aureservoir::ESN

aureservoir::ESN< T > Class Template Reference

#include <esn.h>

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Detailed Description

template<typename T = float>
class aureservoir::ESN< T >

This class implements a basic Echo State Network as described in articles by Herbert Jaeger on the following page:
See also:
http://www.scholarpedia.org/article/Echo_State_Network
The template argument T can be float or double. Single Precision (float) saves quite some computation time.

The "echo state" approach looks at RNNs from a new angle. Large RNNs are interpreted as "reservoirs" of complex, excitable dynamics. Output units "tap" from this reservoir by linearly combining the desired output signal from the rich variety of excited reservoir signals. This idea leads to training algorithms where only the network-to-output connection weights have to be trained. This can be done with known, highly efficient linear regression algorithms. from

See also:
http://www.faculty.iu-bremen.de/hjaeger/esn_research.html
For more information and a complete documentation of this library see
See also:
http://aureservoir.sourceforge.net
Examples:

esn_example.cpp.


Public Types

typedef std::map
< InitParameter, T > 		ParameterMap
typedef SPMatrix< T >
::Type 		SPMatrix
typedef DEMatrix< T >
::Type 		DEMatrix
typedef DEVector< T >
::Type 		DEVector

Public Member Functions

 ESN ()
 ESN (const ESN< T > &src)
const ESN & operator= (const ESN< T > &src)
 ~ESN ()
Algorithm interface
void init () throw (AUExcept)
double adapt (const DEMatrix &in) throw (AUExcept)
void train (const DEMatrix &in, const DEMatrix &out, int washout) throw (AUExcept)
void simulate (const DEMatrix &in, DEMatrix &out)
void resetState ()
C-style Algorithm interface
double adapt (T *inmtx, int inrows, int incols) throw (AUExcept)
void train (T *inmtx, int inrows, int incols, T *outmtx, int outrows, int outcols, int washout) throw (AUExcept)
void simulate (T *inmtx, int inrows, int incols, T *outmtx, int outrows, int outcols) throw (AUExcept)
void simulateStep (T *invec, int insize, T *outvec, int outsize) throw (AUExcept)
Additional Interface for Bandpass and IIR-Filter Neurons


void setBPCutoff (const DEVector &f1, const DEVector &f2) throw (AUExcept)
void setBPCutoff (T *f1vec, int f1size, T *f2vec, int f2size) throw (AUExcept)
void setIIRCoeff (const DEMatrix &B, const DEMatrix &A, int series=1) throw (AUExcept)
void setIIRCoeff (T *bmtx, int brows, int bcols, T *amtx, int arows, int acols, int series=1) throw (AUExcept)
GET parameters
void post ()
int getSize () const
int getInputs () const
int getOutputs () const
double getNoise () const
T getInitParam (InitParameter key)
InitAlgorithm getInitAlgorithm () const
TrainAlgorithm getTrainAlgorithm () const
SimAlgorithm getSimAlgorithm () const
ActivationFunction getReservoirAct () const
ActivationFunction getOutputAct () const
GET internal data
const DEMatrix & getWin ()
const SPMatrix & getW ()
const DEMatrix & getWback ()
const DEMatrix & getWout ()
const DEVector & getX ()
DEMatrix getDelays () throw (AUExcept)
GET internal data C-style interface
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) throw (AUExcept)
void getDelays (T *wmtx, int wrows, int wcols) throw (AUExcept)
SET methods
void setInitAlgorithm (InitAlgorithm alg=INIT_STD) throw (AUExcept)
void setTrainAlgorithm (TrainAlgorithm alg=TRAIN_PI) throw (AUExcept)
void setSimAlgorithm (SimAlgorithm alg=SIM_STD) throw (AUExcept)
void setSize (int neurons=10) throw (AUExcept)
void setInputs (int inputs=1) throw (AUExcept)
void setOutputs (int outputs=1) throw (AUExcept)
void setNoise (double noise) throw (AUExcept)
void setInitParam (InitParameter key, T value=0.)
void setReservoirAct (ActivationFunction f=ACT_TANH) throw (AUExcept)
void setOutputAct (ActivationFunction f=ACT_LINEAR) throw (AUExcept)
SET internal data
Additional method to set all parameters with string key-value pairs, which can be used for bindings from other languages
Parameters:
param the parameter to set
value the value of that parameter


void setWin (const DEMatrix &Win) throw (AUExcept)
void setW (const DEMatrix &W) throw (AUExcept)
void setWback (const DEMatrix &Wback) throw (AUExcept)
void setWout (const DEMatrix &Wout) throw (AUExcept)
void setX (const DEVector &x) throw (AUExcept)
void setLastOutput (const DEVector &last) throw (AUExcept)
SET internal data C-style interface
void setWin (T *inmtx, int inrows, int incols) throw (AUExcept)
void setW (T *inmtx, int inrows, int incols) throw (AUExcept)
void setWback (T *inmtx, int inrows, int incols) throw (AUExcept)
void setWout (T *inmtx, int inrows, int incols) throw (AUExcept)
void setX (T *invec, int insize) throw (AUExcept)
void setLastOutput (T *last, int size) throw (AUExcept)

