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-rwxr-xr-xsrc/ann_mlp.c1503
-rwxr-xr-xsrc/ann_td.c1325
2 files changed, 1415 insertions, 1413 deletions
diff --git a/src/ann_mlp.c b/src/ann_mlp.c
index 096b21a..53f61da 100755
--- a/src/ann_mlp.c
+++ b/src/ann_mlp.c
@@ -1,751 +1,752 @@
-/* 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
-*/
-
-/*
- hacked by Georg Holzmann for some additional methods, bug fixes, ...
- 2005, grh@mur.at
-*/
-
-#include <stdio.h>
-#include <string.h>
-#include "m_pd.h"
-#include "fann.h"
-
-#ifndef VERSION
-#define VERSION "0.2"
-#endif
-
-#ifndef __DATE__
-#define __DATE__ ""
-#endif
-
-#define TRAIN 0
-#define RUN 1
-
-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;
- fann_type *input; // grh: storage for input
- t_atom *output; // grh: storage for output (t_atom)
- fann_type *out_float; // grh: storage for output (fann_type)
- t_canvas *x_canvas;
- t_outlet *l_out, *f_out;
-} t_ann_mlp;
-
-// allocation
-static void ann_mlp_allocate_storage(t_ann_mlp *x)
-{
- unsigned int i;
-
- if(!x->ann)
- return;
-
- x->input = (fann_type *)getbytes(x->ann->num_input*sizeof(fann_type));
- x->output = (t_atom *)getbytes(x->ann->num_output*sizeof(t_atom));
- x->out_float = (fann_type *)getbytes(x->ann->num_output*sizeof(fann_type));
-
- // init storage with zeros
- for (i=0; i<x->ann->num_input; i++)
- x->input[i]=0;
- for (i=0; i<x->ann->num_output; i++)
- {
- SETFLOAT(x->output+i, 0);
- x->out_float[i]=0;
- }
-}
-
-// deallocation
-static void ann_mlp_free(t_ann_mlp *x)
-{
- if(!x->ann)
- return;
-
- freebytes(x->input, x->ann->num_input * sizeof(fann_type));
- freebytes(x->output, x->ann->num_output * sizeof(t_atom));
- freebytes(x->out_float, x->ann->num_output * sizeof(fann_type));
- fann_destroy(x->ann);
-}
-
-static void ann_mlp_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 ann_mlp_createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- unsigned int num_input = 2;
- unsigned int num_output = 1;
- unsigned int num_layers = 3;
- unsigned int *neurons_per_layer = NULL;
- int activated=0;
- int i, count_args = 0;
- float connection_rate = 1;
- float learning_rate = (float)0.7;
-
-
- // okay, start parsing init args ...
-
- if (argc > count_args++)
- num_input = atom_getint(argv++);
-
- if (argc > count_args++)
- num_output = atom_getint(argv++);
-
- if (argc > count_args++)
- {
- int hidden=0;
-
- num_layers = atom_getint(argv++);
- hidden = num_layers-2;
-
- neurons_per_layer = (unsigned int *)getbytes(num_layers*sizeof(unsigned int));
-
- neurons_per_layer[0] = num_input;
-
- // make standard initialization (if there are too few init args)
- for (i=1; i<hidden+1; i++)
- neurons_per_layer[i] = 3;
-
- // now check init args
- for (i=1; i<hidden+1; i++)
- {
- if (argc > count_args++)
- neurons_per_layer[i] = atom_getint(argv++);
- }
-
- neurons_per_layer[num_layers-1] = num_output;
-
- activated=1;
- }
-
- if (argc > count_args++)
- connection_rate = atom_getfloat(argv++);
-
- if (argc > count_args++)
- learning_rate = atom_getfloat(argv++);
-
- // make one hidden layer as standard, if there were too few init args
- if(!activated)
- {
- neurons_per_layer = (unsigned int *)getbytes(3*sizeof(unsigned int));
- neurons_per_layer[0] = num_input;
- neurons_per_layer[1] = 3;
- neurons_per_layer[2] = num_output;
- }
-
- // ... end of parsing init args
-
-
- if(x->ann)
- ann_mlp_free(x);
-
- x->ann = fann_create_array(connection_rate, learning_rate, num_layers, neurons_per_layer);
-
- // deallocate helper array
- freebytes(neurons_per_layer, num_layers * sizeof(unsigned int));
-
- if(!x->ann)
- {
- error("error creating the ann");
- return;
- }
-
- ann_mlp_allocate_storage(x);
- fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);
- fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);
-
- // set error log to stdout, so that you see it in the pd console
- //fann_set_error_log((struct fann_error*)x->ann, stdout);
- // unfortunately this doesn't work ... but it should do in a similar way !!
