initial commit of pix_linNN

svn path=/trunk/externals/grh/; revision=3319

8 files changed, 1581 insertions, 0 deletions

diff --git a/pix_linNN/LinNeuralNet.cpp b/pix_linNN/LinNeuralNet.cpp
new file mode 100755
index 0000000..87318a0
--- /dev/null
+++ b/pix_linNN/LinNeuralNet.cpp
@@ -0,0 +1,147 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class LinNeuralNet
+//
+//   source file
+//
+//   Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
+//
+//   For information on usage and redistribution, and for a DISCLAIMER OF ALL
+//   WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+#include "LinNeuralNet.h"
+
+//--------------------------------------------------
+/* Constructor
+ */
+LinNeuralNet::LinNeuralNet(int netsize) : learn_rate_(0), range_(1), IW_(NULL), b1_(0)
+{
+  // set random seed:
+  srand( (unsigned)time(NULL) );
+
+  netsize_ = (netsize<1) ? 1 : netsize;
+}
+
+//--------------------------------------------------
+/* Destructor
+ */
+LinNeuralNet::~LinNeuralNet()
+{
+  if(IW_)
+    delete[] IW_;
+}
+
+//--------------------------------------------------
+/* creates a new IW-matrix (size: netsize_) and 
+ * b1-vector
+ * ATTENTION: if they exist they'll be deleted
+ */
+bool LinNeuralNet::createNeurons()
+{
+  // delete if they exist
+  if(IW_)
+    delete[] IW_;
+
+  IW_ = new float[netsize_];
+  if(!IW_)
+    return false;
+
+  return true;
+}
+
+//--------------------------------------------------
+/* inits the weight matrix and the bias vector of
+ * the network with random values between [min|max]
+ */
+bool LinNeuralNet::initNetworkRand(const int &min, const int &max)
+{
+  if(!IW_)
+    return false;
+
+  // make randomvalue between 0 and 1
+  // then map it to the bounds
+  b1_ = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+
+  for(int i=0; i<netsize_; i++)
+    {
+      IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+    }
+
+  return true;
+}
+
+//--------------------------------------------------
+/* inits the net with a given weight matrix and bias
+ * (makes a deep copy)
+ * ATTENTION: the dimension of IW-pointer must be the same
+ *            as the netsize !!!
+ * returns false if there's a failure
+ */
+bool LinNeuralNet::initNetwork(const float *IW, float b1)
+{
+  if(!IW_)
+    return false;
+
+  b1_ = b1;
+
+  for(int i=0; i<netsize_; i++)
+      IW_[i] = IW[i];
+
+  return true;
+}
+
+//--------------------------------------------------
+/* calculates the output with the current IW, b1 values
+ * ATTENTION: the array input_data must be in the same
+ *            size as netsize_
+ */
+float LinNeuralNet::calculateNet(float *input_data)
+{
+  if(!IW_)
+    return 0;
+
+  float output = 0;
+
+  // multiply the inputs with the weight matrix IW
+  // and add the bias vector b1
+  for(int i=0; i<netsize_; i++)
+      output += input_data[i] * IW_[i];
+  
+  // map input values to the range
+  output /= range_;
+  
+  return (output+b1_);
+}
+
+//--------------------------------------------------
+/* this method trains the network:
+ * input_data is, as above, the input data, output_data is the 
+ * output of the current net with input_data (output_data is not
+ * calculated in that method !), target_output is the desired
+ * output data
+ * (this is the LMS-algorithm to train linear neural networks)
+ * ATTENTION: the array input_data must be in the same
+ *            size as netsize_
+ */
+bool LinNeuralNet::trainNet(float *input_data, const float &output_data, 
+			    const float &target_output)
+{
+  if(!IW_)
+    return false;
+
+  // this is the LMS-algorithm to train linear
+  // neural networks
+  
+  // calculate the error signal:
+  float error = (target_output - output_data);
+
+  // now change the weights the bias
+  for(int i=0; i<netsize_; i++)
+    IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
+
+  b1_ += 2 * learn_rate_ * error; 
+
+  return true;
+}
diff --git a/pix_linNN/LinNeuralNet.h b/pix_linNN/LinNeuralNet.h
new file mode 100755
index 0000000..c5069dc
--- /dev/null
+++ b/pix_linNN/LinNeuralNet.h
@@ -0,0 +1,154 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class LinNeuralNet
+//
+//   this is an implementation of a simple linear neural net with one neuron
+//   so this net has a Weight-Matrix IW and a bias vector b1
+//   this net can have n input values, but only one output value
+//   (see NeuralNet documentations for more information)
+//
+//   header file
+//
+//   Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
+//
+//   For information on usage and redistribution, and for a DISCLAIMER OF ALL
+//   WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_LIN_NEURAL_NET__
+#define _INCLUDE_LIN_NEURAL_NET__
+
+#include <stdlib.h>
+#include <ctime>
+//#include "m_pd.h"   // for debug
+
+class LinNeuralNet
+{
+ protected:
+
+  /* this is the number of input values, which is