Protected Types

enum  NetInfo {
  RESERVOIR_ACT, OUTPUT_ACT, INIT_ALG, TRAIN_ALG,
  SIMULATE_ALG
}
typedef std::map
< NetInfo, int > 		InfoMap

Protected Member Functions

string getActString (int act)
string getInitString (int alg)
string getSimString (int alg)
string getTrainString (int alg)

Protected Attributes

InitBase< T > * init_
TrainBase< T > * train_
SimBase< T > * sim_
DEMatrix Win_
SPMatrix W_
DEMatrix Wback_
DEMatrix Wout_
DEVector x_
void(* reservoirAct_ )(T *data, int size)
void(* outputAct_ )(T *data, int size)
void(* outputInvAct_ )(T *data, int size)
int neurons_
int inputs_
int outputs_
double noise_
ParameterMap init_params_
InfoMap net_info_

Friends

algorithms are friends
class InitBase< T >
class InitStd< T >
class TrainBase< T >
class TrainPI< T >
class TrainLS< T >
class TrainRidgeReg< T >
class TrainDSPI< T >
class SimBase< T >
class SimStd< T >
class SimSquare< T >
class SimLI< T >
class SimBP< T >
class SimFilter< T >
class SimFilter2< T >
class SimFilterDS< T >

Member Typedef Documentation

template<typename T = float>
typedef std::map<InitParameter,T> aureservoir::ESN< T >::ParameterMap

typedef of a Parameter Map

template<typename T = float>
typedef SPMatrix<T>::Type aureservoir::ESN< T >::SPMatrix

template<typename T = float>
typedef DEMatrix<T>::Type aureservoir::ESN< T >::DEMatrix

template<typename T = float>
typedef DEVector<T>::Type aureservoir::ESN< T >::DEVector

template<typename T = float>
typedef std::map<NetInfo, int> aureservoir::ESN< T >::InfoMap [protected]

Member Enumeration Documentation

template<typename T = float>
enum aureservoir::ESN::NetInfo [protected]

enum used in the InfoMap to query network

Enumerator:
RESERVOIR_ACT  reservoir activation function
OUTPUT_ACT  output activation function
INIT_ALG  initialization algorithm
TRAIN_ALG  training algorithm
SIMULATE_ALG  simulation algorithm

Constructor & Destructor Documentation

template<typename T>
aureservoir::ESN< T >::ESN (  )  [inline]

Constructor.

template<typename T>
aureservoir::ESN< T >::ESN ( const ESN< T > &  src  )  [inline]

Copy Constructor.

template<typename T>
aureservoir::ESN< T >::~ESN (  )  [inline]

Destructor.

Member Function Documentation

template<typename T>
const ESN< T > & aureservoir::ESN< T >::operator= ( const ESN< T > &  src  )  [inline]

assignement operator

template<typename T = float>
void aureservoir::ESN< T >::init (  )  throw (AUExcept) [inline]

Initialization Algorithm for an Echo State Network

See also:
class InitBase

template<typename T>
double aureservoir::ESN< T >::adapt ( const DEMatrix &  in  )  throw (AUExcept) [inline]

Reservoir Adaptation Algorithm Interface At the moment this is only the Gaussian-IP reservoir adaptation method for tanh neurons.