-
- post("created ann with:");
- post("num_input = %i", num_input);
- post("num_output = %i", num_output);
- post("num_layers = %i", num_layers);
- post("connection_rate = %f", connection_rate);
- post("learning_rate = %f", learning_rate);
-}
-
-static void ann_mlp_print_status(t_ann_mlp *x)
-{
- if (x->mode == TRAIN)
- post("nn:training");
- else
- post("nn:running");
-}
-
-static void ann_mlp_train(t_ann_mlp *x)
-{
- x->mode=TRAIN;
- if (x->ann == 0)
- {
- error("ann not initialized");
- return;
- }
- fann_reset_MSE(x->ann);
- ann_mlp_print_status(x);
-}
-
-static void ann_mlp_run(t_ann_mlp *x)
-{
- x->mode=RUN;
- ann_mlp_print_status(x);
-}
-
-static void ann_mlp_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++);
- ann_mlp_print_status(x);
- }
-}
-
-static void ann_mlp_train_on_file(t_ann_mlp *x, t_symbol *s)
-{
- // make correct path
- char patcher_path[MAXPDSTRING];
- char filename[MAXPDSTRING];
-
- if (x->ann == 0)
- {
- error("ann not initialized");
- return;
- }
-
- // make correct path
- canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
- sys_bashfilename(patcher_path, filename);
- x->filenametrain = gensym(filename);
-
- if(!x->filenametrain)
- return;
-
- post("nn: starting training on file %s, please be patient and wait ... (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_mlp: finished training on file %s", x->filenametrain->s_name);
-}
-
-static void ann_mlp_set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- float desired_error = (float)0.001;
- if (0<argc)
- {
- desired_error = atom_getfloat(argv);
- x->desired_error = desired_error;
- post("nn:desired_error set to %f", x->desired_error);
- } else
- {
- error("you must pass me a float");
- }
-}
-
-static void ann_mlp_set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- unsigned int max_iterations = 500000;
- if (argc>0)
- {
- 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 ann_mlp_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 ann_mlp_run_the_net(t_ann_mlp *x, t_symbol *sl, unsigned int argc, t_atom *argv)
-{
- unsigned int i=0;
- fann_type *calc_out;
-
- if (x->ann == 0)
- {
- error("ann not initialized");
- return;
- }
-
- if(argc < x->ann->num_input)
- {
- error("ann_mlp: too few input values!!");
- return;
- }
-
- // fill input array with actual data sent to inlet
- for (i=0;i<x->ann->num_input;i++)
- {
- x->input[i] = atom_getfloat(argv++);
- }
-
- // run the ann
- calc_out = fann_run(x->ann, x->input);
-
- // fill the output array with result from ann
- for (i=0;i<x->ann->num_output;i++)
- SETFLOAT(x->output+i, calc_out[i]);
-
- // send output array to outlet
- outlet_anything(x->l_out, gensym("list"),
- x->ann->num_output, x->output);
-}
-
-static void ann_mlp_train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- int i=0;
- int quantiINs, 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 actual data sent to inlet
- for (i=0;i<quantiINs;i++)
- x->input[i] = atom_getfloat(argv++);
-
- for (i=0;i<quantiOUTs;i++)
- x->out_float[i] = atom_getfloat(argv++);
-
- //fann_reset_MSE(x->ann);
-
- fann_train(x->ann, x->input, x->out_float);
-
- mse = fann_get_MSE(x->ann);
-
- outlet_float(x->f_out, mse);
-}
-
-static void ann_mlp_manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- if (x->mode)
- ann_mlp_run_the_net(x, sl, argc, argv);
- else
- {
- ann_mlp_train_on_the_fly(x, sl, argc, argv);
- }
-}
-
-static void ann_mlp_set_filename(t_ann_mlp *x, t_symbol *s)
-{
- // make correct path
- char patcher_path[MAXPDSTRING];
- char filename[MAXPDSTRING];
-
- if(!s)
- return;
-
- // make correct path
- canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
- sys_bashfilename(patcher_path, filename);
- x->filename = gensym(filename);
-}
-
-static void ann_mlp_load_ann_from_file(t_ann_mlp *x, t_symbol *s)
-{
- ann_mlp_set_filename(x,s);
-
- if(!x->filename)
- {
- error("ann: no filename !!!");
- return;
- }
-
- // deallocate storage
- if(x->ann)
- ann_mlp_free(x);
-
- 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 storage
- ann_mlp_allocate_storage(x);
-}
-
-static void ann_mlp_save_ann_to_file(t_ann_mlp *x, t_symbol *s)
-{
- ann_mlp_set_filename(x,s);
-
- if(!x->filename)
- {
- error("ann: no filename !!!");
- return;
- }
-
- 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 ann_mlp_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 ann_mlp_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 ann_mlp_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 ann_mlp_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 ann_mlp_set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- t_symbol *parametro = 0;
- int funzione = 0;
-
- 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;
- if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
- funzione = FANN_GAUSSIAN;
- if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
- funzione = FANN_GAUSSIAN_STEPWISE;
- if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
- funzione = FANN_ELLIOT;
- if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
- funzione = FANN_ELLIOT_SYMMETRIC;
