Python Util.randomFlipCoin方法代码示例

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def GTreeGPMutatorOperation(genome, **args):
   """ The mutator of GTreeGP, Operation Mutator

   .. versionadded:: 0.6
      The *GTreeGPMutatorOperation* function
   """

   if args["pmut"] <= 0.0:
      return 0
   elements = len(genome)
   mutations = args["pmut"] * elements
   ga_engine = args["ga_engine"]

   gp_terminals = ga_engine.getParam("gp_terminals")
   assert gp_terminals is not None

   gp_function_set = ga_engine.getParam("gp_function_set")
   assert gp_function_set is not None

   if mutations < 1.0:
      mutations = 0
      for i in xrange(len(genome)):
         if Util.randomFlipCoin(args["pmut"]):
            mutations += 1
            rand_node = genome.getRandomNode()
            assert rand_node is not None
            if rand_node.getType() == Consts.nodeType["TERMINAL"]:
               term_operator = rand_choice(gp_terminals)
            else:
               op_len = gp_function_set[rand_node.getData()]
               fun_candidates = []
               for o, l in gp_function_set.items():
                  if l == op_len:
                     fun_candidates.append(o)

               if len(fun_candidates) <= 0:
                  continue

               term_operator = rand_choice(fun_candidates)
            rand_node.setData(term_operator)
   else:
      for it in xrange(int(round(mutations))):
         rand_node = genome.getRandomNode()
         assert rand_node is not None
         if rand_node.getType() == Consts.nodeType["TERMINAL"]:
            term_operator = rand_choice(gp_terminals)
         else:
            op_len = gp_function_set[rand_node.getData()]
            fun_candidates = []
            for o, l in gp_function_set.items():
               if l == op_len:
                  fun_candidates.append(o)

            if len(fun_candidates) <= 0:
               continue

            term_operator = rand_choice(fun_candidates)
         rand_node.setData(term_operator)

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def GTreeMutatorSwap(genome, **args):
   """ The mutator of GTree, Swap Mutator

   .. versionadded:: 0.6
      The *GTreeMutatorSwap* function
   """
   if args["pmut"] <= 0.0:
      return 0
   elements = len(genome)
   mutations = args["pmut"] * elements

   if mutations < 1.0:
      mutations = 0
      for i in xrange(len(genome)):
         if Util.randomFlipCoin(args["pmut"]):
            mutations += 1
            nodeOne = genome.getRandomNode()
            nodeTwo = genome.getRandomNode()
            nodeOne.swapNodeData(nodeTwo)
   else:
      for it in xrange(int(round(mutations))):
         nodeOne = genome.getRandomNode()
         nodeTwo = genome.getRandomNode()
         nodeOne.swapNodeData(nodeTwo)

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def GTreeGPInitializator(genome, **args):
    """This initializator accepts the follow parameters:

    *max_depth*
       The max depth of the tree

    *method*
       The method, accepts "grow", "full" or "ramped"

    .. versionadded:: 0.6
       The *GTreeGPInitializator* function.
    """

    max_depth = genome.getParam("max_depth", 5)
    method = genome.getParam("method", "grow")
    ga_engine = args["ga_engine"]

    if method == "grow":
        root = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth)
    elif method == "full":
        root = GTree.buildGTreeGPFull(ga_engine, 0, max_depth)
    elif method == "ramped":
        if Util.randomFlipCoin(0.5):
            root = GTree.buildGTreeGPFull(ga_engine, 0, max_depth)
        else:
            root = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth)
    else:
        Util.raiseException("Unknown tree initialization method [%s] !" % method)

    genome.setRoot(root)
    genome.processNodes()
    assert genome.getHeight() <= max_depth

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def GTreeMutatorRealRange(genome, **args):
   """ The mutator of GTree, Real Range Mutator

   Accepts the *rangemin* and *rangemax* genome parameters, both optional.

   .. versionadded:: 0.6
      The *GTreeMutatorRealRange* function
   """
   if args["pmut"] <= 0.0:
      return 0
   elements = len(genome)
   mutations = args["pmut"] * elements

   range_min = genome.getParam("rangemin", Consts.CDefRangeMin)
   range_max = genome.getParam("rangemax", Consts.CDefRangeMax)

   if mutations < 1.0:
      mutations = 0
      for i in xrange(len(genome)):
         if Util.randomFlipCoin(args["pmut"]):
            mutations += 1
            rand_node = genome.getRandomNode()
            random_real = rand_uniform(range_min, range_max)
            rand_node.setData(random_real)

   else:
      for it in xrange(int(round(mutations))):
         rand_node = genome.getRandomNode()
         random_real = rand_uniform(range_min, range_max)
         rand_node.setData(random_real)