See also:
"Adapting reservoirs to get Gaussian distributions" by David Verstraeten, Benjamin Schrauwen and Dirk Stroobandt
Parameters:
in matrix of input values (inputs x timesteps), the reservoir will be adapted by this number of timesteps.
Returns:
mean value of differences between all parameters before and after adaptation, can be used to see if learning still makes an progress.

template<typename T = float>
void aureservoir::ESN< T >::train ( const DEMatrix &  in,
const DEMatrix &  out,
int  washout 
) throw (AUExcept) [inline]

Training Algorithm Interface

See also:
class TrainBase
Parameters:
in matrix of input values (inputs x timesteps)
out matrix of desired output values (outputs x timesteps) for teacher forcing
washout washout time in samples, used to get rid of the transient dynamics of the network starting state

template<typename T = float>
void aureservoir::ESN< T >::simulate ( const DEMatrix &  in,
DEMatrix &  out 
) [inline]

Simulation Algorithm Interface

See also:
class SimBase
Parameters:
in matrix of input values (inputs x timesteps)
out matrix for output values (outputs x timesteps)

template<typename T = float>
void aureservoir::ESN< T >::resetState (  )  [inline]

resets the internal state vector x of the reservoir to zero

template<typename T>
double aureservoir::ESN< T >::adapt ( T *  inmtx,
int  inrows,
int  incols 
) throw (AUExcept) [inline]

C-style Reservoir Adaptation Algorithm Interface (data will be copied into a FLENS matrix) At the moment this is only the Gaussian-IP reservoir adaptation method for tanh neurons.

See also:
"Adapting reservoirs to get Gaussian distributions" by David Verstraeten, Benjamin Schrauwen and Dirk Stroobandt
Parameters:
inmtx matrix of input values (inputs x timesteps), the reservoir will be adapted by this number of timesteps.
Returns:
mean value of differences between all parameters before and after adaptation, can be used to see if learning still makes an progress.

template<typename T>
void aureservoir::ESN< T >::train ( T *  inmtx,
int  inrows,
int  incols,
T *  outmtx,
int  outrows,
int  outcols,
int  washout 
) throw (AUExcept) [inline]

C-style Training Algorithm Interface (data will be copied into a FLENS matrix)

See also:
class TrainBase
Parameters:
inmtx input matrix in row major storage (usual C array) (inputs x timesteps)
outmtx output matrix in row major storage (outputs x timesteps) for teacher forcing
washout washout time in samples, used to get rid of the transient dynamics of the network starting state

template<typename T>
void aureservoir::ESN< T >::simulate ( T *  inmtx,
int  inrows,
int  incols,
T *  outmtx,
int  outrows,
int  outcols 
) throw (AUExcept) [inline]

C-style Simulation Algorithm Interface with some additional error checking. (data will be copied into a FLENS matrix)

See also:
class SimBase
Parameters:
inmtx input matrix in row major storage (usual C array) (inputs x timesteps)
outmtx output matrix in row major storage (outputs x timesteps),
Attention:
Data must be already allocated!

template<typename T>
void aureservoir::ESN< T >::simulateStep ( T *  invec,
int  insize,
T *  outvec,
int  outsize 
) throw (AUExcept) [inline]

C-style Simulation Algorithm Interface, for single step simulation

See also:
class SimBase

template<typename T>
void aureservoir::ESN< T >::setBPCutoff ( const DEVector &  f1,
const DEVector &  f2 
) throw (AUExcept) [inline]

Set lowpass/highpass cutoff frequencies for bandpass style neurons. "

See also:
class SimBP
Parameters:
f1 vector with lowpass cutoff for all neurons (size = neurons)
f2 vector with highpass cutoffs (size = neurons)

template<typename T>
void aureservoir::ESN< T >::setBPCutoff ( T *  f1vec,
int  f1size,
T *  f2vec,
int  f2size 
) throw (AUExcept) [inline]

Set lowpass/highpass cutoff frequencies for bandpass style neurons " (C-style Interface).

Parameters:
f1 vector with lowpass cutoff for all neurons (size = neurons)
f2 vector with highpass cutoffs (size = neurons)

template<typename T>
void aureservoir::ESN< T >::setIIRCoeff ( const DEMatrix &  B,
const DEMatrix &  A,
int  series = 1 
) throw (AUExcept) [inline]

sets the IIR-Filter coefficients, like Matlabs filter object.

Parameters:
B matrix with numerator coefficient vectors (m x nb) m ... nr of parallel filters (neurons) nb ... nr of filter coefficients
A matrix with denominator coefficient vectors (m x na) m ... nr of parallel filters (neurons) na ... nr of filter coefficients
seris nr of serial IIR filters, e.g. if series=2 the coefficients B and A will be divided in its half and calculated with 2 serial IIR filters

template<typename T>
void aureservoir::ESN< T >::setIIRCoeff ( T *  bmtx,
int  brows,
int  bcols,
T *  amtx,
int  arows,
int  acols,
int  series = 1 
) throw (AUExcept) [inline]

sets the IIR-Filter coefficients, like Matlabs filter object.

