initial commit of pix_recNN

svn path=/trunk/externals/grh/; revision=3320

13 files changed, 2690 insertions, 0 deletions

diff --git a/pix_recNN/Makefile b/pix_recNN/Makefile
new file mode 100755
index 0000000..ab880e8
--- /dev/null
+++ b/pix_recNN/Makefile
@@ -0,0 +1,46 @@
+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$(GEM-SCR) -I./src
+LIB=-lc -lm -L$(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_recNN.pd_linux
+OBJ=RecurrentNeuron.o Neuron.o pix_recNN.o
+#--------------------------------------------------------
+
+all: pd_linux
+
+pd_linux: $(TARGET)
+
+$(TARGET): $(OBJ)
+	$(LD) $(LD_FLAGS) $(TARGET) $(OBJ) $(LIB)
+	strip --strip-unneeded $(TARGET)
+	chmod 755 $(TARGET)
+
+pix_recNN.o: RecurrentNeuron.o pix_recNN.h pix_recNN.cpp NNet.h NNException.h
+	$(CC) $(CC_FLAGS) $(INCLUDE) pix_recNN.cpp
+
+
+RecurrentNeuron.o: RecurrentNeuron.cpp RecurrentNeuron.h Neuron.o NNActivation.h
+
+Neuron.o: Neuron.cpp Neuron.h NNActivation.h
+
+#--------------------------------------------------------
+
+clean:
+	rm -f $(OBJ) $(TARGET)
+	
+
+install:
+	cp -f $(TARGET) $(PD-PATH)/externs
+	cp -f *.pd $(PD-PATH)/doc/5.reference
diff --git a/pix_recNN/NNActivation.h b/pix_recNN/NNActivation.h
new file mode 100755
index 0000000..e91c046
--- /dev/null
+++ b/pix_recNN/NNActivation.h
@@ -0,0 +1,78 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// NNActivation.h
+//
+//   all the activation functions of the neurons
+//
+//   header file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   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.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_ACTIVATION_NET__
+#define _INCLUDE_ACTIVATION_NET__
+
+
+#include <math.h>
+
+namespace TheBrain
+{
+
+//------------------------------------------------------
+/* implementation of the different activation functions
+ * and it's derivations
+ */
+
+/* Linear activation function.
+ * span: -inf < y < inf
+ * y = x
+*/
+#define LINEAR 0
+
+/* Sigmoid activation function.
+ * span: 0 < y < 1
+ * y = 1/(1 + exp(-x)), y' = y*(1 - y)
+ */
+#define SIGMOID 1
+
+/* Symmetric sigmoid activation function, aka. tanh.
+ * span: -1 < y < 1
+ * y = tanh(x) = 2/(1 + exp(-2*x)) - 1, d = 1-(y*y)
+*/
+#define TANH 2
+
+// linear function
+float act_linear(float value)
+{ return value; }
+
+// derivation of the linear function
+float act_linear_derive(float value)
+{ return 1; }
+
+// sigmoid function
+float act_sigmoid(float value)
+{ return (1.0f/(1.0f + exp(-value))); }
+
+// derivation of the sigmoid function
+float act_sigmoid_derive(float value)
+{ return (value * (1.0f - value)); }
+
+// tanh function
+float act_tanh(float value)
+{ return (2.0f/(1.0f + exp(-2.0f * value)) - 1.0f); }
+
+// derivation of the tanh function
+float act_tanh_derive(float value)
+{ return (1.0f - (value*value)); }
+
+
+} // end of namespace 
+
+#endif // _INCLUDE_ACTIVATION_NET__
diff --git a/pix_recNN/NNException.h b/pix_recNN/NNException.h
new file mode 100755
index 0000000..bcb7be5
--- /dev/null
+++ b/pix_recNN/NNException.h
@@ -0,0 +1,49 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// NNDefines.h
+//
+//   global stuff for all the nets
+//
+//   header file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   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.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_NNDEFINES_NET__
+#define _INCLUDE_NNDEFINES_NET__
+
+#include <string>
+
+using std::string;
+
+namespace TheBrain
+{
+
+//------------------------------------------------------
+/* the exception class for all the neural network stuff
+ */
+class NNExcept
+{
+ protected:
+  string message_;
+
+ public:
+  NNExcept(string message="")
+    { message_ = message; }
+  virtual ~NNExcept() { }
+
+  virtual string what()
+  { return message_; }
+};
+
+} // end of namespace NNet
+
+#endif //_INCLUDE_NNDEFINES_NET__
+
diff --git a/pix_recNN/NNet.h b/pix_recNN/NNet.h
new file mode 100755
index 0000000..349688f
--- /dev/null
+++ b/pix_recNN/NNet.h
@@ -0,0 +1,636 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class NNet
+//
+//   this is a template for all the nets
+//   (see NeuralNet documentations for more information)
+//
+//   header file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   
+//   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.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_NEURAL_TEMPLATE_NET__
+#define _INCLUDE_NEURAL_TEMPLATE_NET__
+
+#include "NNActivation.h"
+#include "NNException.h"
+
+namespace TheBrain
+{
+
+template <class HiddNeuronType,class OutNeuronType>
+class NNet
+{
+ protected:
+
+  /* the number of output values
+   * this is automatically also the 
+   * number of output neurons !
+   */
+  int output_val_;
+
+  /* the number of hidden neurons
+   * per one output neuron
+   * (this net has one hidden layer,
+   * so this is the number of hidden
+   * neurons is hidden_val_*output_val_) 
+   */
+  int hidden_val_;
+
+  /* nr of input values per one output neuron
+   * (so the number of input values are
+   * input_val_*output_val_)
+   */
+  int input_val_;
+
+  /* the memory of the output layer
+   * if you use a recurrent neuron, this
+   * determines how much output values the 
+   * recurrent neurons can remeber
+   * these values are fed back as new input
+   */
+  int memory_out_;
+
+  /* the memory of the hidden layer
+   * if you use a recurrent neuron, this
+   * determines how much output values the 
+   * recurrent neurons can remeber
+   * these values are fed back as new input
+   */
+  int memory_hidden_;
+
+  /* these are the output neurons
+   */
+  OutNeuronType *out_neurons_;
+
+  /* these are the hidden neurons
+   */
+  HiddNeuronType *hidden_neurons_;
+
+  /* function pointer to the activation
+   * function of the output neurons
+   */
+  float (*output_act_f)(float value);
+
+  /* function pointer to the activation
+   * function of the hidden neurons
+   */
+  float (*hidden_act_f)(float value);
+
+  /* function pointer to the derivation of the
+   * activation function of the hidden neurons
+   */
+  float (*hidden_act_f_d)(float value);
+
+
+ public:
+
+  /* Constructor
+   */
+  NNet(int input_val=1, int hidden_val=1, int output_val=1, int memory_out=0,
+       int memory_hidden=1, int HIDDEN_ACT_FUNC=0, int OUT_ACT_FUNC=0);
+
+  /* Destructor
+   */
+  virtual ~NNet();
+
+
+  //-----------------------------------------------------
+
+  /* Set/Get learning rate
+   */
+  virtual void setLearningRate(float learn_rate);
+  virtual float getLearningRate() const;
+
+  /* Set/Get range
+   * (see Neuron.h)
+   */
+  virtual void setRange(float range);
+  virtual float getRange() const;
+
+  /* some more get/set methods
+   */
+  virtual void setOutputVal(int output_val)
+    throw();
+  virtual int getOutputVal() const;
+
+  virtual void setHiddenVal(int hidden_val)
+    throw();
+  virtual int getHiddenVal() const;
+
+  virtual void setInputVal(int input_val)
+    throw();
+  virtual int getInputVal() const;
+
+  virtual void setMemoryOut(int memory)
+    throw();
+  virtual int getMemoryOut() const;
+
+  virtual void setMemoryHidden(int memory)
+    throw();
+  virtual int getMemoryHidden() const;
+
+
+  //-----------------------------------------------------
+
+  /* creates the network
+   */
+  virtual void create()
+    throw(NNExcept);
+
+  /* inits the weight matrix and the bias vector of
+   * the network with random values between [min|max]
+   */
+  virtual void initRand(const int &min, const int &max)
+    throw(NNExcept);
+
+  /* calculates the output with the current Net and writes
+   * it in the array output_data
+   * ATTENTION: array input_data must be a matrix in the form:
+   *              float[output_val_][input_val_]
+   *            array output_data must be in size output_val_
+   *            (there is no checking !!!)
