Class to keep all parameters of an expert MLP for the neuro trigger. More...

#include <GRLMLP.h>

Inheritance diagram for GRLMLP:

Collaboration diagram for GRLMLP:

[legend]

Public Member Functions
	GRLMLP ()
	default constructor.

	GRLMLP (std::vector< unsigned short > &nodes, unsigned short targets, const std::vector< float > &outputscale)
	constructor to set all parameters (not weights and relevantID ranges).

	~GRLMLP ()
	destructor, empty because we don't allocate memory anywhere.

bool	isTrained () const
	check if weights are default values or set by some trainer

unsigned	getNumberOfLayers () const
	get number of layers

unsigned	getNumberOfNodesLayer (unsigned iLayer) const
	get number of nodes in a layer

unsigned	getNumberOfWeights () const
	get number of weights from length of weights vector

unsigned	nWeightsCal () const
	calculate number of weights from number of nodes

unsigned	nBiasCal () const
	calculate number of weights from number of nodes

std::vector< float >	getWeights () const
	get weights vector

std::vector< float >	getBias () const
	get bias vector

void	setWeights (std::vector< float > &weights)
	set weights vector

void	setBias (std::vector< float > &bias)
	set bias vector

void	Trained (bool trained)
	check if weights are default values or set by some trainer

Private Member Functions
	ClassDef (GRLMLP, 2)
	Needed to make the ROOT object storable.

Private Attributes
std::vector< unsigned short >	m_nNodes
	Number of nodes in each layer, not including bias nodes.

std::vector< float >	m_weights
	Weights of the network.

std::vector< float >	m_bias
	bias of the network.

bool	m_trained
	Indicator whether the weights are just default values or have been set by some trainer (set to true when setWeights() is first called).

unsigned short	m_targetVars
	output variables: 1: z, 2: theta, 3: (z, theta)

std::vector< float >	m_outputScale
	Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].

Friends
class	GRLNeuroTrainerModule

Detailed Description

Class to keep all parameters of an expert MLP for the neuro trigger.

Definition at line 21 of file GRLMLP.h.

Constructor & Destructor Documentation

◆ GRLMLP() [1/2]

GRLMLP ( )

default constructor.

Definition at line 14 of file GRLMLP.cc.

              :
  m_nNodes{19, 20, 20, 1}, m_trained(false), m_targetVars(1), m_outputScale{  0., 1.}
{
  m_weights.assign(nWeightsCal(), 0.);
  m_bias.assign(nBiasCal(), 0.);
}

◆ GRLMLP() [2/2]

GRLMLP	(	std::vector< unsigned short > &	nodes,
		unsigned short	targets,
		const std::vector< float > &	outputscale
	)

constructor to set all parameters (not weights and relevantID ranges).

Definition at line 21 of file GRLMLP.cc.

               :
  m_nNodes(nodes), m_trained(false), m_targetVars(targets), m_outputScale(outputscale)
{
  m_weights.assign(nWeightsCal(), 0.);
  m_bias.assign(nBiasCal(), 0.);
}

◆ ~GRLMLP()

~GRLMLP ( )

inline

destructor, empty because we don't allocate memory anywhere.

Definition at line 34 of file GRLMLP.h.

34{ }

Member Function Documentation

◆ getBias()

std::vector< float > getBias ( ) const

inline

get bias vector

Definition at line 51 of file GRLMLP.h.

51{ return m_bias; }

◆ getNumberOfLayers()

unsigned getNumberOfLayers ( ) const

inline

get number of layers

Definition at line 39 of file GRLMLP.h.

39{ return m_nNodes.size(); }

◆ getNumberOfNodesLayer()

unsigned getNumberOfNodesLayer ( unsigned iLayer ) const

inline

get number of nodes in a layer

Definition at line 41 of file GRLMLP.h.

41{ return m_nNodes[iLayer]; }

◆ getNumberOfWeights()

unsigned getNumberOfWeights ( ) const

inline

get number of weights from length of weights vector

Definition at line 43 of file GRLMLP.h.