Parameters:
B matrix with numerator coefficient vectors (m x nb) m ... nr of parallel filters (neurons) nb ... nr of filter coefficients
A matrix with denominator coefficient vectors (m x na) m ... nr of parallel filters (neurons) na ... nr of filter coefficients
seris nr of serial IIR filters, e.g. if series=2 the coefficients B and A will be divided in its half and calculated with 2 serial IIR filters

template<typename T>
void aureservoir::ESN< T >::post (  )  [inline]

posts current parameters to stdout

template<typename T = float>
int aureservoir::ESN< T >::getSize (  )  const [inline]

Returns:
reservoir size (nr of neurons)

template<typename T = float>
int aureservoir::ESN< T >::getInputs (  )  const [inline]

Returns:
nr of inputs to the reservoir

template<typename T = float>
int aureservoir::ESN< T >::getOutputs (  )  const [inline]

Returns:
nr of outputs from the reservoir

template<typename T = float>
double aureservoir::ESN< T >::getNoise (  )  const [inline]

Returns:
current noise level

template<typename T = float>
T aureservoir::ESN< T >::getInitParam ( InitParameter  key  )  [inline]

returns an initialization parametern from the parameter map

Parameters:
key the requested parameter
Returns:
the value of the parameter

template<typename T = float>
InitAlgorithm aureservoir::ESN< T >::getInitAlgorithm (  )  const [inline]

Returns:
initialization algorithm

template<typename T = float>
TrainAlgorithm aureservoir::ESN< T >::getTrainAlgorithm (  )  const [inline]

Returns:
training algorithm

template<typename T = float>
SimAlgorithm aureservoir::ESN< T >::getSimAlgorithm (  )  const [inline]

Returns:
simulation algorithm

template<typename T = float>
ActivationFunction aureservoir::ESN< T >::getReservoirAct (  )  const [inline]

Returns:
reservoir activation function

template<typename T = float>
ActivationFunction aureservoir::ESN< T >::getOutputAct (  )  const [inline]

Returns:
output activation function

template<typename T = float>
const DEMatrix& aureservoir::ESN< T >::getWin (  )  [inline]

Returns:
input weight matrix (neurons x inputs)

template<typename T = float>
const SPMatrix& aureservoir::ESN< T >::getW (  )  [inline]

Returns:
reservoir weight matrix (neurons x neurons)

template<typename T = float>
const DEMatrix& aureservoir::ESN< T >::getWback (  )  [inline]

Returns:
feedback (output to reservoir) weight matrix (neurons x outputs)

template<typename T = float>
const DEMatrix& aureservoir::ESN< T >::getWout (  )  [inline]

Returns:
output weight matrix (outputs x neurons+inputs)

template<typename T = float>
const DEVector& aureservoir::ESN< T >::getX (  )  [inline]

Returns:
internal state vector x (size = neurons)

template<typename T = float>
DEMatrix aureservoir::ESN< T >::getDelays (  )  throw (AUExcept) [inline]

query the trained delays in delay&sum readout

See also:
class SimFilterDS
Returns:
matrix with delay form neurons+inputs to all outputs size = (output x neurons+inputs)

template<typename T>
void aureservoir::ESN< T >::getWin ( T **  mtx,
int *  rows,
int *  cols 
) [inline]

get pointer to input weight matrix data and dimensions (neurons x inputs)

Warning:
This data is in fortran style column major storage !

template<typename T>
void aureservoir::ESN< T >::getWback ( T **  mtx,
int *  rows,
int *  cols 
) [inline]

get pointer to feedback weight matrix data and dimensions (neurons x outputs)

Warning:
This data is in fortran style column major storage !

template<typename T>
void aureservoir::ESN< T >::getWout ( T **  mtx,
int *  rows,
int *  cols 
) [inline]

get pointer to output weight matrix data and dimensions (outputs x neurons+inputs)

Warning:
This data is in fortran style column major storage !

template<typename T>
void aureservoir::ESN< T >::getX ( T **  vec,
int *  length 
) [inline]

get pointer to internal state vector x data and length

template<typename T>
void aureservoir::ESN< T >::getW ( T *  wmtx,
int  wrows,
int  wcols 
) throw (AUExcept) [inline]

Copies data of the sparse reservoir weight matrix into a dense C-style matrix.