+   */
+  virtual void calculate(float **input_data, float *output_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, target_output is 
+   * the desired output data
+   * (this is the a truncated backpropagation through time
+   * algorithm to train the network)
+   * ATTENTION: array input_data must be a matrix in the form:
+   *              float[output_val_][input_val_]
+   *            array output_data must be in size output_val_
+   *            array target_output  must be in size output_val_
+   *            (there is no checking !!!)
+   */
+  virtual void trainBTT(float **input_data, float *output_data, 
+		     float *target_output);
+
+
+  //-----------------------------------------------------
+
+  /* saves the contents of the current net to file
+   */
+  virtual void save(string filename)
+    throw(NNExcept);
+
+  /* loads the parameters of the net from file
+   */
+  virtual void load(string filename)
+    throw(NNExcept);
+
+
+  //-----------------------------------------------------
+ private:
+
+  /* output of the hidden layer with activation function
+   */
+  float *hidden_a_;
+
+  /* output of the hidden layer without activation function
+   */
+  float *hidden_s_;
+
+  /* error signal of the neurons in the hidden layer
+   */
+  float *hidden_error_;
+
+  /* out signal without activation function
+   */
+  float out_s_;
+
+  /* error signal of the output layer
+   */
+  float out_error_;
+
+  /* Copy Construction is not allowed
+   */
+  NNet(const NNet<HiddNeuronType,OutNeuronType> &src)
+    { }
+
+  /* assignement operator is not allowed
+   */
+  const NNet<HiddNeuronType,OutNeuronType>& operator= 
+    (const NNet<HiddNeuronType,OutNeuronType>& src)
+    { return *this; }
+};
+
+
+//--------------------------------------------------
+/* Constructor
+ */
+template <class HiddNeuronType, class OutNeuronType>
+NNet<HiddNeuronType,OutNeuronType>
+  ::NNet(int input_val, int hidden_val, int output_val, int memory_out, 
+	 int memory_hidden, int HIDDEN_ACT_FUNC, int OUT_ACT_FUNC)
+  : out_neurons_(NULL), hidden_neurons_(NULL), hidden_a_(NULL),
+  hidden_s_(NULL), hidden_error_(NULL)
+{
+  output_val_ = (output_val<1) ? 1 : output_val;
+  hidden_val_ = (hidden_val<0) ? 0 : hidden_val;
+  input_val_ = (input_val<1) ? 1 : input_val;
+  memory_out_ = (memory_out<0) ? 0 : memory_out;
+  memory_hidden_ = (memory_hidden<0) ? 0 : memory_hidden;
+
+  // choose hidden activation function:
+  switch(HIDDEN_ACT_FUNC)
+    {
+    case SIGMOID:
+      hidden_act_f = act_sigmoid;
+      hidden_act_f_d = act_sigmoid_derive;
+      break;
+    case TANH:
+      hidden_act_f = act_tanh;
+      hidden_act_f_d = act_tanh_derive;
+      break;
+    default:
+    case LINEAR:
+      hidden_act_f = act_linear;
+      hidden_act_f_d = act_linear_derive;
+      break;
+    }
+
+  // choose out function:
+  switch(OUT_ACT_FUNC)
+    {
+    case SIGMOID:
+      output_act_f = act_sigmoid;
+      break;
+    case TANH:
+      output_act_f = act_tanh;
+      break;
+    default:
+    case LINEAR:
+      output_act_f = act_linear;
+      break;
+    }
+}
+
+//--------------------------------------------------
+/* Destructor
+ */
+template <class HiddNeuronType, class OutNeuronType>
+NNet<HiddNeuronType, OutNeuronType>::~NNet()
+{
+  if(hidden_neurons_)
+    delete[] hidden_neurons_;
+
+  if(out_neurons_)
+    delete[] out_neurons_;
+
+  if(hidden_a_)
+    delete[] hidden_a_;
+
+  if(hidden_s_)
+    delete[] hidden_s_;
+
+  if(hidden_error_)
+    delete[] hidden_error_;
+}
+
+//--------------------------------------------------
+/* creates the network
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::create()
+  throw(NNExcept)
+{
+  // delete if they exist
+  if(out_neurons_)
+    delete[] out_neurons_;
+  if(hidden_neurons_)
+    delete[] hidden_neurons_;
+  if(hidden_a_)
+    delete[] hidden_a_;
+  if(hidden_s_)
+    delete[] hidden_s_;
+  if(hidden_error_)
+    delete[] hidden_error_;
+
+
+  out_neurons_ = new OutNeuronType[output_val_](input_val_,memory_out_);
+  hidden_neurons_ = new HiddNeuronType[hidden_val_*output_val_](input_val_,memory_hidden_);
+
+  if(!out_neurons_ || !hidden_neurons_)
+    throw NNExcept("No memory for Neurons!");
+
+  // create the temporary storage
+  hidden_a_ = new float[hidden_val_];
+  hidden_s_ = new float[hidden_val_];
+  hidden_error_ = new float[hidden_val_];
+
+  if(!hidden_a_ || !hidden_s_ || !hidden_error_)
+    throw NNExcept("No memory for Neurons!");
+
+
+  // create all the neurons
+  for(int i=0; i<output_val_; i++)
+    out_neurons_[i].create();
+  for(int i=0; i<hidden_val_*output_val_; i++)
+    hidden_neurons_[i].create();
+}
+
+//--------------------------------------------------
+/* inits the weight matrix and the bias vector of
+ * the network with random values between [min|max]
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::initRand(const int &min, const int &max)
+    throw(NNExcept)
+{
+  if(!out_neurons_)
+    throw NNExcept("You must first create the Net!");
+
+  // init all the neurons
+  for(int i=0; i<output_val_; i++)
+    out_neurons_[i].initRand(min,max);
+  for(int i=0; i<hidden_val_*output_val_; i++)
+    hidden_neurons_[i].initRand(min,max);
+}
+
+//--------------------------------------------------
+/* calculates the output with the current Net and writes
+ * it in the array output_data
+ * ATTENTION: array input_data must be a matrix in the form:
+ *              float[output_val_][input_val_]
+ *            array output_data must be in size output_val_
+ *            (there is no checking !!!)
