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,