+   * automatically the netsize and the size of IW
+   */
+  int netsize_;
+
+  /* the input weight matrix IW
+   * (size: netsize )
+   */
+  float *IW_;
+
+  /* the bias vector b1
+   */
+  float b1_;
+
+  /* the learning rate of the net
+   */
+  float learn_rate_;
+
+  /* the range of the input values should be from 0
+   * to range_
+   * outputvalues are from -1 to 1
+   */
+  float range_;
+
+
+ public:
+
+  /* Constructor
+   */
+  LinNeuralNet(int netsize);
+
+  /* Destructor
+   */
+  virtual ~LinNeuralNet();
+
+
+  //-----------------------------------------------------
+
+  /* Set/Get learning rate
+   */
+  virtual void setLearningRate(float learn_rate)
+  {  learn_rate_=learn_rate; }
+  virtual float getLearningRate() const
+  {  return learn_rate_; }
+
+  /* Set/Get range
+   */
+  virtual void setRange(float range)
+  {  range_=range; }
+  virtual float getRange() const
+  {  return range_; }
+
+  /* some more get/set methods
+   */
+  virtual int getNetsize() const
+  {  return netsize_; }
+  virtual float *getIW() const
+  {  return IW_; }
+  virtual void setIW(const float *IW)
+  {  for(int i=0; i<netsize_; i++) IW_[i] = IW[i]; }
+  virtual float getb1() const
+  {  return b1_; }
+  virtual void setb1(float b1)
+  {  b1_ = b1; }
+
+
+  //-----------------------------------------------------
+
+  /* creates a new IW-matrix (size: netsize_) and 
+   * b1-vector
+   * returns false if there's a failure
+   * ATTENTION: if they exist they'll be deleted
+   */
+  virtual bool createNeurons();
+
+  /* inits the weight matrix and the bias vector of
+   * the network with random values between [min|max]
+   * returns false if there's a failure
+   */
+  virtual bool initNetworkRand(const int &min, const int &max);
+
+  /* inits the net with a given weight matrix and bias
+   * (makes a deep copy)
+   * ATTENTION: the dimension of IW-pointer must be the same
+   *            as the netsize !!!
+   * returns false if there's a failure
+   */
+  virtual bool initNetwork(const float *IW, float b1);
+
+  /* calculates the output with the current IW, b1 values
+   * ATTENTION: the array input_data must be in the same
+   *            size as netsize_
+   */
+  virtual float calculateNet(float *input_data);
+
+  /* this method trains the network:
+   * input_data is, as above, the input data, output_data is the 
+   * output of the current net with input_data (output_data is not
+   * calculated in that method !), target_output is the desired
+   * output data
+   * (this is the LMS-algorithm to train linear neural networks)
+   * returns false if there's a failure
+   * ATTENTION: the array input_data must be in the same
+   *            size as netsize_
+   */
+  virtual bool trainNet(float *input_data, const float &output_data, 
+			const float &target_output);
+
+ private:
+  /* Copy Construction is not allowed
+   */
+  LinNeuralNet(const LinNeuralNet &src)
+    { }
+
+  /* assignement operator is not allowed
+   */
+  const LinNeuralNet& operator= (const LinNeuralNet& src)
+    { return *this; }
+};
+
+
+
+
+#endif //_INCLUDE_LIN_NEURAL_NET__
diff --git a/pix_linNN/Makefile b/pix_linNN/Makefile
new file mode 100755
index 0000000..48484e1
--- /dev/null
+++ b/pix_linNN/Makefile
@@ -0,0 +1,44 @@
+PD-PATH=/usr/lib/pd
+PD-SCR=/usr/include
+
+# location of the GEM sources and Gem.pd_linux:
+GEM-SCR=/home/Georg/pd-cvs/gem/Gem/src
+GEM-LIB=$(PD-PATH)/extra/Gem.pd_linux 
+
+
+CC = g++
+LD = g++
+INCLUDE=-I$(PD-SCR) -I$(GEM-SCR) -I.
+LIB=-lc -lm -L$(GEM-LIB)
+CC_FLAGS = -c -Wall -g -g -O2 -mmmx -fno-builtin -O3 -funroll-loops -ffast-math
+LD_FLAGS = --export-dynamic -shared -o
+
+
+TARGET=pix_linNN.pd_linux
+OBJ=LinNeuralNet.o pix_linNN.o
+#--------------------------------------------------------
+
+all: pd_linux
+
+pd_linux: $(TARGET)
+
+$(TARGET): $(OBJ)
+	$(LD) $(LD_FLAGS) $(TARGET) $(OBJ) $(LIB)
+	strip --strip-unneeded $(TARGET)
+	chmod 755 $(TARGET)
+
+pix_linNN.o: LinNeuralNet.o pix_linNN.h pix_linNN.cpp
+	$(CC) $(CC_FLAGS) $(INCLUDE) pix_linNN.cpp
+
+LinNeuralNet.o: LinNeuralNet.cpp LinNeuralNet.h
+	$(CC) $(CC_FLAGS) $(INCLUDE) LinNeuralNet.cpp
+
+#--------------------------------------------------------
+
+clean:
+	rm -f $(OBJ) $(TARGET)
+	
+
+install:
+	cp -f $(TARGET) $(PD-PATH)/externs
+	cp -f help/*.pd $(PD-PATH)/doc/5.reference
\ No newline at end of file
diff --git a/pix_linNN/gpl.txt b/pix_linNN/gpl.txt
new file mode 100755
index 0000000..5ea29a7
--- /dev/null
+++ b/pix_linNN/gpl.txt
@@ -0,0 +1,346 @@
+		    GNU GENERAL PUBLIC LICENSE
+		       Version 2, June 1991
+
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+
+    Gnomovision version 69, Copyright (C) 19yy name of author
+    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, the commands you use may
+be called something other than `show w' and `show c'; they could even be
+mouse-clicks or menu items--whatever suits your program.