-
- 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 ann_mlp_set_activation_function_hidden(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;
- if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
- funzione = FANN_GAUSSIAN;
- if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
- funzione = FANN_GAUSSIAN_STEPWISE;
- if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
- funzione = FANN_ELLIOT;
- if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
- funzione = FANN_ELLIOT_SYMMETRIC;
-
- fann_set_activation_function_hidden(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 ann_mlp_randomize_weights(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
-{
- t_float min = -1;
- t_float max = 1;
-
- if(!x->ann)
- {
- post("ann_mlp: ann is not initialized");
- return;
- }
-
- if (argc>0)
- min = atom_getfloat(argv++);
-
- if (argc>1)
- max = atom_getfloat(argv++);
-
- fann_randomize_weights(x->ann, min, max);
-}
-
-static void ann_mlp_learnrate(t_ann_mlp *x, t_float f)
-{
- int learnrate = 0;
-
- if(!x->ann)
- {
- post("ann_mlp: ann is not initialized");
- return;
- }
-
- learnrate = (f<0) ? 0 : f;
- fann_set_learning_rate(x->ann, learnrate);
-}
-
-static void ann_mlp_set_activation_steepness_hidden(t_ann_mlp *x, t_float f)
-{
- if(!x->ann)
- {
- post("ann_mlp: ann is not initialized");
- return;
- }
-
- fann_set_activation_steepness_hidden(x->ann, f);
-}
-
-static void ann_mlp_set_activation_steepness_output(t_ann_mlp *x, t_float f)
-{
- if(!x->ann)
- {
- post("ann_mlp: ann is not initialized");
- return;
- }
-
- fann_set_activation_steepness_output(x->ann, f);
-}
-
-void fann_set_activation_steepness_hidden(struct fann * ann, fann_type steepness);
-
-static void ann_mlp_print_ann_details(t_ann_mlp *x)
-{
- if (x->ann == 0)
- {
- post("ann_mlp: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("ann_mlp:filename not set");
- } else
- {
- post("filename=%s", x->filename->s_name);
- }
- }
-}
-
-static void ann_mlp_print_ann_print(t_ann_mlp *x)
-{
- if(!x->ann)
- {
- post("ann_mlp: ann is not initialized");
- return;
- }
-
- fann_print_connections(x->ann);
- fann_print_parameters(x->ann);
-}
-
-static void *ann_mlp_new(t_symbol *s, int argc, t_atom *argv)
-{
- t_ann_mlp *x = (t_ann_mlp *)pd_new(ann_mlp_class);
- x->l_out = outlet_new(&x->x_obj, &s_list);
- 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->x_canvas = canvas_getcurrent();
- x->filename = NULL;
- x->filenametrain = NULL;
- x->ann = NULL;
- x->input = NULL;
- x->output = NULL;
- x->out_float = NULL;
-
- if (argc>0) {
- x->filename = atom_gensym(argv);
- ann_mlp_load_ann_from_file(x, NULL);
- }
-
- return (void *)x;
-}
-
-void ann_mlp_setup(void) {
- post("");
- post("ann_mlp: multilayer perceptron 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)ann_mlp_new,
- (t_method)ann_mlp_free, sizeof(t_ann_mlp),
- CLASS_DEFAULT, A_GIMME, 0);
-
- // general..
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_help, gensym("help"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_createFann, gensym("create"), A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_train, gensym("train"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_run, gensym("run"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_mode, gensym("setmode"), A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_train_on_file, gensym("train-on-file"), A_DEFSYMBOL, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_manage_list, gensym("data"), A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_filename, gensym("filename"), A_DEFSYMBOL, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_load_ann_from_file, gensym("load"),A_DEFSYMBOL, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_save_ann_to_file, gensym("save"),A_DEFSYMBOL, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_details, gensym("details"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_print, gensym("print"), 0);
-
- // change training parameters
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_desired_error, gensym("desired_error"),A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_learnrate, gensym("learnrate"), A_FLOAT, 0);
-
- // change training and activation algorithms
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_hidden, gensym("set_activation_function_hidden"),A_GIMME, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_hidden, gensym("set_activation_steepness_hidden"), A_FLOAT, 0);
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_output, gensym("set_activation_steepness_output"), A_FLOAT, 0);
-
- // initialization:
- class_addmethod(ann_mlp_class, (t_method)ann_mlp_randomize_weights, gensym("randomize_weights"),A_GIMME, 0);
-
- // the most important one: running the ann
- class_addlist(ann_mlp_class, (t_method)ann_mlp_manage_list);
-
-
-}
+/* ann_mlp : 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
+ USE 1.2 VERSION ONLY
+ this software is licensed under the GNU General Public License
+*/
+
+/*
+ hacked by Georg Holzmann for some additional methods, bug fixes, ...
