This commit was generated by cvs2svn to compensate for changes in r172,svn2git-root

which included commits to RCS files with non-trunk default branches. svn path=/trunk/externals/ann/; revision=173
author: IOhannes m zmölnig <zmoelnig@users.sourceforge.net> 2002-10-14 17:16:51 +0000
committer: IOhannes m zmölnig <zmoelnig@users.sourceforge.net> 2002-10-14 17:16:51 +0000
commit: 3505bf1cfec70eebcf508b67f3fd8ba55eee4421 (patch)
tree: 76b517de2fcc6e6b0333d076ce23674f42324935
8 files changed, 1506 insertions, 0 deletions
diff --git a/GnuGPL.txt b/GnuGPL.txt
new file mode 100644
index 0000000..fa0bef4
--- /dev/null
+++ b/GnuGPL.txt
@@ -0,0 +1,290 @@
+GNU GENERAL PUBLIC LICENSE
+
+Version 2, June 1991 
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+Copyright (C) 1989, 1991 Free Software Foundation, Inc.  
+59 Temple Place - Suite 330, Boston, MA  02111-1307, USA
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+The licenses for most software are designed to take away your freedom
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diff --git a/examples/ann_som.pd b/examples/ann_som.pd
new file mode 100644
index 0000000..9f03979
--- /dev/null
+++ b/examples/ann_som.pd
@@ -0,0 +1,114 @@
+#N canvas 50 -127 640 687 10;
+#X msg 131 495 print;
+#X msg 132 528 new 5 8 8;
+#X msg 127 99 init;
+#X msg 128 274 train;
+#X msg 129 296 test;
+#X msg 128 387 write;
+#X obj 70 559 ann_som 4 9 10;
+#X msg 70 49 1 0 0 1;
+#X msg 70 68 0 1 0 1;
+#X msg 70 87 2 1 0 0;
+#X msg 128 118 init 0.5;
+#X msg 128 138 init 1 0.5 0 0.5;
+#X text 234 101 init all weights with "0";
+#X text 235 120 init all weights with "0.5";
+#X text 235 137 init weights for each sensor;
+#X msg 128 163 learn 1;
+#X msg 128 197 learn 1 0.9 0.1;
+#X text 226 163 set learning rate to 1;
+#X msg 128 180 learn 0.5 0.999;
+#X text 227 179 set learning rate to 0.5 and factor to 0.999;
+#X text 227 197 set learning rate to 1 \, factor to 0.9 and offset to 0.1;
+#X msg 128 214 neighbour 1;
+#X msg 128 231 neighbour 0.5 0.999;
+#X msg 128 248 neighbour 1 0.9 0.1;
+#X text 248 215 set neighbourhood to 1;
+#X text 249 231 set neighbourhoodto 0.5 and factor to 0.999;
+#X text 249 249 set neighbourhood to 1 \, factor to 0.9 and offset to 0.1;
+#X text 180 269 set som to "train" mode (learn from sensor-input and output winning neuron);
+#X text 179 291 set som to "test" mode (output winning neuron for sensor-input \, but do not learn !);
+#X msg 129 328 rule INSTAR;
+#X msg 129 345 rule OUTSTAR;
+#X msg 129 362 rule KOHONEN;
+#X text 218 327 learn with IN-STAR rule;
+#X text 219 345 learn with OUT-STAR rule;
+#X text 219 362 learn with KOHONENrule;
+#X msg 128 405 write mysom.som;
+#X msg 129 429 read;
+#X msg 129 447 read mysom.som;
+#X text 156 68 present various data to the SOM;
+#X text 203 495 for debugging;
+#X text 207 530 create a new SOM with 8x8 neurons \, each having 5 sensors;
+#X text 204 561 create a new SOM with 9x10 neurons \, each having 4 sensors;
+#X floatatom 70 614 4 0 0;
+#X text 113 618 winning neuron;
+#N canvas 13 0 889 630 SOMs 0;
+#X text 76 27 SOM :: Self-Organized Maps;
+#X text 55 53 SOMs are "Artificial Neural Networks" \, that are trying to learn something about the data presented to them without a supervisor/teacher.;
+#X text 59 118 in short:;
+#X text 120 119 the neuron \, whose weight-configuration matches the presented data best is the winner (its number (counting from the lower-left corner) is sent to the output);
+#X text 121 163 to match the data better the next time it is presented \, the weights of the winning neuron are adjusted.;
+#X text 121 188 the weights of the neurons neighbouring the winner are adjusted to match the data too \, but not so strong as the winner's.;
+#X text 121 276 lr(n+1)=lr(n)*factor;
+#X text 275 277 learning_rate=lr+offset;
+#X text 121 289 nb(n+1)=nb(n)*factor;
+#X text 275 290 neighbourhood=nb+offset;
+#X text 121 230 both neighbourhood and learning-rate (==amount of how much the weights of the winner (and \, proportional \, the weights of the neighbours) are adjusted) are decreasing recursively with time.;
