Class for handling the parameters for the neural-network PID. More...

#include <PIDNeuralNetworkParameters.h>

Inheritance diagram for PIDNeuralNetworkParameters:

Collaboration diagram for PIDNeuralNetworkParameters:

[legend]

Public Member Functions
	PIDNeuralNetworkParameters ()
	Constructor.

	PIDNeuralNetworkParameters (const std::string &description, const std::vector< std::string > &inputNames, const std::string &modelDefinition, const std::vector< int > &outputSpeciesPdg, const std::vector< float > &meanValues, const std::vector< float > &standardDeviations, const PIDNNMissingInputs &handleMissingInputs, const PIDNNInputsToCut &inputsToCut)
	Construct with individual neural-network parameters.

int	pdg2OutputIndex (const int pdg, const bool throwException=true) const
	Convert pdg code to the index of the neural-network output that represents the corresponding probability.

bool	hasPdgCode (const int pdg, const bool throwException=false) const

const std::string &	getModelDefinition () const
	Get the neural network model-definition string for frugally-deep.

const std::vector< float > &	getMeanValues () const
	Get the mean values of the inputs for normalization.

const std::vector< float > &	getStandardDeviations () const
	Get the standard deviations of the inputs for normalization.

const PIDNNMissingInputs &	getHandleMissingInputs () const
	Get vector of input indices and corresponding values that are set if the corresponding input is NaN.

const PIDNNInputsToCut &	getInputsToCut () const
	Get vector of input indices, whose values are overwritten if other input variables are in a certain range.

size_t	getInputSize () const
	Get number of inputs.

const std::vector< std::string > &	getInputNames () const
	Get input names.

size_t	getInputIndex (const std::string &name) const
	Get input index for input name.

const std::string &	getDescription () const
	Get neural network description.

const std::vector< int > &	getOutputSpeciesPdg () const
	Get the list of pdg codes of species hypotheses, for which the network predicts the probability in the order defined by the network output layer.

Private Member Functions
	ClassDef (PIDNeuralNetworkParameters, 1)
	ClassDef as this is a TObject.

Private Attributes
std::string	m_description
	description of neural network parameters

std::vector< std::string >	m_inputNames
	list of input names

std::string	m_modelDefinition
	neural network string for frugally-deep

std::vector< int >	m_outputSpeciesPdg
	PDG codes of hypotheses of neural-network output.

std::vector< float >	m_meanValues
	mean values of inputs

std::vector< float >	m_standardDeviations
	standard deviations of inputs

PIDNNMissingInputs	m_handleMissingInputs
	handling of NaN inputs

PIDNNInputsToCut	m_inputsToCut
	overwrite certain input variables

Detailed Description

Class for handling the parameters for the neural-network PID.

Definition at line 42 of file PIDNeuralNetworkParameters.h.

Constructor & Destructor Documentation

◆ PIDNeuralNetworkParameters() [1/2]

PIDNeuralNetworkParameters ( )

inline

Constructor.

Definition at line 46 of file PIDNeuralNetworkParameters.h.

46{}

◆ PIDNeuralNetworkParameters() [2/2]

PIDNeuralNetworkParameters	(	const std::string &	description,
		const std::vector< std::string > &	inputNames,
		const std::string &	modelDefinition,
		const std::vector< int > &	outputSpeciesPdg,
		const std::vector< float > &	meanValues,
		const std::vector< float > &	standardDeviations,
		const PIDNNMissingInputs &	handleMissingInputs,
		const PIDNNInputsToCut &	inputsToCut )

inline

Construct with individual neural-network parameters.

Definition at line 51 of file PIDNeuralNetworkParameters.h.

                               :
      m_description(description),
      m_inputNames(inputNames),
      m_modelDefinition(modelDefinition),
      m_outputSpeciesPdg(outputSpeciesPdg),
      m_meanValues(meanValues),
      m_standardDeviations(standardDeviations),
      m_handleMissingInputs(handleMissingInputs),
      m_inputsToCut(inputsToCut)
    {}

Member Function Documentation

◆ getDescription()

const std::string & getDescription ( ) const

inline

Get neural network description.

Returns: const std::string& neural network description

Definition at line 132 of file PIDNeuralNetworkParameters.h.

132{return m_description;}

◆ getHandleMissingInputs()

const PIDNNMissingInputs & getHandleMissingInputs ( ) const

inline

Get vector of input indices and corresponding values that are set if the corresponding input is NaN.

Returns: const PIDNNMissingInputs& missing input handnling information

Definition at line 105 of file PIDNeuralNetworkParameters.h.

105{return m_handleMissingInputs;}

◆ getInputIndex()

size_t getInputIndex ( const std::string & name ) const

Get input index for input name.

Parameters

name	Input name that should be matched to a index

Returns: size_t input index, -1 if name not in inputs

Definition at line 31 of file PIDNeuralNetworkParameters.cc.

{
  const auto itr = std::find(m_inputNames.begin(), m_inputNames.end(), name);
  if (itr == m_inputNames.end()) B2FATAL("Invalid input name: " << name);
  return static_cast<size_t>(itr - m_inputNames.begin());
}

◆ getInputNames()

const std::vector< std::string > & getInputNames ( ) const

inline

Get input names.

Returns: const std::vector<std::string>& input names

Definition at line 120 of file PIDNeuralNetworkParameters.h.

120{return m_inputNames;}

◆ getInputSize()

size_t getInputSize ( ) const

inline

Get number of inputs.