+ 2005, grh@mur.at
+*/
+
+#include <stdio.h>
+#include <string.h>
+#include "m_pd.h"
+#include "fann.h"
+
+#ifndef VERSION
+#define VERSION "0.2"
+#endif
+
+#ifndef __DATE__
+#define __DATE__ ""
+#endif
+
+#define TRAIN 0
+#define RUN 1
+
+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;
+ fann_type *input; // grh: storage for input
+ t_atom *output; // grh: storage for output (t_atom)
+ fann_type *out_float; // grh: storage for output (fann_type)
+ t_canvas *x_canvas;
+ t_outlet *l_out, *f_out;
+} t_ann_mlp;
+
+// allocation
+static void ann_mlp_allocate_storage(t_ann_mlp *x)
+{
+ unsigned int i;
+
+ if(!x->ann)
+ return;
+
+ x->input = (fann_type *)getbytes(x->ann->num_input*sizeof(fann_type));
+ x->output = (t_atom *)getbytes(x->ann->num_output*sizeof(t_atom));
+ x->out_float = (fann_type *)getbytes(x->ann->num_output*sizeof(fann_type));
+
+ // init storage with zeros
+ for (i=0; i<x->ann->num_input; i++)
+ x->input[i]=0;
+ for (i=0; i<x->ann->num_output; i++)
+ {
+ SETFLOAT(x->output+i, 0);
+ x->out_float[i]=0;
+ }
+}
+
+// deallocation
+static void ann_mlp_free(t_ann_mlp *x)
+{
+ if(!x->ann)
+ return;
+
+ freebytes(x->input, x->ann->num_input * sizeof(fann_type));
+ freebytes(x->output, x->ann->num_output * sizeof(t_atom));
+ freebytes(x->out_float, x->ann->num_output * sizeof(fann_type));
+ fann_destroy(x->ann);
+}
+
+static void ann_mlp_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 ann_mlp_createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ unsigned int num_input = 2;
+ unsigned int num_output = 1;
+ unsigned int num_layers = 3;
+ unsigned int *neurons_per_layer = NULL;
+ int activated=0;
+ int i, count_args = 0;
+ float connection_rate = 1;
+ float learning_rate = (float)0.7;
+
+
+ // okay, start parsing init args ...
+
+ if (argc > count_args++)
+ num_input = atom_getint(argv++);
+
+ if (argc > count_args++)
+ num_output = atom_getint(argv++);
+
+ if (argc > count_args++)
+ {
+ int hidden=0;
+
+ num_layers = atom_getint(argv++);
+ hidden = num_layers-2;
+
+ neurons_per_layer = (unsigned int *)getbytes(num_layers*sizeof(unsigned int));
+
+ neurons_per_layer[0] = num_input;
+
+ // make standard initialization (if there are too few init args)
+ for (i=1; i<hidden+1; i++)
+ neurons_per_layer[i] = 3;
+
+ // now check init args
+ for (i=1; i<hidden+1; i++)
+ {
+ if (argc > count_args++)
+ neurons_per_layer[i] = atom_getint(argv++);
+ }
+
+ neurons_per_layer[num_layers-1] = num_output;
+
+ activated=1;
+ }
+
+ if (argc > count_args++)
+ connection_rate = atom_getfloat(argv++);
+
+ if (argc > count_args++)
+ learning_rate = atom_getfloat(argv++);
+
+ // make one hidden layer as standard, if there were too few init args
+ if(!activated)
+ {
+ neurons_per_layer = (unsigned int *)getbytes(3*sizeof(unsigned int));
+ neurons_per_layer[0] = num_input;
+ neurons_per_layer[1] = 3;
+ neurons_per_layer[2] = num_output;
+ }
+
+ // ... end of parsing init args
+
+
+ if(x->ann)
+ ann_mlp_free(x);
+
+ x->ann = fann_create_array(connection_rate, learning_rate, num_layers, neurons_per_layer);
+
+ // deallocate helper array
+ freebytes(neurons_per_layer, num_layers * sizeof(unsigned int));
+
+ if(!x->ann)
+ {
+ error("error creating the ann");
+ return;
+ }
+
+ ann_mlp_allocate_storage(x);
+ fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);
+ fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);
+
+ // set error log to stdout, so that you see it in the pd console
+ //fann_set_error_log((struct fann_error*)x->ann, stdout);
+ // unfortunately this doesn't work ... but it should do in a similar way !!
