Print graphe

graphKnapsack.py

def evolve_several_generation_with_limited_time(item_set, size_of_population, number_of_child, time_limit,
                                                mutationRate):
    temps_init = time.time()
    value0 = 0
    result = []
    population = generate_first_population(item_set, size_of_population)
    value0 = max(value0, value(get_best_individual_in_population(population), item_set))
    result.append(value0)
    while (time.time() - temps_init < time_limit):
        population_sorted = sort_population_by_fitness(population, item_set)
        breeders = select_breeders(population_sorted, size_of_population)
        population = create_children(breeders, number_of_child)
        population = mutate_population(population, mutationRate)
        population = sort_population_by_fitness(population, item_set)
        value0 = max(value0, value(get_best_individual_in_population(population), item_set))
    return value0def mean_result_evolve(item_set, size_of_population, number_of_child, number_of_sample, mutationRate, time_limit):
    meanResult = 0
    for i in range(number_of_sample):
        meanResult += evolve_several_generation_with_limited_time(item_set, size_of_population, number_of_child,
                                                                  time_limit, mutationRate)
    return (meanResult / number_of_sample)
  
def mean_result_evolve(item_set, size_of_population, number_of_child, number_of_sample, mutationRate, time_limit):
    meanResult = 0
    for i in range(number_of_sample):
        meanResult += evolve_several_generation_with_limited_time(item_set, size_of_population, number_of_child,
                                                                  time_limit, mutationRate)
    return (meanResult / number_of_sample)


def print_graph(number_of_child, number_of_sample, mutationRate, size_of_population, time_limit, item_set):
	fig = plt.figure()
	ax = fig.add_subplot(111, projection='3d')
	
	ax.set_xlabel('Population size')
	ax.set_ylabel('Mutation rate')
	ax.set_zlabel('Efficiency')

	graphSize = []
	graphMutation = []
	graphResult = []
	for i in range(20):
		mutationRate = 5*i
		for j in range(19):
			size_of_population = 5*(j+1)
			graphSize.append(size_of_population)
			graphMutation.append(mutationRate)
			graphResult.append(mean_result_evolve(item_set, size_of_population, number_of_child, number_of_sample, mutationRate, time_limit))

	ax.scatter(graphSize, graphMutation, graphResult)
	plt.show()