invertmotornstep.h

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/***************************************************************************
 *   Copyright (C) 2005-2011 by                                            *
 *    Georg Martius  <georg dot martius at web dot de>                     *
 *    Frank Hesse    <frank at nld dot ds dot mpg dot de>                  *
 *    Ralf Der       <ralfder at mis dot mpg dot de>                       *
 *                                                                         *
 *   ANY COMMERCIAL USE FORBIDDEN!                                         *
 *   LICENSE:                                                              *
 *   This work is licensed under the Creative Commons                      *
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 *   or send a letter to Creative Commons, 543 Howard Street, 5th Floor,   *
 *   San Francisco, California, 94105, USA.                                *
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 *   This program is distributed in the hope that it will be useful,       *
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 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.                  *
 *                                                                         *
 ***************************************************************************/
#ifndef __INVERTMOTORNSTEP_H
#define __INVERTMOTORNSTEP_H

#include "invertmotorcontroller.h"
#include "teachable.h"

#include <assert.h>
#include <cmath>

#include <selforg/matrix.h>
#include <selforg/noisegenerator.h>

typedef struct InvertMotorNStepConf {
  int buffersize; ///< buffersize size of the time-buffer for x,y,eta
  matrix::Matrix initialC; ///< initial controller matrix C (if not given then randomly chosen, see cInit, cNonDiag...)
  double cInit; ///< cInit size of the C matrix to initialised with.
  double cNonDiag; ///< cNonDiag is the size of the nondiagonal elements in respect to the diagonal (cInit) ones
  double cNonDiagAbs; ///< cNonDiag is the value of the nondiagonal elements
  bool useS;    ///< useS decides whether to use the S matrix in addition to the A matrix (sees sensors)
  /** useSD decides whether to use the SD matrix in addition to the A
      matrix (sees first and second derivatives)  */
  bool useSD;
  /** number of context sensors(considered at the end of the sensor
      vector. If not 0, then S will only get them as input  */
  int numberContext;
  bool someInternalParams;  ///< someInternalParams if true only some internal parameters are exported, all otherwise
} InvertMotorNStepConf;

/**
 * class for robot controller that uses the georg's matrixlib for
 *  direct matrix inversion for n channels
 * (simple one layer networks)
 *
 * Implements standart parameters: eps, rho, mu, stepnumber4avg, stepnumber4delay
 */
class InvertMotorNStep : public InvertMotorController, public Teachable {

public:
  InvertMotorNStep(const InvertMotorNStepConf& conf = getDefaultConf());

  virtual void init(int sensornumber, int motornumber, RandGen* randGen = 0);

  virtual ~InvertMotorNStep();

  /// returns the number of sensors the controller was initialised with or 0 if not initialised
  virtual int getSensorNumber() const { return number_sensors; }
  /// returns the mumber of motors the controller was initialised with or 0 if not initialised
  virtual int getMotorNumber() const  { return number_motors; }

  /// performs one step (includes learning).
  /// Calulates motor commands from sensor inputs.
  virtual void step(const sensor* , int number_sensors, motor* , int number_motors);

  /// performs one step without learning. Calulates motor commands from sensor inputs.
  virtual void stepNoLearning(const sensor* , int number_sensors,
                              motor* , int number_motors);

  /**** STOREABLE ****/
  /** stores the controller values to a given file. */
  virtual bool store(FILE* f) const;
  /** loads the controller values from a given file. */
  virtual bool restore(FILE* f);

  /**** INSPECTABLE ****/
  virtual std::list<ILayer> getStructuralLayers() const;
  virtual std::list<IConnection> getStructuralConnections() const;

  /**** TEACHING ****/
  /** The given motor teaching signal is used for this timestep.
      It is used as a feed forward teaching signal for the controller.
      Please note, that the teaching signal has to be given each timestep
       for a continuous teaching process.
   */
  virtual void setMotorTeachingSignal(const motor* teaching, int len);

  /** The given sensor teaching signal (distal learning) is used for this timestep.
      First the belonging motor teachung signal is calculated by the inverse model.
      See setMotorTeachingSignal
   */
  virtual void setSensorTeachingSignal(const sensor* teaching, int len);
  void getLastMotors(motor* motors, int len);
  void getLastSensors(sensor* sensors, int len);