+
+You should also get your employer (if you work as a programmer) or your
+school, if any, to sign a "copyright disclaimer" for the program, if
+necessary.  Here is a sample; alter the names:
+
+  Yoyodyne, Inc., hereby disclaims all copyright interest in the program
+  `Gnomovision' (which makes passes at compilers) written by James Hacker.
+
+  <signature of Ty Coon>, 1 April 1989
+  Ty Coon, President of Vice
+
+This General Public License does not permit incorporating your program into
+proprietary programs.  If your program is a subroutine library, you may
+consider it more useful to permit linking proprietary applications with the
+library.  If this is what you want to do, use the GNU Library General
+Public License instead of this License.
+
diff --git a/pix_linNN/help-pix_linNN.pd b/pix_linNN/help-pix_linNN.pd
new file mode 100755
index 0000000..932116f
--- /dev/null
+++ b/pix_linNN/help-pix_linNN.pd
@@ -0,0 +1,135 @@
+#N canvas 871 74 498 738 10;
+#X obj 28 237 gemwin;
+#X msg 28 211 create \, 1;
+#N canvas 463 0 765 790 pix2sig_stuff~ 0;
+#X obj 120 35 gemhead;
+#X obj 120 132 pix_texture;
+#X obj 119 274 outlet~;
+#X obj 139 185 square 4;
+#X obj 139 163 separator;
+#X obj 61 165 separator;
+#X obj 120 101 pix_video;
+#X msg 186 64 dimen 640 480;
+#X obj 26 36 block~ 2048;
+#X msg 186 38 dimen 320 240;
+#X msg 76 535 getprecision;
+#X msg 93 696 getlearnrate;
+#X msg 65 671 learnrate 0.2;
+#X msg 424 459 getneurons;
+#X msg 404 206 train;
+#X obj 31 227 inlet~;
+#X msg 65 647 learnrate 0.05;
+#X text 296 49 <- input dimension;
+#X msg 76 498 precision \$1;
+#X floatatom 76 481 5 0 0 0 - - -;
+#X text 42 335 precision:;
+#X text 53 358 1: means every pixel is used in calculation;
+#X text 53 372 2: only every second pixel;
+#X text 53 386 ...;
+#X obj 62 411 loadbang;
+#X msg 407 401 neurons 2048;
+#X msg 407 422 neurons 64;
+#X text 403 336 neurons:;
+#X text 416 357 nr. of neurons used in the calculation;
+#X text 415 370 (_MUST_ be the same as the buffersize !!!);
+#X text 43 615 learnrate:;
+#X msg 62 456 precision 1;
+#X msg 62 436 precision 4;
+#X text 397 126 train:;
+#X text 417 152 trains the neural net;
+#X text 418 166 (the current video frame to;
+#X text 425 178 the current audio block);
+#X obj 61 252 pix_linNN;
+#X text 346 592 save/load;
+#X text 359 614 saves/load the actual trained net to/from a file;
+#X msg 440 684 load net.dat;
+#X msg 440 664 save net.dat;
+#X obj 78 226 r \$0-linNN;
+#X obj 404 233 s \$0-linNN;
+#X obj 62 564 s \$0-linNN;
+#X obj 407 492 s \$0-linNN;
+#X obj 65 725 s \$0-linNN;
+#X obj 440 723 s \$0-linNN;
+#X connect 0 0 6 0;
+#X connect 1 0 4 0;
+#X connect 1 0 5 0;
+#X connect 4 0 3 0;
+#X connect 5 0 37 0;
+#X connect 6 0 1 0;
+#X connect 7 0 6 0;
+#X connect 9 0 6 0;
+#X connect 10 0 44 0;
+#X connect 11 0 46 0;
+#X connect 12 0 46 0;
+#X connect 13 0 45 0;
+#X connect 14 0 43 0;
+#X connect 15 0 37 0;
+#X connect 16 0 46 0;
+#X connect 18 0 44 0;
+#X connect 19 0 18 0;
+#X connect 24 0 32 0;
+#X connect 25 0 45 0;
+#X connect 26 0 45 0;
+#X connect 31 0 44 0;
+#X connect 32 0 44 0;
+#X connect 37 1 2 0;
+#X connect 40 0 47 0;
+#X connect 41 0 47 0;
+#X connect 42 0 37 0;
+#X restore 87 492 pd pix2sig_stuff~;
+#X msg 102 212 0 \, destroy;
+#X obj 114 537 unsig~;
+#X obj 204 382 osc~ 440;
+#X obj 203 406 *~;
+#X obj 235 406 tgl 15 0 empty empty empty 0 -6 0 8 -262144 -1 -1 0
+1;
+#X obj 205 446 sig~ 0;
+#X floatatom 115 558 8 0 0 0 - - -;
+#X text 199 230 <- create gemwin;
+#X obj 39 392 readsf~;
+#X obj 39 351 openpanel;
+#X msg 39 371 open \$1;
+#X obj 39 330 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 65 329 <- load sample for training;
+#X obj 120 367 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0
+1;
+#X floatatom 204 364 5 0 0 0 - - -;
+#X text 270 381 <- simple osc for training;
+#X text 260 447 <- to train silence;
+#X obj 83 413 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 214 491 <- audio/video work;
+#X obj 88 634 dac~;
+#X obj 88 609 *~;
+#X obj 116 609 dbtorms;
+#X floatatom 116 591 5 0 0 0 - - -;
