input);
+ post("num_output=%i", x->ann->num_output);
+ post("learning_rate=%f", x->ann->learning_rate);
+ post("connection_rate=%f", x->ann->connection_rate);
+ post("total_neurons=%i", x->ann->total_neurons);
+ post("total_connections=%i", x->ann->total_connections);
+ post("last error=%i", x->ann->errstr);
+ if (x->filename == 0)
+ {
+ post("filename not set");
+ } else
+ {
+ post("filename=%s", x->filename->s_name);
+ }
+ }
+}
+
+void set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames)
+{
+ x->num_input = ins;
+ x->frames = frames;
+ x->ins_frames_set=1;
+}
+
+void *nn_new(t_symbol *s, int argc, t_atom *argv)
+{
+ t_ann_td *x = (t_ann_td *)pd_new(ann_td_class);
+ x->l_out = outlet_new(&x->x_obj, &s_list);
+ x->f_out = outlet_new(&x->x_obj, &s_float);
+
+ x->desired_error = (float)0.001; + x->max_iterations = 500000; + x->iterations_between_reports = 1000;
+ x->mode=RUN;
+ x->ins_frames_set=0;
+
+ if (argc<2)
+ {
+ error("2 arguments needed: num_input and frames. filename optional");
+ return (void *)x;
+ }
+
+ if (argc>0) {
+ x->num_input = atom_getint(argv++);
+ }
+
+ if (argc>1) {
+ x->frames = atom_getint(argv++);
+ x->ins_frames_set=1;
+ allocate_inputs(x);
+ }
+
+ if (argc>2) {
+ x->filename = atom_gensym(argv);
+ load_ann_from_file(x, NULL , 0, NULL);
+ }
+
+ return (void *)x;
+}
+
+// free resources
+static void nn_free(t_ann_td *x)
+{
+ struct fann *ann = x->ann;
+ fann_destroy(ann);
+ deallocate_inputs(x);
+ // TODO: free other resources!
+}
+
+void ann_td_setup(void) {
+
+ post("");
+ post("ann_td: time delay neural nets for PD");
+ post("version: "VERSION"");
+ post("compiled: "__DATE__);
+ post("author: Davide Morelli");
+ post("contact: info@davidemorelli.it www.davidemorelli.it");
+
+ ann_td_class = class_new(gensym("ann_td"),
+ (t_newmethod)nn_new,
+ (t_method)nn_free, sizeof(t_ann_td),
+ CLASS_DEFAULT, A_GIMME, 0);
+
+ // general..
+ class_addmethod(ann_td_class, (t_method)help, gensym("help"), 0);
+ class_addmethod(ann_td_class, (t_method)createFann, gensym("create"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)train, gensym("train"), 0);
+ class_addmethod(ann_td_class, (t_method)run, gensym("run"), 0);
+ class_addmethod(ann_td_class, (t_method)set_mode, gensym("setmode"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)train_on_file, gensym("train-on-file"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)manage_list, gensym("data"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)set_filename, gensym("filename"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)load_ann_from_file, gensym("load"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)save_ann_to_file, gensym("save"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)print_ann_details, gensym("details"), 0);
+
+ // change training parameters
+ class_addmethod(ann_td_class, (t_method)set_desired_error, gensym("desired_error"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
+
+ // change training and activation algorithms
+ class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
+ class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
+ class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
+ class_addmethod(ann_td_class, (t_method)set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
+ class_addmethod(ann_td_class, (t_method)set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
+
+ class_addmethod(ann_td_class, (t_method)set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0);
+
+ // the most important one: running the ann
+ class_addlist(ann_td_class, (t_method)manage_list);
+
+ // help patch
+ class_sethelpsymbol(ann_td_class, gensym("help-ann_td"));
+
+
+}
\ No newline at end of file |