+
+ post("created ann with:");
+ post("num_input = %i", num_input);
+ post("num_output = %i", num_output);
+ post("num_layers = %i", num_layers);
+ post("connection_rate = %f", connection_rate);
+ post("learning_rate = %f", learning_rate);
+}
+
+static void ann_mlp_print_status(t_ann_mlp *x)
+{
+ if (x->mode == TRAIN)
+ post("nn:training");
+ else
+ post("nn:running");
+}
+
+static void ann_mlp_train(t_ann_mlp *x)
+{
+ x->mode=TRAIN;
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+ fann_reset_MSE(x->ann);
+ ann_mlp_print_status(x);
+}
+
+static void ann_mlp_run(t_ann_mlp *x)
+{
+ x->mode=RUN;
+ ann_mlp_print_status(x);
+}
+
+static void ann_mlp_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++);
+ ann_mlp_print_status(x);
+ }
+}
+
+static void ann_mlp_train_on_file(t_ann_mlp *x, t_symbol *s)
+{
+ // make correct path
+ char patcher_path[MAXPDSTRING];
+ char filename[MAXPDSTRING];
+
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+
+ // make correct path
+ canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
+ sys_bashfilename(patcher_path, filename);
+ x->filenametrain = gensym(filename);
+
+ if(!x->filenametrain)
+ return;
+
+ post("nn: starting training on file %s, please be patient and wait ... (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_mlp: finished training on file %s", x->filenametrain->s_name);
+}
+
+static void ann_mlp_set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ float desired_error = (float)0.001;
+ if (0<argc)
+ {
+ desired_error = atom_getfloat(argv);
+ x->desired_error = desired_error;
+ post("nn:desired_error set to %f", x->desired_error);
+ } else
+ {
+ error("you must pass me a float");
+ }
+}
+
+static void ann_mlp_set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ unsigned int max_iterations = 500000;
+ if (argc>0)
+ {
+ 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 ann_mlp_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 ann_mlp_run_the_net(t_ann_mlp *x, t_symbol *sl, unsigned int argc, t_atom *argv)
+{
+ unsigned int i=0;
+ fann_type *calc_out;
+
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+
+ if(argc < x->ann->num_input)
+ {
+ error("ann_mlp: too few input values!!");
+ return;
+ }
+
+ // fill input array with actual data sent to inlet
+ for (i=0;i<x->ann->num_input;i++)
+ {
+ x->input[i] = atom_getfloat(argv++);
+ }
+
+ // run the ann
+ calc_out = fann_run(x->ann, x->input);
+
+ // fill the output array with result from ann
+ for (i=0;i<x->ann->num_output;i++)
+ SETFLOAT(x->output+i, calc_out[i]);
+
+ // send output array to outlet
+ outlet_anything(x->l_out, gensym("list"),
+ x->ann->num_output, x->output);
+}
+
+static void ann_mlp_train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ int i=0;
+ int quantiINs, 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 actual data sent to inlet
+ for (i=0;i<quantiINs;i++)
+ x->input[i] = atom_getfloat(argv++);
+
+ for (i=0;i<quantiOUTs;i++)
+ x->out_float[i] = atom_getfloat(argv++);
+
+ //fann_reset_MSE(x->ann);
+
+ fann_train(x->ann, x->input, x->out_float);
+
+ mse = fann_get_MSE(x->ann);
+
+ outlet_float(x->f_out, mse);
+}
+
+static void ann_mlp_manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (x->mode)
+ ann_mlp_run_the_net(x, sl, argc, argv);
+ else
+ {
+ ann_mlp_train_on_the_fly(x, sl, argc, argv);
+ }
+}
+
+static void ann_mlp_set_filename(t_ann_mlp *x, t_symbol *s)
+{
+ // make correct path
+ char patcher_path[MAXPDSTRING];
+ char filename[MAXPDSTRING];
+
+ if(!s)
+ return;
+
+ // make correct path
+ canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
+ sys_bashfilename(patcher_path, filename);
+ x->filename = gensym(filename);
+}
+
+static void ann_mlp_load_ann_from_file(t_ann_mlp *x, t_symbol *s)
+{
+ ann_mlp_set_filename(x,s);
+
+ if(!x->filename)
+ {
+ error("ann: no filename !!!");
+ return;
+ }
+
+ // deallocate storage
+ if(x->ann)
+ ann_mlp_free(x);
+
+ 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 storage
+ ann_mlp_allocate_storage(x);
+}
+
+static void ann_mlp_save_ann_to_file(t_ann_mlp *x, t_symbol *s)
+{
+ ann_mlp_set_filename(x,s);
+
+ if(!x->filename)
+ {
+ error("ann: no filename !!!");
+ return;
+ }
+
+ 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 ann_mlp_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 ann_mlp_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 ann_mlp_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 ann_mlp_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 ann_mlp_set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ t_symbol *parametro = 0;
+ int funzione = 0;
+
+ 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;
+ if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
+ funzione = FANN_GAUSSIAN;
+ if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
+ funzione = FANN_GAUSSIAN_STEPWISE;
+ if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
+ funzione = FANN_ELLIOT;
+ if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
+ funzione = FANN_ELLIOT_SYMMETRIC;
+
+ 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 ann_mlp_set_activation_function_hidden(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;
+ if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
+ funzione = FANN_GAUSSIAN;
+ if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
+ funzione = FANN_GAUSSIAN_STEPWISE;
+ if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
+ funzione = FANN_ELLIOT;
+ if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
+ funzione = FANN_ELLIOT_SYMMETRIC;
+
+ fann_set_activation_function_hidden(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 ann_mlp_randomize_weights(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ t_float min = -1;
+ t_float max = 1;
+
+ if(!x->ann)
+ {
+ post("ann_mlp: ann is not initialized");
+ return;
+ }