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::calculate(float **input_data, float *output_data)
+{
+  for(int i=0; i<output_val_; i++)
+    {
+
+      // 1.: calculation of the hidden layer
+      for(int j=0; j<hidden_val_; j++)
+	{
+	  hidden_a_[j] = hidden_act_f( 
+	    hidden_neurons_[i*hidden_val_+j].calculate(input_data[i]) );
+	}
+
+      // 2.: calculation of the output layer
+      *output_data++ = output_act_f( out_neurons_[i].calculate(hidden_a_) );
+    }
+}
+
+//--------------------------------------------------
+/* 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, target_output is 
+ * the desired output data
+ * (this is the a truncated backpropagation through time
+ * algorithm to train the network)
+ * ATTENTION: array input_data must be a matrix in the form:
+ *              float[output_val_][input_val_]
+ *            array output_data must be in size output_val_
+ *            array target_output  must be in size output_val_
+ *            (there is no checking !!!)
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::trainBTT(float **input_data, float *output_data, 
+			 float *target_output)
+{
+  post("train");
+
+  for(int i=0; i<output_val_; i++)
+    {
+
+
+      //---------------------------------------------------------
+      // 1. Forward - Pass:
+      // 
+      // the output of the hidden and the output-layer
+      // are calculated and saved (before and after
+      // the activation function)
+
+      // calculation of the hidden layer
+      for(int j=0; j<hidden_val_; j++)
+	{
+	  hidden_s_[j] = hidden_neurons_[i*hidden_val_+j].calculate(input_data[i]);
+	  hidden_a_[j] = hidden_act_f(hidden_s_[j]);
+	}
+
+      // calculation of the output layer
+      out_s_ = out_neurons_[i].calculate(hidden_a_);
+      output_data[i] = output_act_f(out_s_);
+ 
+
+      //---------------------------------------------------------
+      // 2. Backward - Pass:
+      // 
+      // calculation of the error signals
+      // (they are also stored)
+
+      // output layer
+      out_error_ = output_data[i] - target_output[i];
+      
+      // hidden layer:
+      for(int j=0; j<hidden_val_; j++)
+	{
+	  hidden_error_[j] = hidden_act_f_d( hidden_s_[j]+0.1 ) *
+	    ( out_error_ * out_neurons_[i].getIW(j) );
+	}
+
+
+      //---------------------------------------------------------
+      // 3. Modification of the weights:
+
+      for(int j=0; j<hidden_val_; j++)
+      {
+	// output layer:
+	out_neurons_[i].setIW(j, 
+	       out_neurons_[i].getIW(j) - 
+	       getLearningRate() * out_error_ 
+	       * hidden_a_[j] );
+
+	// hidden layer:
+	for(int k=0; k<input_val_; k++)
+	{
+	  hidden_neurons_[i*hidden_val_+j].setIW(k,
+	       hidden_neurons_[i*hidden_val_+j].getIW(k) -
+	       getLearningRate() * hidden_error_[j]
+	       * input_data[i][k]/hidden_neurons_[0].getRange() );
+	}
+
+
+	// recurrent part of the hidden layer:
+	float delta = getLearningRate() * hidden_error_[j] * hidden_a_[j];
+	for(int k=0; k<memory_hidden_; k++)
+	  {
+	    hidden_neurons_[i*hidden_val_+j].setLW(k,
+	       hidden_neurons_[i*hidden_val_+j].getLW(k) - delta);
+	  }
+      }
+
+      // recurrent part of the output layer:
+      float delta = getLearningRate() * out_error_ * output_data[i];
+      for(int j=0; j<memory_out_; j++)
+	{
+	  out_neurons_[i].setLW(j,
+	     out_neurons_[i].getLW(j) - delta);
+	}
+    
+
+    }
+}
+
+//--------------------------------------------------
+/* saves the contents of the current net to file
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::save(string filename)
+  throw(NNExcept)
+{
+
+}
+
+//--------------------------------------------------
+  /* loads the parameters of the net from file
+   */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::load(string filename)
+  throw(NNExcept)
+{
+
+}
+
+//-----------------------------------------------------
+/* Set/Get learning rate
+ * (see Neuron.h)
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setLearningRate(float learn_rate)
+{
+  learn_rate = (learn_rate<0) ? 0 : learn_rate;
+
+  for(int i=0; i<output_val_; i++)
+    out_neurons_[i].setLearningRate(learn_rate);
+  for(int i=0; i<hidden_val_*output_val_; i++)
+    hidden_neurons_[i].setLearningRate(learn_rate);
+}
+template <class HiddNeuronType, class OutNeuronType>
+float NNet<HiddNeuronType, OutNeuronType>::getLearningRate() const
+{
+  return out_neurons_[0].getLearningRate();
+}
+
+//-----------------------------------------------------
+/* Set/Get range
+ * (see Neuron.h)
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setRange(float range)
+{
+  for(int i=0; i<output_val_; i++)
+    out_neurons_[i].setRange(1);
+
+  for(int i=0; i<hidden_val_*output_val_; i++)
+    hidden_neurons_[i].setRange(range);
+}
+template <class HiddNeuronType, class OutNeuronType>
+float NNet<HiddNeuronType, OutNeuronType>::getRange() const
+{
+  return hidden_neurons_[0].getRange();
+}
+
+//-----------------------------------------------------
+/* get/set output_val_
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setOutputVal(int output_val)
+  throw()
+{
+  output_val_ = (output_val<1) ? 1 : output_val;
+
+  create();
+}
+template <class HiddNeuronType, class OutNeuronType>
+int NNet<HiddNeuronType,OutNeuronType>::getOutputVal() const
+{
+  return output_val_;
+}
+
+//-----------------------------------------------------
+/* get/set hidden_val_
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setHiddenVal(int hidden_val)
+  throw()
+{
+  hidden_val_ = (hidden_val<1) ? 1 : hidden_val;
+
+  create();
+}
+template <class HiddNeuronType, class OutNeuronType>
+int NNet<HiddNeuronType,OutNeuronType>::getHiddenVal() const
+{
+  return hidden_val_;
+}
+
+//-----------------------------------------------------
+/* get/set input_val_
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setInputVal(int input_val)
+    throw()
+{
+  input_val_ = (input_val<1) ? 1 : input_val;
+
+  create();
+}
+template <class HiddNeuronType, class OutNeuronType>
+int NNet<HiddNeuronType,OutNeuronType>::getInputVal() const
+{
+  return input_val_;
+}
+
+//-----------------------------------------------------
+/* get/set memory of the output layer
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setMemoryOut(int memory)
+    throw()
+{
+  memory_out_ = (memory<0) ? 0 : memory;
+
+  create();
+}
+template <class HiddNeuronType, class OutNeuronType>
+int NNet<HiddNeuronType,OutNeuronType>::getMemoryOut() const
+{
+  return memory_out_;
+}
+
+//-----------------------------------------------------
+/* get/set memory of the hidden layer
+ */
+template <class HiddNeuronType, class OutNeuronType>
+void NNet<HiddNeuronType,OutNeuronType>::setMemoryHidden(int memory)
+    throw()
+{
+  memory_hidden_ = (memory<0) ? 0 : memory;
+
+  create();
+}
+template <class HiddNeuronType, class OutNeuronType>
+int NNet<HiddNeuronType,OutNeuronType>::getMemoryHidden() const
+{
+  return memory_hidden_;
+}
+
+
+} // end of namespace
+
+#endif //_INCLUDE_LIN_NEURAL_NET__
diff --git a/pix_recNN/Neuron.cpp b/pix_recNN/Neuron.cpp
new file mode 100755
index 0000000..c020c1c
--- /dev/null
+++ b/pix_recNN/Neuron.cpp
@@ -0,0 +1,169 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class Neuron
+//
+//   source file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   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. 