Attention:
Memory of the C array must be allocated before!
Parameters:
wmtx pointer to matrix of size (neurons_ x neurons_)

template<typename T>
void aureservoir::ESN< T >::getDelays ( T *  wmtx,
int  wrows,
int  wcols 
) throw (AUExcept) [inline]

query the trained delays in delay&sum readout

See also:
class SimFilterDS and copies the data into a C-style matrix
Attention:
Memory of the C array must be allocated before!
Parameters:
wmtx matrix with delay form neurons+inputs to all outputs size = (output x neurons+inputs)

template<typename T>
void aureservoir::ESN< T >::setInitAlgorithm ( InitAlgorithm  alg = INIT_STD  )  throw (AUExcept) [inline]

set initialization algorithm

template<typename T>
void aureservoir::ESN< T >::setTrainAlgorithm ( TrainAlgorithm  alg = TRAIN_PI  )  throw (AUExcept) [inline]

set training algorithm

template<typename T>
void aureservoir::ESN< T >::setSimAlgorithm ( SimAlgorithm  alg = SIM_STD  )  throw (AUExcept) [inline]

set simulation algorithm

template<typename T>
void aureservoir::ESN< T >::setSize ( int  neurons = 10  )  throw (AUExcept) [inline]

set reservoir size (nr of neurons)

template<typename T>
void aureservoir::ESN< T >::setInputs ( int  inputs = 1  )  throw (AUExcept) [inline]

set nr of inputs to the reservoir

template<typename T>
void aureservoir::ESN< T >::setOutputs ( int  outputs = 1  )  throw (AUExcept) [inline]

set nr of outputs from the reservoir

template<typename T>
void aureservoir::ESN< T >::setNoise ( double  noise  )  throw (AUExcept) [inline]

set noise level for training/simulation algorithm

Parameters:
noise with uniform distribution within [-noise|+noise]

template<typename T>
void aureservoir::ESN< T >::setInitParam ( InitParameter  key,
T  value = 0. 
) [inline]

set initialization parameter

template<typename T>
void aureservoir::ESN< T >::setReservoirAct ( ActivationFunction  f = ACT_TANH  )  throw (AUExcept) [inline]

set reservoir activation function

template<typename T>
void aureservoir::ESN< T >::setOutputAct ( ActivationFunction  f = ACT_LINEAR  )  throw (AUExcept) [inline]

set output activation function

template<typename T>
void aureservoir::ESN< T >::setWin ( const DEMatrix &  Win  )  throw (AUExcept) [inline]

set input weight matrix (neurons x inputs)

template<typename T>
void aureservoir::ESN< T >::setW ( const DEMatrix &  W  )  throw (AUExcept) [inline]

set reservoir weight matrix (neurons x neurons)

template<typename T>
void aureservoir::ESN< T >::setWback ( const DEMatrix &  Wback  )  throw (AUExcept) [inline]

set feedback weight matrix (neurons x outputs)

template<typename T>
void aureservoir::ESN< T >::setWout ( const DEMatrix &  Wout  )  throw (AUExcept) [inline]

set output weight matrix (outputs x neurons+inputs)

template<typename T>
void aureservoir::ESN< T >::setX ( const DEVector &  x  )  throw (AUExcept) [inline]

set internal state vector (size = neurons)

template<typename T>
void aureservoir::ESN< T >::setLastOutput ( const DEVector &  last  )  throw (AUExcept) [inline]

set last output, stored by the simulation algorithm needed in singleStep simulation with feedback

Parameters:
last vector with length = outputs

template<typename T>
void aureservoir::ESN< T >::setWin ( T *  inmtx,
int  inrows,
int  incols 
) throw (AUExcept) [inline]

set input weight matrix C-style interface (neurons x inputs) (data will be copied into a FLENS matrix)

Parameters:
inmtx pointer to win matrix in row major storage

template<typename T>
void aureservoir::ESN< T >::setW ( T *  inmtx,
int  inrows,
int  incols 
) throw (AUExcept) [inline]

set reservoir weight matrix C-style interface (neurons x neurons) (data will be copied into a FLENS matrix)

Parameters:
inmtx pointer to a dense reservoir matrix in row major storage

template<typename T>
void aureservoir::ESN< T >::setWback ( T *  inmtx,
int  inrows,
int  incols 
) throw (AUExcept) [inline]

set feedback weight matrix C-style interface (neurons x outputs) (data will be copied into a FLENS matrix)

Parameters:
inmtx pointer to wback matrix in row major storage

template<typename T>
void aureservoir::ESN< T >::setWout ( T *  inmtx,
int  inrows,
int  incols 
) throw (AUExcept) [inline]

set output weight matrix C-style interface (outputs x neurons+inputs) (data will be copied into a FLENS matrix)