+#X text 119 319 thus you will sooner or (most of the time) later get a "brain map" \, where similar inputs will activate neurons in specifique regions (like there are regions for seeing and regions for hearing in our brains);
+#X text 97 381 there are various rules \, how to re-adjust the weights of the neurons : in-star \, out-star and kohonen (maybe there are others \, but these i found in literature);
+#X obj 607 220 +;
+#X text 640 182 ...;
+#X obj 579 185 * \$1;
+#X obj 607 185 * \$2;
+#X obj 670 185 * \$0;
+#X obj 579 128 unpack 0 0 0 0 0;
+#X text 602 111 n sensors;
+#X text 705 186 weights 1 to n;
+#X obj 579 90 inlet;
+#X obj 607 288 outlet;
+#X text 594 62 a neuron;
+#X text 566 307 the neuron with the highest weighted sum;
+#X text 567 318 matches best and is therefore the winner;
+#X text 53 452 notes:;
+#X text 101 453 each neuron of the SOM has n sensors. you have to present a list of n floats to the SOM to make it work;
+#X text 102 482 you should init the weights for each sensor with the expected mean of the sensor values before you start training to get best and fastest results;
+#X text 55 87 they were first proposed by the Finnish scientist T.Kohonen in the 80ies (i think).;
+#X text 98 543 if you have no clue \, what's this all about \, maybe you do not need SOMs (which i doubt) or you should have a look at;
+#X text 118 577 http://www.eas.asu.edu/~eee511;
+#X text 118 591 http://www.cis.hut.fi/projects/ica;
+#X connect 13 0 22 0;
+#X connect 15 0 13 0;
+#X connect 16 0 13 0;
+#X connect 17 0 13 0;
+#X connect 18 0 15 0;
+#X connect 18 1 16 0;
+#X connect 18 4 17 0;
+#X connect 21 0 18 0;
+#X restore 535 44 pd SOMs;
+#X text 81 13 ann_som :: train and test Self-Organized Maps;
+#X obj 73 660 ann_som test.som;
+#X text 211 664 load a SOM-file;
+#X connect 0 0 6 0;
+#X connect 1 0 6 0;
+#X connect 2 0 6 0;
+#X connect 3 0 6 0;
+#X connect 4 0 6 0;
+#X connect 5 0 6 0;
+#X connect 6 0 42 0;
+#X connect 7 0 6 0;
+#X connect 8 0 6 0;
+#X connect 9 0 6 0;
+#X connect 11 0 6 0;
+#X connect 15 0 6 0;
+#X connect 16 0 6 0;
+#X connect 18 0 6 0;
+#X connect 21 0 6 0;
+#X connect 22 0 6 0;
+#X connect 23 0 6 0;
+#X connect 29 0 6 0;
+#X connect 30 0 6 0;
+#X connect 31 0 6 0;
+#X connect 35 0 6 0;
+#X connect 36 0 6 0;
+#X connect 37 0 6 0;
diff --git a/examples/test.som b/examples/test.som
new file mode 100644
index 0000000..f1074db
--- /dev/null
+++ b/examples/test.som
@@ -0,0 +1,206 @@
+SOM:
+10 10 10 INSTAR
+0.999999999999999 0.999999999000000 0.000000000000000
+0.999999500000777 0.999999999666666 1.000000000000000
+weights:
+ -3.399999539504940 -1.000001787943173 -1.600001854036666 -3.600000136648053 -1.599999240440176 -4.599999658971642 2.200000908266854 -2.600003123982305 -3.800002398132818 3.999999663428766
+ -3.664408811633732 -0.293151033136177 -0.987533543750226 -2.380008660046391 -1.570271300167646 -4.070760204272482 1.171876563866566 -2.077239071278385 -3.123878873088140 3.214371110932181
+ -3.850183412803100 0.180009854785169 -1.042011297665122 -1.010214762676719 -1.261593950708803 -3.602933720042998 0.223488363849274 -1.757965969969787 -2.823378035872298 2.474907850267799
+ -3.267525288182409 0.051764920257421 -0.861189147291411 0.346768964752324 -1.086401295987099 -3.010025855790309 0.216062192056090 -2.054030541621496 -1.665247654188838 1.772114086164674
+ -2.531908811257504 -0.228362572156031 0.283020028903598 0.691545049584991 -1.317946071617238 -2.780427630196057 0.669072905841086 -2.095166482257043 -0.357067595055914 1.194836568762597
+ -1.670410067094406 -0.850456374464438 1.177079303231737 0.975765076637809 -1.598632087371286 -2.628074788624443 1.200071995849616 -2.338536290646471 0.508359948325128 0.694655469059352
+ -0.972839748009842 -1.432819542418945 1.713902285702235 0.848126099774984 -2.005987681503301 -2.656928282231532 1.910400404106563 -2.720742652779477 0.819178516157051 0.589653431079632
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diff --git a/src/ann.c b/src/ann.c
new file mode 100644
index 0000000..8b0cb27
--- /dev/null
+++ b/src/ann.c
@@ -0,0 +1,64 @@
+/* ...this is an externals for comouting Aritficial Neural Networks...