+//
+/////////////////////////////////////////////////////////////////////////////
+
+#include "Neuron.h"
+
+namespace TheBrain
+{
+
+//--------------------------------------------------
+/* Constructor
+ */
+Neuron::Neuron(int inputs, int dummy)
+  : learn_rate_(0), range_(1), IW_(NULL), b1_(0)
+{
+  inputs_ = (inputs<1) ? 1 : inputs;
+}
+
+//--------------------------------------------------
+/* Destructor
+ */
+Neuron::~Neuron()
+{
+  if(IW_)
+    delete[] IW_;
+}
+
+//--------------------------------------------------
+/* creates a new IW-matrix (size: inputs_) and 
+ * b1-vector
+ * ATTENTION: if they exist they'll be deleted
+ */
+void Neuron::create()
+  throw(NNExcept)
+{
+  // delete if they exist
+  if(IW_)
+    delete[] IW_;
+
+  IW_ = new float[inputs_];
+  if(!IW_)
+    throw NNExcept("No memory for Neurons!");
+}
+
+//--------------------------------------------------
+/* inits the weight matrix and the bias vector of
+ * the network with random values between [min|max]
+ */
+void Neuron::initRand(const int &min, const int &max)
+  throw(NNExcept)
+{
+  if(!IW_)
+    throw NNExcept("You must first create the Net!");
+
+  // 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<inputs_; i++)
+    {
+      IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+    }
+
+  //post("b1: %f, IW: %f %f %f", b1_, IW_[0], IW_[1], IW_[2]);
+}
+
+//--------------------------------------------------
+/* 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 inputs !!!
+ */
+void Neuron::init(const float *IW, float b1)
+  throw(NNExcept)
+{
+  if(!IW_)
+    throw NNExcept("You must first create the Net!");
+
+  b1_ = b1;
+
+  for(int i=0; i<inputs_; i++)
+      IW_[i] = IW[i];
+}
+
+//--------------------------------------------------
+/* calculates the output with the current IW, b1 values
+ * ATTENTION: the array input_data must be in the same
+ *            size as inputs_
+ */
+float Neuron::calculate(float *input_data)
+{
+  float output = 0;
+
+  // multiply the inputs with the weight matrix IW
+  // and add the bias vector b1
+  for(int i=0; i<inputs_; i++)
+    {
+      output += input_data[i] * IW_[i];
+    }
+
+  // map input values to the range
+  output /= range_; 
+  
+  //post("b1: %f, IW: %f %f %f", b1_, IW_[0], IW_[1], IW_[2]);
+  //post("range: %f, in: %f %f %f, out: %f",range_,input_data[0],
+  //     input_data[1], input_data[2], output+b1_);
+
+  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 inputs_
+ * returns the calculated value
+ */
+// float Neuron::trainLMS(const float *input_data, 
+// 		       const float &target_output)
+// {
+//   float output = 0;
+
+//   // multiply the inputs with the weight matrix IW
+//   // and add the bias vector b1
+//   for(int i=0; i<inputs_; i++)
+//     {
+//       output += input_data[i] * IW_[i];
+//     }
+
+//   // map input values to the range
+//   output /= range_; 
+
+//   output += b1_;
+
+//   //------------
+
+//   // this is the LMS-algorithm to train linear
+//   // neural networks
+  
+//   // calculate the error signal:
+//   float error = (target_output - output);
+
+//   // now change the weights the bias
+//   for(int i=0; i<inputs_; i++)
+//     IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
+
+//   b1_ += 2 * learn_rate_ * error; 
+
+//   //------------
+
+//   return (output);
+// }
+
+} // end of namespace
diff --git a/pix_recNN/Neuron.h b/pix_recNN/Neuron.h
new file mode 100755
index 0000000..f10d993
--- /dev/null
+++ b/pix_recNN/Neuron.h
@@ -0,0 +1,191 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class Neuron
+//
+//   this is an implementation of one neuron of a Neural Network
+//   so this neuron has a Weight-Matrix IW and a bias vector b1
+//   this neuron can have n input values, but only one output value
+//   (see NeuralNet documentations for more information)
+//
+//   header file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   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.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_NEURON_NET__
+#define _INCLUDE_NEURON_NET__
+
+#include <stdlib.h>
+#include <stdexcept>
+#include "NNException.h"
+#include "m_pd.h" //debug
+
+namespace TheBrain
+{
+
+//------------------------------------------------------
+/* class of one neuron
+ */
+class Neuron
+{
+ protected:
+
+  /* this is the number of input values, which is
+   * automatically the input and the size of IW
+   */
+  int inputs_;
+
+  /* the input weight matrix IW
+   * (size: inputs )
+   */
+  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
+   */
+  Neuron(int inputs, int dummy=0);
+
+  /* Destructor
+   */
+  virtual ~Neuron();
+
+
+  //-----------------------------------------------------
+
+  /* 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 getInputs() const
+  {  return inputs_; }
+
+  virtual float *getIW() const
+  {  return IW_; }
+  virtual float getIW(int index) const
+  {  return IW_[index]; }
+  
+  virtual void setIW(const float *IW)
+  {  for(int i=0; i<inputs_; i++) IW_[i] = IW[i]; }
+  virtual void setIW(int index, float value)
+  {  IW_[index] = value; }
+  
+  virtual float getb1() const
+  {  return b1_; }
+  virtual void setb1(float b1)
+  {  b1_ = b1; }
+
+
+  /* dummies
+   */
+
+  virtual int getMemory() const
+  {  return 0; }
+
+  virtual float *getLW() const
+  {  return NULL; }
+  virtual float getLW(int index) const
+  {  return 0; }
+
+  virtual void setLW(const float *LW)
+  {   }
+  virtual void setLW(int index, float value)
+  {   }
+
+
+  //-----------------------------------------------------
+
+  /* creates a new IW-matrix (size: inputs_) and 
+   * b1-vector
+   * ATTENTION: if they exist they'll be deleted
+   */
+  virtual void create()
+    throw(NNExcept);
+
+  /* inits the weight matrix and the bias vector of
+   * the network with random values between [min|max]
+   */
+  virtual void initRand(const int &min, const int &max)
+    throw(NNExcept);
+
+  /* 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 inputs !!!
