def full_example():
ploter = Plot()
num_of_start_population = 10
i = 2
min = -2
max = 2
chance_for_crossingover = 0.5
popul = population.Population([])
#step 1 - random start population
for j in range(num_of_start_population):
rand_arr = [random.uniform(min, max) for k in range(i)]
popul.append([rand_arr])
#step 1b - calculate f_x
'''
for j in range(num_of_start_population):
popul.chromosome[j].f_x = functions.Goldstein_Price(popul.chromosome[j].x)
ploter.addData('przed', popul.chromosome[j].f_x)
print(popul.get_max_f_x())
#step 2 - make selection
test_population = selection.Selection.proportional(popul)
print(test_population)
population_after_selection = population.Population(test_population)
#step 3 - crossingover and mutation of best specimens in population
#kryzwanie na wyseekcjonowanych rodziach
#mutacje sie robi na potmokach(z krzyzywaonia) zeby je zmienic
#ponizej testowo
child_population = population.Population([])
for j in range(int(0.5*chance_for_crossingover*num_of_start_population)):
rand_dad = random.randint(0, num_of_start_population-1)
rand_mom = random.randint(0, num_of_start_population-1)
kid = crossingovers.Crossingovers.aritmetic(population_after_selection.chromosome[rand_dad], population_after_selection.chromosome[rand_mom])
child_population.append(kid)
child_population.append([population_after_selection.chromosome[rand_dad].x, population_after_selection.chromosome[rand_mom].x])
if child_population.size != num_of_start_population:
rand_dad = random.randint(0, num_of_start_population-1)
rand_mom = random.randint(0, num_of_start_population-1)
child_population.append([population_after_selection.chromosome[rand_dad].x, population_after_selection.chromosome[rand_mom].x])
print(child_population)
after_mutation = mutation.Mutation.reciprocal_exchange(0.5, child_population.array_of_float)
child_population_after_mutation = population.Population(after_mutation)
ploter.addKind('po')
#step 3b - calculate f_x
#for j in range(child_population_after_mutation.size):
# child_population_after_mutation.chromosome[j].f_x = functions.Goldstein_Price(child_population_after_mutation.chromosome[j].x)
# ploter.addData('po', child_population_after_mutation.chromosome[j].f_x)
popul.add_child(child_population_after_mutation)
#step 4 - stategis to choose new population from the old one
popul = stategies.Strategies.full(popul)
print(popul.chromosome[1].f_x)
'''
func = functions.Goldstein_Price
func_mutation = mutation.Mutation.reciprocal_exchange
func_crossingovers = crossingovers.Crossingovers.aritmetic
func_selection = selection.Selection.tournament
computing_loop(func, popul, 10, ploter, 'Randomowa', 100, func_selection,
0.8, func_crossingovers, func_mutation, 0.8)
ploter.showPlot()
pass
