Class to keep all parameters of an expert MLP for the neuro trigger.
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#include <GRLMLP.h>
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| GRLMLP () |
| default constructor.
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| GRLMLP (std::vector< unsigned short > &nodes, unsigned short targets, const std::vector< float > &outputscale) |
| constructor to set all parameters (not weights and relevantID ranges).
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| ~GRLMLP () |
| destructor, empty because we don't allocate memory anywhere.
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bool | isTrained () const |
| check if weights are default values or set by some trainer
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unsigned | getNumberOfLayers () const |
| get number of layers
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unsigned | getNumberOfNodesLayer (unsigned iLayer) const |
| get number of nodes in a layer
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unsigned | getNumberOfWeights () const |
| get number of weights from length of weights vector
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unsigned | nWeightsCal () const |
| calculate number of weights from number of nodes
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unsigned | nBiasCal () const |
| calculate number of weights from number of nodes
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std::vector< float > | getWeights () const |
| get weights vector
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std::vector< float > | getBias () const |
| get bias vector
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void | setWeights (std::vector< float > &weights) |
| set weights vector
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void | setBias (std::vector< float > &bias) |
| set bias vector
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void | Trained (bool trained) |
| check if weights are default values or set by some trainer
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| ClassDef (GRLMLP, 2) |
| Needed to make the ROOT object storable.
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std::vector< unsigned short > | m_nNodes |
| Number of nodes in each layer, not including bias nodes.
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std::vector< float > | m_weights |
| Weights of the network.
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std::vector< float > | m_bias |
| bias of the network.
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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).
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unsigned short | m_targetVars |
| output variables: 1: z, 2: theta, 3: (z, theta)
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std::vector< float > | m_outputScale |
| Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].
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Class to keep all parameters of an expert MLP for the neuro trigger.
Definition at line 21 of file GRLMLP.h.
◆ GRLMLP() [1/2]
default constructor.
Definition at line 14 of file GRLMLP.cc.
14 :
16{
19}
std::vector< float > m_weights
Weights of the network.
unsigned nWeightsCal() const
calculate number of weights from number of nodes
std::vector< float > m_outputScale
Output[i] of the MLP is scaled from [-1, 1] to [outputScale[2i], outputScale[2i+1]].
std::vector< unsigned short > m_nNodes
Number of nodes in each layer, not including bias nodes.
std::vector< float > m_bias
bias of the network.
unsigned nBiasCal() const
calculate number of weights from number of nodes
unsigned short m_targetVars
output variables: 1: z, 2: theta, 3: (z, theta)
bool m_trained
Indicator whether the weights are just default values or have been set by some trainer (set to true w...
◆ GRLMLP() [2/2]
GRLMLP |
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std::vector< unsigned short > & | nodes, |
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unsigned short | targets, |
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const std::vector< float > & | outputscale ) |
constructor to set all parameters (not weights and relevantID ranges).
Definition at line 21 of file GRLMLP.cc.
◆ ~GRLMLP()
destructor, empty because we don't allocate memory anywhere.
Definition at line 34 of file GRLMLP.h.
◆ getBias()
std::vector< float > getBias |
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const |
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inline |
get bias vector
Definition at line 51 of file GRLMLP.h.
◆ getNumberOfLayers()
unsigned getNumberOfLayers |
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const |
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inline |
get number of layers
Definition at line 39 of file GRLMLP.h.
39{ return m_nNodes.size(); }
◆ getNumberOfNodesLayer()
unsigned getNumberOfNodesLayer |
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unsigned | iLayer | ) |
const |
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inline |
get number of nodes in a layer
Definition at line 41 of file GRLMLP.h.
41{ return m_nNodes[iLayer]; }
◆ getNumberOfWeights()
unsigned getNumberOfWeights |
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const |
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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 |
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const |
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inline |
get weights vector
Definition at line 49 of file GRLMLP.h.
◆ isTrained()
check if weights are default values or set by some trainer
Definition at line 37 of file GRLMLP.h.
◆ nBiasCal()
unsigned nBiasCal |
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const |
calculate number of weights from number of nodes
Definition at line 45 of file GRLMLP.cc.
46{
47 unsigned nbias = 0;
51 }
52 }
53 return nbias;
54}
unsigned getNumberOfLayers() const
get number of layers
◆ nWeightsCal()
unsigned nWeightsCal |
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const |
calculate number of weights from number of nodes
Definition at line 32 of file GRLMLP.cc.
33{
34 unsigned nWeights = 0;
39 }
40 }
41 return nWeights;
42}
◆ setBias()
void setBias |
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std::vector< float > & | bias | ) |
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inline |
set bias vector
Definition at line 55 of file GRLMLP.h.
◆ setWeights()
void setWeights |
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std::vector< float > & | weights | ) |
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inline |
set weights vector
Definition at line 53 of file GRLMLP.h.
53{ m_weights = weights; }
◆ Trained()
void Trained |
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bool | trained | ) |
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inline |
check if weights are default values or set by some trainer
Definition at line 58 of file GRLMLP.h.
58{ m_trained = trained; }
◆ GRLNeuroTrainerModule
◆ m_bias
std::vector<float> m_bias |
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private |
bias of the network.
Definition at line 66 of file GRLMLP.h.
◆ m_nNodes
std::vector<unsigned short> m_nNodes |
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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 |
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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 |
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private |
output variables: 1: z, 2: theta, 3: (z, theta)
Definition at line 72 of file GRLMLP.h.
◆ 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).
Definition at line 69 of file GRLMLP.h.
◆ m_weights
std::vector<float> m_weights |
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private |
Weights of the network.
Definition at line 64 of file GRLMLP.h.
The documentation for this class was generated from the following files: