def Crossover():
global funEval
for i in range(0, popSize):
if (random.random() <= CR):
# Choose two random indices
i1, i2 = random.sample(range(0, popSize), 2)
# Parents
p1 = deepcopy(pop[i1])
p2 = deepcopy(pop[i2])
# Choose a crossover point
pt = random.randint(1, ff.D - 2)
# Generate new childs
c1 = p1.chromosome[0:pt] + p2.chromosome[pt:]
c2 = p2.chromosome[0:pt] + p1.chromosome[pt:]
# Get the fitness of childs
c1Fitness = ff.FitnessFunction(c1)
funEval = funEval + 1
c2Fitness = ff.FitnessFunction(c2)
funEval = funEval + 1
# Select between parent and child
if c1Fitness < p1.fitness:
pop[i1].fitness = c1Fitness
pop[i1].chromosome = c1
if c2Fitness < p2.fitness:
pop[i2].fitness = c2Fitness
pop[i2].chromosome = c2
def Mutation():
global UB, LB, funEval
for i in range(0, popSize):
if (random.random() <= MR):
# Choose random index
r = random.randint(0, popSize - 1)
# Choose a parent
p = deepcopy(pop[r])
# Choose mutation point
pt = random.randint(0, ff.D - 1)
# Generate new childs
c = deepcopy(p.chromosome)
# Mutation
c[pt] = round(random.uniform(ff.LB, ff.UB), 2)
#Get the fitness of childs
cFitness = ff.FitnessFunction(c)
funEval = funEval + 1
# Select between parent and child
if cFitness < p.fitness:
pop[r].fitness = cFitness
pop[r].chromosome = c
def Init():
global funEval
for i in range(0, popSize):
chromosome = []
for j in range(0, ff.D):
chromosome.append(round(random.uniform(ff.LB, ff.UB), 2))
fitness = ff.FitnessFunction(chromosome)
funEval = funEval + 1
newIndividual = Individual(chromosome, fitness)
pop.append(newIndividual)
y2 = y1 + SubGroupSize
y3 = y2 + SubGroupSize
y4 = y3 + SubGroupSize
print('Begin of training SubGroup: ' + str(y1))
print('Begin of select SubGroup: ' + str(y2))
print('Begin of testing SubGroup: ' + str(y3))
print('End of testing SubGroup: ' + str(y4))
if (y4 + SubGroupSize) > len(parsed):
y4 = len(parsed) - 1
print('Final end of testing SubGroup: ' + str(y4))
flogic = fuzzy.FuzzyLogic(y1, y3, parsed.loc[:, :], True, False)
for j in range(0,GA_Iterations):
print("Processing Group ", i+1, "out of ", NumberOfGroups, "GA iteration ", j+1, "out of ", GA_Iterations)
FFTrain =FitnessFunction.FitnessFunction(y1,y3,parsed.loc[:,:],collection_records[j],flogic,[10000000.0,0,0,0,0,0,0],True,False,TradeWhenIntersection)
resultTrain = FFTrain.getRreturn(parsed.loc[:,:])
FFSelection =FitnessFunction.FitnessFunction(y2,y3,parsed.loc[:,:],collection_records[j],flogic,[10000000.0,0,0,0,0,0,0],True,False,TradeWhenIntersection)
resultSelection = FFSelection.getRreturn(parsed.loc[:,:])
ReturnAverage = ((((1 + resultTrain) ** 0.5) - 1)+resultSelection)*0.5
BestReturnAverage = max(ReturnAverage)
BestIndividualAverage = collection_records[j][ReturnAverage.idxmax(axis=0)]
if j == 0:
BestIndividual.append(BestIndividualAverage)
rreturnLog.append(ReturnAverage[ReturnAverage.idxmax(axis=0)])
BestReturn = resultSelection[ReturnAverage.idxmax(axis=0)]
elif BestReturnAverage < BestReturn:
BestIndividual.append(BestIndividual[j-1])
