print("*************************************") print(str(dpll_results)) # p5_e = Totals(numpy.divide(dpll_results.efficacy.eff, # dpll_results.efficacy.eff)) # # p5_rt = Totals(numpy.divide(dpll_results.running_time.eff, # dpll_results.running_time.eff)) p5_e = Totals(dpll_results.efficacy.eff) p5_rt = Totals(dpll_results.running_time.eff) print("\nQuality of Solutions") print("*************************************") print(p5_e.print_results()) print("\nQuality of Running Time") print("*************************************") print(p5_rt.print_results()) # GSAT # gsat_results = gsat(sat_instances) # print("\n\nGSAT ALGORITHM") # print("*************************************") # print(str(gsat_results)) # # # p6_e = Totals(numpy.divide(gsat_results.efficacy.eff, # # gsat_results.efficacy.eff)) # # # # p6_rt = Totals(numpy.divide(gsat_results.running_time.eff,
print("\n\nMIN COST ALGORITHM") print("*************************************") print(str(min_cost_results)) # p2_e = Totals(numpy.divide(min_cost_results.efficacy.eff, # optimal_dynamic_programming_results.efficacy.eff)) # # p2_rt = Totals(numpy.divide(min_cost_results.running_time.eff, # optimal_dynamic_programming_results.running_time.eff)) p2_e = Totals(min_cost_results.efficacy.eff) p2_rt = Totals(min_cost_results.running_time.eff) print("\nQuality of Solutions") print("*************************************") print(p2_e.print_results()) print("\nQuality of Running Time") print("*************************************") print(p2_rt.print_results()) # The greedy 2-approximation from the textbook. greedy_two_approximation_results = greedy_two_approximation(k_instances) print("\n\nGREEDY TWO APPROXIMATION ALGORITHM") print("*************************************") print(str(greedy_two_approximation_results)) # p3_e = Totals(numpy.divide(optimal_dynamic_programming_results.efficacy.eff, # greedy_two_approximation_results.efficacy.eff)) # # p3_rt = Totals(numpy.divide(optimal_dynamic_programming_results.running_time.eff,