CLUS::Machine Class Reference

Every machine has an input vector, an output one and a real output one should provide a constructor from file. More...

#include <machine.h>

Inheritance diagram for CLUS::Machine:

[legend]Collaboration diagram for CLUS::Machine:

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Public Member Functions
	Machine ()
	Machine (Subscript InDim, Subscript OutDim, Vector< double > *Input=NULL)
virtual int	InDim (void)
	Get the number of input dimensions.
int	OutDim (void)
	Get the number of output dimensions.
virtual void	Infer (void)
	Do the inference.
virtual bool	Tick (void)
	Process more data for the epoch.
virtual void	Identify (void)
	Use the training data to learn a new structure.
virtual void	Prune (void)
	Prune the structure.
virtual double	FindBestParameter (char *name)
void	SetTrainingData (TrainingData *Training)
	Set the training data for this machine.
void	SetPruningData (TrainingData *Pruning)
	Set the pruning data for this machine.
void	ScaleData (Scale::NormType type)
	Scale the training data.
const Vector< Scale > &	GetScaleFactors (void) const
virtual void	GenerateStructure (string Type)
void	SaveToFile (char *filename)
	Output the structure data to a file.
virtual void	SaveToStream (ostream &out)
	Output the structure data to a stream.
virtual string	TypeName (void)
	Get the type of this object.
virtual bool	SetOptionOnComponents (char *optName, double value)
virtual int	SetOption (char *name, char *val)
	Set an option for the machine.
Protected Member Functions
virtual double	IdentifyStep (int iter)
Protected Attributes
Subscript	maxIter
double	convergenceLim
double	Criterion
Vector< Scale >	scale
TrainingData *	training
TrainingData *	pruning
char *	savePattern
char *	saveInferPattern
int	inDim
int	outDim
int	N
	number of subparts

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Detailed Description

Every machine has an input vector, an output one and a real output one should provide a constructor from file.

A constructor of the form Cons(int nrpar, int* pararray) and one of the form Cons(FILE* filename, int inDim, int outDim)

Definition at line 73 of file machine.h.

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Constructor & Destructor Documentation

CLUS::Machine::Machine (   )  [inline]

Definition at line 93 of file machine.h.

CLUS::Machine::Machine (  Subscript  InDim, Subscript  OutDim, Vector< double > *  Input = NULL )  [inline]

Definition at line 97 of file machine.h.

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Member Function Documentation

virtual double CLUS::Machine::FindBestParameter (  char *  name  )  [inline, virtual]

Deprecated: Definition at line 169 of file machine.h.

virtual void CLUS::Machine::GenerateStructure (  string  Type  )  [inline, virtual]

Deprecated: Definition at line 208 of file machine.h.

const Vector<Scale>& CLUS::Machine::GetScaleFactors (  void   )  const [inline]

Definition at line 202 of file machine.h.

virtual void CLUS::Machine::Identify (  void   )  [inline, virtual]

Use the training data to learn a new structure. Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 131 of file machine.h.

virtual double CLUS::Machine::IdentifyStep (  int  iter  )  [inline, protected, virtual]

Definition at line 88 of file machine.h.

virtual int CLUS::Machine::InDim (  void   )  [inline, virtual]

Get the number of input dimensions. Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 107 of file machine.h.

virtual void CLUS::Machine::Infer (  void   )  [inline, virtual]

Do the inference. Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 119 of file machine.h. Referenced by Tick().

int CLUS::Machine::OutDim (  void   )  [inline]

Get the number of output dimensions. Definition at line 113 of file machine.h.

virtual void CLUS::Machine::Prune (  void   )  [inline, virtual]

Prune the structure. Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 165 of file machine.h.

void CLUS::Machine::SaveToFile (  char *  filename  )  [inline]

Output the structure data to a file. Parameters: filename  name of save file Definition at line 214 of file machine.h. Referenced by Identify().

virtual void CLUS::Machine::SaveToStream (  ostream &  out  )  [inline, virtual]

