本文整理汇总了Python中GPopulation.GPopulation.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Python GPopulation.evaluate方法的具体用法？Python GPopulation.evaluate怎么用？Python GPopulation.evaluate使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类GPopulation.GPopulation的用法示例。
在下文中一共展示了GPopulation.evaluate方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: step
# 需要导入模块: from GPopulation import GPopulation [as 别名]
# 或者: from GPopulation.GPopulation import evaluate [as 别名]
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
logging.debug("Population was cloned.")
size_iterate = len(self.internalPop)
# Odd population size
if size_iterate % 2 != 0: size_iterate -= 1
crossover_empty = self.select(popID=self.currentGeneration).crossover.isEmpty()
for i in xrange(0, size_iterate, 2):
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
if not crossover_empty and Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
newPop.internalPop.append(brother)
if len(self.internalPop) % 2 != 0:
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=1):
(sister, brother) = it
else:
sister = random.choice([genomeMom, genomeDad])
sister = sister.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
logging.debug("Evaluating the new created population.")
newPop.evaluate()
if self.elitism:
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration == self.nGenerations)示例2: step
# 需要导入模块: from GPopulation import GPopulation [as 别名]
# 或者: from GPopulation.GPopulation import evaluate [as 别名]
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
if (MPI.COMM_WORLD.Get_rank() == 0): ### assumes WE are on top of hierarchy!
popsize = len(self.internalPop)
numAdded = 0
maxTries = 1000
numTries = 0
crossover_empty = self.select(popID=self.currentGeneration).crossover.isEmpty()
###TODO: enforce constraints!###
while numAdded < popsize:
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
if not crossover_empty and Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
# logging.debug("done cloning")
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
if (numTries > maxTries or self.owner.eval_constraints(sister)):
newPop.internalPop.append(sister)
numAdded += 1
print "successfully added sister"
if (numAdded < popsize and (numTries > maxTries or self.owner.eval_constraints(brother))):
newPop.internalPop.append(brother)
print "successfully added brother"
numAdded += 1
numTries += 1
#end rank0 onlye
# print "rank %d start eval pop" % MPI.COMM_WORLD.Get_rank()
logging.debug("Evaluating the newly created population.")
newPop.evaluate()
# print "rank %d done eval pop" % MPI.COMM_WORLD.Get_rank()
# if (MPI.COMM_WORLD.Get_rank() == 0):
# print "after eval, new pop's positions are:"
# for p in newPop:
# print p.wt_positions
if (MPI.COMM_WORLD.Get_rank() == 0): ### assumes WE are on top of hierarchy!
if self.elitism:
logging.debug("Doing elitism, %d" % self.nElitismReplacement)
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
# self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
# self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
if (MPI.COMM_WORLD.Get_rank() == 0): ### assumes WE are on top of hierarchy!
self.internalPop.sort()
# if (MPI.COMM_WORLD.Get_rank() == 0):
# print "after sort, internal pop's positions are:"
# for p in self.internalPop:
# print p.wt_positions
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
if (MPI.COMM_WORLD.Get_rank() == 0): ### assumes WE are on top of hierarchy!
self.saveState ()
return (self.currentGeneration == self.nGenerations)示例3: your_func
# 需要导入模块: from GPopulation import GPopulation [as 别名]
# 或者: from GPopulation.GPopulation import evaluate [as 别名]
#.........这里部分代码省略.........
if not crossover_empty and self.pCrossover >= 1.0:
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
if not crossover_empty and Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
newPop.internalPop.append(brother)
if len(self.internalPop) % 2 != 0:
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=1):
(sister, brother) = it
else:
sister = random.choice([genomeMom, genomeDad])
sister = sister.clone()
sister.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
logging.debug("Evaluating the new created population.")
newPop.evaluate()
if self.elitism:
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
#re-evaluate before being sure this is the best
self.internalPop.bestRaw(i).evaluate()
if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration == self.nGenerations)
def printStats(self):
""" Print generation statistics
:rtype: the printed statistics as string
.. versionchanged:: 0.6
The return of *printStats* method.示例4: step
# 需要导入模块: from GPopulation import GPopulation [as 别名]
# 或者: from GPopulation.GPopulation import evaluate [as 别名]
def step(self):
""" Just do one step in evolution, one generation """
genomeMom = None
genomeDad = None
newPop = GPopulation(self.internalPop)
logging.debug("Population was cloned.")
size_iterate = len(self.internalPop)
# Odd population size
if size_iterate % 2 != 0: size_iterate -= 1
#Check on the crossover function by picking a random individual - is it empty?
crossover_empty = self.select(popID=self.currentGeneration).crossover.isEmpty()
for i in xrange(0, size_iterate, 2):
#Ok, we select 2 parents using the selector (RouletteWheel, etc.)
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if not crossover_empty and self.pCrossover >= 1.0:
#Crossover all of them
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
#Filp a coin each time to determine if you should crossover
if not crossover_empty and Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2):
(sister, brother) = it
else:
sister = genomeMom.clone()
brother = genomeDad.clone()
#And "pre" mutate them
sister.premutate(pmut=self.pPreMutation, ga_engine=self)
brother.premutate(pmut=self.pPreMutation, ga_engine=self)
#Now each offspring is mutated
sister.mutate(pmut=self.pMutation, ga_engine=self)
brother.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
newPop.internalPop.append(brother)
if len(self.internalPop) % 2 != 0:
#Odd-numbered population
genomeMom = self.select(popID=self.currentGeneration)
genomeDad = self.select(popID=self.currentGeneration)
if Util.randomFlipCoin(self.pCrossover):
for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=1):
(sister, brother) = it
else:
sister = random.choice([genomeMom, genomeDad])
sister = sister.clone()
#Do the 2 mutations
sister.premutate(pmut=self.pPreMutation, ga_engine=self)
sister.mutate(pmut=self.pMutation, ga_engine=self)
newPop.internalPop.append(sister)
#---- Evaluate fitness ------
logging.debug("Evaluating the new created population.")
newPop.evaluate()
#Niching methods- Petrowski's clearing
self.clear()
if self.elitism:
#Avoid too much elitism
if self.nElitismReplacement >= len(self.internalPop):
self.nElitismReplacement = len(self.internalPop)-1
logging.debug("Doing elitism.")
if self.getMinimax() == Consts.minimaxType["maximize"]:
#Replace the n-th worst new ones with the nth best old ones
for i in xrange(self.nElitismReplacement):
if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
elif self.getMinimax() == Consts.minimaxType["minimize"]:
for i in xrange(self.nElitismReplacement):
if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score:
newPop[len(newPop)-1-i] = self.internalPop.bestRaw(i)
self.internalPop = newPop
self.internalPop.sort()
logging.debug("The generation %d was finished.", self.currentGeneration)
self.currentGeneration += 1
return (self.currentGeneration >= self.nGenerations)