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+/////////////////////////////////////////////////////////////////////////////
+//
+// class LinNeuralNet
+//
+//   this is an implementation of a simple linear neural net with one neuron
+//   so this net has a Weight-Matrix IW and a bias vector b1
+//   this net can have n input values, but only one output value
+//   (see NeuralNet documentations for more information)
+//
+//   header file
+//
+//   Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
+//
+//   For information on usage and redistribution, and for a DISCLAIMER OF ALL
+//   WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+
+#ifndef _INCLUDE_LIN_NEURAL_NET__
+#define _INCLUDE_LIN_NEURAL_NET__
+
+#include <stdlib.h>
+#include <ctime>
+//#include "m_pd.h"   // for debug
+
+class LinNeuralNet
+{
+ protected:
+
+  /* this is the number of input values, which is
+   * automatically the netsize and the size of IW
+   */
+  int netsize_;
+
+  /* the input weight matrix IW
+   * (size: netsize )
+   */
+  float *IW_;
+
+  /* the bias vector b1
+   */
+  float b1_;
+
+  /* the learning rate of the net
+   */
+  float learn_rate_;
+
+  /* the range of the input values should be from 0
+   * to range_
+   * outputvalues are from -1 to 1
+   */
+  float range_;
+
+
+ public:
+
+  /* Constructor
+   */
+  LinNeuralNet(int netsize);
+
+  /* Destructor
+   */
+  virtual ~LinNeuralNet();
+
+
+  //-----------------------------------------------------
+
+  /* Set/Get learning rate
+   */
+  virtual void setLearningRate(float learn_rate)
+  {  learn_rate_=learn_rate; }
+  virtual float getLearningRate() const
+  {  return learn_rate_; }
+
+  /* Set/Get range
+   */
+  virtual void setRange(float range)
+  {  range_=range; }
+  virtual float getRange() const
+  {  return range_; }
+
+  /* some more get/set methods
+   */
+  virtual int getNetsize() const
+  {  return netsize_; }
+  virtual float *getIW() const
+  {  return IW_; }
+  virtual void setIW(const float *IW)
+  {  for(int i=0; i<netsize_; i++) IW_[i] = IW[i]; }
+  virtual float getb1() const
+  {  return b1_; }
+  virtual void setb1(float b1)
+  {  b1_ = b1; }
+
+
+  //-----------------------------------------------------
+
+  /* creates a new IW-matrix (size: netsize_) and 
+   * b1-vector
+   * returns false if there's a failure
+   * ATTENTION: if they exist they'll be deleted
+   */
+  virtual bool createNeurons();
+
+  /* inits the weight matrix and the bias vector of
+   * the network with random values between [min|max]
+   * returns false if there's a failure
+   */
+  virtual bool initNetworkRand(const int &min, const int &max);
+
+  /* inits the net with a given weight matrix and bias
+   * (makes a deep copy)
+   * ATTENTION: the dimension of IW-pointer must be the same
+   *            as the netsize !!!
+   * returns false if there's a failure
+   */
+  virtual bool initNetwork(const float *IW, float b1);
+
+  /* calculates the output with the current IW, b1 values
+   * ATTENTION: the array input_data must be in the same
+   *            size as netsize_
+   */
+  virtual float calculateNet(float *input_data);
+
+  /* this method trains the network:
+   * input_data is, as above, the input data, output_data is the 
+   * output of the current net with input_data (output_data is not
+   * calculated in that method !), target_output is the desired
+   * output data
+   * (this is the LMS-algorithm to train linear neural networks)
+   * returns false if there's a failure
+   * ATTENTION: the array input_data must be in the same
+   *            size as netsize_
+   */
+  virtual bool trainNet(float *input_data, const float &output_data, 
+			const float &target_output);
+
+ private:
+  /* Copy Construction is not allowed
+   */
+  LinNeuralNet(const LinNeuralNet &src)
+    { }
+
+  /* assignement operator is not allowed
+   */
+  const LinNeuralNet& operator= (const LinNeuralNet& src)
+    { return *this; }
+};
+
+
+
+
+#endif //_INCLUDE_LIN_NEURAL_NET__