def ga(df, start, end, _positionList, ranges=[(20,100),(0.01, 1),(0.01, 1),(0.01, 1),(1, 5)], eps=0.01):
indv_template = BinaryIndividual(ranges=ranges, eps=eps)
population = Population(indv_template=indv_template, size=100)
population.init() # Initialize population with individuals.
# Use built-in operators here.
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=0.8, pe=0.5)
mutation = FlipBitMutation(pm=0.3)
engine = GAEngine(population=population, selection=selection,
crossover=crossover, mutation=mutation,
analysis=[FitnessStore])
@engine.fitness_register
def fitness(indv):
n, upper, lower, adds, cutoff = indv.solution
df['KAMA'] = talib.KAMA(df.close, int(n))
df['VAR'] = talib.VAR(df.close-df.KAMA.shift(1) - df.close.shift(1)+df.KAMA.shift(2),10)
profitsList, buypriceList, sellpriceList, fits,positionList = profitsCal(df, start, end, _positionList, upper=upper, lower=lower, adds = adds, cutoff=cutoff)
return float(fits)
@engine.analysis_register
class ConsoleOutput(OnTheFlyAnalysis):
master_only = True
interval = 1
def register_step(self, g, population, engine):
best_indv = population.best_indv(engine.fitness)
msg = 'Generation: {}, best fitness: {:.3f}'.format(g, engine.fmax)
print(best_indv.solution)
engine.logger.info(msg)
engine.run(ng=30)
return population.best_indv(engine.fitness).solution, _positionList
def doga(units, ploads, hload, size=50, ng=5, pc=0.8, pe=0.5, pm=0.1):
# 计算不同典型日下，最小运行成本均值
def calcost(indv):
# 输入改造方案
for chpunit, rtype in zip(chpunits, indv):
chpunit.rtype = rtype
meancost = 0
# ploads为字典，key:典型日出现的概率,value:[典型日负荷曲线,风电出力极限曲线1,...,风电出力极限曲线n]
for p in ploads.keys():
x = ProSimu()
x.pload = ploads[p][0]
x.hload = hload
wn = 0
for wpunit in wpunits:
wn += 1
wpunit.maxwp = ploads[p][wn]
x.units = units
meancost += x.getoptvalue()*p
return meancost
ranges = list()
wpunits = list()
chpunits = list()
for unit in units:
if unit.ptype == 0: # Wind Power Unit
wpunits += [unit]
elif unit.ptype == 2: # CHP Unit-1
ranges += [(0, 4)]
chpunits += [unit]
elif unit.ptype == 3: # CHP Unit-2
ranges += [(0, 3)]
chpunits += [unit]
template = BinaryIndividual(ranges, eps=1)
population = Population(indv_template=template, size=size).init()
selection = TournamentSelection()
crossover = UniformCrossover(pc=pc, pe=pe)
mutation = FlipBitMutation(pm=pm)
engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation,
analysis=[ConsoleOutput, FitnessStore])
@engine.fitness_register
@engine.minimize
def fitness(indv):
# print(type(float(calcost(indv.solution))))
return float(calcost(indv.solution))
engine.run(ng=ng)
bestindv = population.best_indv(engine.fitness).solution
for unitt, rtype in zip(chpunits, bestindv):
unitt.rtype = rtype
result = calcost(bestindv)
print('Best individual:', bestindv)
print('Optimal result:', result)
class optimize_ga:
def __init__(self ,k ,total_implied_variance ,slice_before ,slice_after ,tau):
self.k =k
self.total_implied_variance =total_implied_variance
self.slice_before =slice_before
self.slice_after = slice_after
self.tau = tau
# Define population.
indv_template = BinaryIndividual(ranges=[(1e-5, 20),(1e-5, 20),(1e-5, 20)], eps=0.001)
self.population = Population(indv_template=indv_template, size=30).init()
# Create genetic operators.
selection = TournamentSelection()
crossover = UniformCrossover(pc=0.8, pe=0.5)
mutation = FlipBitMutation(pm=0.1)
# Create genetic algorithm engine.
self.engine = GAEngine(population=self.population, selection=selection,
crossover=crossover, mutation=mutation,
analysis=[FitnessStore])
# Define fitness function.
@self.engine.fitness_register
@self.engine.minimize
def fitness(indv):
a, b, m, rho, sigma = indv.solution
model_total_implied_variance=svi_raw(self.k,np.array([a, b, m, rho, sigma]),self.tau)
value = norm(self.total_implied_variance - model_total_implied_variance,ord=2)
# if bool(len(self.slice_before)) and np.array(model_total_implied_variance < self.slice_before).any():
# value +=(np.count_nonzero(~np.array(model_total_implied_variance < self.slice_before))*100)
# # value = 1e6
#
# if bool(len(self.slice_after)) and np.array(model_total_implied_variance > self.slice_after).any():
# value += float(np.count_nonzero(~np.array(model_total_implied_variance > self.slice_after)) * 100)
# # value = 1e6
# if np.isnan(value):
# value = 1e6
value = float(value)
return value
# Define on-the-fly analysis.
# @self.engine.analysis_register
# class ConsoleOutputAnalysis(OnTheFlyAnalysis):
# interval = 1
# master_only = True
#
# def register_step(self, g, population, engine):
# best_indv = population.best_indv(engine.fitness)
# msg = 'Generation: {}, best fitness: {:.3f}'.format(g, engine.ori_fmax)
# self.logger.info(msg)
#
# def finalize(self, population, engine):
# best_indv = population.best_indv(engine.fitness)
# x = best_indv.solution
# y = engine.ori_fmax
# msg = 'Optimal solution: ({}, {})'.format(x, y)
# self.logger.info(msg)
def optimize(self):
self.engine.run(ng=500)
return self.population.best_indv(self.engine.fitness).solution
class ConsoleOutput(OnTheFlyAnalysis):
master_only = True
interval = 20
def register_step(self, generacija, population, gen_algo):
best_indv = population.best_indv(gen_algo.fitness)
msg = 'Generacija: {}, best fitness: {:.3f}'.format(generacija, gen_algo.fmax)
gen_algo.logger.info(msg)
for generacija in range(individual_template):
trenutna_generacija = generacija
if mpi.is_master:
best_indv = population.best_indv(fitness)
else:
best_indv = None
best_indv = mpi.bcast(best_indv)
local_indvs = []
local_size = mpi.split_size(population.size // 2)
for _ in range(local_size):
roditelji = self.selection.select(population, fitness=fitness)
djeca = crossover.cross(*roditelji)
dijete= [mtation.mutation(dijete) for dijete in djeca]
local_indvs.extend(djeca)
indvs = mpi.merge_seq(local_indvs)