pm4py.algo.analysis.marking_equation.variants.classic module

class pm4py.algo.analysis.marking_equation.variants.classic.Parameters(*values)

Bases: Enum

CASE_ID_KEY = 'pm4py:param:case_id_key'

ACTIVITY_KEY = 'pm4py:param:activity_key'

COSTS = 'costs'

INCIDENCE_MATRIX = 'incidence_matrix'

A = 'A_matrix'

FULL_BOOTSTRAP_REQUIRED = 'full_bootstrap_required'

class pm4py.algo.analysis.marking_equation.variants.classic.MarkingEquationSolver(net: PetriNet, im: Marking, fm: Marking, parameters: Dict[Any, Any] | None = None)

Bases: object

get_components() → Tuple[Any, Any, Any, Any, Any]

Retrieve the components (Numpy matrixes) of the problem

Returns:

• c – objective function

• Aub – Inequalities matrix

• bub – Inequalities vector

• Aeq – Equalities matrix

• beq – Equalities vector

change_ini_vec(ini: Marking)

Changes the initial marking of the synchronous product net

Parameters:

ini – Initial marking

get_x_vector(sol_points: List[int]) → List[int]

Returns the x vector of the solution

Parameters:

sol_points – Solution of the integer problem

Returns:

X vector

Return type:

x

get_h(sol_points: List[int]) → int

Returns the value of the heuristics

Parameters:

sol_points – Solution of the integer problem

Returns:

Heuristics value

Return type:

h

get_activated_transitions(sol_points: List[int]) → List[Transition]

Gets the transitions of the synchronous product net that are non-zero in the solution of the marking equation

Parameters:

sol_points – Solution of the integer problem

Returns:

Activated transitions

Return type:

act_trans

solve() → Tuple[int, List[int]]

Solves the marking equation, returning the heuristics and the x vector

Returns:

• h – Heuristics value

• x – X vector

solve_given_components(c, Aub, bub, Aeq, beq)

Solves the linear problem given the components

Parameters:

• c – Objective vector

• Aub – Inequalities matrix

• bub – Inequalities vector

• Aeq – Equalities matrix

• beq – Equalities vector

Returns:

• h – Heuristics value

• x – X vector

get_firing_sequence(x: List[int]) → Tuple[List[Transition], bool, int]

Gets a firing sequence from the X vector

Parameters:

x – X vector

Returns:

• firing_sequence – Firing sequence

• reach_fm – Boolean value that is true whether the firing sequence reaches the final marking

• explained_events – Number of explaned events by the firing sequence

pm4py.algo.analysis.marking_equation.variants.classic.build(net: PetriNet, im: Marking, fm: Marking, parameters: Dict[Any, Any] | None = None) → MarkingEquationSolver

Builds the marking equation out of a Petri net

Parameters:

• net – Petri net

• im – Initial marking

• fm – Final marking

• parameters – Parameters of the algorithm, including: - Parameters.CASE_ID_KEY => attribute to use as case identifier - Parameters.ACTIVITY_KEY => attribute to use as activity - Parameters.COSTS => (if provided) the cost function (otherwise the default cost function is applied) - Parameters.INCIDENCE_MATRIX => (if provided) the incidence matrix of the Petri net - Parameters.A => (if provided) the A numpy matrix of the incidence matrix - Parameters.FULL_BOOTSTRAP_REQUIRED => The preset/postset of places/transitions need to be inserted

pm4py.algo.analysis.marking_equation.variants.classic.get_h_value(solver: MarkingEquationSolver, parameters: Dict[Any, Any] | None = None) → int

Gets the heuristics value from the marking equation

Parameters:

• solver – Marking equation solver (class in this file)

• parameters – Possible parameters of the algorithm