  /**** New TEACHING interface ****/
  /** The given motor teaching signal is used for this timestep. 
      It is used as a feed forward teaching signal for the controller.
      Please note, that the teaching signal has to be given each timestep 
       for a continuous teaching process.
     @param teaching: matrix with dimensions (motornumber,1)
   */
  virtual void setMotorTeaching(const matrix::Matrix& teaching);

  /** The given sensor teaching signal (distal learning) is used for this timestep. 
      The belonging motor teachung signal is calculated by the inverse model.
      See setMotorTeaching
     @param teaching: matrix with dimensions (motorsensors,1)
   */
  virtual void setSensorTeaching(const matrix::Matrix& teaching);  
  /// returns the last motor values (useful for cross motor coupling)
  virtual matrix::Matrix getLastMotorValues();
  /// returns the last sensor values (useful for cross sensor coupling)
  virtual matrix::Matrix getLastSensorValues();

  

  // UNUSED! OLD IMPLEMENTATION which hat some consistency arguments
  void calcCandHUpdatesTeaching(matrix::Matrix& C_update, matrix::Matrix& H_update, int y_delay);

  /**** REINFORCEMENT ****/
  /** set the reinforcement signal for this timestep.
      It is used to calculate a factor for the update.
      Factor = 1-0.95*reinforcement.
      @param reinforcement value between -1 and 1 (-1 bad, 0 neutral, 1 good)
   */
  virtual void setReinforcement(double reinforcement);



  static InvertMotorNStepConf getDefaultConf(){
    InvertMotorNStepConf c;
    c.buffersize = 50;
    c.cInit = 1.0;
    c.cNonDiag = 0;
    c.useS  = false;
    c.useSD  = false;
    c.numberContext  = 0;
    c.someInternalParams = true;
    c.cNonDiagAbs=0.0;
    return c;
  }

  /// sets the sensor channel weights (matrix should be (getSensorNumber() x 1)
  void setSensorWeights(const matrix::Matrix& weights);
  matrix::Matrix getSensorWeights() const {return sensorweights; }
  /// reference to C-matrix
  matrix::Matrix& getC(){return C;};

  double getE() const {return v.multTM().val(0,0);}

protected:
  unsigned short number_sensors;
  unsigned short number_motors;

 public:
  matrix::Matrix A; ///< Model Matrix (motors to sensors)
  matrix::Matrix S; ///< additional Model Matrix (sensors to sensors)
  matrix::Matrix SD; ///< additional Model Matrix (sensors derivatives to sensors)
  matrix::Matrix C; ///< Controller Matrix
  matrix::Matrix H; ///< Controller Bias
  matrix::Matrix B; ///< Model Bias
protected:
  NoiseGenerator* BNoiseGen; ///< Noisegenerator for noisy bias
  NoiseGenerator* YNoiseGen; ///< Noisegenerator for noisy motor output
  matrix::Matrix R; ///< C*A
  matrix::Matrix SmallID; ///< small identity matrix in the dimension of R
  matrix::Matrix xsi; ///< current output error
  matrix::Matrix v;   ///< current reconstructed error
  double E_val; ///< value of Error function
  double xsi_norm; ///< norm of matrix
  double xsi_norm_avg; ///< average norm of xsi (used to define whether Modell learns)
  double pain;         ///< if the modelling error (xsi) is too high we have a pain signal
  matrix::Matrix* x_buffer;
  matrix::Matrix* y_buffer;
  matrix::Matrix* eta_buffer;
  matrix::Matrix zero_eta; // zero initialised eta
  matrix::Matrix x_smooth;
  //   matrix::Matrix z; ///< membrane potential
  matrix::Matrix y_teaching; ///< teaching motor signal
  bool useTeaching; ///< flag whether there is an actual teachning signal or not
  double reinforcement; ///< reinforcement value (set via setReinforcement())
  double reinforcefactor; ///< reinforcement factor (set to 1 every step after learning)
  int t_rand; ///< initial random time to avoid syncronous management of all controllers

  matrix::Matrix sensorweights; ///< sensor channel weight (each channel gets a certain importance)