+   thikn aboiut this
+	
+   0201:forum::f�r::uml�ute:2001
+*/
+
+#include "ann.h"
+
+typedef struct ann 
+{
+  t_object t_ob;
+} t_ann;
+
+t_class *ann_class;
+
+/* do a little help thing */
+
+static void ann_help(void)
+{
+  post("\n\n...this is the ann external "VERSION"..\n");
+  post("self-organized maps"
+       "\n\tann_som"
+       "");
+  post("\n(l) forum::f�r::uml�ute 2001\n"
+       "this software is under the GnuGPL that is provided with these files");
+}
+
+void *ann_new(void)
+{
+  t_ann *x = (t_ann *)pd_new(ann_class);
+  return (void *)x;
+}
+
+void ann_som_setup();
+
+/*
+  waiting to be released in near future:
+  ANN_SOM : self organized maps
+  ANN_PERCEPTRON : perceptrons
+  ANN_MLP : multilayer perceptrons
+  
+  waiting to be realeased sometimes
+  ANN_RBF : radial basis functions
+*/
+
+
+
+void ann_setup(void) 
+{
+  ann_som_setup();
+
+  
+  /* ************************************** */
+  
+  post("\n\t................................");
+  post("\t...artificial neural networks...");
+  post("\t..........version "VERSION"..........");
+  post("\t....forum::f�r::uml�ute 2001....");
+  post("\t....send me a 'help' message....");
+  post("\t................................\n");
+  
+  ann_class = class_new(gensym("ann"), ann_new, 0, sizeof(t_ann), 0, 0);
+  class_addmethod(ann_class, ann_help, gensym("help"), 0);
+}
diff --git a/src/ann.h b/src/ann.h
new file mode 100644
index 0000000..48452d8
--- /dev/null
+++ b/src/ann.h
@@ -0,0 +1,15 @@
+/* ********************************************** */
+/* the ANN external                              */
+/* ********************************************** */
+/*                            forum::f�r::uml�ute */
+/* ********************************************** */
+
+#ifndef INCLUDE_ANN_H__
+#define INCLUDE_ANN_H__
+
+#include "m_pd.h"
+
+/* to beautify the logo make sure to make the VERSION-info 4 chars long */
+#define VERSION "0.1."
+
+#endif
diff --git a/src/ann_som.c b/src/ann_som.c
new file mode 100644
index 0000000..3247e39
--- /dev/null
+++ b/src/ann_som.c
@@ -0,0 +1,721 @@
+/* ann_som :
+   part of the ARTIFICIAL NEURAL NETWORK external for PURE DATA
+   SELF-ORGANIZED MAP : instar learning-rule
+
+   (l) 0201:forum::f�r::uml�ute:2001
+   this software is licensed under the GNU General Public License
+*/
+
+#include "ann.h"
+#include <math.h>
+#ifdef NT
+#define sqrtf sqrt
+#endif
+
+#if 1
+#include <stdio.h>
+#include <fcntl.h>
+#include <string.h>
+#ifdef linux
+#include <unistd.h>
+#endif
+#ifdef NT
+#include <io.h>
+#endif
+#endif
+
+/* ****************************************************************************** */
+/* som : save and load messages... */
+
+#define INSTAR  1
+#define OUTSTAR 2
+#define KOHONEN 0
+
+/* learning-rule
+ INSTAR : instar learning-rule
+*/
+
+#define TRAIN 0
+#define TEST  1
+
+
+typedef struct _som {
+  t_object x_obj;
+  t_outlet *left, *right;
+
+  int rule;  /* INSTAR, OUTSTAR, KOHONEN */
+  int mode;  /* TRAIN, TEST */
+
+  t_symbol *filename;
+  int defaultfilename; /* TRUE if filename is still "default.som" */
+
+  int num_neurX, num_neurY; /* for 2dim-fields */
+  int num_neurons; /* num_neurX * num_neurY */
+  int num_sensors;
+
+  t_float **weights; /* the neural network (pointer to neuron (neuron is a pointer to an array of weights)) */
+  t_float **dist; /* squaredistances between neurons (for neighbourhood) (pointer to neuron (is a pointer to an array of distances))*/
+
+  t_float *workingspace; /* a for comparing data*/
+
+  double lr, lr_factor, lr_bias; /* learning rate: lr(n)=(lr(n-1)*lr_factor; LR=lr(n)+lr_bias */
+  double nb, nb_factor, nb_bias; /* neighbourhood */
+
+  /* something for reading/writing to files */
+  t_canvas *x_canvas;
+  t_symbol *x_dir;
+
+} t_som;
+
+static t_class *som_class;
+
+/* -----------------  private functions -------------------- */
+
+static void som_killsom(t_som *x)
+{
+  /* kill the weights-field */