+
+ if (argc>0)
+ min = atom_getfloat(argv++);
+
+ if (argc>1)
+ max = atom_getfloat(argv++);
+
+ fann_randomize_weights(x->ann, min, max);
+}
+
+static void ann_mlp_learnrate(t_ann_mlp *x, t_float f)
+{
+ int learnrate = 0;
+
+ if(!x->ann)
+ {
+ post("ann_mlp: ann is not initialized");
+ return;
+ }
+
+ learnrate = (f<0) ? 0 : f;
+ fann_set_learning_rate(x->ann, learnrate);
+}
+
+static void ann_mlp_set_activation_steepness_hidden(t_ann_mlp *x, t_float f)
+{
+ if(!x->ann)
+ {
+ post("ann_mlp: ann is not initialized");
+ return;
+ }
+
+ fann_set_activation_steepness_hidden(x->ann, f);
+}
+
+static void ann_mlp_set_activation_steepness_output(t_ann_mlp *x, t_float f)
+{
+ if(!x->ann)
+ {
+ post("ann_mlp: ann is not initialized");
+ return;
+ }
+
+ fann_set_activation_steepness_output(x->ann, f);
+}
+
+void fann_set_activation_steepness_hidden(struct fann * ann, fann_type steepness);
+
+static void ann_mlp_print_ann_details(t_ann_mlp *x)
+{
+ if (x->ann == 0)
+ {
+ post("ann_mlp: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("ann_mlp:filename not set");
+ } else
+ {
+ post("filename=%s", x->filename->s_name);
+ }
+ }
+}
+
+static void ann_mlp_print_ann_print(t_ann_mlp *x)
+{
+ if(!x->ann)
+ {
+ post("ann_mlp: ann is not initialized");
+ return;
+ }
+
+ fann_print_connections(x->ann);
+ fann_print_parameters(x->ann);
+}
+
+static void *ann_mlp_new(t_symbol *s, int argc, t_atom *argv)
+{
+ t_ann_mlp *x = (t_ann_mlp *)pd_new(ann_mlp_class);
+ x->l_out = outlet_new(&x->x_obj, &s_list);
+ 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->x_canvas = canvas_getcurrent();
+ x->filename = NULL;
+ x->filenametrain = NULL;
+ x->ann = NULL;
+ x->input = NULL;
+ x->output = NULL;
+ x->out_float = NULL;
+
+ if (argc>0) {
+ x->filename = atom_gensym(argv);
+ ann_mlp_load_ann_from_file(x, NULL);
+ }
+
+ return (void *)x;
+}
+
+void ann_mlp_setup(void) {
+ post("");
+ post("ann_mlp: multilayer perceptron 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)ann_mlp_new,
+ (t_method)ann_mlp_free, sizeof(t_ann_mlp),
+ CLASS_DEFAULT, A_GIMME, 0);
+
+ // general..
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_help, gensym("help"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_createFann, gensym("create"), A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_train, gensym("train"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_run, gensym("run"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_mode, gensym("setmode"), A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_train_on_file, gensym("train-on-file"), A_DEFSYMBOL, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_manage_list, gensym("data"), A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_filename, gensym("filename"), A_DEFSYMBOL, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_load_ann_from_file, gensym("load"),A_DEFSYMBOL, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_save_ann_to_file, gensym("save"),A_DEFSYMBOL, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_details, gensym("details"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_print, gensym("print"), 0);
+
+ // change training parameters
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_desired_error, gensym("desired_error"),A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_learnrate, gensym("learnrate"), A_FLOAT, 0);
+
+ // change training and activation algorithms
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_hidden, gensym("set_activation_function_hidden"),A_GIMME, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_hidden, gensym("set_activation_steepness_hidden"), A_FLOAT, 0);
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_output, gensym("set_activation_steepness_output"), A_FLOAT, 0);
+
+ // initialization:
+ class_addmethod(ann_mlp_class, (t_method)ann_mlp_randomize_weights, gensym("randomize_weights"),A_GIMME, 0);
+
+ // the most important one: running the ann
+ class_addlist(ann_mlp_class, (t_method)ann_mlp_manage_list);
+
+
+}
diff --git a/src/ann_td.c b/src/ann_td.c
index 307ab8d..f135d09 100755
--- a/src/ann_td.c
+++ b/src/ann_td.c
@@ -1,662 +1,663 @@
-/* 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"
-
-#ifndef VERSION
-#define VERSION "0.2"
-#endif
-
-#ifndef __DATE__
-#define __DATE__ ""
-#endif
-
-#define TRAIN 0
-#define RUN 1
-
-#define MAXINPUT 1024
-#define MAXOUTPUT 256
-
-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 ann_td_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 ann_td_deallocate_inputs(t_ann_td *x)
-{
- if (x->inputs != 0)
- {
- freebytes(x->inputs, sizeof(x->inputs));
- x->inputs = 0;
- }
-}
-
-static void ann_td_allocate_inputs(t_ann_td *x)
-{
- unsigned int i;
- ann_td_deallocate_inputs(x);
- // allocate space for inputs array
- x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float));
- for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f;
-}
-
-static void ann_td_createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- unsigned int num_input = 2;
- unsigned int num_output = 1;
- 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);
-
- ann_td_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 ann_td_print_status(t_ann_td *x)
-{
- if (x->mode == TRAIN)
- post("ann_td:training");
- else
- post("ann_td:running");
-}
-
-static void ann_td_train(t_ann_td *x)
-{
- x->mode=TRAIN;
- if (x->ann == 0)
- {
- error("ann not initialized");
- return;
- }
- fann_reset_MSE(x->ann);
- ann_td_print_status(x);
-}
-
-static void ann_td_run(t_ann_td *x)
-{
- x->mode=RUN;
- ann_td_print_status(x);
-}
-
-static void ann_td_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++);
- ann_td_print_status(x);
- }
-}
-
-
-
-static void ann_td_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 ann_td_set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- float desired_error = (float)0.001;