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G2DBinaryStringXUniform(genome, **args):
   """ The G2DBinaryString Uniform Crossover

   .. versionadded:: 0.6
      The *G2DBinaryStringXUniform* function
   """
   sister = None
   brother = None
   gMom = args["mom"]
   gDad = args["dad"]

   sister = gMom.clone()
   brother = gDad.clone()
   sister.resetStats()
   brother.resetStats()

   h, w = gMom.getSize()

   for i in xrange(h):
      for j in xrange(w):
         if Util.randomFlipCoin(Consts.CDefG2DBinaryStringUniformProb):
            temp = sister.getItem(i, j)
            sister.setItem(i, j, brother.getItem(i, j))
            brother.setItem(i, j, temp)

   return (sister, brother)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G1DBinaryStringMutatorFlip(genome, **args):
   """ The classical flip mutator for binary strings """
   if args["pmut"] <= 0.0:
      return 0
   stringLength = len(genome)
   mutations = args["pmut"] * (stringLength)

   if mutations < 1.0:
      mutations = 0
      for it in xrange(stringLength):
         if Util.randomFlipCoin(args["pmut"]):
            if genome[it] == 0:
               genome[it] = 1
            else:
               genome[it] = 0
            mutations += 1

   else:
      for it in xrange(int(round(mutations))):
         which = rand_randint(0, stringLength - 1)
         if genome[which] == 0:
            genome[which] = 1
         else:
            genome[which] = 0

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G2DListMutatorSwap(genome, **args):
   """ The mutator of G1DList, Swap Mutator

   .. note:: this mutator is :term:`Data Type Independent`

   """

   if args["pmut"] <= 0.0:
      return 0
   height, width = genome.getSize()
   elements = height * width

   mutations = args["pmut"] * elements

   if mutations < 1.0:
      mutations = 0
      for i in xrange(height):
         for j in xrange(width):
            if Util.randomFlipCoin(args["pmut"]):
               index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
               Util.list2DSwapElement(genome.genomeList, (i, j), index_b)
               mutations += 1
   else:
      for it in xrange(int(round(mutations))):
         index_a = (rand_randint(0, height - 1), rand_randint(0, width - 1))
         index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
         Util.list2DSwapElement(genome.genomeList, index_a, index_b)

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G2DListMutatorIntegerRange(genome, **args):
   """ Simple integer range mutator for G2DList

   Accepts the *rangemin* and *rangemax* genome parameters, both optional.

   """
   if args["pmut"] <= 0.0:
      return 0
   height, width = genome.getSize()
   elements = height * width

   mutations = args["pmut"] * elements

   range_min = genome.getParam("rangemin", Consts.CDefRangeMin)
   range_max = genome.getParam("rangemax", Consts.CDefRangeMax)

   if mutations < 1.0:
      mutations = 0
      for i in xrange(genome.getHeight()):
         for j in xrange(genome.getWidth()):
            if Util.randomFlipCoin(args["pmut"]):
               random_int = rand_randint(range_min, range_max)
               genome.setItem(i, j, random_int)
               mutations += 1

   else:
      for it in xrange(int(round(mutations))):
         which_x = rand_randint(0, genome.getWidth() - 1)
         which_y = rand_randint(0, genome.getHeight() - 1)
         random_int = rand_randint(range_min, range_max)
         genome.setItem(which_y, which_x, random_int)

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G1DListMutatorAllele(genome, **args):
   """ The mutator of G1DList, Allele Mutator

   To use this mutator, you must specify the *allele* genome parameter with the
   :class:`GAllele.GAlleles` instance.

   """
   if args["pmut"] <= 0.0:
      return 0
   listSize = len(genome)
   mutations = args["pmut"] * listSize

   allele = genome.getParam("allele", None)
   if allele is None:
      Util.raiseException("to use the G1DListMutatorAllele, you must specify the 'allele' parameter", TypeError)

   if mutations < 1.0:
      mutations = 0
      for it in xrange(listSize):
         if Util.randomFlipCoin(args["pmut"]):
            new_val = allele[it].getRandomAllele()
            genome[it] = new_val
            mutations += 1
   else:
      for it in xrange(int(round(mutations))):
         which_gene = rand_randint(0, listSize - 1)
         new_val = allele[which_gene].getRandomAllele()
         genome[which_gene] = new_val

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G1DListMutatorSIM(genome, **args):
   """ The mutator of G1DList, Simple Inversion Mutation

