Namespaces
namespace	nonTT

namespace	TT

Classes
class	KLScore
	Class storing a KL Score, specializes the Score class. More...

class	L1Regulator
	Class storing an L1 Regulator, specializes the Regulator class. More...

class	L2Regulator
	Class storing an L2 Regulator, specializes the Regulator class. More...

class	Regulator
	Class storing all relevant information for a regulator. More...

class	Score
	Class storing all relevant information for a score. More...

class	sKLdynScore
	Class storing a dynamic sKL-Divergence Score, specializes the Score class. More...

class	sKLScore
	Class storing a static sKL-Divergence Score, specializes the Score class. More...

Functions
template<class T , pRC::Size D>
decltype(expand(pRC::makeConstantSequence< pRC::Size, D, 2 >(), [](auto const ... Ns) { return pRC::Tensor< T, Ns... >{};}))	calculatePTheta (nonTT::MHNOperator< T, D > const &op)
	Calculates the vector pTheta given a nonTT MHN Operator.

template<pRC::Size R, class T , class X , pRC::Size D>
X	calculatePTheta (TT::MHNOperator< T, D > const &op, X const &pInit, T const &toleranceSolver)
	Calculates the vector pTheta (as a TT) given a TT MHN Operator and a tolerance.

template<pRC::Size R, class T , pRC::Size D>
auto	calculatePTheta (TT::MHNOperator< T, D > const &op, T const &toleranceSolver)
	Calculates the vector pTheta (as a TT) given a TT MHN Operator and a tolerance.

template<class T , pRC::Size D, class S >
T	calculateScore (nonTT::MHNOperator< T, D > const &op, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator)
	Calculate score of a theta matrix given some data distribution pD.

template<pRC::Size RP, class T , pRC::Size D, class S >
T	calculateScore (TT::MHNOperator< T, D > const &op, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, T const &toleranceSolverP=1e-4)
	Calculate score of a theta matrix given some data distribution pD (using TTs)

template<class T , pRC::Size D, class S >
std::tuple< T, pRC::Tensor< T, D, D > >	calculateScoreAndGradient (nonTT::MHNOperator< T, D > const &op, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, T const &toleranceSolverQ=1e-8)
	Calculate score and gradient of a theta matrix given some data distribution pD.

template<pRC::Size RP, pRC::Size RQ, class T , pRC::Size D, class S , class X >
std::tuple< T, pRC::Tensor< T, D, D > >	calculateScoreAndGradient (TT::MHNOperator< T, D > const &op, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, X &pInit, T const &toleranceSolverP=1e-4, T const &toleranceSolverQ=1e-4)
	Calculate score and gradient of a theta matrix given some distribution pD (using TTs)

template<pRC::Size RP, pRC::Size RQ, class T , pRC::Size D, class S >
std::tuple< T, pRC::Tensor< T, D, D > >	calculateScoreAndGradient (TT::MHNOperator< T, D > const &op, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, T const &toleranceSolverP=1e-4, T const &toleranceSolverQ=1e-4)
	Calculate score and gradient of a theta matrix given some distribution pD (using TTs)

template<class T , class S >
static auto	learnIndependenceModel (std::map< S, T > const &pD)
	Calculate the theta matrix corresponding to the independence model for a given data distribution.

template<class T , class S >
static auto	combinePDs (std::vector< std::map< S, T > > const &pDs, std::vector< pRC::Index > const &lengths)
	Combines multiple data distributions into a single distribution.

template<class T , pRC::Size D, class F >
static auto	generateData (pRC::RandomEngine< F > &rng, pRC::Tensor< T, D, D > const &smallThetaGT, pRC::Size const &size, std::string const &header, std::string const &filename)
	Generates a data file from a given ground truth model.

template<class T , pRC::Size D>
static auto	generateData (pRC::Tensor< T, D, D > const &smallThetaGT, pRC::Size const &size, std::string const &header, std::string const &filename)
	Generates a data file from a given ground truth model.

template<class T , pRC::Size D, class F >
static auto	generatePD (pRC::RandomEngine< F > &rng, pRC::Tensor< T, D, D > const &smallThetaGT, pRC::Size const &size)
	Generates a data distribution from a given ground truth model.

template<class T , pRC::Size D>
static auto	generatePD (pRC::Tensor< T, D, D > const &smallThetaGT, pRC::Size const &size)
	Generates a data distribution from a given ground truth model.