  /** factor to teach for continuity: subsequent motor commands
      should not differ too much */
  double continuity;
  double modelCompliant; ///< learning factor for model (or sensor) compliant learning
  int managementInterval; ///< interval between subsequent management function calls
  paramval inhibition; ///< inhibition strength for sparce kwta strategy (is scaled with epsC)
  paramval kwta;       ///< (int) number of synapses that get strengthend
  paramval limitRF;    ///< (int) receptive field of motor neurons (number of offcenter sensors) if null then no limitation. Mutual exclusive with inhibition
  paramval dampS;     ///< damping of S matrix
  paramval dampC;     ///< damping of C matrix
  paramval activeExplore; ///< decides whether and how strong the backpropagated error is used as a control signal
  paramval cfactor;
  paramval cnondiagabs;
  paramval cdiagabs;


  paramval noiseY; ///< noise strength for y

  InvertMotorNStepConf conf;

  /// puts the sensors in the ringbuffer, generate controller values and put them in the
  //  ringbuffer as well
  virtual void fillBuffersAndControl(const sensor* x_, int number_sensors,
                             motor* y_, int number_motors);

  /// calculates the first shift into the motor space useing delayed motor values.
  //  @param delay 0 for no delay and n>0 for n timesteps delay in the time loop
  virtual void calcEtaAndBufferIt(int delay);
  /// calculates xsi for the current time step using the delayed y values
  //  and x delayed by one
  //  @param delay 0 for no delay and n>0 for n timesteps delay in the time loop
  virtual void calcXsi(int delay);

  /// learn H,C with motors y and corresponding sensors x
  //  @param delay 0 for no delay and n>0 for n timesteps delay in the time loop
  virtual void learnController(int delay);

  /// calculates the Update for C and H
  // @param delay timesteps to delay the y-values.  (usually 0)
  //  Please note that the delayed values are NOT used for the error calculation
  //  (this is done in calcXsi())
  virtual void calcCandHUpdates(matrix::Matrix& C_update, matrix::Matrix& H_update, int delay);

  /// updates the matrix C and H
  virtual void updateCandH(const matrix::Matrix& C_update, const matrix::Matrix& H_update, double squashSize);

  /// learn A, (and S) using motors y and corresponding sensors x
  //  @param delay 0 for no delay and n>0 for n timesteps delay in the time loop
  virtual void learnModel(int delay);

  /// calculates the predicted sensor values
  virtual matrix::Matrix model(const matrix::Matrix* x_buffer, int delay, const matrix::Matrix& y);

  /// handles inhibition damping etc.
  virtual void management();

  /// returns controller output for given sensor values
  virtual matrix::Matrix calculateControllerValues(const matrix::Matrix& x_smooth);

  /** Calculates first and second derivative and returns both in on matrix (above).
      We use simple discrete approximations:
      \f[ f'(x) = (f(x) - f(x-1)) / 2 \f]
      \f[ f''(x) = f(x) - 2f(x-1) + f(x-2) \f]
      where we have to go into the past because we do not have f(x+1). The scaling can be neglegted.
  */
  matrix::Matrix calcDerivatives(const matrix::Matrix* buffer, int delay);

public:
  /** k-winner take all inhibition for synapses. k largest synapses are strengthed and the rest are inhibited.
      strong synapes are scaled by 1+(damping/k) and weak synapses are scaled by 1-(damping/(n-k)) where n is the
      number of synapes
      @param weightmatrix reference to weight matrix. Synapses for a neuron are in one row.
             The inhibition is done for all rows independently
      @param k number of synapes to strengthen
      @param damping strength of supression and exitation (typically 0.001)
   */
  void kwtaInhibition(matrix::Matrix& weightmatrix, unsigned int k, double damping);

  /** sets all connections to zero which are further away then rfSize
      from the diagonal.
      If rfSize == 1 then only main diagonal is left.
      If rfSize = 2: main diagonal and upper and lower side diagonal are kept and so on and so forth.
   */
  void limitC(matrix::Matrix& weightmatrix, unsigned int rfSize);


  static double clip095(double x);
  static double regularizedInverse(double v);

};

#endif