Parameters:
inmtx pointer to wout matrix in row major storage

template<typename T>
void aureservoir::ESN< T >::setX ( T *  invec,
int  insize 
) throw (AUExcept) [inline]

set internal state vector C-style interface (size = neurons) (data will be copied into a FLENS matrix)

Parameters:
invec pointer to state vector

template<typename T>
void aureservoir::ESN< T >::setLastOutput ( T *  last,
int  size 
) throw (AUExcept) [inline]

set last output, stored by the simulation algorithm needed in singleStep simulation with feedback

Parameters:
last vector with size = outputs

template<typename T>
string aureservoir::ESN< T >::getActString ( int  act  )  [inline, protected]

Returns:
string of activation function enum

template<typename T>
string aureservoir::ESN< T >::getInitString ( int  alg  )  [inline, protected]

Returns:
string of init algorithm enum

template<typename T>
string aureservoir::ESN< T >::getSimString ( int  alg  )  [inline, protected]

Returns:
string of simulation algorithm enum

template<typename T>
string aureservoir::ESN< T >::getTrainString ( int  alg  )  [inline, protected]

Returns:
string of training algorithm enum

Friends And Related Function Documentation

template<typename T = float>
friend class InitBase< T > [friend]

template<typename T = float>
friend class InitStd< T > [friend]

template<typename T = float>
friend class TrainBase< T > [friend]

template<typename T = float>
friend class TrainPI< T > [friend]

template<typename T = float>
friend class TrainLS< T > [friend]

template<typename T = float>
friend class TrainRidgeReg< T > [friend]

template<typename T = float>
friend class TrainDSPI< T > [friend]

template<typename T = float>
friend class SimBase< T > [friend]

template<typename T = float>
friend class SimStd< T > [friend]

template<typename T = float>
friend class SimSquare< T > [friend]

template<typename T = float>
friend class SimLI< T > [friend]

template<typename T = float>
friend class SimBP< T > [friend]

template<typename T = float>
friend class SimFilter< T > [friend]

template<typename T = float>
friend class SimFilter2< T > [friend]

template<typename T = float>
friend class SimFilterDS< T > [friend]

Field Documentation

template<typename T = float>
InitBase<T>* aureservoir::ESN< T >::init_ [protected]

function object for initialization algorithm

template<typename T = float>
TrainBase<T>* aureservoir::ESN< T >::train_ [protected]

function object for training algorithm

template<typename T = float>
SimBase<T>* aureservoir::ESN< T >::sim_ [protected]

function object for simulation algorithm

template<typename T = float>
DEMatrix aureservoir::ESN< T >::Win_ [protected]

input weight matrix

template<typename T = float>
SPMatrix aureservoir::ESN< T >::W_ [protected]

reservoir weight matrix

template<typename T = float>
DEMatrix aureservoir::ESN< T >::Wback_ [protected]

feedback (output to reservoir) weight matrix

template<typename T = float>
DEMatrix aureservoir::ESN< T >::Wout_ [protected]

output weight matrix (this will be trained)

template<typename T = float>
DEVector aureservoir::ESN< T >::x_ [protected]

internal state vector holding the current value of each neuron in the reservoir

template<typename T = float>
void(* aureservoir::ESN< T >::reservoirAct_)(T *data, int size) [protected]

activation function for the reservoir

See also:
activations.h

template<typename T = float>
void(* aureservoir::ESN< T >::outputAct_)(T *data, int size) [protected]

activation function for the outputs

See also:
activations.h

template<typename T = float>
void(* aureservoir::ESN< T >::outputInvAct_)(T *data, int size) [protected]

inverse activation function for the outputs

See also:
activations.h

template<typename T = float>
int aureservoir::ESN< T >::neurons_ [protected]

nr of neurons in the reservoir (= reservoir size)

template<typename T = float>
int aureservoir::ESN< T >::inputs_ [protected]

nr of inputs to the reservoir

template<typename T = float>
int aureservoir::ESN< T >::outputs_ [protected]

nr of outputs from the reservoir

template<typename T = float>
double aureservoir::ESN< T >::noise_ [protected]

noise level

template<typename T = float>
ParameterMap aureservoir::ESN< T >::init_params_ [protected]

parameter map for initialization arguments

template<typename T = float>
InfoMap aureservoir::ESN< T >::net_info_ [protected]

holds strings of various ESN settings

The documentation for this class was generated from the following files:
Generated on Wed Mar 12 21:16:08 2008 for aureservoir by  doxygen 1.5.3