[PD-cvs] externals/grh/pix_recNN Makefile, NONE, 1.1 NNActivation.h, NONE, 1.1 NNException.h, NONE, 1.1 NNet.h, NONE, 1.1 Neuron.cpp, NONE, 1.1 Neuron.h, NONE, 1.1 RecurrentNeuron.cpp, NONE, 1.1 RecurrentNeuron.h, NONE, 1.1 gpl.txt, NONE, 1.1 help-pix_recNN.pd, NONE, 1.1 pix_recNN.cpp, NONE, 1.1 pix_recNN.h, NONE, 1.1 readme.txt, NONE, 1.1 - Pd-cvs

12 Jul 2005


      Update of /cvsroot/pure-data/externals/grh/pix_recNN
In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv14254/pix_recNN
Added Files:
    Makefile NNActivation.h NNException.h NNet.h Neuron.cpp 
    Neuron.h RecurrentNeuron.cpp RecurrentNeuron.h gpl.txt 
    help-pix_recNN.pd pix_recNN.cpp pix_recNN.h readme.txt 
Log Message:
initial commit of pix_recNN
--- NEW FILE: RecurrentNeuron.cpp ---
/////////////////////////////////////////////////////////////////////////////
//
// 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
--- NEW FILE: gpl.txt ---
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--- NEW FILE: NNet.h ---
/////////////////////////////////////////////////////////////////////////////
//
// 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__
--- NEW FILE: NNActivation.h ---
/////////////////////////////////////////////////////////////////////////////
//
// 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__
--- NEW FILE: help-pix_recNN.pd ---
#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);
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#X connect 18 0 40 0;
#X connect 19 0 40 0;
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#X msg 110 302 0 , destroy;
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#X obj 205 456 *~;
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1;
#X obj 207 496 sig~ 0;
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#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 $1;
#X obj 41 380 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
-1;
#X text 67 379 <- load sample for training;
#X obj 122 417 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0
1;
#X floatatom 206 414 5 0 0 0 - - -;
#X text 272 431 <- simple osc for training;
#X text 262 497 <- to train silence;
#X obj 85 463 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
-1;
#X text 216 541 <- audio/video work;
#X obj 90 684 dac~;
#X obj 90 659 *~;
#X obj 118 659 dbtorms;
#X floatatom 118 641 5 0 0 0 - - -;
#X text 168 638 <- outvol in dB;
#X text 22 170 pix_recNN uses a 2 layer recurrent neural net (for more
detailed info look at the source code.);
#X text 119 737 Georg Holzmann grh@mur.at , 2004;
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--- NEW FILE: Makefile ---
PD-PATH=/usr/lib/pd
PD-SCR=/usr/include
# location of the GEM sources and Gem.pd_linux:
GEM-SCR=/home/Georg/pd-cvs/gem/Gem/src
GEM-LIB=$(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
--- NEW FILE: NNException.h ---
/////////////////////////////////////////////////////////////////////////////
//
// 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__
--- NEW FILE: pix_recNN.cpp ---
/////////////////////////////////////////////////////////////////////////////
//
//   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);
}
--- NEW FILE: Neuron.cpp ---
/////////////////////////////////////////////////////////////////////////////
//
// 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
--- NEW FILE: RecurrentNeuron.h ---
/////////////////////////////////////////////////////////////////////////////
//
// 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__
--- NEW FILE: pix_recNN.h ---
/////////////////////////////////////////////////////////////////////////////
//
//   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
--- NEW FILE: Neuron.h ---
/////////////////////////////////////////////////////////////////////////////
//
// 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__
--- NEW FILE: readme.txt ---
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