+   */
+  virtual void init(const float *IW, float b1)
+    throw(NNExcept);
+
+  /* calculates the output with the current IW, b1 values
+   * ATTENTION: the array input_data must be in the same
+   *            size as inputs_
+   */
+  virtual float calculate(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)
+   * ATTENTION: the array input_data must be in the same
+   *            size as inputs_
+   * returns the calculated value
+   */
+/*   virtual float trainLMS(const float *input_data,  */
+/* 			 const float &target_output); */
+
+
+  //-----------------------------------------------------
+ private:
+
+  /* Copy Construction is not allowed
+   */
+  Neuron(const Neuron &src)
+    { }
+
+  /* assignement operator is not allowed
+   */
+  const Neuron& operator= (const Neuron& src)
+    { return *this; }
+};
+
+
+} // end of namespace
+
+#endif //_INCLUDE_NEURON_NET__
diff --git a/pix_recNN/RecurrentNeuron.cpp b/pix_recNN/RecurrentNeuron.cpp
new file mode 100755
index 0000000..1b322c1
--- /dev/null
+++ b/pix_recNN/RecurrentNeuron.cpp
@@ -0,0 +1,226 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class RecurrentNeuron
+//
+//   source file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   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. 
+//
+/////////////////////////////////////////////////////////////////////////////
+
+#include "RecurrentNeuron.h"
+
+namespace TheBrain
+{
+
+//--------------------------------------------------
+/* Constructor
+ */
+RecurrentNeuron::RecurrentNeuron(int inputs, int memory)
+  : Neuron(inputs), LW_(NULL), mem_data_(NULL)
+{
+  memory_ = (memory<0) ? 1 : memory+1;
+}
+
+//--------------------------------------------------
+/* Destructor
+ */
+RecurrentNeuron::~RecurrentNeuron()
+{
+   if(LW_)
+     delete[] LW_;
+
+   if(mem_data_)
+     delete[] mem_data_;
+}
+
+//--------------------------------------------------
+/* creates a new IW-matrix (size: inputs_) and 
+ * b1-vector
+ * ATTENTION: if they exist they'll be deleted
+ */
+void RecurrentNeuron::create()
+  throw(NNExcept)
+{
+  // delete if they exist
+  if(IW_)
+    delete[] IW_;
+  if(LW_)
+    delete[] LW_;
+  if(mem_data_)
+    delete[] mem_data_;
+
+  IW_ = new float[inputs_];
+  LW_ = new float[memory_];
+  mem_data_ = new float[memory_];
+
+  if(!IW_ || !LW_ || !mem_data_)
+    throw NNExcept("No memory for Neurons!");
+
+  index_=0;
+}
+
+//--------------------------------------------------
+/* inits the weight matrix and the bias vector of
+ * the network with random values between [min|max]
+ */
+void RecurrentNeuron::initRand(const int &min, const int &max)
+  throw(NNExcept)
+{
+  if(!IW_ || !LW_)
+    throw NNExcept("You must first create the Net!");
+
+  // 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<inputs_; i++)
+    {
+      IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+    }
+  for(int i=0; i<memory_; i++)
+    {
+      //LW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+      LW_[i] = ((float)rand()/(float)RAND_MAX)*(min);
+    }
+}
+
+//--------------------------------------------------
+/* inits the net with given weight matrix and bias
+ * (makes a deep copy)
+ * ATTENTION: the dimension of IW-pointer must be the same
+ *            as the inputs (also for LW) !!!
+ */
+void RecurrentNeuron::init(const float *IW, const float *LW, float b1)
+  throw(NNExcept)
+{
+  if(!IW_ || !LW_)
+    throw NNExcept("You must first create the Net!");
+
+  b1_ = b1;
+
+  for(int i=0; i<inputs_; i++)
+      IW_[i] = IW[i];
+  for(int i=0; i<memory_; i++)
+      LW_[i] = LW[i];
+}
+
+//--------------------------------------------------
+/* calculates the output with the current IW, b1 values
+ * ATTENTION: the array input_data must be in the same
+ *            size as inputs_
+ */
+float RecurrentNeuron::calculate(float *input_data)
+{
+  float output = 0;
+
+  // multiply the inputs with the weight matrix IW
+  for(int i=0; i<inputs_; i++)
+    {
+      output += input_data[i] * IW_[i];
+    }
+
+  // map input values to the range
+  output /= range_; 
+
+  // multiply memory with weight matrix LW
+  // the index is used to make something
+  // like a simple list or ringbuffer
+  for(int i=0; i<memory_; i++)
+    {
+      output += mem_data_[index_] * LW_[i];
+      index_ = (index_+1) % memory_;
+    }
+
+  // now add bias
+  output += b1_;
+
+  // finally save the new output in memory
+  mem_data_[index_] = output;
+  index_ = (index_+1) % memory_;
+
+  //post("input: %f %f %f, IW: %f %f %f, b: %f",
+  //      input_data[0], input_data[1], input_data[2],
+  //      IW_[0], IW_[1], IW_[2], b1_);
+  //post("output: %f",output);
+
+  return (output);
+}
+
+//--------------------------------------------------
+/* 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 inputs_
+ * returns the calculated output
+ */
+// float RecurrentNeuron::trainLMS(const float *input_data, 
+// 				const float &target_output)
+// {
+//   // calculate output value:
+
+//   float output = 0;
+
+//   // multiply the inputs with the weight matrix IW
+//   for(int i=0; i<inputs_; i++)
+//     {
+//       output += input_data[i] * IW_[i];
+//     }
+
+//   // map input values to the range
+//   output /= range_; 
+
+//   // multiply memory with weight matrix LW
+//   // the index is used to make something
+//   // like a simple list or ringbuffer
+//   for(int i=0; i<memory_; i++)
+//     {
+//       output += mem_data_[index_] * LW_[i];
+//       index_ = (index_+1) % memory_;
+//     }
+
+//   // now add bias
+//   output += b1_;
+
+//   //----------------
+
+//   // this is the LMS-algorithm to train linear
+//   // neural networks
+
+//   // calculate the error signal:
+//   float error = (target_output - output);
+
+//   // now change IW
+//   for(int i=0; i<inputs_; i++)
+//     IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
+
+//   // change LW
+//   for(int i=0; i<memory_; i++)
+//     {
+//       LW_[i] += 2 * learn_rate_ * error * mem_data_[index_];
+//       index_ = (index_+1) % memory_;     
+//     }
+
+//   // and the bias
+//   b1_ += 2 * learn_rate_ * error; 
+
+//   //-----------------
+
+//   // finally save the new output in memory
+//   mem_data_[index_] = output;
+//   index_ = (index_+1) % memory_;
+
+//   return (output);
+// }
+
+
+} // end of namespace
diff --git a/pix_recNN/RecurrentNeuron.h b/pix_recNN/RecurrentNeuron.h
new file mode 100755
index 0000000..ee87068
--- /dev/null
+++ b/pix_recNN/RecurrentNeuron.h
@@ -0,0 +1,149 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class RecurrentNeuron
+//
+//   this is an implementation of one neuron of a Recurrent Neural Network
+//   this neuron can have n input values, m values in it's memory and
+//   one output value
+//   (see NeuralNet documentations for more information)
+//
+//   header file
+//
+//   Copyright (c) 2005 Georg Holzmann <grh@gmx.at>