+  int i=x->num_neurons;
+
+  while (i--) {
+    freebytes(x->weights[i], sizeof(x->weights[i]));
+    x->weights[i]=0;
+  }
+  freebytes(x->weights, sizeof(x->weights));
+  x->weights = 0;
+
+  /* kill the dist-field */
+  i=x->num_neurons;
+
+  while (i--) {
+    freebytes(x->dist[i], sizeof(x->dist[i]));
+    x->dist[i]=0;
+  }
+  freebytes(x->dist, sizeof(x->dist));
+  x->dist = 0;
+
+  /* kill the working-space */
+  freebytes(x->workingspace, sizeof(x->workingspace));
+  x->workingspace = 0;
+}
+
+static void som_makedist(t_som *x)
+{
+  int i, j;
+
+  x->dist = (t_float **)getbytes(x->num_neurons * sizeof(t_float *));
+
+  for (i=0; i<x->num_neurons; i++) {
+    int x1 = (i%x->num_neurX), y1 = (i/x->num_neurX);
+    x->dist[i]=(t_float *)getbytes(x->num_neurons * sizeof(t_float));
+
+    for (j=0; j<x->num_neurons; j++) {
+      int x2 = (j%x->num_neurX), y2 = (j/x->num_neurX);
+      x->dist[i][j] = sqrt((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2));
+    }
+  }
+}
+
+static int som_whosthewinner(t_som *x, t_float *senses)
+{
+  t_float min_dist = 0;
+  int min_n = x->num_neurons-1;
+  t_float *weight = x->weights[min_n];
+  int n = x->num_sensors;
+
+  while (n--) {
+    t_float f = senses[n] - weight[n];
+    min_dist += f*f;
+  }
+
+  n=x->num_neurons-1;
+  while (n--) {
+    int s = x->num_sensors;
+  
+    t_float dist = 0;
+    weight = x->weights[n];
+
+    while (s--) {
+      t_float f;
+      f = senses[s] - weight[s];
+      dist += f*f;
+    }
+    if (dist<min_dist) {
+      min_dist = dist;
+      min_n    = n;
+    }
+  }
+
+  return min_n;
+}
+
+static void som_createnewsom(t_som *x, int sens, int nx, int ny)
+{ /* create a new SOM */
+  int i, j;
+
+  /* clean up the old SOM */
+  som_killsom(x);
+
+
+  /* make new SOM */
+  x->num_neurons = nx * ny;
+  x->num_neurX = nx;
+  x->num_neurY = ny;
+  x->num_sensors = sens;
+
+  x->weights = (t_float **)getbytes(x->num_neurons * sizeof(t_float *));
+  for (i=0; i<x->num_neurons; i++) {
+    x->weights[i]=(t_float *)getbytes(x->num_sensors * sizeof(t_float));
+
+    for (j=0; j<x->num_sensors; j++) x->weights[i][j] = 0;
+  }
+
+  /* make new dist */
+  som_makedist(x);
+
+  /* make new workingspace */
+  x->workingspace = (t_float *)getbytes(x->num_sensors * sizeof(t_float));
+  for (i=0; i<x->num_sensors; i++) x->workingspace[i]=0.f;
+}
+
+/* ----------------- public functions ---------------------- */
+
+static void som_list(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* present the data */
+  int i = x->num_sensors;
+  //  t_float *data = (t_float *)getbytes(sizeof(t_float) * i);
+  t_float *data = x->workingspace;
+  t_float *dummy = data;
+  int winner;
+
+  t_float learningrate = x->lr+x->lr_bias, neighbourhood = x->nb+x->nb_bias;
+
+  /* first: extract the data */
+  /* check if there is enough input data; fill up with zeros if not; if there's plenty, maybe forget about the rest */
+  if ((i = x->num_sensors - argc) > 0) {
+    dummy = data + argc;
+    while (i--) *dummy++ = 0;
+    i = x->num_sensors;
+  } else i = x->num_sensors;
+  dummy = data;
+  /* really get the data */
+  while (i--) *dummy++ = atom_getfloat(argv++);
+
+  /* second: get the winning neuron */
+  winner = som_whosthewinner(x, data);
+
+  if (x->mode == TRAIN) {
+    /* third: learn something */
+    /* update all the neurons that are within the neighbourhood */
+    i=x->num_neurons;
+    switch (x->rule) {
+    case OUTSTAR:
+      while (i--) {
+	t_float dist = x->dist[winner][i];
+	if (neighbourhood > dist) {
+	  t_float factor = 1 - dist/neighbourhood;
+	  t_float *weight=x->weights[i];
+	  int s = x->num_sensors;
+
+	  while (s--) weight[s] += learningrate*data[s]*(factor-weight[s]);
+	}
+      }
+      break;
+    case INSTAR:
+      while (i--) {
+	t_float dist = x->dist[winner][i];
+	if (neighbourhood > dist) {
+	  t_float factor = learningrate * (1 - dist/neighbourhood);
+	  t_float *weight=x->weights[i];
