def __createSATformula(self, pn, m0, mf, max_d, max_t, traces_xes,
nbTraces):
'''
This function creates and solve the SAT formula of the clustering problem.
:param pn (Petrinet)
:param m0 (Marking)
:param mf (Marking)
:param max_d (int)
:param max_t (int)
:param traces_xes (Log)
'''
# this object creates variable numbers of the SAT formula
self.__vars = VariablesGenerator()
# formula version of data event log
log_to_PN_w_formula, self.__traces = log_to_Petri_with_w(
traces_xes,
self.__transitions,
self.__vars,
self.__size_of_run,
self.__wait_transition_trace,
self.__wait_transition_model,
label_l=BOOLEAN_VAR_TRACES_ACTIONS,
max_nbTraces=nbTraces)
# creates the boolean variables for the next formulas
self.__createBooleanVariables()
# formula of centroids
centroidsFormulasList = self.__createCentroids(m0, mf)
# formula that describes maximal distance
diffTracesCentroids = self.__getDiffTracesCentroids(
self.__vars.getFunction(BOOLEAN_VAR_CHI_TRANSITIONS),
self.__vars.getFunction(BOOLEAN_VAR_DIFF_l),
self.__vars.getFunction(BOOLEAN_VAR_DIFF_m),
self.__vars.getFunction(BOOLEAN_VAR_TRACES_ACTIONS))
# formula that create BOOLEAN_VAR_COMMON_T variables
listOfCommonTransitions = self.__commonTransitions(
self.__vars.getFunction(BOOLEAN_VAR_COMMON_T),
self.__vars.getFunction(BOOLEAN_VAR_K_CONTAINS_T))
# formula that describes that a trace belongs to at most one cluster
aClusterMax = self.__tracesInAClusterOnly(
self.__vars.getFunction(BOOLEAN_VAR_J_CLUSTERISED),
self.__vars.getFunction(BOOLEAN_VAR_J_IN_K))
# concat the formula
full_formula = And([], [], log_to_PN_w_formula +
centroidsFormulasList + diffTracesCentroids +
listOfCommonTransitions + aClusterMax)
# formula to cnf
cnf = full_formula.operatorToCnf(self.__vars.iterator)
# CNF is completed with minimisation and solved
self.__createWCNFWithMinimization(cnf)
def __createWncf(self, initialisationFormulas, distanceFormula,
artefactForMinimization):
'''
This method creates the wncf formulas with the weighted variables depending on the distance and artefact.
:param initialisationFormulas: @see __artefactsInitialisation
:param distanceFormula: @see __compute_distance
:param artefactForMinimization: MULTI_ALIGNMENT or ANTI_ALIGNMENT or EXACT_ALIGNMENT
:return:
'''
formulas = initialisationFormulas + distanceFormula + self.__sup_to_minimize(
artefactForMinimization)
full_formula = And([], [], formulas)
cnf = full_formula.operatorToCnf(self.__vars.iterator)
wcnf = WCNF()
wcnf.extend(cnf)
wcnf = self.__createWeights(wcnf, artefactForMinimization)
self.__formula_time = time.time()
return wcnf