+//
+//   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.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_RECURRENT_NEURON_NET__
+#define _INCLUDE_RECURRENT_NEURON_NET__
+
+#include <stdlib.h>
+#include <stdexcept>
+#include "Neuron.h"
+
+namespace TheBrain
+{
+
+//------------------------------------------------------
+/* class of one neuron
+ */
+class RecurrentNeuron : public Neuron
+{
+ protected:
+
+  /* this determines how much output values the net
+   * can remeber
+   * these values are fed back as new input
+   */
+  int memory_;
+
+  /* the weight matrix for the recurrent 
+   * values (size: memory_)
+   */
+  float *LW_;
+
+
+ public:
+
+  /* Constructor
+   */
+  RecurrentNeuron(int inputs, int memory);
+
+  /* Destructor
+   */
+  virtual ~RecurrentNeuron();
+
+
+  //-----------------------------------------------------
+  /* some more get/set methods
+   */
+
+  virtual int getMemory() const
+  {  return memory_; }
+
+  virtual float *getLW() const
+  {  return LW_; }
+  virtual float getLW(int index) const
+  {  return LW_[index]; }
+
+  virtual void setLW(const float *LW)
+  {  for(int i=0; i<inputs_; i++) LW_[i] = LW[i]; }
+  virtual void setLW(int index, float value)
+  {  LW_[index] = value; }
+
+
+  //-----------------------------------------------------
+
+  /* creates a new IW-matrix (size: inputs_) and 
+   * b1-vector
+   * ATTENTION: if they exist they'll be deleted
+   */
+  virtual void create()
+    throw(NNExcept);
+
+  /* inits the weight matrix and the bias vector of
+   * the network with random values between [min|max]
+   */
+  virtual void initRand(const int &min, const int &max)
+    throw(NNExcept);
+
+  /* inits the net with given weight matrix and bias
+   * (makes a deep copy)
+   * ATTENTION: the dimension of IW-pointer must be the same
+   *            as the inputs (also for LW) !!!
+   */
+  virtual void init(const float *IW, const float *LW, float b1)
+    throw(NNExcept);
+
+  /* calculates the output with the current IW, b1 values
+   * ATTENTION: the array input_data must be in the same
+   *            size as inputs_
+   */
+  virtual float calculate(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)
+   * ATTENTION: the array input_data must be in the same
+   *            size as inputs_
+   * returns the calculated output
+   */
+/*   virtual float trainLMS(const float *input_data,  */
+/* 			 const float &target_output); */
+
+
+  //-----------------------------------------------------
+ private:
+
+  /* the storage for the memory data
+   */
+  float *mem_data_;
+
+  /* this index is used to make something
+   * like a simple list or ringbuffer
+   */
+  int index_;
+
+  /* Copy Construction is not allowed
+   */
+  RecurrentNeuron(const RecurrentNeuron &src) : Neuron(1)
+    { }
+
+  /* assignement operator is not allowed
+   */
+    const RecurrentNeuron& operator= (const RecurrentNeuron& src)
+    { return *this; }
+};
+
+
+} // end of namespace
+
+#endif //_INCLUDE_RECURRENT_NEURON_NET__
diff --git a/pix_recNN/gpl.txt b/pix_recNN/gpl.txt
new file mode 100755
index 0000000..5ea29a7
--- /dev/null
+++ b/pix_recNN/gpl.txt
@@ -0,0 +1,346 @@
+		    GNU GENERAL PUBLIC LICENSE
+		       Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc.
+                       59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+			    Preamble
+ 
+  The licenses for most software are designed to take away your
+freedom to share and change it.  By contrast, the GNU General Public
+License is intended to guarantee your freedom to share and change free
+software--to make sure the software is free for all its users.  This
+General Public License applies to most of the Free Software
+Foundation's software and to any other program whose authors commit to
+using it.  (Some other Free Software Foundation software is covered by
+the GNU Library General Public License instead.)  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
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+possible use to the public, the best way to achieve this is to make it
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+
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+This General Public License does not permit incorporating your program into
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+
diff --git a/pix_recNN/help-pix_recNN.pd b/pix_recNN/help-pix_recNN.pd
new file mode 100755
index 0000000..4236941
--- /dev/null
+++ b/pix_recNN/help-pix_recNN.pd
@@ -0,0 +1,146 @@
+#N canvas 871 74 498 783 10;
+#X obj 36 327 gemwin;
+#X msg 36 301 create \, 1;
+#N canvas 75 72 765 790 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 msg 381 708 getmemory;
+#X msg 361 639 memory 0;
+#X msg 361 660 memory 1;
+#X obj 61 252 pix_recNN;
+#X text 296 49 <- input dimension;
+#X obj 78 226 r \$0-recNN;
+#X obj 62 564 s \$0-recNN;
+#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 obj 407 492 s \$0-recNN;
+#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 obj 65 725 s \$0-recNN;
+#X msg 361 681 memory 3;
+#X obj 361 741 s \$0-recNN;
+#X text 343 543 memory:;
+#X text 356 565 this determines \, how much values from the past the
+recurrent net considers in the calculation;
+#X text 357 604 (be carefull with large values !!!);
+#X msg 62 456 precision 1;
+#X msg 62 436 precision 4;
+#X obj 404 233 s \$0-recNN;
+#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 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 20 0;
+#X connect 6 0 1 0;
+#X connect 7 0 6 0;
+#X connect 9 0 6 0;
+#X connect 10 0 23 0;
+#X connect 11 0 38 0;
+#X connect 12 0 38 0;
+#X connect 13 0 33 0;
+#X connect 14 0 46 0;
+#X connect 15 0 20 0;
+#X connect 16 0 38 0;
+#X connect 17 0 40 0;
+#X connect 18 0 40 0;
+#X connect 19 0 40 0;
+#X connect 20 1 2 0;
+#X connect 22 0 20 0;
+#X connect 24 0 23 0;
+#X connect 25 0 24 0;
+#X connect 30 0 45 0;
+#X connect 31 0 33 0;
+#X connect 32 0 33 0;
+#X connect 39 0 40 0;
+#X connect 44 0 23 0;
+#X connect 45 0 23 0;
+#X restore 89 542 pd pix2sig_stuff~;
+#X msg 110 302 0 \, destroy;
+#X obj 116 587 unsig~;
+#X obj 206 432 osc~ 440;
+#X obj 205 456 *~;
+#X obj 237 456 tgl 15 0 empty empty empty 0 -6 0 8 -262144 -1 -1 0
+1;
+#X obj 207 496 sig~ 0;
+#X floatatom 117 608 8 0 0 0 - - -;
+#X text 25 23 pix_recNN:;
+#X text 24 57 pix_recNN is an instument/interface. This instrument
+should be useful as a general experimental video interface to generate
+audio. You can train the neural net with playing audio samples to specific
+video frames in real-time. The main interest for me was not to train
+the net exactly to reproduce these samples \, but to make experimental
+sounds \, which are "between" all the trained samples.;
+#X text 22 214 (but this version is unfinished - e.g. the training
+algorithm must be tuned etc. - so it's only a very basic prototype...)
