def findSol(self, timewindow):
pyevolve.logEnable()
genome = G1DList.G1DList(4)
# range is constrained by the solutions found by MR
genome.setParams(rangemin=-12.0, rangemax=2.0)
# Change the initializator to Real Values
genome.initializator.set(Initializators.G1DListInitializatorReal)
# Change the mutator to Gaussian
genome.mutator.set(Mutators.G1DListMutatorRealGaussian)
# The evaluator function (objective function)
genome.evaluator.set(self.eval_func(self.features))
genome.crossover.set(Crossovers.G1DListCrossoverTwoPoint)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
# Set the Roulette Wheel selector method, the number of generations and
# the termination criteria
ga.selector.set(Selectors.GRouletteWheel)
# set default parameters for the engine
ga.setGenerations(100)
#ga.setPopulationSize(80)
#ga.setMutationRate(0.2)
#ga.setCrossoverRate(0.8)
ga.setMinimax(Consts.minimaxType["minimize"])
ga.setElitism(True)
ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
dbPath = createPath('pyevolve.db')
sqlite_adapter = DBAdapters.DBSQLite(dbname=dbPath,
identify="timewindow" +
str(timewindow),
resetIdentify=True,
resetDB=False)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump frequency of 20 generations
ga.evolve(freq_stats=20)
# Best individual
best = ga.bestIndividual()
stats = {
'constant': best[0],
'EMA': best[1],
'RSI': best[2],
'MACD': best[3]
}
return stats
def findSol(self, timewindow):
pyevolve.logEnable()
genome = G1DList.G1DList(4)
# range is constrained by the solutions found by MR
genome.setParams(rangemin=-12.0, rangemax=2.0)
# Change the initializator to Real Values
genome.initializator.set(Initializators.G1DListInitializatorReal)
# Change the mutator to Gaussian
genome.mutator.set(Mutators.G1DListMutatorRealGaussian)
# The evaluator function (objective function)
genome.evaluator.set(self.eval_func(self.features))
genome.crossover.set(Crossovers.G1DListCrossoverTwoPoint)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
# Set the Roulette Wheel selector method, the number of generations and
# the termination criteria
ga.selector.set(Selectors.GRouletteWheel)
# set default parameters for the engine
ga.setGenerations(100)
#ga.setPopulationSize(80)
#ga.setMutationRate(0.2)
#ga.setCrossoverRate(0.8)
ga.setMinimax(Consts.minimaxType["minimize"])
ga.setElitism(True)
ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
dbPath = createPath('pyevolve.db')
sqlite_adapter = DBAdapters.DBSQLite(dbname=dbPath, identify="timewindow" + str(timewindow), resetIdentify=True, resetDB=False)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump frequency of 20 generations
ga.evolve(freq_stats=20)
# Best individual
best = ga.bestIndividual()
stats = {'constant': best[0],
'EMA': best[1],
'RSI': best[2],
'MACD': best[3]
}
return stats
from pyevolve import DBAdapters import pyevolve from math import sin # This function is the evaluation function, we want # to give high score to more zero'ed chromosomes def eval_func(chromosome): for xval in chromosome: score = xval * sin(31.4*xval) + 1 return score # Enable the pyevolve logging system pyevolve.logEnable() # Genome instance, 1D List of 50 elements genome = G1DList.G1DList(50) # Sets the range max and min of the 1D List genome.setParams(rangemin=-1, rangemax=2) # The evaluator function (evaluation function) genome.evaluator.set(eval_func) # Genetic Algorithm Instance ga = GSimpleGA.GSimpleGA(genome) #Change scaling method pop = ga.getPopulation() pop.scaleMethod.set(Scaling.SaturatedScaling)
]
# Fitness evaluation function.
def eval_func(chromosome):
player = Player(chromosome)
game = Game([player] + player_list, verbose=False,
quiet=True) # Start the game in quiet mode.
ranking = game.play_game()
score = ranking.index('GA Player')
return score * score
# Enable the pyevolve logging system
pyevolve.logEnable()
# Genome instance, 1D List of 7 elements
genome = G1DList.G1DList(7)
# Sets the range max and min of the 1D List
bestScore = len(player_list) * len(player_list) + 1 # run forever
genome.setParams(rangemin=0, rangemax=100)
# The evaluator function (evaluation function)
genome.evaluator.set(eval_func)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
# Set the Roulette Wheel selector method, the number of generations and
def handle(self, *args, **options):
# This function is the evaluation function, we want
# to give high score to more zero'ed chromosomes
r = redis.Redis()
evaluator_set = getattr(settings, 'HAIKU_EVALUATORS', HAIKU_EVALUATORS)
def eval_func(chromosome):
closeness = []
EVALUATORS = []
i = 0
for eval_cls in evaluator_set:
print chromosome
EVALUATORS.append((eval_cls, float(chromosome[i])/100))
i += 1
try:
rated_comments = BaseHaiku.get_concrete_child().objects.rated()
except AttributeError:
raise CommandError("BaseHaiku child-classes' manager must implement rated/unrated (see django_haikus.models.HaikuManager")
result_queue = "haiku:results_" + ''.join([random.choice(string.ascii_uppercase) for i in range(5)])
for c in rated_comments:
data = (c.pk, EVALUATORS, result_queue)
r.lpush(settings.REDIS_CALCULATE_QUEUE, cPickle.dumps(data))
results = []
while True:
results.append(float(r.blpop(result_queue)[1]))
if(len(results) == len(rated_comments)):
break
# `results` contains the *distance* from the target score
# i.e. a lower average score is better.
# results = [100, 91, 80, 100, ...,]
print results
average_fitness = sum(results) / len(results)
print "Average fitness: %s, scaled: %s" % (average_fitness, 100 - average_fitness)
return 100 - average_fitness
pyevolve.logEnable()
genome = G1DList.G1DList(len(evaluator_set))
# Sets the range max and min of the 1D List
genome.setParams(rangemin=0, rangemax=100)
# The evaluator function (evaluation function)
genome.evaluator.set(eval_func)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
# Set the Roulette Wheel selector method, the number of generations and
# the termination criteria
#ga.selector.set(Selectors.GRouletteWheel)
ga.setGenerations(5)
# ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
# Sets the DB Adapter, the resetDB flag will make the Adapter recreate
# the database and erase all data every run, you should use this flag
# just in the first time, after the pyevolve.db was created, you can
# omit it.
sqlite_adapter = DBAdapters.DBSQLite(identify="ex1", resetDB=True)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump
# frequency of 20 generations
ga.evolve(freq_stats=1)
# Best individual
print ga.bestIndividual()