template<pRC::Size D, class T = pRC::Float<>, class F >
static auto	generateTheta (pRC::RandomEngine< F > &rng, pRC::Float<> const &fullness=0.5, T const &diagonalMean=-2, T const &diagonalStd=2, T const &offDiagonalMean=0, T const &offDiagonalB=0.75)
	Generates a random theta matrix according to given distributions, and with given fullness.

template<pRC::Size D, class T = pRC::Float<>>
static auto	generateTheta (pRC::Float<> const &fullness=0.5, T const &diagonalMean=-2, T const &diagonalStd=2, T const &offDiagonalMean=0, T const &offDiagonalB=0.75)
	Generates a random theta matrix according to given distributions, and with given fullness.

template<class T , pRC::Size D>
static auto	getCrossValSplits (std::string const &filename, pRC::Index const &k)
	Splits the samples in a dataset into k as equal as possible sized sets that can be used for k-fold cross-validated training.

template<class T , pRC::Size D>
static auto	readData (std::string const &filename)
	Reads a dataset from file, where the first line is the header (containing event names) and all subsequent lines contain one sample, with entries separated by commas or spaces.

static auto	readHeader (std::string const &filename)
	Reads the header of a dataset from file.

template<class T , pRC::Size D>
static auto	readTheta (std::string const &filename)
	Reads a theta matrix from file, discarding its header.

template<class S >
static auto	getRandomSubscripts ()
	Generates a uniformly distributed random Subscripts element for a Tensor or a TT.

template<class T , pRC::Size D>
static auto	writeTheta (std::string const &filename, std::string const &header, pRC::Tensor< T, D, D > const &theta, std::map< std::string, std::string > const &logInfoNames={}, std::map< std::string, double > const &logInfoNumbers={})
	Writes a theta matrix to file, including additional logging information at the bottom.

Function Documentation

◆ calculatePTheta() [1/3]

template<class T , pRC::Size D>

decltype(expand(pRC::makeConstantSequence< pRC::Size, D, 2 >(),[](auto const ... Ns) { return pRC::Tensor< T, Ns... >{}; })) cMHN::calculatePTheta ( nonTT::MHNOperator< T, D > const & op )

Calculates the vector pTheta given a nonTT MHN Operator.

This solves the linear equation (1-Q)*pTheta = p0, where Q is an MHN Operator and p0 is the initial distribution, i.e. (100%, 0%, ..., 0%). This operation is exact, so no tolerance can be provided.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)

Parameters

op	nonTT MHN Operator constructed from a theta matrix

◆ calculatePTheta() [2/3]

template<pRC::Size R, class T , pRC::Size D>

auto cMHN::calculatePTheta	(	TT::MHNOperator< T, D > const &	op,
		T const &	toleranceSolver )

Calculates the vector pTheta (as a TT) given a TT MHN Operator and a tolerance.

This solves the linear equation (1-Q)*pTheta = p0, where Q is an MHN Operator and p0 is the initial distribution, i.e. (100%, 0%, ..., 0%).

Template Parameters

R	max TT rank of pTheta
T	type used for calculations (inferred)
D	number of events of the dataset (inferred)

Parameters

op	TT MHN Operator constructed from a theta matrix
toleranceSolver	tolerance passed to the solver of the linear system

◆ calculatePTheta() [3/3]

template<pRC::Size R, class T , class X , pRC::Size D>

X cMHN::calculatePTheta	(	TT::MHNOperator< T, D > const &	op,
		X const &	pInit,
		T const &	toleranceSolver )

Calculates the vector pTheta (as a TT) given a TT MHN Operator and a tolerance.

This solves the linear equation (1-Q)*pTheta = p0, where Q is an MHN Operator and p0 is the initial distribution, i.e. (100%, 0%, ..., 0%).