+;
+#X text 207 320 <- create gemwin;
+#X obj 41 442 readsf~;
+#X obj 41 401 openpanel;
+#X msg 41 421 open \$1;
+#X obj 41 380 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 67 379 <- load sample for training;
+#X obj 122 417 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0
+1;
+#X floatatom 206 414 5 0 0 0 - - -;
+#X text 272 431 <- simple osc for training;
+#X text 262 497 <- to train silence;
+#X obj 85 463 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
+-1;
+#X text 216 541 <- audio/video work;
+#X obj 90 684 dac~;
+#X obj 90 659 *~;
+#X obj 118 659 dbtorms;
+#X floatatom 118 641 5 0 0 0 - - -;
+#X text 168 638 <- outvol in dB;
+#X text 22 170 pix_recNN uses a 2 layer recurrent neural net (for more
+detailed info look at the source code.);
+#X text 119 737 Georg Holzmann <grh@mur.at> \, 2004;
+#X connect 1 0 0 0;
+#X connect 2 0 4 0;
+#X connect 2 0 26 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 14 0 2 0;
+#X connect 14 1 23 0;
+#X connect 15 0 16 0;
+#X connect 16 0 14 0;
+#X connect 17 0 15 0;
+#X connect 19 0 14 0;
+#X connect 20 0 5 0;
+#X connect 26 0 25 0;
+#X connect 26 0 25 1;
+#X connect 27 0 26 1;
+#X connect 28 0 27 0;
diff --git a/pix_recNN/pix_recNN.cpp b/pix_recNN/pix_recNN.cpp
new file mode 100755
index 0000000..299625a
--- /dev/null
+++ b/pix_recNN/pix_recNN.cpp
@@ -0,0 +1,423 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+//   GEM - Graphics Environment for Multimedia
+//
+//   pix_recNN
+//
+//   Implementation file
+//
+//   Copyright (c) 2005 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_recNN.h"
+
+CPPEXTERN_NEW_WITH_THREE_ARGS(pix_recNN, t_floatarg, A_DEFFLOAT,
+            t_floatarg, A_DEFFLOAT, t_floatarg, A_DEFFLOAT)
+
+//----------------------------------------------------------
+/* Constructor
+ */
+  pix_recNN::pix_recNN(t_floatarg arg0=64, t_floatarg arg1=1, t_floatarg arg2=1) :
+    m_data_(NULL), m_xsize_(0), m_ysize_(0), m_csize_(0),
+    train_on_(false), net_(NULL), temp_pix_(NULL)
+{
+  // init args ?????????????????????????????????
+  neuron_nr_=2048;          //static_cast<int>((arg0<0)?2:arg0);
+  memory_=0;
+  precision_=2;          //static_cast<int>((arg2<1)?1:arg2);
+  //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) );
+
+  // build the net
+  buildNewNet();
+}
+
+//----------------------------------------------------------
+/* Destructor
+ */
+pix_recNN::~pix_recNN()
+{
+  outlet_free(out0_);
+  m_data_ = NULL;
+  m_xsize_ = 0;
+  m_ysize_ = 0;
+
+  // delete net
+  delete net_;
+
+  // delete temp_pix_
+  for(int i=0; i<neuron_nr_; i++)
+    delete[] temp_pix_[i];
+  delete[] temp_pix_;
+}
+
+//----------------------------------------------------------
+/* a helper to build a new net
+ */
+void pix_recNN::buildNewNet()
+{
+  try
+    {
+      if(net_)
+  delete net_;
+
+      if(temp_pix_)
+  {
+    for(int i=0; i<neuron_nr_; i++)
+      delete[] temp_pix_[i];
+    delete[] temp_pix_;
+  }
+
+      // create the net
+      net_ = new NNet<RecurrentNeuron,RecurrentNeuron>(3,3,neuron_nr_,memory_,
+                   0,TANH,LINEAR);
+      if(!net_)
+  {
+    post("pix_recNN~: no memory for neural nets!");
+    net_=NULL;
+    return;
+  }
+
+      // create the temp_pix
+      temp_pix_ = new float*[neuron_nr_];
+      if(!temp_pix_)
+  {
+    post("pix_recNN~: no memory for temp_pix_!");
+    temp_pix_=NULL;
+    return;
+  }
+      for(int i=0; i<neuron_nr_; i++)
+  {
+    temp_pix_[i] = new float[3];
+    if(!temp_pix_[i])
+      {
+        post("pix_recNN~: no memory for temp_pix_!");
+        temp_pix_=NULL;
+        return;
+      }
+  }
+
+      // initialize temp_pix_ with 0
+      for(int i=0; i<neuron_nr_; i++)
+  {
+    for(int j=0; j<3; j++)
+      {
+        temp_pix_[i][j] = 0;
+      }
+  }
+
+      // init the net
+      net_->create();
+      net_->initRand(-1,1);
+      net_->setRange(255);
+      net_->setLearningRate(0.01);
+    }
+  catch(NNExcept &exc)
+     {
+       post("pix_recNN: %s", exc.what().c_str());
+     }
+}
+
+//----------------------------------------------------------
+/* processImage
+ */
+void pix_recNN::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_recNN::perform(t_int* w)
+{
+  pix_recNN *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_recNN~: 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
+      // the storage to sum the pixels:
+      unsigned long int temp_data[3] = { 0, 0, 0 };
+
+      // 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++;
+      }
+        }
+
+      x->temp_pix_[n][0] = temp_data[0] / add_count;
+      x->temp_pix_[n][1] = temp_data[1] / add_count;
+      x->temp_pix_[n][2] = temp_data[2] / add_count;
+    }
+
+  // learning, or calculation:
+  if(!x->train_on_)
+    x->net_->calculate(x->temp_pix_, out_signal);
+  else
+    x->net_->trainBTT(x->temp_pix_, 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_recNN::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
+ */
+void pix_recNN::saveNet(string filename)
+{
+  try
+    {
+      net_->save(filename);
+      post("pix_recNN~: saved to output-file %s", filename.c_str());
+    }
+  catch(NNExcept &exc)
+     {
+       post("pix_recNN: %s", exc.what().c_str());
+     }
+}
+
+//----------------------------------------------------------
+/* loads the parameters of the net from file
+ */
+void pix_recNN::loadNet(string filename)
+{
+  try
+    {
+      net_->load(filename);
+      post("pix_recNN~: loaded file %s", filename.c_str());
+    }
+  catch(NNExcept &exc)
+     {
+       post("pix_recNN: %s", exc.what().c_str());
+     }
+}
+
+//----------------------------------------------------------
+/* setup callback
+ */
+void pix_recNN::obj_setupCallback(t_class *classPtr)
+{
+  class_addcreator((t_newmethod)_classpix_recNN, gensym("pix_recNN~"), A_NULL);
+
+  class_addmethod(classPtr, (t_method)pix_recNN::setNeurons,
+      gensym("neurons"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::getNeurons,
+      gensym("getneurons"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::setMemory,
+      gensym("memory"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::getMemory,
+      gensym("getmemory"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::setPrecision,
