shark::MultiChainApproximator< MarkovChainType > Class Template Reference

Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel. More...

#include <shark/Unsupervised/RBM/GradientApproximations/MultiChainApproximator.h>

+ Inheritance diagram for shark::MultiChainApproximator< MarkovChainType >:

Public Types

typedef MarkovChainType::RBM RBM
 
- Public Types inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
enum  Feature {
  HAS_VALUE = 1, HAS_FIRST_DERIVATIVE = 2, HAS_SECOND_DERIVATIVE = 4, CAN_PROPOSE_STARTING_POINT = 8,
  IS_CONSTRAINED_FEATURE = 16, HAS_CONSTRAINT_HANDLER = 32, CAN_PROVIDE_CLOSEST_FEASIBLE = 64, IS_THREAD_SAFE = 128,
  IS_NOISY = 256
}
 List of features that are supported by an implementation. More...
 
typedef PointType SearchPointType
 
typedef ResultT ResultType
 
typedef boost::mpl::if_< std::is_arithmetic< ResultT >, SearchPointType, RealMatrix >::type FirstOrderDerivative
 
typedef TypedFlags< FeatureFeatures
 This statement declares the member m_features. See Core/Flags.h for details. More...
 
typedef TypedFeatureNotAvailableException< FeatureFeatureNotAvailableException
 

Public Member Functions

 MultiChainApproximator (RBM *rbm)
 
std::string name () const
 From INameable: return the class name. More...
 
void setK (unsigned int k)
 
void setNumberOfSamples (std::size_t samples)
 
void setBatchSize (std::size_t batchSize)
 
MarkovChainType & chain ()
 
MarkovChainType const & chain () const
 
std::size_t numBatches () const
 Returns the number of batches of the dataset that are used in every iteration. More...
 
std::size_t & numBatches ()
 Returns a reference to the number of batches of the dataset that are used in every iteration. More...
 
void setData (UnlabeledData< RealVector > const &data)
 
SearchPointType proposeStartingPoint () const
 Proposes a starting point in the feasible search space of the function. More...
 
std::size_t numberOfVariables () const
 Accesses the number of variables. More...
 
void setRegularizer (double factor, SingleObjectiveFunction *regularizer)
 
double evalDerivative (SearchPointType const &parameter, FirstOrderDerivative &derivative) const
 Evaluates the objective function and calculates its gradient. More...
 
- Public Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
const Featuresfeatures () const
 
virtual void updateFeatures ()
 
bool hasValue () const
 returns whether this function can calculate it's function value More...
 
bool hasFirstDerivative () const
 returns whether this function can calculate the first derivative More...
 
bool hasSecondDerivative () const
 returns whether this function can calculate the second derivative More...
 
bool canProposeStartingPoint () const
 returns whether this function can propose a starting point. More...
 
bool isConstrained () const
 returns whether this function can return More...
 
bool hasConstraintHandler () const
 returns whether this function can return More...
 
bool canProvideClosestFeasible () const
 Returns whether this function can calculate thee closest feasible to an infeasible point. More...
 
bool isThreadSafe () const
 Returns true, when the function can be usd in parallel threads. More...
 
bool isNoisy () const
 Returns true, when the function can be usd in parallel threads. More...
 
 AbstractObjectiveFunction ()
 Default ctor. More...
 
virtual ~AbstractObjectiveFunction ()
 Virtual destructor. More...
 
virtual void init ()
 
void setRng (random::rng_type *rng)
 Sets the Rng used by the objective function. More...
 
virtual bool hasScalableDimensionality () const
 
virtual void setNumberOfVariables (std::size_t numberOfVariables)
 Adjusts the number of variables if the function is scalable. More...
 
virtual std::size_t numberOfObjectives () const
 
virtual bool hasScalableObjectives () const
 
virtual void setNumberOfObjectives (std::size_t numberOfObjectives)
 Adjusts the number of objectives if the function is scalable. More...
 
std::size_t evaluationCounter () const
 Accesses the evaluation counter of the function. More...
 
AbstractConstraintHandler< SearchPointType > const & getConstraintHandler () const
 Returns the constraint handler of the function if it has one. More...
 
virtual bool isFeasible (const SearchPointType &input) const
 Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled. More...
 
virtual void closestFeasible (SearchPointType &input) const
 If supported, the supplied point is repaired such that it satisfies all of the function's constraints. More...
 
virtual ResultType eval (SearchPointType const &input) const
 Evaluates the objective function for the supplied argument. More...
 
ResultType operator() (SearchPointType const &input) const
 Evaluates the function. Useful together with STL-Algorithms like std::transform. More...
 
virtual ResultType evalDerivative (SearchPointType const &input, SecondOrderDerivative &derivative) const
 Evaluates the objective function and calculates its gradient. More...
 