Returns: size_t number of inputs

Definition at line 115 of file PIDNeuralNetworkParameters.h.

115{return m_inputNames.size();}

◆ getInputsToCut()

const PIDNNInputsToCut & getInputsToCut ( ) const

inline

Get vector of input indices, whose values are overwritten if other input variables are in a certain range.

Returns: const PIDNNInputsToCut& overwrite inputs information

Definition at line 110 of file PIDNeuralNetworkParameters.h.

110{return m_inputsToCut;}

◆ getMeanValues()

const std::vector< float > & getMeanValues ( ) const

inline

Get the mean values of the inputs for normalization.

Returns: const std::vector<float>& input mean values

Definition at line 95 of file PIDNeuralNetworkParameters.h.

95{return m_meanValues;}

◆ getModelDefinition()

const std::string & getModelDefinition ( ) const

inline

Get the neural network model-definition string for frugally-deep.

Returns: const std::string& model-definition string

Definition at line 90 of file PIDNeuralNetworkParameters.h.

90{return m_modelDefinition;}

◆ getOutputSpeciesPdg()

const std::vector< int > & getOutputSpeciesPdg ( ) const

inline

Get the list of pdg codes of species hypotheses, for which the network predicts the probability in the order defined by the network output layer.

Returns: const std::vector<int>& list of pdg codes of hypotheses predicted by the network

Definition at line 139 of file PIDNeuralNetworkParameters.h.

139{return m_outputSpeciesPdg;}

◆ getStandardDeviations()

const std::vector< float > & getStandardDeviations ( ) const

inline

Get the standard deviations of the inputs for normalization.

Returns: const std::vector<float>& input standard deviations

Definition at line 100 of file PIDNeuralNetworkParameters.h.

100{return m_standardDeviations;}

◆ hasPdgCode()

bool hasPdgCode	(	const int	pdg,
		const bool	throwException = false ) const

inline

Parameters

pdg	pdg code of hypothesis
throwException	throw exception if pdg code is not predicted

Returns: true if neural network predicts probability for the given hypothesis; false if neural network does not predict probability for the given hypothesis

Definition at line 84 of file PIDNeuralNetworkParameters.h.

84{return pdg2OutputIndex(pdg, throwException) >= 0;}

◆ pdg2OutputIndex()

int pdg2OutputIndex	(	const int	pdg,
		const bool	throwException = true ) const

Convert pdg code to the index of the neural-network output that represents the corresponding probability.

Parameters

pdg	PDG code for particle-species hypothesis
throwException	throw exception if pdg code is not predicted

Returns: int Index of neural-network output. -1 if pdg code not predicted by neural network

Definition at line 18 of file PIDNeuralNetworkParameters.cc.

{
  const auto itr = std::find(m_outputSpeciesPdg.begin(), m_outputSpeciesPdg.end(), pdg);
  if (itr == m_outputSpeciesPdg.end()) {
    if (throwException)
      B2FATAL("Invalid particle-species hypothesis: " << pdg
              << ". PID neural network is not trained for this particle-species hypothesis");
    return -1;
  }
  return static_cast<size_t>(itr - m_outputSpeciesPdg.begin());
}

Member Data Documentation

◆ m_description

std::string m_description

private

description of neural network parameters

Definition at line 143 of file PIDNeuralNetworkParameters.h.

◆ m_handleMissingInputs

PIDNNMissingInputs m_handleMissingInputs

private

handling of NaN inputs

Definition at line 149 of file PIDNeuralNetworkParameters.h.

◆ m_inputNames

std::vector<std::string> m_inputNames

private

list of input names

Definition at line 144 of file PIDNeuralNetworkParameters.h.

◆ m_inputsToCut

PIDNNInputsToCut m_inputsToCut

private

overwrite certain input variables

Definition at line 150 of file PIDNeuralNetworkParameters.h.

◆ m_meanValues

std::vector<float> m_meanValues

private

mean values of inputs

Definition at line 147 of file PIDNeuralNetworkParameters.h.

◆ m_modelDefinition

std::string m_modelDefinition

private

neural network string for frugally-deep

Definition at line 145 of file PIDNeuralNetworkParameters.h.

◆ m_outputSpeciesPdg

std::vector<int> m_outputSpeciesPdg

private

PDG codes of hypotheses of neural-network output.

Definition at line 146 of file PIDNeuralNetworkParameters.h.

◆ m_standardDeviations

std::vector<float> m_standardDeviations

private

standard deviations of inputs

Definition at line 148 of file PIDNeuralNetworkParameters.h.

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

analysis/dbobjects/include/PIDNeuralNetworkParameters.h
analysis/dbobjects/src/PIDNeuralNetworkParameters.cc

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ PIDNeuralNetworkParameters() [1/2]

◆ PIDNeuralNetworkParameters() [2/2]

Member Function Documentation

◆ getDescription()

◆ getHandleMissingInputs()

◆ getInputIndex()

◆ getInputNames()

◆ getInputSize()

◆ getInputsToCut()

◆ getMeanValues()

◆ getModelDefinition()

◆ getOutputSpeciesPdg()

◆ getStandardDeviations()

◆ hasPdgCode()

◆ pdg2OutputIndex()

Member Data Documentation

◆ m_description

◆ m_handleMissingInputs

◆ m_inputNames

◆ m_inputsToCut

◆ m_meanValues

◆ m_modelDefinition

◆ m_outputSpeciesPdg

◆ m_standardDeviations