def get_map_exponential_from_aggstatistics(agg_statistics, parameters=None):
"""
Get transition stochastic distribution map (exponential variable forced) given the log and the Petri net
Parameters
-----------
agg_statistics
Aggregated statistics calculated on the DFG graph and the Petri net
parameters
Parameters of the algorithm
Returns
-----------
stochastic_map
Map that to each transition associates a random variable
"""
stochastic_map = {}
if parameters is None:
parameters = {}
del parameters
for el in agg_statistics:
if type(el) is PetriNet.Transition:
rand = RandomVariable()
rand.random_variable = Exponential()
rand.random_variable.scale = agg_statistics[el]["performance"]
stochastic_map[el] = rand
return stochastic_map
#saving possible traces in a dictionary
possible_traces = {}
possible_traces[0] = ['a', 'b', 'e']
possible_traces[1] = ['a', 'b', 'd', 'e']
possible_traces[2] = ['a', 'd', 'b', 'e']
possible_traces[3] = ['a', 'c', 'e']
possible_traces[4] = ['a', 'c', 'd', 'e']
possible_traces[5] = ['a', 'd', 'c', 'e']
#creating stochastic map assigning probabilities to transitions
smap = {}
for t in transitions:
rand = RandomVariable()
#we do not use the uniform distribution, but we need some kind of distribution to initialize
#the random_variable of the RandomVariable() object, so that we can call set_weight and get_weight later
rand.random_variable = Uniform()
smap[t] = rand
if t.label == "b":
rand.set_weight(0.9)
elif t.label == "c":
rand.set_weight(0.1)
elif t.label == "d":
rand.set_weight(0.2)
elif t.label == None:
rand.set_weight(0.8)
else:
rand.set_weight(1)
simulated_log2 = simulator.apply(
net,