- Public Member Functions inherited from shark::INameable
virtual ~INameable ()
 

Additional Inherited Members

- Protected Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
void announceConstraintHandler (AbstractConstraintHandler< SearchPointType > const *handler)
 helper function which is called to announce the presence of an constraint handler. More...
 
- Protected Attributes inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
Features m_features
 
std::size_t m_evaluationCounter
 Evaluation counter, default value: 0. More...
 
AbstractConstraintHandler< SearchPointType > const * m_constraintHandler
 
random::rng_type * mep_rng
 

Detailed Description

template<class MarkovChainType>
class shark::MultiChainApproximator< MarkovChainType >

Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel.

The advantage is, that every chain can produce samples of a different mode of the distribution. The disadvantage is however, that mixing is slower and a higher value of sampling steps between subsequent samples need to be chosen.

Definition at line 44 of file MultiChainApproximator.h.

Member Typedef Documentation

◆ RBM

template<class MarkovChainType >
typedef MarkovChainType::RBM shark::MultiChainApproximator< MarkovChainType >::RBM

Definition at line 46 of file MultiChainApproximator.h.

Constructor & Destructor Documentation

◆ MultiChainApproximator()

Member Function Documentation

◆ chain() [1/2]

template<class MarkovChainType >
MarkovChainType& shark::MultiChainApproximator< MarkovChainType >::chain ( )
inline

Definition at line 74 of file MultiChainApproximator.h.

◆ chain() [2/2]

template<class MarkovChainType >
MarkovChainType const& shark::MultiChainApproximator< MarkovChainType >::chain ( ) const
inline

Definition at line 77 of file MultiChainApproximator.h.

◆ evalDerivative()

template<class MarkovChainType >
double shark::MultiChainApproximator< MarkovChainType >::evalDerivative ( SearchPointType const &  input,
FirstOrderDerivative derivative 
) const
inlinevirtual

Evaluates the objective function and calculates its gradient.

Parameters
[in]inputThe argument to eval the function for.
[out]derivativeThe derivate is placed here.
Returns
The result of evaluating the function for the supplied argument.
Exceptions
FeatureNotAvailableExceptionin the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 136 of file MultiChainApproximator.h.

References shark::swap().

◆ name()

template<class MarkovChainType >
std::string shark::MultiChainApproximator< MarkovChainType >::name ( ) const
inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 59 of file MultiChainApproximator.h.

◆ numBatches() [1/2]

template<class MarkovChainType >
std::size_t shark::MultiChainApproximator< MarkovChainType >::numBatches ( ) const
inline

Returns the number of batches of the dataset that are used in every iteration.

If it is less than all batches, the batches are chosen at random. if it is 0, all batches are used

Definition at line 84 of file MultiChainApproximator.h.

◆ numBatches() [2/2]

template<class MarkovChainType >
std::size_t& shark::MultiChainApproximator< MarkovChainType >::numBatches ( )
inline

Returns a reference to the number of batches of the dataset that are used in every iteration.

If it is less than all batches, the batches are chosen at random.if it is 0, all batches are used.

Definition at line 91 of file MultiChainApproximator.h.

◆ numberOfVariables()

template<class MarkovChainType >
std::size_t shark::MultiChainApproximator< MarkovChainType >::numberOfVariables ( ) const
inlinevirtual

Accesses the number of variables.

Implements shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 127 of file MultiChainApproximator.h.

◆ proposeStartingPoint()

template<class MarkovChainType >
SearchPointType shark::MultiChainApproximator< MarkovChainType >::proposeStartingPoint ( ) const
inlinevirtual

Proposes a starting point in the feasible search space of the function.

Returns
The generated starting point.
Exceptions
FeatureNotAvailableExceptionin the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 123 of file MultiChainApproximator.h.

◆ setBatchSize()

template<class MarkovChainType >
void shark::MultiChainApproximator< MarkovChainType >::setBatchSize ( std::size_t  batchSize)
inline

◆ setData()

template<class MarkovChainType >
void shark::MultiChainApproximator< MarkovChainType >::setData ( UnlabeledData< RealVector > const &  data)
inline

◆ setK()

template<class MarkovChainType >
void shark::MultiChainApproximator< MarkovChainType >::setK ( unsigned int  k)
inline

Definition at line 62 of file MultiChainApproximator.h.

◆ setNumberOfSamples()

template<class MarkovChainType >
void shark::MultiChainApproximator< MarkovChainType >::setNumberOfSamples ( std::size_t  samples)
inline

◆ setRegularizer()

template<class MarkovChainType >
void shark::MultiChainApproximator< MarkovChainType >::setRegularizer ( double  factor,
SingleObjectiveFunction regularizer 
)
inline

Definition at line 131 of file MultiChainApproximator.h.


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