/***************************************************************************//*! *  \file   aureservoir.i * *  \brief  SWIG interface to aureservoir library * *  \author Georg Holzmann, grh _at_ mur _dot_ at *  \date   Oct 2007 * *   ::::_aureservoir_:::: *   C++ library for analog reservoir computing neural networks * *   This library is free software; you can redistribute it and/or *   modify it under the terms of the GNU Lesser General Public *   License as published by the Free Software Foundation; either *   version 2.1 of the License, or (at your option) any later version. * ***************************************************************************//***************************************************************************/// module docstring%define DOCSTRING"aureservoir is an open-source (L-GPL) C++ library for analogreservoir computing neural networks. The basic class is the ESN(Echo State Network) class, which can be used in single or doubleprecision (SingleESN, DoubleESN).Echo State Networks can be used with different initialization,training and simulation algorithms - which can be choosen at runtimefrom the definitions in the DATA section of the module documentation.For more info on ESNs see docstrings of DoubleESN or SingleESN.You can find autogenerated docstrings for most of the methods - formore detailed documentation and more information seehttp://aureservoir.sourceforge.net.2007-2008, byGeorg Holzmanngrh _at_ mur _dot_ athttp://grh.mur.at"%enddef/***************************************************************************/// module definition%module(docstring=DOCSTRING) aureservoir%{#define SWIG_FILE_WITH_INIT#include "../aureservoir/aureservoir.h"using namespace aureservoir;%}// numpy initialization%include "numpy.i"%init %{import_array();%}// general exception handling for AUExcept%exception{   try {      $action   } catch (AUExcept &e) {      PyErr_SetString( PyExc_RuntimeError, e.what().c_str() );      return NULL;   }}/***************************************************************************/// C++ arrays to numpy conversions%apply (float *INPLACE_ARRAY2, int DIM1, int DIM2){  (float *inmtx, int inrows, int incols),   (float *outmtx, int outrows, int outcols),   (float *wmtx, int wrows, int wcols),    (float *amtx, int arows, int acols),   (float *bmtx, int brows, int bcols) };%apply (double *INPLACE_ARRAY2, int DIM1, int DIM2){  (double *inmtx, int inrows, int incols),   (double *outmtx, int outrows, int outcols),   (double *wmtx, int wrows, int wcols),   (double *amtx, int arows, int acols),   (double *bmtx, int brows, int bcols) };%apply (float* INPLACE_ARRAY1, int DIM1){  (float *invec, int insize),   (float *outvec, int outsize),   (float *f1vec, int f1size),   (float *f2vec, int f2size),   (float *last, int size) };%apply (double* INPLACE_ARRAY1, int DIM1){  (double *invec, int insize),   (double *outvec, int outsize),   (double *f1vec, int f1size),   (double *f2vec, int f2size),   (double *last, int size) };%apply (float** ARGOUTVIEW_ARRAY1, int* DIM1){ (float **vec, int *length) };%apply (double** ARGOUTVIEW_ARRAY1, int* DIM1){ (double **vec, int *length) };%apply (float** ARGOUTVIEW_FARRAY2, int* DIM1, int* DIM2){ (float **mtx, int *rows, int *cols) };%apply (double** ARGOUTVIEW_FARRAY2, int* DIM1, int* DIM2){ (double **mtx, int *rows, int *cols) };/***************************************************************************/// documentation// use autodoc%feature("autodoc", "1");// include doxygen documentation%include ESN.i// so gehts (ohne namespace!):// %feature("docstring", "TTTTTTTTTTTTTTTTTTTT")  ESN::simulate;/***************************************************************************/// class declarations// NOTE: don't add throw(AUExcept) to the declarations !template <typename T>class ESN{ public:  ESN();  ESN(const ESN<T> &src);  ~ESN();  void init();  void resetState();  double adapt(T *inmtx, int inrows, int incols);  inline void train(T *inmtx, int inrows, int incols,                    T *outmtx, int