Output the structure data to a stream. Parameters: out  stream for output Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 223 of file machine.h. Referenced by SaveToFile().

void CLUS::Machine::ScaleData (  Scale::NormType  type  )  [inline]

Scale the training data. Parameters: type  interval or distribution Definition at line 193 of file machine.h.

virtual int CLUS::Machine::SetOption (  char *  name, char *  val )  [inline, virtual]

Set an option for the machine. Parameters: name  name of option to be set val  value of option Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 267 of file machine.h.

virtual bool CLUS::Machine::SetOptionOnComponents (  char *  optName, double  value )  [inline, virtual]

Deprecated: Definition at line 258 of file machine.h.

void CLUS::Machine::SetPruningData (  TrainingData *  Pruning  )  [inline]

Set the pruning data for this machine. Parameters: Pruning  Definition at line 185 of file machine.h.

void CLUS::Machine::SetTrainingData (  TrainingData *  Training  )  [inline]

Set the training data for this machine. Parameters: Training  Definition at line 177 of file machine.h.

virtual bool CLUS::Machine::Tick (  void   )  [inline, virtual]

Process more data for the epoch. Reimplemented from CLUS::Filter. Definition at line 124 of file machine.h.

virtual string CLUS::Machine::TypeName (  void   )  [inline, virtual]

Get the type of this object. Returns: name of object type as a string Reimplemented in CLUS::BinaryDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticDecisionTree< T_Splitter >, CLUS::MultiDecisionTree< T_Splitter >, CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >, and CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >. Definition at line 252 of file machine.h.

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Field Documentation

double CLUS::Machine::convergenceLim [protected]

Definition at line 77 of file machine.h. Referenced by CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Identify(), CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Identify(), Identify(), Machine(), and SetOption().

double CLUS::Machine::Criterion [protected]

Definition at line 77 of file machine.h. Referenced by Identify().

int CLUS::Machine::inDim [protected]

Definition at line 83 of file machine.h. Referenced by InDim(), Machine(), and ScaleData().

Subscript CLUS::Machine::maxIter [protected]

Definition at line 76 of file machine.h. Referenced by Identify(), Machine(), and SetOption().

int CLUS::Machine::N [protected]

number of subparts Definition at line 86 of file machine.h. Referenced by Identify().

int CLUS::Machine::outDim [protected]

Definition at line 83 of file machine.h. Referenced by Machine(), and OutDim().

TrainingData* CLUS::Machine::pruning [protected]

Definition at line 80 of file machine.h. Referenced by CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Prune(), CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Prune(), CLUS::MultiDecisionTree< T_Splitter >::Prune(), CLUS::BinaryProbabilisticDecisionTree< T_Splitter >::Prune(), CLUS::BinaryDecisionTree< T_Splitter >::Prune(), and SetPruningData().

char* CLUS::Machine::saveInferPattern [protected]

Definition at line 82 of file machine.h. Referenced by Machine(), and SetOption().

char* CLUS::Machine::savePattern [protected]

Definition at line 81 of file machine.h. Referenced by Identify(), Machine(), and SetOption().

Vector<Scale> CLUS::Machine::scale [protected]

Definition at line 78 of file machine.h. Referenced by GetScaleFactors(), CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Infer(), CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Infer(), Machine(), CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Prune(), CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Prune(), CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >::SaveToStream(), CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::SaveToStream(), and ScaleData().

TrainingData* CLUS::Machine::training [protected]

Definition at line 79 of file machine.h. Referenced by CLUS::BinaryRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Identify(), CLUS::BinaryProbabilisticRegressionTree< T_Distribution, T_Regressor, T_Splitter >::Identify(), CLUS::MultiDecisionTree< T_Splitter >::Identify(), Identify(), CLUS::BinaryProbabilisticDecisionTree< T_Splitter >::Identify(), CLUS::BinaryDecisionTree< T_Splitter >::Identify(), ScaleData(), and SetTrainingData().

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

• machine.h

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