+	  int s = x->num_sensors;
+
+	  while (s--) weight[s] += (data[s]-weight[s])*factor;
+	}
+      }
+      break;
+    default:
+      /* KOHONEN rule */
+      while (i--) {
+	t_float dist = x->dist[winner][i];
+	if (neighbourhood > dist) {
+	  t_float *weight=x->weights[i];
+	  int s = x->num_sensors;
+
+	  while (s--) weight[s] += (data[s]-weight[s])*learningrate;
+	}
+      }
+    }
+
+    /* update learning-rate and neighbourhood */
+    x->lr *= x->lr_factor;
+    x->nb *= x->nb_factor;
+  }
+
+  /* finally: do the output thing */
+  /* do the output thing */
+  outlet_float(x->x_obj.ob_outlet, winner);
+
+  //  freebytes(data, sizeof(t_float)*x->num_sensors);
+}
+
+static void som_bang(t_som *x)
+{ /* re-trigger the last output */
+  error("som_bang: nothing to do");
+}
+
+static void som_init(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* initialize the neuron-weights */
+  int i, j;
+  t_float f;
+
+  switch (argc) {
+  case 0:
+  case 1:
+    f = (argc)?atom_getfloat(argv):0;
+    for (i=0; i<x->num_neurons; i++)
+      for (j=0; j<x->num_sensors; j++)
+	x->weights[i][j]=f;
+    break;
+  default:
+    if (argc == x->num_sensors) {
+      for (i=0; i<x->num_neurons; i++)
+	for (j=0; j<x->num_sensors; j++)
+	  x->weights[i][j]=atom_getfloat(&argv[j]);
+    } else 
+      error("som_init: you should pass a list of expected mean-values for each sensor to the SOM");
+  }
+}
+
+
+static void som_makenewsom(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* create a new SOM */
+  int sens, nx, ny;
+
+
+  /* check whether there is sufficient data to create a new SOM */
+  if ((argc != 2) && (argc !=3)) {
+    error("som_new: wrong number of arguments (only 2 or 3 parameters are allowed)");
+    return;
+  }
+
+  /* 3 arguments : #sensors #neurX #neurY :: 2D-field of neurons with neurX * neurY  items
+     2 arguments : #sensors #neurXY       :: 2D-field of neurons with neurXY* neurXY items
+
+     to create more-dimensional fields, we now have to manually adjust the SOM-file (change the distances...)
+     LATER, we might do a function "ann_makedist"
+  */
+
+  sens = atom_getfloat(argv);
+  if (sens <= 0) {
+    error("some_new: number of sensors must be >= 1");
+    return;
+  }
+
+  if (argc==3) {
+    nx = atom_getint(argv+1);
+    ny = atom_getint(argv+2);
+    if ((nx<=0) || (ny<=0)) {
+      error("some_new: number of neurons must be >= 1");
+      return;
+    }
+  } else {
+    nx = atom_getint(argv+1);
+    if (nx<=0) {
+      error("some_new: number of neurons must be >= 1");
+      return;
+    }
+    ny = nx;
+  }
+
+  som_createnewsom(x, sens, nx, ny);
+}
+
+static void som_train(t_som *x)
+{ /* set the mode to TRAIN */
+  x->mode = TRAIN;
+}
+static void som_test(t_som *x)
+{ /* set the mode to TEST */
+  x->mode = TEST;
+}
+
+static void som_rule(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* set the learning rule */
+  int rule=-1;
+
+  if (argc>1) {
+    error("som_rule: only 1 argument may be specified");
+    return;
+  }
+  if (argc == 0) {
+    post("som_rule: you are currently training with the %s rule", (x->rule==INSTAR)?"INSTAR":(x->rule==OUTSTAR)?"OUTSTAR":"KOHONEN");
+    return;
+  }
+
+  if (argv->a_type==A_FLOAT) rule=atom_getint(argv);
+  else if (argv->a_type==A_SYMBOL) {
+    char *name=atom_getsymbol(argv)->s_name;
+    if      (!strcmp(name, "instar")  && !strcmp(name, "INSTAR"))  rule=INSTAR;
+    else if (!strcmp(name, "outstar") && !strcmp(name, "OUTSTAR")) rule=OUTSTAR;
+    else if (!strcmp(name, "kohonen") && !strcmp(name, "KOHONEN")) rule=KOHONEN;
+  }
+
+  switch (rule) {
+  case KOHONEN:
+  case INSTAR:
+  case OUTSTAR:
+    x->rule=rule;
+    break;
+  default:
+    error("som_rule: you specified an invalid rule !");
+  }
+}
+
+
+static void som_learn(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* set a new LEARNINGRATE */
+  switch (argc) {
+  case 3:
+    x->lr_bias = atom_getfloat(&argv[2]);
