Classes
class	MHNOperator
	Class storing an MHN operator represented by a theta matrix (for non TT calculations) More...

Functions
template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, class T , class X , pRC::Size D, class RX = pRC::RemoveConstReference<X>>
X	jacobi (nonTT::MHNOperator< T, D > const &op, X const &b)
	Solves the linear system (1-Q)x=b or (1-Q)^Tx=b.

template<class T , pRC::Size D, class S >
std::tuple< pRC::Tensor< T, D, D >, std::map< std::string, std::string >, std::map< std::string, double > >	learnTheta (pRC::Tensor< T, D, D > const &theta, std::string const &header, std::string const &output, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, T const &toleranceOptimizer, T const &toleranceSolverQ)
	Optimizes an MHN represented by a theta matrix to best describe a given data distribution.

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns> requires (OR == pRC::Operator::Restrict::None) && (OT == pRC::Operator::Transform::None)
static constexpr auto	applyDerivative (MHNOperator< T1, D > const &op, pRC::Tensor< T2, Ns... > const &x, pRC::Index const &i)
	apply the derivative of an MHN Q wrt to theta_ii to a vector x

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns> requires (OR == pRC::Operator::Restrict::None)
static constexpr auto	apply_diag (MHNOperator< T1, D > const &op, pRC::Tensor< T2, Ns... > const &x)
	apply the diagonal part of (1-Q) to a vector x, given an MHN Q

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns> requires (OR == pRC::Operator::Restrict::None)
static constexpr auto	apply_offdiag (MHNOperator< T1, D > const &op, pRC::Tensor< T2, Ns... > const &x)
	apply the off-diagonal part of Q or its transposed to a vector x, given an MHN Q

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns> requires (OR == pRC::Operator::Restrict::None)
static constexpr auto	apply (MHNOperator< T1, D > const &op, pRC::Tensor< T2, Ns... > const &x)
	apply (1-Q) or its transposed to a vector x, given an MHN Q

Function Documentation

◆ apply()

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns>
requires (OR == pRC::Operator::Restrict::None)

static constexpr auto cMHN::nonTT::apply	(	MHNOperator< T1, D > const &	op,
		pRC::Tensor< T2, Ns... > const &	x )

inlinestaticconstexpr

apply (1-Q) or its transposed to a vector x, given an MHN Q

Template Parameters

T1	type used for calculations (from MHN Operator, inferred)
T2	type used for calculations (from vector x, inferred)
D	number of events of the dataset (inferred)
Ns...	Mode sizes of the D events (usually D instances of 2, inferred)

Parameters

op	MHN Operator to apply
x	vector to apply (1-Q) (or its transposed) to

◆ apply_diag()

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns>
requires (OR == pRC::Operator::Restrict::None)

static constexpr auto cMHN::nonTT::apply_diag	(	MHNOperator< T1, D > const &	op,
		pRC::Tensor< T2, Ns... > const &	x )

inlinestaticconstexpr

apply the diagonal part of (1-Q) to a vector x, given an MHN Q

Template Parameters

T1	type used for calculations (from MHN Operator, inferred)
T2	type used for calculations (from vector x, inferred)
D	number of events of the dataset (inferred)
Ns...	Mode sizes of the D events (usually D instances of 2, inferred)

Parameters

op	MHN Operator to apply
x	vector to apply diagonal of (1-Q) (or its transposed) to

◆ apply_offdiag()

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns>
requires (OR == pRC::Operator::Restrict::None)

static constexpr auto cMHN::nonTT::apply_offdiag	(	MHNOperator< T1, D > const &	op,
		pRC::Tensor< T2, Ns... > const &	x )

inlinestaticconstexpr

apply the off-diagonal part of Q or its transposed to a vector x, given an MHN Q

Template Parameters

T1	type used for calculations (from MHN Operator, inferred)
T2	type used for calculations (from vector x, inferred)
D	number of events of the dataset (inferred)
Ns...	Mode sizes of the D events (usually D instances of 2, inferred)

Parameters

op	MHN Operator to apply
x	vector to apply off-diagonal of Q (or its transposed) to

◆ applyDerivative()

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, pRC::Operator::Restrict OR = pRC::Operator::Restrict::None, pRC::Operator::Hint OH = pRC::Operator::Hint::None, class T1 , pRC::Size D, class T2 , pRC::Size... Ns>
requires (OR == pRC::Operator::Restrict::None) && (OT == pRC::Operator::Transform::None)

static constexpr auto cMHN::nonTT::applyDerivative	(	MHNOperator< T1, D > const &	op,
		pRC::Tensor< T2, Ns... > const &	x,
		pRC::Index const &	i )

inlinestaticconstexpr

apply the derivative of an MHN Q wrt to theta_ii to a vector x

Template Parameters

T1	type used for calculations (from MHN Operator, inferred)
T2	type used for calculations (from vector x, inferred)
D	number of events of the dataset (inferred)
Ns...	Mode sizes of the D events (usually D instances of 2)

Parameters

op	MHN Operator to calculate derivative of
x	vector to apply derivative to
i	index of theta entry wrt which derivative is taken of

◆ jacobi()

template<pRC::Operator::Transform OT = pRC::Operator::Transform::None, class T , class X , pRC::Size D, class RX = pRC::RemoveConstReference<X>>

X cMHN::nonTT::jacobi	(	nonTT::MHNOperator< T, D > const &	op,
		X const &	b )

Solves the linear system (1-Q)x=b or (1-Q)^Tx=b.

Template Parameters

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

Parameters

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

◆ learnTheta()

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

std::tuple< pRC::Tensor< T, D, D >, std::map< std::string, std::string >, std::map< std::string, double > > cMHN::nonTT::learnTheta	(	pRC::Tensor< T, D, D > const &	theta,
		std::string const &	header,
		std::string const &	output,
		std::map< S, T > const &	pD,
		cMHN::Score< T > const &	Score,
		cMHN::Regulator< T, D > const &	Regulator,
		T const &	toleranceOptimizer,
		T const &	toleranceSolverQ )

Optimizes an MHN represented by a theta matrix to best describe a given data distribution.

The optimization is done by LBFGS. Logging information containing Score and Regulator names, Iteration number, Time, Score and Regularization strength is written to file after every iteration.

The optimized theta is returned. Additionally, two std::maps are returned containing logging information:

std::map<std::string, std::string> logInfoNames: Info like Score name and Regulator name
std::map<std::string, double> logInfoNumbers: Info like final score and runtime

Template Parameters

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

Parameters

theta	theta matrix representing the initial MHN
header	header of the data (usually containing event names)
output	file to write the theta matrix to
pD	data distribution to learn
Score	Score object representing the score used
Regulator	Regulator object representing the regulator used
toleranceOptimizer	tolerance of the optimizer
toleranceSolverQ	tolerance of the solver for calculating q (intermediate result for calculating the derivatives)

Classes

Functions

Function Documentation

◆ apply()

◆ apply_diag()

◆ apply_offdiag()

◆ applyDerivative()

◆ jacobi()

◆ learnTheta()