+#X text 166 588 <- outvol in dB;
+#X text 110 703 Georg Holzmann <grh@mur.at> \, 2004;
+#X text 24 23 pix_linNN:;
+#X text 22 58 (see also pix_recNN !!!);
+#X text 24 90 pix_linNN~ calculates an audio signal out of a video
+frame with a linear neural network \, which can be trained.;
+#X text 24 124 The network has one neuron per audio sample: this neuron
+has three inputs (a RGB-signal) \, a weight vector for each of the
+inputs \, a bias value and a linear output function.;
+#X connect 1 0 0 0;
+#X connect 2 0 4 0;
+#X connect 2 0 23 0;
+#X connect 3 0 0 0;
+#X connect 4 0 9 0;
+#X connect 5 0 6 0;
+#X connect 6 0 2 0;
+#X connect 7 0 6 1;
+#X connect 8 0 2 0;
+#X connect 11 0 2 0;
+#X connect 11 1 20 0;
+#X connect 12 0 13 0;
+#X connect 13 0 11 0;
+#X connect 14 0 12 0;
+#X connect 16 0 11 0;
+#X connect 17 0 5 0;
+#X connect 23 0 22 0;
+#X connect 23 0 22 1;
+#X connect 24 0 23 1;
+#X connect 25 0 24 0;
diff --git a/pix_linNN/pix_linNN.cpp b/pix_linNN/pix_linNN.cpp
new file mode 100755
index 0000000..515a6a2
--- /dev/null
+++ b/pix_linNN/pix_linNN.cpp
@@ -0,0 +1,541 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+//   GEM - Graphics Environment for Multimedia
+//
+//   pix_linNN
+//
+//   Implementation file
+//
+//   Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
+//   (and of course lot's of other developers for PD and GEM)
+//
+//   For information on usage and redistribution, and for a DISCLAIMER OF ALL
+//   WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+#include "pix_linNN.h"
+
+CPPEXTERN_NEW_WITH_TWO_ARGS(pix_linNN, t_floatarg, A_DEFFLOAT, t_floatarg, A_DEFFLOAT)
+
+//----------------------------------------------------------
+/* Constructor
+ */
+  pix_linNN::pix_linNN(t_floatarg arg0=64, t_floatarg arg1=1) : 
+    m_data_(NULL), m_xsize_(0), m_ysize_(0), m_csize_(0), 
+    train_on_(false), net_(NULL)
+{
+  // init args ?????????????????????????????????
+  neuron_nr_=2048;          //static_cast<int>((arg0<0)?2:arg0);
+  precision_=2;          //static_cast<int>((arg1<1)?1:arg1);
+  //post("arg0: %d, arg1: %d",arg0,arg1);
+
+  // generate the in- and outlet:
+  out0_ = outlet_new(this->x_obj, &s_signal);
+  inlet_new(this->x_obj, &this->x_obj->ob_pd, &s_signal, &s_signal);
+
+  // set random seed:
+  srand( (unsigned)time(NULL) );
+
+  // creates the nets
+  net_ = new LinNeuralNet[neuron_nr_](3);
+  if(!net_)
+    {
+      post("pix_linNN~: no memory for neural nets!");
+      return;
+    }
+
+  for(int i=0; i<neuron_nr_; i++)
+    {
+      if( !net_[i].createNeurons() )
+	{
+	  post("pix_linNN~: error in creating the net!");
+	  return;
+	}
+      if( !net_[i].initNetworkRand(-1,1) )
+	{
+	  post("pix_linNN~: error in initializing the net!");
+	  return;
+	}	
+
+      net_[i].setRange(255);
+      net_[i].setLearningRate(0.01);
+    }
+}
+
+//----------------------------------------------------------
+/* Destructor
+ */
+pix_linNN::~pix_linNN()
+{
+  outlet_free(out0_);
+  m_data_ = NULL;
+  m_xsize_ = 0;
+  m_ysize_ = 0;
+
+  // delete weight matrix and bias vector
+  delete[] net_;
+}
+
+//----------------------------------------------------------
+/* processImage
+ */
+void pix_linNN::processImage(imageStruct &image)
+{
+  m_data_ = image.data;
+  m_xsize_ = image.xsize;
+  m_ysize_ = image.ysize;
+  m_csize_ = image.csize;
+  m_format_ = image.format;
+}
+
+//----------------------------------------------------------
+/* DSP perform
+ */
+t_int* pix_linNN::perform(t_int* w)
+{
+  pix_linNN *x = GetMyClass((void*)w[1]);
+  t_float* in_signal = (t_float*)(w[2]);
+  t_float* out_signal = (t_float*)(w[3]);
+  int blocksize = (t_int)(w[4]);
+
+  if(blocksize != x->neuron_nr_)
+    {
+      post("pix_linNN~: neurons and buffersize are different! You MUST have the same neuron nr as the buffersize !!!");
+      post("neurons: %d, buffersize: %d", x->neuron_nr_, blocksize);
+      return (w+5);
+    }
+
+  
+  // some needed data
+  long int pix_size = x->m_xsize_ * x->m_ysize_;
+  int pix_blocksize  = (blocksize<pix_size)?blocksize:pix_size;
+
+  // splits the frame into slices, so that the average