+  case 2:
+    x->lr_factor = atom_getfloat(&argv[1]);
+  case 1:
+    x->lr = atom_getfloat(&argv[0]);
+    break;
+  default:
+    error("som_learn: you should pass up to 4 learning-rate parameters");
+  }
+}
+static void som_neighbour(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* set a new NEIGHBOURHOOD */
+  switch (argc) {
+  case 3:
+    x->nb_bias = atom_getfloat(&argv[2]);
+  case 2:
+    x->nb_factor = atom_getfloat(&argv[1]);
+  case 1:
+    x->nb = atom_getfloat(&argv[0]);
+    break;
+  default:
+    error("som_neighbour: you should pass up to 4 neighbourhood parameters");
+  }
+} 
+
+static void som_read(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* read a som-file */
+
+  int fd;
+  char filnam[MAXPDSTRING];
+  char buf[MAXPDSTRING], *bufptr;
+
+  int neuronsX, neuronsY, sensors, rule=0;
+  double lr[3], nb[3];
+  t_float dummy;
+  char *text=0;
+  int i, j;
+  t_float *fp;
+
+  FILE *f=0;
+
+  text = (char *)getbytes(MAXPDSTRING*sizeof(char));
+
+  if (argc>0) {
+    x->filename = atom_gensym(argv);
+    x->defaultfilename = 0;
+  }
+  if (x->defaultfilename) error("som_read: reading from default file \"%s\"", x->filename->s_name);
+
+  if ((fd = open_via_path(canvas_getdir(x->x_canvas)->s_name,
+		  x->filename->s_name, "", buf, &bufptr, MAXPDSTRING, 0)) < 0) {
+    error("%s: can't open", x->filename->s_name);
+    return;
+  }
+  else
+    close (fd);
+
+ /* open */
+  sys_bashfilename(x->filename->s_name, filnam);
+  dummy = 0;
+
+  while (f == 0) {
+    if (!(f = fopen(filnam, "r"))) {
+      error("msgfile_read: unable to open %s", filnam);
+      return;
+    }
+
+    /* read */
+
+    /* read header */
+    if ( (dummy=fscanf(f,"SOM:\n%d",&sensors)) != 1) {
+      error("som_read: error reading file\n");
+      break;
+    }
+    if ( (dummy=fscanf(f,"%d",&neuronsX)) != 1) {
+      error("som_read: error reading file\n");
+      break;
+    }
+    if ( (dummy=fscanf(f,"%d",&neuronsY)) != 1) {
+     error("som_read: error reading file\n");
+      break;
+    }
+    fscanf(f,"%s",text);
+    if (!strcmp("INSTAR", text)) rule = INSTAR;
+    else if (!strcmp("OUTSTAR", text)) rule = OUTSTAR;
+    else if (!strcmp("KOHONEN", text)) rule = KOHONEN;
+
+    for (i=0; i<3; i++)
+      if ( (fscanf(f,"%lf",&lr[i])) != 1) {
+	error("som_read: error reading file\n");
+	break;
+      }
+    for (i=0; i<3; i++)
+      if ( (fscanf(f,"%lf",&nb[i])) != 1) {
+	error("som_read: error reading file\n");
+	break;
+      }
+
+    /* we now have a valid SOM-definition
+       let's create a dummy SOM */
+
+    som_createnewsom(x, sensors, neuronsX, neuronsY);
+ 
+    x->rule = rule;
+
+    x->lr=lr[0];
+    x->lr_factor=lr[1];
+    x->lr_bias=lr[2];
+
+    x->nb=nb[0];
+    x->nb_factor=nb[1];
+    x->nb_bias=nb[2];
+
+    /* read the weights */
+
+    if ((fscanf(f,"\nweights:\n %f",&dummy)) != 1) {
+      break;
+    }
+    
+    i=0;
+    while (i<x->num_neurons) {
+      j = x->num_sensors;
+      fp= x->weights[i];
+      while (j--) {
+	*fp++=dummy;
+	if ((fscanf(f,"%f",&dummy)) != 1) {
+	  break;
+	}
+      }
+      j = x->num_sensors;
+      i++;
+    }
+
+    /* finally read the distances */
+    if ((fscanf(f,"\ndists:\n %f",&dummy)) != 1) {
+      break;
+    }
+    
+    i=0;
+    while (i<x->num_neurons) {
+      j = x->num_neurons;
+      fp= x->dist[i];
+      while (j--) {
+	*fp++=dummy;
+	if ((fscanf(f,"%f",&dummy)) != 1) {
+	  break;
+	}
+      }
+      j = x->num_sensors;
+      i++;
+    }
+
+  }
+
+  /* close file */
+  if (f) fclose(f);
+
+
+}
+
+static void som_write(t_som *x, t_symbol *s, int argc, t_atom *argv)
+{ /* write a som-file */
+  char filnam[MAXPDSTRING];
+  char buf[MAXPDSTRING];
+  char *text=0;
+  int textlen;
+
+  FILE *f=0;
+
+  int i;
+
+  if (argc>0) {
+    x->filename = atom_gensym(argv);
+    x->defaultfilename = 0;
+  }
+  if (x->defaultfilename) error("som_write: writing to default file \"%s\"", x->filename->s_name);