outrows, int outcols,                    int washout);  inline void simulate(T *inmtx, int inrows, int incols,                       T *outmtx, int outrows, int outcols);  inline void simulateStep(T *invec, int insize, T *outvec, int outsize);  void setBPCutoff(T *f1vec, int f1size, T *f2vec, int f2size);  void setIIRCoeff(T *bmtx, int brows, int bcols,                   T *amtx, int arows, int acols, int series=1);  void post();  int getSize();  int getInputs();  int getOutputs();  double getNoise();  T getInitParam(InitParameter key);  InitAlgorithm getInitAlgorithm();  TrainAlgorithm getTrainAlgorithm();  SimAlgorithm getSimAlgorithm();  ActivationFunction getReservoirAct();  ActivationFunction getOutputAct();  void getWin(T **mtx, int *rows, int *cols);  void getWback(T **mtx, int *rows, int *cols);  void getWout(T **mtx, int *rows, int *cols);  void getX(T **vec, int *length);  void getW(T *wmtx, int wrows, int wcols);  void getDelays(T *wmtx, int wrows, int wcols);  void setInitAlgorithm(InitAlgorithm alg=INIT_STD);  void setTrainAlgorithm(TrainAlgorithm alg=TRAIN_LEASTSQUARE);  void setSimAlgorithm(SimAlgorithm alg=SIM_STD);  void setSize(int neurons=10);  void setInputs(int inputs=1);  void setOutputs(int outputs=1);  void setNoise(double noise);  void setInitParam(InitParameter key, T value=0.);  void setReservoirAct(ActivationFunction f=ACT_TANH);  void setOutputAct(ActivationFunction f=ACT_LINEAR);  void setWin(T *inmtx, int inrows, int incols);  void setW(T *inmtx, int inrows, int incols);  void setWback(T *inmtx, int inrows, int incols);  void setWout(T *inmtx, int inrows, int incols);  void setX(T *invec, int insize);  void setLastOutput(T *last, int size);};%template(DoubleESN) ESN<double>;%template(SingleESN) ESN<float>;/***************************************************************************/// additional enumsenum InitParameter{  CONNECTIVITY,     //!< connectivity of the reservoir weight matrix  ALPHA,            //!< spectral radius of the reservoir weight matrix  IN_CONNECTIVITY,  //!< connectivity of the input weight matrix  IN_SCALE,         //!< scale input weight matrix random vaules  IN_SHIFT,         //!< shift input weight matrix random vaules  FB_CONNECTIVITY,  //!< connectivity of the feedback weight matrix  FB_SCALE,         //!< scale feedback weight matrix random vaules  FB_SHIFT,         //!< shift feedback weight matrix random vaules  LEAKING_RATE,     //!< leaking rate for Leaky Integrator ESNs  TIKHONOV_FACTOR,  //!< regularization factor for TrainRidgeReg  DS_USE_CROSSCORR, //!< use simple cross-correlation for delay calculation  DS_USE_GCC,       //!< use generalized cross-correlation for delay calculation  DS_MAXDELAY,      //!< maximum delay for delay&sum readout  IP_LEARNRATE,     //!< learnrate for Gaussian-IP reservoir adaptation  IP_MEAN,          //!< desired mean for Gaussian-IP reservoir adaptation  IP_VAR            //!< desired variance for Gaussian-IP reservoir adaptation};enum InitAlgorithm{  INIT_STD};enum SimAlgorithm{  SIM_STD,     //!< standard simulation \sa class SimStd  SIM_SQUARE,  //!< additional squared state updates \sa class SimSquare  SIM_LI,      //!< simulation with leaky integrator neurons \sa class SimLI  SIM_BP,      //!< simulation with bandpass neurons \sa class SimBP  SIM_FILTER,  //!< simulation with IIR-Filter neurons \sa class SimFilter  SIM_FILTER2, //!< IIR-Filter before nonlinearity \sa class SimFilter2  SIM_FILTER_DS};enum TrainAlgorithm{  TRAIN_PI,        //!< offline, pseudo inverse based \sa class TrainPI  TRAIN_LS,        //!< offline least square algorithm, \sa class TrainLS  TRAIN_RIDGEREG,  //!< with ridge regression, \sa class TrainRidgeReg  TRAIN_DS_PI      //!< trains a delay&sum readout with PI \sa class TrainDSPI};enum ActivationFunction{  ACT_LINEAR,      //!< linear activation function  ACT_TANH,        //!< tanh activation function  ACT_TANH2,       //!< tanh activation function with local slope and bias  ACT_SIGMOID      //!< sigmoid activation function};