import Parameters as P import Problem1 as Cls import SupportSteadyState as Support # create a cohort of patients for when the drug is not available cohortFairCoin = Cls.Cohort(id=1, pop_size=P.SIM_POP_SIZE, mortality_prob=P.MORTALITY_PROB) # simulate the cohort FairCoinOutcome = cohortFairCoin.simulate(P.TIME_STEPS) # create a cohort of patients for when the drug is available cohortUnfairCoin = Cls.Cohort(id=2, pop_size=P.SIM_POP_SIZE, mortality_prob=P.MORTALITY_PROB * P.DRUG_EFFECT_RATIO) # simulate the cohort UnfairCoinOutcome = cohortUnfairCoin.simulate(P.TIME_STEPS) # print outcomes of each cohort Support.print_outcomes(FairCoinOutcome, 'When coin is fair:') Support.print_outcomes(UnfairCoinOutcome, 'When coin is unfair:') # draw survival curves and histograms Support.draw_survival_curves_and_histograms(FairCoinOutcome, UnfairCoinOutcome) # print comparative outcomes Support.print_comparative_outcomes(FairCoinOutcome, UnfairCoinOutcome)
import Problem1 as pb unfair = pb.Cohort(2,1000,20,0.4) print('Average reward for unfair coin is', unfair.get_average())