+
+  canvas_makefilename(x->x_canvas, x->filename->s_name, buf, MAXPDSTRING);
+  sys_bashfilename(x->filename->s_name, filnam);
+
+  while (f==0) {
+    /* open file */
+    if (!(f = fopen(filnam, "w"))) {
+      error("msgfile : failed to open %s", filnam);
+    } else {
+
+      /* write header information */
+      text=(char *)getbytes(sizeof(char)*MAXPDSTRING);
+      sprintf(text, "SOM:\n%d %d %d %s\n%.15f %.15f %.15f\n%.15f %.15f %.15f\nweights:\n",
+	      x->num_sensors, x->num_neurX, x->num_neurY, (x->rule==INSTAR)?"INSTAR":(x->rule==OUTSTAR)?"OUTSTAR":"KOHONEN",
+	      x->lr, x->lr_factor, x->lr_bias,
+	      x->nb, x->nb_factor, x->nb_bias);
+      textlen = strlen(text);
+
+      if (fwrite(text, textlen*sizeof(char), 1, f) < 1) {
+	error("msgfile : failed to write %s", filnam); break;
+      }
+    
+      /* write weights */
+      for (i=0; i<x->num_neurons; i++) {
+	int j=x->num_sensors;
+	t_float *weight = x->weights[i];
+	while (j--) {
+	  sprintf(text, " %.15f", *weight++);
+	  textlen=strlen(text);
+	  if (fwrite(text, textlen*sizeof(char), 1, f) < 1) {
+	    error("msgfile : failed to write %s", filnam); break;
+	  }
+	}
+	if (fwrite("\n", sizeof(char), 1, f) < 1) {
+	  error("msgfile : failed to write %s", filnam); break;
+	}
+      }
+
+      /* write dists */
+      if (fwrite("dists:\n", 7*sizeof(char), 1, f) < 1) {
+	error("msgfile : failed to write %s", filnam); break;
+      }
+      for (i=0; i<x->num_neurons; i++) {
+	int j=x->num_neurons;
+	t_float *dist = x->dist[i];
+	while (j--) {
+	  sprintf(text, " %.15f", *dist++);
+	  textlen=strlen(text);
+	  if (fwrite(text, textlen*sizeof(char), 1, f) < 1) {
+	    error("msgfile : failed to write %s", filnam); break;
+	  }
+	}
+	if (fwrite("\n", sizeof(char), 1, f) < 1) {
+	  error("msgfile : failed to write %s", filnam); break;
+	}
+      }   
+    }
+  }
+  /* close file */
+  if (f) fclose(f);
+
+  freebytes(text, sizeof(text));
+}
+
+
+static void som_help(t_som *x)
+{
+  post("\nann_som\t:: self orgranized map");
+  post("<f1> <f2> <f3>... <fn>\t: train/test som with data"
+       "\nlearn\t\t:... "
+
+       "\nhelp\t\t: show this help");
+  post("creation: \"ann_som <som-file>\": <som-file> defines a file to be loeaded as a som");
+}
+
+
+static void som_print(t_som *x)
+{
+  char c = (x->defaultfilename)?'\0':'\"';
+  post("\nann_som\t:: self orgranized map");
+  post("rule=%s\tmode=%s", (x->rule==INSTAR)?"INSTAR":(x->rule==OUTSTAR)?"OUTSTAR":"KOHONEN", (x->mode==TEST)?"TEST":"TRAIN");
+  post("file = %c%s%c", c, x->filename->s_name,c );
+  post("neurons = %d*%d = %d\tsensors=%d", x->num_neurX, x->num_neurY, x->num_neurons, x->num_sensors);
+  post("learning-rate : lr=%.15f\tlr_x=%.15f\tlr_o=%.15f", x->lr, x->lr_factor, x->lr_bias);
+  post("neighbourhood : nb=%.15f\tnb_x=%.15f\tnb_o=%.15f\n", x->nb, x->nb_factor, x->nb_bias);
+
+}
+static void som_free(t_som *x)
+{
+  som_killsom(x);
+}
+
+static void *som_new(t_symbol *s, int argc, t_atom *argv)
+{
+    t_som *x = (t_som *)pd_new(som_class);
+
+    outlet_new(&x->x_obj, 0);
+
+    x->rule = INSTAR;
+    x->mode = TRAIN;
+
+    x->filename = gensym("default.som");
+    x->defaultfilename = 1;
+
+    x->num_neurX   = 0;
+    x->num_neurY   = 0;
+    x->num_neurons = 0;
+
+    x->num_sensors = 0;
+
+    x->weights     = 0;
+    x->dist        = 0;
+
+    x->lr          = 1;
+    x->lr_factor   = 0.999999999;
+    x->lr_bias     = 0;
+
+    x->nb          = 10;
+    x->nb_factor   = 0.999999999;
+    x->nb_bias     = 0.999999999;
+
+    x->x_canvas = canvas_getcurrent();
+
+
+    if ((argc==0) || (argv->a_type == A_SYMBOL)) {
+      /* load the som-file */
+      if (argc != 0) x->defaultfilename = 0;
+      som_read(x, s, argc, argv);
+    } else {
+      /* create a new som */
+      som_makenewsom(x, s, argc, argv);
+    }
+
+    return (x);
+}
+
+static void som_setup(void)
+{
+  som_class = class_new(gensym("ann_som"), (t_newmethod)som_new,