- if (0<argc)
- {
- 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");
- }
-}
-
-static void ann_td_set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- unsigned int max_iterations = 500000;
- if (argc>0)
- {
- 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 ann_td_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 ann_td_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 ann_td_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;
-
- ann_td_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);
-
-}
-
-static void ann_td_train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- int i=0;
- unsigned int j=0;
- 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;
- }
-
- ann_td_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);
-
-
-}
-
-static void ann_td_manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- if (x->mode)
- ann_td_run_the_net(x, sl, argc, argv);
- else
- {
- ann_td_train_on_the_fly(x, sl, argc, argv);
- }
-}
-
-static void ann_td_set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- if (argc>0) {
- x->filename = atom_gensym(argv);
- } else
- {
- error("you must specify the filename");
- }
- post("nn:filename set to %s", x->filename->s_name);
-}
-
-static void ann_td_load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- if (x->ins_frames_set==0)
- {
- 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);
-
- ann_td_allocate_inputs(x);
-}
-
-static void ann_td_save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- if (argc>0) {
- x->filename = atom_gensym(argv);
- }
- 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 ann_td_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 ann_td_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 ann_td_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 ann_td_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 ann_td_set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
-{
- t_symbol *parametro = 0;
- int funzione = 0;
-
- 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 ann_td_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 ann_td_set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames)
-{
- x->num_input = ins;
- x->frames = frames;
- x->ins_frames_set=1;
-}
-
-static void *ann_td_new(t_symbol *s, int argc, t_atom *argv)
-{
- t_ann_td *x = (t_ann_td *)pd_new(ann_td_class);
- x->l_out = outlet_new(&x->x_obj, &s_list);
- 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;
- ann_td_allocate_inputs(x);
- }
-
- if (argc>2) {
- x->filename = atom_gensym(argv);
- ann_td_load_ann_from_file(x, NULL , 0, NULL);
- }
-
- return (void *)x;
-}
-
-// free resources
-static void ann_td_free(t_ann_td *x)
-{
- struct fann *ann = x->ann;
- fann_destroy(ann);
- ann_td_deallocate_inputs(x);
- // TODO: free other resources!
-}
-
-void ann_td_setup(void) {
- post("");
- post("ann_td: time delay neural nets for PD");
- post("version: "VERSION"");
- post("compiled: "__DATE__);
- post("author: Davide Morelli");
- post("contact: info@davidemorelli.it www.davidemorelli.it");
-
- ann_td_class = class_new(gensym("ann_td"),
- (t_newmethod)ann_td_new,
- (t_method)ann_td_free, sizeof(t_ann_td),
- CLASS_DEFAULT, A_GIMME, 0);
-
- // general..
- class_addmethod(ann_td_class, (t_method)ann_td_help, gensym("help"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_createFann, gensym("create"), A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_train, gensym("train"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_run, gensym("run"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_mode, gensym("setmode"), A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_train_on_file, gensym("train-on-file"), A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_manage_list, gensym("data"), A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_filename, gensym("filename"), A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_load_ann_from_file, gensym("load"),A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_save_ann_to_file, gensym("save"),A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_print_ann_details, gensym("details"), 0);
-
- // change training parameters
- class_addmethod(ann_td_class, (t_method)ann_td_set_desired_error, gensym("desired_error"),A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
-
- // change training and activation algorithms
- class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
- class_addmethod(ann_td_class, (t_method)ann_td_set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
-
- class_addmethod(ann_td_class, (t_method)ann_td_set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0);
-
- // the most important one: running the ann
- class_addlist(ann_td_class, (t_method)ann_td_manage_list);
-
-
-}
+/* 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
+ use 1.2 version only
+ this software is licensed under the GNU General Public License
+*/
+#include <stdio.h>
+#include <string.h>
+#include "m_pd.h"
+#include "fann.h"
+
+#ifndef VERSION
+#define VERSION "0.2"
+#endif
+
+#ifndef __DATE__
+#define __DATE__ ""
+#endif
+
+#define TRAIN 0
+#define RUN 1
+
+#define MAXINPUT 1024
+#define MAXOUTPUT 256
+
+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 ann_td_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 ann_td_deallocate_inputs(t_ann_td *x)
+{
+ if (x->inputs != 0)
+ {
+ freebytes(x->inputs, sizeof(x->inputs));
+ x->inputs = 0;
+ }
+}
+
+static void ann_td_allocate_inputs(t_ann_td *x)
+{
+ unsigned int i;
+ ann_td_deallocate_inputs(x);
+ // allocate space for inputs array
+ x->inputs = (t_float *)getbytes((x->frames) * (x->num_input) * sizeof(t_float));
+ for (i=0; i<(x->frames * x->num_input); i++) x->inputs[i]=0.f;
+}
+