Template Parameters

R	max TT rank of pTheta
T	type used for calculations (inferred)
X	class of pTheta (inferred)
D	number of events of the dataset (inferred)

Parameters

op	TT MHN Operator constructed from a theta matrix
pInit	initial value for linear equation
toleranceSolver	tolerance passed to the solver of the linear system

◆ calculateScore() [1/2]

template<class T , pRC::Size D, class S >

T cMHN::calculateScore	(	nonTT::MHNOperator< T, D > const &	op,
		std::map< S, T > const &	pD,
		cMHN::Score< T > const &	Score,
		cMHN::Regulator< T, D > const &	Regulator )

Calculate score of a theta matrix given some data distribution pD.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
S	Subscripts type used as keys in data distributions (inferred)

Parameters

op	MHNOperator to use as ground truth model
pD	data distribution
Score	Score to use
Regulator	Regulator to used

◆ calculateScore() [2/2]

template<pRC::Size RP, class T , pRC::Size D, class S >

T cMHN::calculateScore	(	TT::MHNOperator< T, D > const &	op,
		std::map< S, T > const &	pD,
		cMHN::Score< T > const &	Score,
		cMHN::Regulator< T, D > const &	Regulator,
		T const &	toleranceSolverP = 1e-4 )

Calculate score of a theta matrix given some data distribution pD (using TTs)

Template Parameters

RP	max TT rank of pTheta
T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
S	Subscripts type used as keys in data distributions (inferred)

Parameters

op	MHNOperator to use as ground truth model
pD	data distribution
Score	Score to use
Regulator	Regulator to used
toleranceSolverP	tolerance to use when calculating pTheta (default: 1e-4)

◆ calculateScoreAndGradient() [1/3]

template<class T , pRC::Size D, class S >

std::tuple< T, pRC::Tensor< T, D, D > > cMHN::calculateScoreAndGradient	(	nonTT::MHNOperator< T, D > const &	op,
		std::map< S, T > const &	pD,
		cMHN::Score< T > const &	Score,
		cMHN::Regulator< T, D > const &	Regulator,
		T const &	toleranceSolverQ = 1e-8 )

Calculate score and gradient of a theta matrix given some data distribution pD.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
S	Subscripts type used as keys in data distributions (inferred)

Parameters

op	MHNOperator to calculate score and gradient of
pD	data distribution
Score	Score to use
Regulator	Regulator to use
toleranceSolverQ	tolerance to use when calculating q (default: 1e-8)

◆ calculateScoreAndGradient() [2/3]

template<pRC::Size RP, pRC::Size RQ, class T , pRC::Size D, class S >

std::tuple< T, pRC::Tensor< T, D, D > > cMHN::calculateScoreAndGradient	(	TT::MHNOperator< T, D > const &	op,
		std::map< S, T > const &	pD,
		cMHN::Score< T > const &	Score,
		cMHN::Regulator< T, D > const &	Regulator,
		T const &	toleranceSolverP = 1e-4,
		T const &	toleranceSolverQ = 1e-4 )

Calculate score and gradient of a theta matrix given some distribution pD (using TTs)

Template Parameters

RP	max TT rank of pTheta
RQ	max TT rank of q
T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
S	Subscripts type used as keys in data distributions (inferred)

Parameters

op	MHNOperator to calculate score and gradient of
pD	data distribution
Score	Score to use
Regulator	Regulator to use
toleranceSolverP	tolerance to use when calculating pTheta (default: 1e-4)
toleranceSolverQ	tolerance to use when calculating q (default: 1e-4)

◆ calculateScoreAndGradient() [3/3]

template<pRC::Size RP, pRC::Size RQ, class T , pRC::Size D, class S , class X >

std::tuple< T, pRC::Tensor< T, D, D > > cMHN::calculateScoreAndGradient	(	TT::MHNOperator< T, D > const &	op,
		std::map< S, T > const &	pD,
		cMHN::Score< T > const &	Score,
		cMHN::Regulator< T, D > const &	Regulator,
		X &	pInit,
		T const &	toleranceSolverP = 1e-4,
		T const &	toleranceSolverQ = 1e-4 )

Calculate score and gradient of a theta matrix given some distribution pD (using TTs)

pInit is used as the starting point of the calculation of pTheta. Beware that it is updated in place to the solution pTheta after it is obtained.

Template Parameters

RP	max TT rank of pTheta
RQ	max TT rank of q
T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
S	Subscripts type used as keys in data distributions (inferred)
X	class of pInit (inferred)

Parameters

op	MHNOperator to calculate score and gradient of
pD	data distribution
Score	Score to use
Regulator	Regulator to use
pInit	initial value for linear equation
toleranceSolverP	tolerance to use when calculating pTheta (default: 1e-4)
toleranceSolverQ	tolerance to use when calculating q (default: 1e-4)

◆ combinePDs()

template<class T , class S >

static auto cMHN::combinePDs	(	std::vector< std::map< S, T > > const &	pDs,
		std::vector< pRC::Index > const &	lengths )

inlinestatic

Combines multiple data distributions into a single distribution.