+			    (t_method)som_free, sizeof(t_som), 0, A_GIMME, 0);
+
+  class_addlist(som_class, som_list);
+  class_addbang(som_class, som_bang);
+
+  class_addmethod(som_class, (t_method)som_makenewsom, gensym("new"), A_GIMME, 0);
+  class_addmethod(som_class, (t_method)som_init, gensym("init"), A_GIMME, 0);
+
+  class_addmethod(som_class, (t_method)som_learn, gensym("learn"), A_GIMME, 0);
+  class_addmethod(som_class, (t_method)som_neighbour, gensym("neighbour"), A_GIMME, 0);
+ 
+  class_addmethod(som_class, (t_method)som_train, gensym("train"), 0);
+  class_addmethod(som_class, (t_method)som_test, gensym("test"), 0);
+  class_addmethod(som_class, (t_method)som_rule, gensym("rule"), A_GIMME, 0);
+
+  class_addmethod(som_class, (t_method)som_read, gensym("read"), A_GIMME, 0);
+  class_addmethod(som_class, (t_method)som_write, gensym("write"), A_GIMME, 0);
+
+  class_addmethod(som_class, (t_method)som_print, gensym("print"), 0);
+  class_addmethod(som_class, (t_method)som_help, gensym("help"), 0);
+  class_sethelpsymbol(som_class, gensym("ann/som"));
+
+}
+
+void ann_som_setup(void)
+{
+  som_setup();
+}
+
diff --git a/src/makefile.irix b/src/makefile.irix
new file mode 100644
index 0000000..12272d1
--- /dev/null
+++ b/src/makefile.irix
@@ -0,0 +1,20 @@
+current: irix5
+
+TARGETS = linux \
+	ann_som
+
+SGI5OBJECTS = $(TARGETS:%=%.pd_irix5)
+
+# ----------------------- IRIX ----------------------------
+.SUFFIXES: .pd_irix5
+SGIINCLUDE = -I../../src
+
+irix5: $(SGIOBJECTS)
+
+.c.pd_irix5:
+	cc -g -w2 -fullwarn -mips2 -DFTS $(SGIINCLUDE) -c $*.c
+	ld -elf -shared -rdata_shared -o $*.pd_irix5 $*.o
+	rm $*.o
+
+clean::
+	rm *.o obj/* *.pd_irix5 so_locations rm *.pd_linux *~
diff --git a/src/makefile.linux b/src/makefile.linux
new file mode 100644
index 0000000..0df06bb
--- /dev/null
+++ b/src/makefile.linux
@@ -0,0 +1,76 @@
+current: all
+
+
+# the ANN-EXTERNAL-makefile
+# everything is GnuGPL that should come with the ann.tgz
+# NO WARRANTIES FOR ANYTHING
+# et cetera
+# (l) forum::f�r::uml�ute 2001
+
+# make sure that the "m_pd.h" is somehow available either by putting it into this
+# directory, by adding it's path to the INCLUDE-path or by putting it into an
+# already included path, e.g. "/usr/local/include/"
+
+#these are the user adjustables : adjust them to fit into your system
+# PD will install to $(DESTDIR)$(INSTALLL_PREFIX)$(PDLIBDIR), which is /usr/local/lib/pd
+# by default
+DESTDIR =
+INSTALL_PREFIX = /usr/local
+PDLIBDIR = /lib/pd
+#these were the user adjustables
+
+
+TARGETS = ann \
+	ann_som \
+
+
+# ----------------------- LINUX ----------------------------
+.SUFFIXES: .pd_linux
+
+
+LINUXOBJECTS = $(TARGETS:%=%.o)
+ARCH = $(shell uname --machine)
+
+PD_DIR = $(DESTDIR)$(INSTALL_PREFIX)$(PDLIBDIR)
+
+ifeq (${ARCH},alpha)
+AFLAGS = -mieee  -mcpu=ev56
+endif
+
+LINCLUDE =
+
+$(LINUXOBJECTS): *.h
+
+#CFLAGS = -O2 -g -Wall $(LINCLUDE) $(UCFLAGS) $(AFLAGS)
+CFLAGS = -O2 -O6 -funroll-loops -mpentiumpro -fomit-frame-pointer -Wall $(LINCLUDE) $(UCFLAGS) $(AFLAGS) 
+
+everything: clean all install distclean
+
+distclean:
+	touch dummy.o
+	touch dummy.pd_linux
+	touch dummy~
+	rm *.o *.pd_linux *~
+
+clean:
+	touch dummy.o
+	touch dummy.pd_linux
+	rm *.o *.pd_linux
+
+all: $(LINUXOBJECTS)
+
+	@echo :: $(LINUXOBJECTS)
+
+	ld -export_dynamic  -shared -o ann.pd_linux *.o -lc -lm
+	strip --strip-unneeded ann.pd_linux
+
+.c.pd_linux:
+	cc $(CFLAGS) -O2 -DPD -fPIC $(INCLUDE) -c -o $*.o $*.c
+
+
+install: installdocs
+	install -m 644 ann.pd_linux $(PD_DIR)/externs
+
+installdocs:
+	install -d $(PD_DIR)/doc/5.reference/ann
+	install -m644 ../examples/* $(PD_DIR)/doc/5.reference/ann
author	IOhannes m zmölnig <zmoelnig@users.sourceforge.net>	2002-10-14 17:16:51 +0000
committer	IOhannes m zmölnig <zmoelnig@users.sourceforge.net>	2002-10-14 17:16:51 +0000
commit	3505bf1cfec70eebcf508b67f3fd8ba55eee4421 (patch)
tree	76b517de2fcc6e6b0333d076ce23674f42324935