43{ return m_weights.size(); }

◆ getWeights()

std::vector< float > getWeights ( ) const

inline

get weights vector

Definition at line 49 of file GRLMLP.h.

49{ return m_weights; }

◆ isTrained()

bool isTrained ( ) const

inline

check if weights are default values or set by some trainer

Definition at line 37 of file GRLMLP.h.

37{ return m_trained; }

◆ nBiasCal()

unsigned nBiasCal ( ) const

calculate number of weights from number of nodes

Definition at line 45 of file GRLMLP.cc.

{
  unsigned nbias = 0;
  if (getNumberOfLayers() > 1) {
    for (unsigned il = 1; il < getNumberOfLayers(); ++il) {
      nbias += m_nNodes[il] ;
    }
  }
  return nbias;
}

◆ nWeightsCal()

unsigned nWeightsCal ( ) const

calculate number of weights from number of nodes

Definition at line 32 of file GRLMLP.cc.

{
  unsigned nWeights = 0;
  if (getNumberOfLayers() > 1) {
    nWeights = m_nNodes[0] * m_nNodes[1];
    for (unsigned il = 1; il < getNumberOfLayers() - 1; ++il) {
      nWeights += m_nNodes[il] * m_nNodes[il + 1];
    }
  }
  return nWeights;
}

◆ setBias()

void setBias ( std::vector< float > & bias )

inline

set bias vector

Definition at line 55 of file GRLMLP.h.

55{ m_bias = bias; }

◆ setWeights()

void setWeights ( std::vector< float > & weights )

inline

set weights vector

Definition at line 53 of file GRLMLP.h.

53{ m_weights = weights; }

◆ Trained()

void Trained ( bool trained )

inline

check if weights are default values or set by some trainer

Definition at line 58 of file GRLMLP.h.

58{ m_trained = trained; }

Friends And Related Function Documentation

◆ GRLNeuroTrainerModule

friend class GRLNeuroTrainerModule

friend

Definition at line 24 of file GRLMLP.h.

Member Data Documentation

◆ m_bias

std::vector<float> m_bias

private

bias of the network.

Definition at line 66 of file GRLMLP.h.

◆ m_nNodes

std::vector<unsigned short> m_nNodes

private

Number of nodes in each layer, not including bias nodes.

Definition at line 62 of file GRLMLP.h.

◆ m_outputScale

std::vector<float> m_outputScale

private

Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].

Definition at line 75 of file GRLMLP.h.

◆ m_targetVars

unsigned short m_targetVars

private

output variables: 1: z, 2: theta, 3: (z, theta)

Definition at line 72 of file GRLMLP.h.

◆ m_trained

bool m_trained

private

Indicator whether the weights are just default values or have been set by some trainer (set to true when setWeights() is first called).

Definition at line 69 of file GRLMLP.h.

◆ m_weights

std::vector<float> m_weights

private

Weights of the network.

Definition at line 64 of file GRLMLP.h.

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

trg/grl/dataobjects/include/GRLMLP.h
trg/grl/dataobjects/src/GRLMLP.cc

Public Member Functions

Private Member Functions

Private Attributes

Friends

Detailed Description

Constructor & Destructor Documentation

◆ GRLMLP() [1/2]

◆ GRLMLP() [2/2]

◆ ~GRLMLP()

Member Function Documentation

◆ getBias()

◆ getNumberOfLayers()

◆ getNumberOfNodesLayer()

◆ getNumberOfWeights()

◆ getWeights()

◆ isTrained()

◆ nBiasCal()

◆ nWeightsCal()

◆ setBias()

◆ setWeights()

◆ Trained()

Friends And Related Function Documentation

◆ GRLNeuroTrainerModule

Member Data Documentation

◆ m_bias

◆ m_nNodes

◆ m_outputScale

◆ m_targetVars

◆ m_trained

◆ m_weights