+  // of one slice can be used for the network input
+  // there are as much slices as the buffsize is
+
+  float nr = sqrt(blocksize); // the number of slices at the
+                              // x- and y-axis
+
+  float x_slice = x->m_xsize_ / nr; // x size of a slice in pixels
+  float y_slice = x->m_ysize_ / nr; // x size of a slice in pixels
+  int x_slice_int = static_cast<int>( x_slice );
+  int y_slice_int = static_cast<int>( y_slice );
+
+  // the number of slices on one axis (is the float nr
+  // from above rounded up)
+  int slice_nr = static_cast<int>(nr) + 1;
+
+  if (x->m_data_)
+  {
+    switch(x->m_format_)
+    {
+    case GL_RGBA:
+      for(int n=0; n<pix_blocksize; n++)
+      {
+	//post("Block %d:",n);
+
+	// calulate the pixel in left upper edge of every slice
+	int lu_pix_x = static_cast<int>( (n % slice_nr) * x_slice );
+	int lu_pix_y = static_cast<int>( static_cast<int>(n / slice_nr) * y_slice );
+
+	//post("lu_pix: %d, %d", lu_pix_x, lu_pix_y);
+
+	// now sum up all the pixels of one slice and then divide through the
+	// number of pixels
+	unsigned long int temp_data[3] = { 0, 0, 0 };  // the storage to sum the pixels
+	t_float average_pix[3] = { 0, 0, 0 };  // the average of the pixels
+	
+	// only for optimization:
+	int helper1 = x->m_xsize_ * x->m_csize_;
+	int add_count = 0;
+
+	for(int i=0; i<x_slice_int; i+=x->precision_)
+	  {
+	    for(int j=0; j<y_slice_int; j+=x->precision_)
+	      {
+		// the way to access the pixels: (C=chRed, chBlue, ...)
+		//data[Y * xsize * csize + X * csize + C]
+		
+		//post("current pixel: %d %d", 
+		//     ((lu_pix_x+i)%x->m_xsize), ((lu_pix_y+j)%x->m_ysize) );
+
+		temp_data[0] += x->m_data_[ 
+			     (lu_pix_y+j) * helper1
+			     + (lu_pix_x+i) * x->m_csize_ + chRed ];
+		
+		temp_data[1] += x->m_data_[ 
+			     ((lu_pix_y+j)) * helper1
+			     + ((lu_pix_x+i)) * x->m_csize_ + chGreen ];
+		
+		temp_data[2] += x->m_data_[ 
+			     ((lu_pix_y+j)%x->m_ysize_) * helper1
+			     + ((lu_pix_x+i)%x->m_xsize_) * x->m_csize_ + chBlue ];
+		
+		add_count++;
+	      }
+	  }
+	average_pix[0] = temp_data[0] / add_count;
+	average_pix[1] = temp_data[1] / add_count;
+	average_pix[2] = temp_data[2] / add_count;
+
+	// the calculation of the network:
+	*out_signal = x->net_[n].calculateNet(average_pix);
+
+	//post("%d: RGBav: %f %f %f, out_signal: %f",
+	//n,average_pix[0],average_pix[1],average_pix[2],*out_signal);
+	
+	// learning:
+	if(x->train_on_)
+	  x->net_[n].trainNet(average_pix, *out_signal, *in_signal);
+
+	out_signal++;
+	in_signal++;
+      }
+      break;
+    default:
+      post("RGB only for now");
+    }
+  } 
+  else 
+    {
+      pix_blocksize=blocksize;
+      while (pix_blocksize--) *out_signal++=0;
+    }
+
+  x->train_on_=false;
+  return (w+5);
+}
+
+//----------------------------------------------------------
+/* DSP-Message
+ */
+void pix_linNN::dspMess(void *data, t_signal** sp)
+{
+  dsp_add(perform, 4, data, sp[0]->s_vec, sp[1]->s_vec, sp[0]->s_n);
+}
+
+//----------------------------------------------------------
+/* saves the contents of the current net to file
+ * (it saves the neuron_nr_, learning rate
+ * IW-matrix and b1-vector of the net)
+ */
+void pix_linNN::saveNet(string filename)
+{
+  // open and check outfile
+  ofstream outfile;
+  outfile.open(filename.c_str());
+  if(!outfile)
+    {
+      post("pix_linNN~: failed to open output-file!");
+      return;
+    }
+  
+  // write XML-header
+  outfile << "<?xml version=\"1.0\" encoding=\"ISO-8859-1\" ?>" << endl;
+
+  // start-tag
+  outfile << "<linNN>" << endl;
+
+  // neuron_nr_(=size) and learning rate
+  outfile << "\t<neurons> " << neuron_nr_ << " </neurons>" << endl;
+  outfile << "\t<learnrate> " << net_[0].getLearningRate() 
+	  << " </learnrate>" << endl;
+
+  // now the IW-matrix of the neural net
+  outfile << "\t<IW>" << endl;
+  for(int i=0; i<neuron_nr_; i++)
+    {
+      outfile << "\t\t" << net_[i].getIW()[0] << " "
+	      << net_[i].getIW()[1] << " "
+	      << net_[i].getIW()[2] << endl;
+    }
