icl::math::StochasticOptimizer< T > Class Template Reference

The StochasticOptimizer is a tiny frame-work for simple stochastic optimization processes. More...

#include <StochasticOptimizer.h>

List of all members.

Public Types
typedef StochasticOptimizerResult< T >	Result
	Result structure.
Public Member Functions
	StochasticOptimizer (int dataDim)
	create a stochastic optimizer with given data dimension
Result	optimize (int maxTimeSteps)
	start optimization process with given step count
Result	optimize (T minError, int maxSteps)
	start optimization process with given step count and minimal error stop-criterion
Protected Member Functions
virtual T *	getData ()=0
	must return the current data vector
virtual T	getError (const T *data)=0
	must return current error value (>=0)
virtual const T *	getNoise (int currentTime, int endTime)=0
	returns a noise vector (of size dataDim, which was passed to the constructor)
virtual void	reinitialize ()=0
	this function is called before the optimization is started
virtual void	notifyProgress (int t, int numSteps, int startError, int currBestError, int currError, const T *data, int dataDim)
	a pure utility function, which can be implemented in derived classes to notify optization progress (somehow)
Private Attributes
int	m_dataDim
	internal data-dimension variable

---

Detailed Description

template<class T = float>
class icl::math::StochasticOptimizer< T >

The StochasticOptimizer is a tiny frame-work for simple stochastic optimization processes.

Stochastic optimization is minimization of an error function, which depends on a set of latent variables (here: called data). The naive approach origins from starting configuration, which must be created by the pure virtual reinitialize function. Now in each optimization step, the current data vector is changed slightly using an additive noise vector which must be provided by the also pure virtual getNoise-function. If the error arising from the changed data is less then the current minimal error, then the change data vector use hold, otherwise the last change of data is reverted. This procedure is iterated until either a maximum number of iterations is reached or until a given minimal error is reached.

NEW** now this class is a template (defined for float and double)

---

Member Typedef Documentation

template<class T = float>

typedef StochasticOptimizerResult<T> icl::math::StochasticOptimizer< T >::Result

Result structure.

---

Constructor & Destructor Documentation

template<class T = float>

icl::math::StochasticOptimizer< T >::StochasticOptimizer

(

int

dataDim

)

create a stochastic optimizer with given data dimension

---

Member Function Documentation

template<class T = float>

virtual T* icl::math::StochasticOptimizer< T >::getData

(

)

[protected, pure virtual]

must return the current data vector

data is un-const, as it is changed in each step if the reached error is less then the current best error. Returned pointer must have at least the length of dataDim passed to the constructor

template<class T = float>

virtual T icl::math::StochasticOptimizer< T >::getError

(

const T *

data

)

[protected, pure virtual]

must return current error value (>=0)

returns the error measurement dependent on given data vector

template<class T = float>

virtual const T* icl::math::StochasticOptimizer< T >::getNoise	(	int	currentTime,
		int	endTime
	)		[protected, pure virtual]

returns a noise vector (of size dataDim, which was passed to the constructor)

optionally, the noise-strength might depend on the current time-progress. Therefore, currentTime and endTime is also passed to this functions

template<class T = float>

virtual void icl::math::StochasticOptimizer< T >::notifyProgress	(	int	t,
		int	numSteps,
		int	startError,
		int	currBestError,
		int	currError,
		const T *	data,
		int	dataDim
	)		[protected, virtual]

a pure utility function, which can be implemented in derived classes to notify optization progress (somehow)

template<class T = float>

Result icl::math::StochasticOptimizer< T >::optimize

(

int

maxTimeSteps

)

[inline]

start optimization process with given step count

template<class T = float>

Result icl::math::StochasticOptimizer< T >::optimize	(	T	minError,
		int	maxSteps
	)

start optimization process with given step count and minimal error stop-criterion

template<class T = float>

virtual void icl::math::StochasticOptimizer< T >::reinitialize

(

)

[protected, pure virtual]

this function is called before the optimization is started

Internally data must be initialized

---

Member Data Documentation

template<class T = float>

int icl::math::StochasticOptimizer< T >::m_dataDim [private]

internal data-dimension variable

---

The documentation for this class was generated from the following file:

• /home/jenkins/workspace/icl-manual-trunk/icl-manual/ICLMath/src/ICLMath/StochasticOptimizer.h