+      gensym("precision"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::getPrecision,
+      gensym("getprecision"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::setTrainOn,
+      gensym("train"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::setLearnrate,
+      gensym("learnrate"), A_FLOAT, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::getLearnrate,
+      gensym("getlearnrate"), A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::saveToFile,
+      gensym("save"), A_SYMBOL, A_NULL);
+  class_addmethod(classPtr, (t_method)pix_recNN::loadFromFile,
+      gensym("load"), A_SYMBOL, A_NULL);
+
+  class_addmethod(classPtr, (t_method)pix_recNN::dspMessCallback,
+      gensym("dsp"), A_NULL);
+  class_addmethod(classPtr, nullfn, gensym("signal"), A_NULL);
+}
+
+//----------------------------------------------------------
+/* DSP callback
+ */
+void pix_recNN::dspMessCallback(void *data, t_signal** sp)
+{
+  GetMyClass(data)->dspMess(data, sp);
+}
+
+//----------------------------------------------------------
+/* sets the precision
+ */
+void pix_recNN::setPrecision(void *data, t_floatarg precision)
+{
+  GetMyClass(data)->precision_ =
+    (precision<1) ? 1 : static_cast<int>(precision);
+}
+void pix_recNN::getPrecision(void *data)
+{
+  post("pix_recNN~: precision: %d",GetMyClass(data)->precision_);
+}
+
+//----------------------------------------------------------
+/* method to train the network
+ */
+void pix_recNN::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 net will be initialized
+ */
+void pix_recNN::setNeurons(void *data, t_floatarg neurons)
+{
+  GetMyClass(data)->neuron_nr_ =
+    (neurons<1) ? 1 : static_cast<int>(neurons);
+
+  GetMyClass(data)->buildNewNet();
+}
+void pix_recNN::getNeurons(void *data)
+{
+  post("pix_recNN~: nr of neurons: %d (MUST be the same as buffersize!)",
+       GetMyClass(data)->neuron_nr_);
+}
+
+//----------------------------------------------------------
+/* changes the nblock size
+ * ATTENTION: a new net will be initialized
+ */
+void pix_recNN::setMemory(void *data, t_floatarg memory)
+{
+  GetMyClass(data)->memory_ =
+    (memory<0) ? 0 : static_cast<int>(memory);
+
+  GetMyClass(data)->buildNewNet();
+}
+void pix_recNN::getMemory(void *data)
+{
+  post("pix_recNN~: memory: %d",
+       GetMyClass(data)->memory_);
+}
+
+//----------------------------------------------------------
+/* sets the learnrate of the net
+ */
+void pix_recNN::setLearnrate(void *data, t_floatarg learn_rate)
+{
+  GetMyClass(data)->net_->setLearningRate(learn_rate);
+}
+void pix_recNN::getLearnrate(void *data)
+{
+  post("pix_recNN~: learning rate: %f",GetMyClass(data)->net_->getLearningRate());
+}
+
+//----------------------------------------------------------
+/* FileIO-stuff
+ */
+void pix_recNN::saveToFile(void *data, t_symbol *filename)
+{
+  GetMyClass(data)->saveNet(filename->s_name);
+}
+void pix_recNN::loadFromFile(void *data, t_symbol *filename)
+{
+  GetMyClass(data)->loadNet(filename->s_name);
+}
diff --git a/pix_recNN/pix_recNN.h b/pix_recNN/pix_recNN.h
new file mode 100755
index 0000000..944ebd3
--- /dev/null
+++ b/pix_recNN/pix_recNN.h
@@ -0,0 +1,204 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+//   GEM - Graphics Environment for Multimedia
+//
+//   pix_recNN~
+//   Calculates an audio signal out of a video frame
+//   with a recurrent neural network
+//
+//   (see RecurrentNeuralNet.h for more info)
+//
+//   header file
+//
+//   Copyright (c) 2005 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_RECNN_H__
+#define _INCLUDE_PIX_RECNN_H__
+
+#include <string>
+#include <sstream>
+#include <fstream>
+#include "Base/GemPixObj.h"
+#include "NNet.h"
+#include "RecurrentNeuron.h"
+
+
+using std::string;
+using std::endl;
+using std::ifstream;
+using std::ofstream;
+using std::istringstream;
+
+using namespace TheBrain;
+
+
+/*-----------------------------------------------------------------
+ *  CLASS
+ * pix_recNN~
+ *
+ * calculates an audio signal out of a video frame with
+ * a recurrent neural network
+ *
+ * KEYWORDS
+ * pix audio
+ *
+ * DESCRIPTION
+ * 1 signal-outlet
+ */
+class GEM_EXTERN pix_recNN : public GemPixObj
+{
+  CPPEXTERN_HEADER(pix_recNN, GemPixObj)
+
+ public:
+
+  /* Constructor
+   */
+  pix_recNN(t_floatarg arg0, t_floatarg arg1, t_floatarg arg2);
+
+ protected:
+
+  /* Destructor
+   */
+  virtual ~pix_recNN();
+
+
+  //-----------------------------------
+  /* 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_;
+
+  /* temporary float for calculation
+   */
+  float **temp_pix_;
+
+  /* processImage
+   */
+  virtual void processImage(imageStruct &image);
+
+
+  //-----------------------------------
+  /* Neural Network STUFF:
+   */
+
+  /* the neural net
+   * (size: buffsize)
+   */
+  NNet<RecurrentNeuron,RecurrentNeuron> *net_;
+
+  /* training modus on
+   * (will only be on for one audio buffer)
+   */
+  bool train_on_;
+
+  /* the number of neurons, which should be
+   * THE SAME as the audio buffer size
+   */
+  int neuron_nr_;
+
+  /* memory determines, how much results from the past
+   * are used to calculate an output value
+   * (0 means only the result from the current frame,
+   * 2 also from the last frame, etc.)
+   */
+  int memory_;
+
+
+  //-----------------------------------
+  /* 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
+   */
+  virtual void saveNet(string filename);
+
+  /* loads the parameters of the net from file
+   */
+  virtual void loadNet(string filename);
+
+ private:
+
+  /* a helper to build a new net
+   */
+  virtual void buildNewNet();
+
+  //-----------------------------------
+  /* 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 net will be initialized
+   */
+  static void setNeurons(void *data, t_floatarg neurons);
+  static void getNeurons(void *data);
+
+  /* changes the nblock size
+   * ATTENTION: a new net will be initialized
+   */
+  static void setMemory(void *data, t_floatarg memory);
+  static void getMemory(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_recNN/readme.txt b/pix_recNN/readme.txt
new file mode 100755
index 0000000..6372504
--- /dev/null
+++ b/pix_recNN/readme.txt
@@ -0,0 +1,27 @@
+pix_recNN - 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