   .. note:: this mutator is :term:`Data Type Independent`

   """
   mutations = 0
   if args["pmut"] <= 0.0:
      return 0

   cuts = [rand_randint(0, len(genome)), rand_randint(0, len(genome))]

   if cuts[0] > cuts[1]:
      Util.listSwapElement(cuts, 0, 1)

   if (cuts[1] - cuts[0]) <= 0:
      cuts[1] = rand_randint(cuts[0], len(genome))

   if Util.randomFlipCoin(args["pmut"]):
      part = genome[cuts[0]:cuts[1]]
      if len(part) == 0:
         return 0
      part.reverse()
      genome[cuts[0]:cuts[1]] = part
      mutations += 1

   return mutations

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def GTreeMutatorRealGaussian(genome, **args):
   """ A gaussian mutator for GTree of Real numbers

   Accepts the *rangemin* and *rangemax* genome parameters, both optional. Also
   accepts the parameter *gauss_mu* and the *gauss_sigma* which respectively
   represents the mean and the std. dev. of the random distribution.

   """
   if args["pmut"] <= 0.0:
      return 0
   elements = len(genome)
   mutations = args["pmut"] * elements

   mu = genome.getParam("gauss_mu", Consts.CDefG1DListMutRealMU)
   sigma = genome.getParam("gauss_sigma", Consts.CDefG1DListMutRealSIGMA)

   if mutations < 1.0:
      mutations = 0
      for i in xrange(len(genome)):
         if Util.randomFlipCoin(args["pmut"]):
            mutations += 1
            rand_node = genome.getRandomNode()
            final_value = rand_node.getData() + rand_gauss(mu, sigma)
            final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
            final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
            rand_node.setData(final_value)
   else:
      for it in xrange(int(round(mutations))):
         rand_node = genome.getRandomNode()
         final_value = rand_node.getData() + rand_gauss(mu, sigma)
         final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
         final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
         rand_node.setData(final_value)

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def Reshuffle(genome, **args):
  """Reshuffle the order of the shapes"""
  if Util.randomFlipCoin(args['pmut']):
    shuffle(genome.shapes)
    return 1
  else:
    return 0

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G1DListMutatorRealRange(genome, **args):
   """ Simple real range mutator for G1DList

   Accepts the *rangemin* and *rangemax* genome parameters, both optional.

   """
   if args["pmut"] <= 0.0:
      return 0
   listSize = len(genome)
   mutations = args["pmut"] * (listSize)

   if mutations < 1.0:
      mutations = 0
      for it in xrange(listSize):
         if Util.randomFlipCoin(args["pmut"]):
            genome[it] = rand_uniform(genome.getParam("rangemin", Consts.CDefRangeMin),
                                      genome.getParam("rangemax", Consts.CDefRangeMax))
            mutations += 1

   else:
      for it in xrange(int(round(mutations))):
         which_gene = rand_randint(0, listSize - 1)
         genome[which_gene] = rand_uniform(genome.getParam("rangemin", Consts.CDefRangeMin),
                                           genome.getParam("rangemax", Consts.CDefRangeMax))

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G1DListMutatorIntegerBinary(genome, **args):
   """ The mutator of G1DList, the binary mutator

   This mutator will random change the 0 and 1 elements of the 1D List.

   """
   if args["pmut"] <= 0.0:
      return 0
   listSize = len(genome)
   mutations = args["pmut"] * (listSize)

   if mutations < 1.0:
      mutations = 0
      for it in xrange(listSize):
         if Util.randomFlipCoin(args["pmut"]):
            if genome[it] == 0:
               genome[it] = 1
            elif genome[it] == 1:
               genome[it] = 0

            mutations += 1
   else:
      for it in xrange(int(round(mutations))):
         which_gene = rand_randint(0, listSize - 1)
         if genome[which_gene] == 0:
            genome[which_gene] = 1
         elif genome[which_gene] == 1:
            genome[which_gene] = 0

   return int(mutations)

# 需要导入模块: from pyevolve import Util [as 别名]
# 或者: from pyevolve.Util import randomFlipCoin [as 别名]
def G2DBinaryStringMutatorSwap(genome, **args):
   """ The mutator of G2DBinaryString, Swap Mutator

   .. versionadded:: 0.6
      The *G2DBinaryStringMutatorSwap* function
   """

   if args["pmut"] <= 0.0:
      return 0
   height, width = genome.getSize()
   elements = height * width

   mutations = args["pmut"] * elements

   if mutations < 1.0:
      mutations = 0
      for i in xrange(height):
         for j in xrange(width):
            if Util.randomFlipCoin(args["pmut"]):
               index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
               Util.list2DSwapElement(genome.genomeString, (i, j), index_b)
               mutations += 1
   else:
      for it in xrange(int(round(mutations))):
         index_a = (rand_randint(0, height - 1), rand_randint(0, width - 1))
         index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
         Util.list2DSwapElement(genome.genomeString, index_a, index_b)

   return int(mutations)