Template Parameters

T	type used for calculations (inferred)
S	Subscripts type used as keys in data distribution (inferred)

Parameters

pDs	data distributions to combine

◆ generateData() [1/2]

template<class T , pRC::Size D, class F >

static auto cMHN::generateData	(	pRC::RandomEngine< F > &	rng,
		pRC::Tensor< T, D, D > const &	smallThetaGT,
		pRC::Size const &	size,
		std::string const &	header,
		std::string const &	filename )

inlinestatic

Generates a data file from a given ground truth model.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
F	base class of pRC::RandomEngine (inferred)

Parameters

rng	RandomEngine to use for generating random numbers (needed not to get the same rng every time)
smallThetaGT	small theta matrix of ground truth model (pRC::Tensor<T, D, D> object)
size	number of samples to take into account when constructing the data distribution pTheta
header	header of the output file, i.e. event names
filename	output filename

◆ generateData() [2/2]

template<class T , pRC::Size D>

static auto cMHN::generateData	(	pRC::Tensor< T, D, D > const &	smallThetaGT,
		pRC::Size const &	size,
		std::string const &	header,
		std::string const &	filename )

inlinestatic

Generates a data file from a given ground truth model.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)

Parameters

smallThetaGT	small theta matrix of ground truth model (pRC::Tensor<T, D, D> object)
size	number of samples to take into account when constructing the data distribution
header	header of the output file, i.e. event names
filename	output filename

◆ generatePD() [1/2]

template<class T , pRC::Size D, class F >

static auto cMHN::generatePD	(	pRC::RandomEngine< F > &	rng,
		pRC::Tensor< T, D, D > const &	smallThetaGT,
		pRC::Size const &	size )

inlinestatic

Generates a data distribution from a given ground truth model.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)
F	base class of pRC::RandomEngine (inferred)

Parameters

rng	RandomEngine to use for generating random numbers (needed not to get the same rng every time)
smallThetaGT	small theta matrix of ground truth model (pRC::Tensor<T, D, D> object)
size	number of samples to take into account when constructing the data distribution

◆ generatePD() [2/2]

template<class T , pRC::Size D>

static auto cMHN::generatePD	(	pRC::Tensor< T, D, D > const &	smallThetaGT,
		pRC::Size const &	size )

inlinestatic

Generates a data distribution from a given ground truth model.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)

Parameters

smallThetaGT	small theta matrix of ground truth model (pRC::Tensor<T, D, D> object)
size	number of samples to take into account when constructing the data distribution
toleranceSolverP	tolerance passed to the solver calculating pTheta

◆ generateTheta() [1/2]

template<pRC::Size D, class T = pRC::Float<>>

static auto cMHN::generateTheta	(	pRC::Float<> const &	fullness = 0.5,
		T const &	diagonalMean = -2,
		T const &	diagonalStd = 2,
		T const &	offDiagonalMean = 0,
		T const &	offDiagonalB = 0.75 )

inlinestatic

Generates a random theta matrix according to given distributions, and with given fullness.

This function is used when no pRC::RandomEngine is passed.

Diagonal entries of the theta matrix (small thetas!) are generated using a Gaussian distribution, whose mean and standard deviation can be specified. Off diagonal entries are generated using a Laplace distribution, whose mean and b-parameter can be specified. The distribution's standard deviation is given by sqrt(2)*b.

Template Parameters

D	number of events of the dataset
T	type used for calculations (inferred)

Parameters

fullness	Percentage of entries in theta matrix that are allowed to differ from 0 (stochastically)
diagonalMean	mean of the distribution generating the diagonal entries
diagonalStd	standard deviation of the distribution generating the diagonal entries
offDiagonalMean	mean of the distribution generating the off diagonal entries
offDiagonalB	B parameter of the distribution generating the off diagonal entries

◆ generateTheta() [2/2]

template<pRC::Size D, class T = pRC::Float<>, class F >

static auto cMHN::generateTheta	(	pRC::RandomEngine< F > &	rng,
		pRC::Float<> const &	fullness = 0.5,
		T const &	diagonalMean = -2,
		T const &	diagonalStd = 2,
		T const &	offDiagonalMean = 0,
		T const &	offDiagonalB = 0.75 )

inlinestatic

Generates a random theta matrix according to given distributions, and with given fullness.

