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:

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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	nLayers () const
	get number of layers

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

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

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

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

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

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

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

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

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

Detailed Description

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

Definition at line 13 of file GRLMLP.h.

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

Public Member Functions