The source code and dockerfile for the GSW2024 AI Lab.
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import stormpy import stormpy.core
import stormpy.examples import stormpy.examples.files
import stormpy.info import pycarl
def example_building_models_02():
import stormpy.pars if stormpy.info.storm_ratfunc_use_cln(): import pycarl.cln as pc else: import pycarl.gmp as pc
def make_factorized_rf(var, cache): num = pc.FactorizedPolynomial(pc.Polynomial(var), cache) denom = pc.FactorizedPolynomial(pc.Rational(1)) return pc.FactorizedRationalFunction(num, denom)
# And the parametric path = stormpy.examples.files.drn_pdtmc_die model = stormpy.build_parametric_model_from_drn(path)
parameters = model.collect_probability_parameters() bar_parameters = dict() for p in parameters: # Ensure that variables with that name are not recognized by pycarl. assert pycarl.variable_with_name(p.name + "_bar").is_no_variable bar_parameters[p] = pycarl.Variable(p.name + "_bar")
substitutions = dict([[pc.Polynomial(1) - p, bar_parameters[p]] for p in parameters]) print(substitutions)
matrix = model.transition_matrix for e in matrix: val = e.value() if val.is_constant(): continue val_pol = val.numerator.polynomial() cache = val.numerator.cache() for sub, repl in substitutions.items(): if val_pol - sub == 0: print("Found substitution") e.set_value(make_factorized_rf(repl, cache)) break # Assume only one substitution per entry print(matrix)
if __name__ == '__main__': example_building_models_02()
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