cMHN::nonTT Namespace Reference

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

Functions
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 &toleranceSolverP, T const &toleranceSolverQ)
	Optimizes an MHN represented by a theta matrix to best describe a given data distribution. More...
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, pRC::If< pRC::IsSatisfied<(OR==pRC::Operator::Restrict::None)> > = 0, pRC::If< pRC::IsSatisfied<(OT==pRC::Operator::Transform::None)> > = 0>
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 More...
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, pRC::If< pRC::IsSatisfied<(OR==pRC::Operator::Restrict::None)> > = 0>
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 More...

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, pRC::If< pRC::IsSatisfied<(OR==pRC::Operator::Restrict::None)> > = 0>

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

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, pRC::If< pRC::IsSatisfied<(OR==pRC::Operator::Restrict::None)> > = 0, pRC::If< pRC::IsSatisfied<(OT==pRC::Operator::Transform::None)> > = 0>

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

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 &	toleranceSolverP,
		T const &	toleranceSolverQ
	)

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

The optimization is done by BFGS. 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
toleranceSovlerP	tolerance of the solver for calculating pTheta
toleranceSolverQ	tolerance of the solver for calculating q (intermediate result for calculating the derivatives)