+  outfile << "\t</IW>" << endl;
+
+  // and the b1-vector
+  outfile << "\t<b1>" << endl << "\t\t";
+  for(int i=0; i<neuron_nr_; i++)
+    {
+      outfile << net_[i].getb1() << " ";
+    }
+  outfile << endl << "\t</b1>" << endl;
+
+  // end-tag
+  outfile << "</linNN>" << endl;
+
+
+  outfile.close();
+  post("pix_linNN~: saved to output-file %s", filename.c_str());
+  return;
+}
+
+//----------------------------------------------------------
+/* loads the parameters of the net from file
+ * (it loads the neuron_nr_, learning rate
+ * IW-matrix and b1-vector of the net)
+ */
+void pix_linNN::loadNet(string filename)
+{
+  // temp variables
+  float IW[3];
+  float b1, learnrate;
+
+  ifstream infile;
+  infile.open(filename.c_str());
+
+  if(!infile)
+    {
+      post("pix_linNN~: cannot open input-file!");
+      return;
+    }
+
+  post("pix_linNN~: loading input-file %s",filename.c_str());
+
+  int state = 0, IWcount = 0, b1count = 0;
+  bool tag=false;
+  string line, temp;
+
+  while (getline(infile, line))
+    {
+      istringstream instream(line);
+      instream >> temp;
+
+      // specify the tags
+      //post("input: %s",temp.c_str());
+      if( temp == "<neurons>" ) 
+	{state=1; }
+      if( temp == "<learnrate>" ) 
+	{state=2; }
+      if( temp == "<IW>" ) 
+	{state=3; }
+      if( temp == "<b1>" ) 
+	{state=4; }
+      if( !strncmp(temp.c_str(),"</",2) ) 
+	{state=0;}
+
+      if( !strncmp(temp.c_str(),"<",1) ) 
+	{tag=true; }
+      else 
+	{tag=false; }
+
+      // make string stream again
+      instream.str(line);
+      if(tag)
+	instream >> temp; // if theres a tag, stream it
+
+
+      bool go_on=false;
+      while(!go_on)
+	{
+	  // end of a line
+	  if(instream.eof() || !state)
+	    {
+	      go_on=true;
+	      break;
+	    }
+
+
+	  // <neuron>
+	  if(state == 1)
+	    {
+	      instream >> neuron_nr_;
+	      if(!net_)
+		{
+		  // creates new nets
+		  net_ = new LinNeuralNet[neuron_nr_](3);
+		  if(!net_)
+		    {
+		      post("pix_linNN~: no memory for neural nets!");
+		      break;
+		    }
+		}
+	      for(int i=0; i<neuron_nr_; i++)
+		{
+		  if( !net_[i].createNeurons() )
+		    {
+		  post("pix_linNN~: error in creating the net!");
+		  break;
+		    }
+		}
+
+	      go_on=false;
+	      break;
+	    }
+	  
+	  // <learnrate>
+	  if(state == 2)
+	    {
+	      instream >> learnrate;
+
+	      for(int i=0; i<neuron_nr_; i++)
+		net_[i].setLearningRate(learnrate);
+
+	      go_on=false;
+	      break;
+	    }
+	  
+	  // <IW>
+	  if(state == 3)
+	    {
+	      instream >> IW[0];
+	      instream >> IW[1];
+	      instream >> IW[2];
+	      
+	      if(IWcount<neuron_nr_)
+		net_[IWcount++].setIW(IW);
+	      else
+		{
+		  go_on = false;
+		  break;
+		}
+	    }
+	  
+	  // <b1>
+	  if(state == 4)
+	    {
+	      for(int i=0; i<neuron_nr_; i++)
+		{	      
+		  instream >> b1;
+		  net_[b1count++].setb1(b1);
+		}
+
+	      go_on = false;
+	      break;
+	    }
+
+	  //else:
+	  go_on=false;
+	  break;
+	}
+    }
+
+  infile.close();
+  return;
+}
+
+//----------------------------------------------------------
+/* setup callback
+ */
+void pix_linNN::obj_setupCallback(t_class *classPtr)
+{
+  class_addcreator((t_newmethod)_classpix_linNN, gensym("pix_linNN~"), A_NULL);
+
+  class_addmethod(classPtr, (t_method)pix_linNN::setNeurons,
+		  gensym("neurons"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::getNeurons,
+		  gensym("getneurons"), A_NULL); 
+  class_addmethod(classPtr, (t_method)pix_linNN::setPrecision,
+		  gensym("precision"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::getPrecision,
+		  gensym("getprecision"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::setTrainOn,
+		  gensym("train"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::setLearnrate,
+		  gensym("learnrate"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::getLearnrate,
+		  gensym("getlearnrate"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::saveToFile,
+		  gensym("save"), A_SYMBOL, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_linNN::loadFromFile,