+static void ann_td_createFann(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ unsigned int num_input = 2;
+ unsigned int num_output = 1;
+ 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);
+
+ ann_td_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 ann_td_print_status(t_ann_td *x)
+{
+ if (x->mode == TRAIN)
+ post("ann_td:training");
+ else
+ post("ann_td:running");
+}
+
+static void ann_td_train(t_ann_td *x)
+{
+ x->mode=TRAIN;
+ if (x->ann == 0)
+ {
+ error("ann not initialized");
+ return;
+ }
+ fann_reset_MSE(x->ann);
+ ann_td_print_status(x);
+}
+
+static void ann_td_run(t_ann_td *x)
+{
+ x->mode=RUN;
+ ann_td_print_status(x);
+}
+
+static void ann_td_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++);
+ ann_td_print_status(x);
+ }
+}
+
+
+
+static void ann_td_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 ann_td_set_desired_error(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ float desired_error = (float)0.001;
+ if (0<argc)
+ {
+ 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");
+ }
+}
+
+static void ann_td_set_max_iterations(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ unsigned int max_iterations = 500000;
+ if (argc>0)
+ {
+ 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 ann_td_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 ann_td_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 ann_td_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;
+
+ ann_td_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);
+
+}
+
+static void ann_td_train_on_the_fly(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ int i=0;
+ unsigned int j=0;
+ 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;
+ }
+
+ ann_td_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);
+
+
+}
+
+static void ann_td_manage_list(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (x->mode)
+ ann_td_run_the_net(x, sl, argc, argv);
+ else
+ {
+ ann_td_train_on_the_fly(x, sl, argc, argv);
+ }
+}
+
+static void ann_td_set_filename(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (argc>0) {
+ x->filename = atom_gensym(argv);
+ } else
+ {
+ error("you must specify the filename");
+ }
+ post("nn:filename set to %s", x->filename->s_name);
+}
+
+static void ann_td_load_ann_from_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (x->ins_frames_set==0)
+ {
+ 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);
+
+ ann_td_allocate_inputs(x);
+}
+
+static void ann_td_save_ann_to_file(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ if (argc>0) {
+ x->filename = atom_gensym(argv);
+ }
+ 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 ann_td_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 ann_td_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 ann_td_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 ann_td_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 ann_td_set_activation_function_output(t_ann_td *x, t_symbol *sl, int argc, t_atom *argv)
+{
+ t_symbol *parametro = 0;
+ int funzione = 0;
+
+ 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 ann_td_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 ann_td_set_num_input_frames(t_ann_td *x, t_floatarg ins, t_floatarg frames)
+{
+ x->num_input = ins;
+ x->frames = frames;
+ x->ins_frames_set=1;
+}
+
+static void *ann_td_new(t_symbol *s, int argc, t_atom *argv)
+{
+ t_ann_td *x = (t_ann_td *)pd_new(ann_td_class);
+ x->l_out = outlet_new(&x->x_obj, &s_list);
+ 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;
+ ann_td_allocate_inputs(x);
+ }
+
+ if (argc>2) {
+ x->filename = atom_gensym(argv);
+ ann_td_load_ann_from_file(x, NULL , 0, NULL);
+ }
+
+ return (void *)x;
+}
+
+// free resources
+static void ann_td_free(t_ann_td *x)
+{
+ struct fann *ann = x->ann;
+ fann_destroy(ann);
+ ann_td_deallocate_inputs(x);
+ // TODO: free other resources!
+}
+
+void ann_td_setup(void) {
+ post("");
+ post("ann_td: time delay neural nets for PD");
+ post("version: "VERSION"");
+ post("compiled: "__DATE__);
+ post("author: Davide Morelli");
+ post("contact: info@davidemorelli.it www.davidemorelli.it");
+
+ ann_td_class = class_new(gensym("ann_td"),
+ (t_newmethod)ann_td_new,
+ (t_method)ann_td_free, sizeof(t_ann_td),
+ CLASS_DEFAULT, A_GIMME, 0);
+
+ // general..
+ class_addmethod(ann_td_class, (t_method)ann_td_help, gensym("help"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_createFann, gensym("create"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_train, gensym("train"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_run, gensym("run"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_mode, gensym("setmode"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_train_on_file, gensym("train-on-file"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_manage_list, gensym("data"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_filename, gensym("filename"), A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_load_ann_from_file, gensym("load"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_save_ann_to_file, gensym("save"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_print_ann_details, gensym("details"), 0);
+
+ // change training parameters
+ class_addmethod(ann_td_class, (t_method)ann_td_set_desired_error, gensym("desired_error"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
+
+ // change training and activation algorithms
+ class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
+ class_addmethod(ann_td_class, (t_method)ann_td_set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
+
+ class_addmethod(ann_td_class, (t_method)ann_td_set_num_input_frames, gensym("inputs_frames"),A_DEFFLOAT, A_DEFFLOAT, 0);
+
+ // the most important one: running the ann
+ class_addlist(ann_td_class, (t_method)ann_td_manage_list);
+
+
+}
generated by cgit v1.2.1 (git 2.18.0) at 2024-12-22 10:30:42 +0000