Diagonal entries of the theta matrix (small thetas!) are generated using a Gaussian distribution, whose mean and standard deviation can be specified. Off diagonal entries are generated using a Laplace distribution, whose mean and b-parameter can be specified. The distribution's standard deviation is given by sqrt(2)*b.

Template Parameters

D	number of events of the dataset
T	type used for calculations (inferred)
F	base class of pRC::RandomEngine (inferred)

Parameters

rng	RandomEngine to use for generating random numbers (needed not to get the same rng every time)
fullness	Percentage of entries in theta matrix that are allowed to differ from 0 (stochastically)
diagonalMean	mean of the distribution generating the diagonal entries
diagonalStd	standard deviation of the distribution generating the diagonal entries
offDiagonalMean	mean of the distribution generating the off diagonal entries
offDiagonalB	B parameter of the distribution generating the off diagonal entries

◆ getCrossValSplits()

template<class T , pRC::Size D>

static auto cMHN::getCrossValSplits	(	std::string const &	filename,
		pRC::Index const &	k )

inlinestatic

Splits the samples in a dataset into k as equal as possible sized sets that can be used for k-fold cross-validated training.

Template Parameters

T	type used for calculations (usually pRC::Float<>)
D	number of events of the dataset

Parameters

filename	name of the file containing the dataset
k	number of equally sized sets to produce

◆ getRandomSubscripts()

template<class S >

static auto cMHN::getRandomSubscripts ( )

inlinestatic

Generates a uniformly distributed random Subscripts element for a Tensor or a TT.

Template Parameters

S	Subscripts Class to generate random element of

◆ learnIndependenceModel()

template<class T , class S >

static auto cMHN::learnIndependenceModel ( std::map< S, T > const & pD )

inlinestatic

Calculate the theta matrix corresponding to the independence model for a given data distribution.

Template Parameters

T	type used for calculations (inferred)
S	Subscripts type used as keys in data distributions (inferred)

Parameters

pD	data distribution

◆ readData()

template<class T , pRC::Size D>

static auto cMHN::readData ( std::string const & filename )

inlinestatic

Reads a dataset from file, where the first line is the header (containing event names) and all subsequent lines contain one sample, with entries separated by commas or spaces.

Template Parameters

T	type used for calculations (usually pRC::Float<>)
D	number of events of the dataset

Parameters

filename name of the file containing the dataset

◆ readHeader()

static auto cMHN::readHeader ( std::string const & filename )

inlinestatic

Reads the header of a dataset from file.

Parameters

filename name of the file to read

◆ readTheta()

template<class T , pRC::Size D>

static auto cMHN::readTheta ( std::string const & filename )

inlinestatic

Reads a theta matrix from file, discarding its header.

Template Parameters

T	type used for calculations
D	number of events of the dataset

Parameters

filename name of the file to read from

◆ writeTheta()

template<class T , pRC::Size D>

static auto cMHN::writeTheta	(	std::string const &	filename,
		std::string const &	header,
		pRC::Tensor< T, D, D > const &	theta,
		std::map< std::string, std::string > const &	logInfoNames = {},
		std::map< std::string, double > const &	logInfoNumbers = {} )

inlinestatic

Writes a theta matrix to file, including additional logging information at the bottom.

Template Parameters

T	type used for calculations (inferred)
D	number of events of the dataset (inferred)

Parameters

filename	name of the file to write to
header	header of the input file, i.e. event names
theta	theta matrix
logInfoNames	logging information as a map string -> string (usually used for score and regulator names)
logInfoNumbers	logging information as a map string -> double (usually used for score, time, etc.)

Namespaces

Classes

Functions

Function Documentation

◆ calculatePTheta() [1/3]

◆ calculatePTheta() [2/3]

◆ calculatePTheta() [3/3]

◆ calculateScore() [1/2]

◆ calculateScore() [2/2]

◆ calculateScoreAndGradient() [1/3]

◆ calculateScoreAndGradient() [2/3]

◆ calculateScoreAndGradient() [3/3]

◆ combinePDs()

◆ generateData() [1/2]

◆ generateData() [2/2]

◆ generatePD() [1/2]

◆ generatePD() [2/2]

◆ generateTheta() [1/2]

◆ generateTheta() [2/2]

◆ getCrossValSplits()

◆ getRandomSubscripts()

◆ learnIndependenceModel()

◆ readData()

◆ readHeader()

◆ readTheta()

◆ writeTheta()