+		  gensym("load"), A_SYMBOL, A_NULL);
+
+  class_addmethod(classPtr, (t_method)pix_linNN::dspMessCallback, 
+		  gensym("dsp"), A_NULL);
+  class_addmethod(classPtr, nullfn, gensym("signal"), A_NULL);
+}
+
+//----------------------------------------------------------
+/* DSP callback
+ */
+void pix_linNN::dspMessCallback(void *data, t_signal** sp)
+{
+  GetMyClass(data)->dspMess(data, sp);
+}
+
+//----------------------------------------------------------
+/* sets the precision
+ */
+void pix_linNN::setPrecision(void *data, t_floatarg precision)
+{
+  GetMyClass(data)->precision_ = 
+    (precision<1) ? 1 : static_cast<int>(precision);
+}
+void pix_linNN::getPrecision(void *data)
+{
+  post("pix_linNN~: precision: %d",GetMyClass(data)->precision_);
+}
+
+//----------------------------------------------------------
+/* method to train the network
+ */
+void pix_linNN::setTrainOn(void *data)
+{
+  GetMyClass(data)->train_on_ = true; 
+}
+
+//----------------------------------------------------------
+/* changes the number of neurons
+ * (which should be the same as the audio buffer)
+ * ATTENTION: a new IW-matrix and b1-vector will be initialized
+ */
+void pix_linNN::setNeurons(void *data, t_floatarg neurons)
+{
+  GetMyClass(data)->neuron_nr_ =
+    (neurons<1) ? 1 : static_cast<int>(neurons);
+
+  if(GetMyClass(data)->net_)
+    delete[] GetMyClass(data)->net_;
+
+  // creates the nets
+  GetMyClass(data)->net_ = new LinNeuralNet[GetMyClass(data)->neuron_nr_](3);
+  if(!GetMyClass(data)->net_)
+    {
+      post("pix_linNN~: no memory for neural nets!");
+      return;
+    }
+
+  for(int i=0; i<GetMyClass(data)->neuron_nr_; i++)
+    {
+      if( !GetMyClass(data)->net_[i].createNeurons() )
+	{
+	  post("pix_linNN~: error in creating the net!");
+	  return;
+	}
+      if( !GetMyClass(data)->net_[i].initNetworkRand(-1,1) )
+	{
+	  post("pix_linNN~: error in initializing the net!");
+	  return;
+	}
+    }
+}
+void pix_linNN::getNeurons(void *data)
+{
+  post("pix_linNN~: nr of neurons: %d (MUST be the same as buffersize!)",
+       GetMyClass(data)->neuron_nr_);
+}
+
+//----------------------------------------------------------
+/* sets the learnrate of the net
+ */
+void pix_linNN::setLearnrate(void *data, t_floatarg learn_rate)
+{
+  for(int i=0; i<GetMyClass(data)->neuron_nr_; i++)
+    GetMyClass(data)->net_[i].setLearningRate(learn_rate);
+}
+void pix_linNN::getLearnrate(void *data)
+{
+  post("pix_linNN~: learning rate: %f",GetMyClass(data)->net_[0].getLearningRate());
+}
+
+//----------------------------------------------------------
+/* FileIO-stuff
+ */
+void pix_linNN::saveToFile(void *data, t_symbol *filename)
+{
+  GetMyClass(data)->saveNet(filename->s_name);
+}
+void pix_linNN::loadFromFile(void *data, t_symbol *filename)
+{
+  GetMyClass(data)->loadNet(filename->s_name);
+}
diff --git a/pix_linNN/pix_linNN.h b/pix_linNN/pix_linNN.h
new file mode 100755
index 0000000..4ebc10c
--- /dev/null
+++ b/pix_linNN/pix_linNN.h
@@ -0,0 +1,188 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+//   GEM - Graphics Environment for Multimedia
+//
+//   pix_linNN~
+//   Calculates an audio signal out of a video frame
+//   with a linear neural network, which can be trained
+//
+//   the network has one neuron per audio sample: this neuron has
+//   three inputs (a RGB-signal), a weight vector for each of the inputs,
+//   a bias value and a linear output function
+//   (see LinNeuralNet.h for more info)
+//
+//   header file
+//
+//   Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
+//   (and of course lot's of other developers for PD and GEM)
+//
+//   For information on usage and redistribution, and for a DISCLAIMER OF ALL
+//   WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_PIX_LINNN_H__
+#define _INCLUDE_PIX_LINNN_H__
+
+#include <string>
+#include <sstream>
+#include <fstream>
+#include "Base/GemPixObj.h"
+#include "LinNeuralNet.h"
+
+
+using std::string;
+using std::endl;
+using std::ifstream;
+using std::ofstream;
+using std::istringstream;
+
+
+/*-----------------------------------------------------------------
+ *  CLASS
+ * pix_linNN~
+ *   
+ * calculates an audio signal out of a video frame with
+ * a linear neural network
+ *   
+ * KEYWORDS
+ * pix audio
+ *   
+ * DESCRIPTION
+ * 1 signal-outlet
+ */
+class GEM_EXTERN pix_linNN : public GemPixObj
+{
+  CPPEXTERN_HEADER(pix_linNN, GemPixObj)
+
+ public:
+
+  /* Constructor
+   */
+  pix_linNN(t_floatarg arg0, t_floatarg arg1);
+    	
+ protected:
+    	
+  /* Destructor
+   */  
+  virtual ~pix_linNN();
+
+
+  //-----------------------------------
+  /* Image STUFF:
+   */
+    
+  /* The pixBlock with the current image
+   *  pixBlock    	m_pixBlock;
+   */
+  unsigned char *m_data_;
+  int            m_xsize_;
+  int            m_ysize_;
+  int            m_csize_;
+  int            m_format_;
+
+  /* precision of the image:
+   * 1 means every pixel is taken for the calculation,
+   * 2 every second pixel, 3 every third, ...
+   */
+  int precision_;
+
+  /* processImage
+   */
+  virtual void processImage(imageStruct &image);
+
+
+  //-----------------------------------
+  /* Neural Network STUFF:
+   */
+
+  /* the linear neural nets
+   * (size: buffsize)
+   */
+  LinNeuralNet *net_;
+
+  /* training modus on
+   * (will only be on for one audio buffer)
+   */
+  bool train_on_;
+
+  /* the number of neurons, which should be
+   * (= size of the array nets_)
+   * THE SAME as the audio buffer size
+   */
+  int neuron_nr_;
+
+
+  //-----------------------------------
+  /* Audio STUFF:
+   */
+
+  /* the outlet
+   */
+  t_outlet *out0_;
+
+  /* DSP perform
+   */
+  static t_int* perform(t_int* w);
+
+  /* DSP-Message
+   */
+  virtual void dspMess(void *data, t_signal** sp);
+
+
+  //-----------------------------------
+  /* File IO:
+   */
+
+  /* saves the contents of the current net to file
+   * (it saves the neuron_nr_, learning rate
+   * IW-matrix and b1-vector of the net)
+   */
+  virtual void saveNet(string filename);
+
+  /* loads the parameters of the net from file
+   * (it loads the neuron_nr_, learning rate
+   * IW-matrix and b1-vector of the net)
+   */
+  virtual void loadNet(string filename);
+
+ private:
+
+  //-----------------------------------
+  /* static members
+   * (interface to the PD world)
+   */
+
+  /* set/get the precision of the image calculation
+   */
+  static void setPrecision(void *data, t_floatarg precision);
+  static void getPrecision(void *data);
+
+  /* method to train the network
+   */
+  static void setTrainOn(void *data);
+
+  /* changes the number of neurons
+   * (which should be the same as the audio buffer)
+   * ATTENTION: a new IW-matrix and b1-vector will be initialized
+   */
+  static void setNeurons(void *data, t_floatarg neurons);
+  static void getNeurons(void *data);
+
+  /* sets the learnrate of the net
+   */
+  static void setLearnrate(void *data, t_floatarg learn_rate);
+  static void getLearnrate(void *data);
+
+  /* DSP callback
+   */
+  static void dspMessCallback(void* data, t_signal** sp);
+
+  /* File IO:
+   */
+  static void saveToFile(void *data, t_symbol *filename);
+  static void loadFromFile(void *data, t_symbol *filename); 
+};
+
+#endif	// for header file
diff --git a/pix_linNN/readme.txt b/pix_linNN/readme.txt
new file mode 100755
index 0000000..aa3ce84
--- /dev/null
+++ b/pix_linNN/readme.txt
@@ -0,0 +1,26 @@
+pix_linNN - by Georg Holzmann <grh@mur.at>, 2004
+look at: http://grh.mur.at/software/thebrain.html
+
+--------------------------------license---------------------------------------
+
+This program is free software; you can redistribute it and/or
+modify it under the terms of the GNU General Public License
+as published by the Free Software Foundation; either version 2
+of the License, or (at your option) any later version.
+ 
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
+
+In the official pix_recNN distribution, the GNU General Public License is
+in the file gpl.txt
+
+
+-------------------------------information-----------------------------------
+
+see the PD help patch
\ No newline at end of file