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 range(genome.getHeight()):
for j in range(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 range(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)
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.get_size()
elements = height * width
mutations = args["pmut"] * elements
range_min = genome.get_param("rangemin", CDefRangeMin)
range_max = genome.get_param("rangemax", CDefRangeMax)
if mutations < 1.0:
mutations = 0
for i in xrange(genome.get_height()):
for j in xrange(genome.get_width()):
if utils.random_flip_coin(args["pmut"]):
random_int = rand_randint(range_min, range_max)
genome.set_item(i, j, random_int)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.get_width()-1)
which_y = rand_randint(0, genome.get_height()-1)
random_int = rand_randint(range_min, range_max)
genome.set_item(which_y, which_x, random_int)
return int(mutations)
def G1DBinaryStringXTwoPoint(genome, **args):
""" The 1D Binary String crossover, Two Point
.. warning:: You can't use this crossover method for binary strings with length of 1.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) == 1:
Util.raiseException("The Binary String have one element, can't use the Two Point Crossover method !", TypeError)
cuts = [rand_randint(1, len(gMom)-1), rand_randint(1, len(gMom)-1)]
if cuts[0] > cuts[1]:
Util.listSwapElement(cuts, 0, 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister[cuts[0]:cuts[1]] = gDad[cuts[0]:cuts[1]]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cuts[0]:cuts[1]] = gMom[cuts[0]:cuts[1]]
return (sister, brother)
def G2DBinaryStringMutatorFlip(genome, **args):
""" A flip mutator for G2DBinaryString
.. versionadded:: 0.6
The *G2DBinaryStringMutatorFlip* 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(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
if genome[i][j] == 0: genome.setItem(i, j, 1)
else: genome.setItem(i, j, 0)
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)
if genome[i][j] == 0: genome.setItem(which_y, which_x, 1)
else: genome.setItem(which_y, which_x, 0)
return int(mutations)
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
def G2DBinaryStringMutatorFlip(genome, **args):
""" A flip mutator for G2DBinaryString
.. versionadded:: 0.6
The *G2DBinaryStringMutatorFlip* function
"""
if args["pmut"] <= 0.0: return 0
height, width = genome.get_size()
elements = height * width
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(genome.get_height()):
for j in xrange(genome.get_width()):
if utils.random_flip_coin(args["pmut"]):
if genome[i][j] == 0: genome.set_item(i, j, 1)
else: genome.set_item(i, j, 0)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.get_width()-1)
which_y = rand_randint(0, genome.get_height()-1)
if genome[i][j] == 0: genome.set_item(which_y, which_x, 1)
else: genome.set_item(which_y, which_x, 0)
return int(mutations)
def G1DBinaryStringXTwoPoint(genome, **args):
""" The 1D Binary String crossover, Two Point
.. warning:: You can't use this crossover method for binary strings with length of 1.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) == 1:
Util.raiseException(
"The Binary String have one element, can't use the Two Point Crossover method !",
TypeError)
cuts = [rand_randint(1, len(gMom) - 1), rand_randint(1, len(gMom) - 1)]
if cuts[0] > cuts[1]:
Util.listSwapElement(cuts, 0, 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister[cuts[0]:cuts[1]] = gDad[cuts[0]:cuts[1]]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cuts[0]:cuts[1]] = gMom[cuts[0]:cuts[1]]
return (sister, brother)
def G1DListMutatorDisplacement(genome: G1DList, **args):
""" The mutator of G1DList, Swap Mutator
.. note:: this mutator is :term:`Data Type Independent`
"""
mutations = 0
pmut = args["pmut"]
if pmut <= 0.0:
return mutations
if not Util.randomFlipCoin(pmut):
return mutations
listSize = len(genome)
to_remove = rand_randint(0, listSize - 1)
while True:
to_insert = rand_randint(0, listSize - 1)
if to_remove != to_insert:
break
val = genome.genomeList.pop(to_remove)
genome.genomeList.insert(to_insert, val)
mutations = mutations + 1
return mutations
def GTreeMutatorIntegerRange(genome, **args):
""" The mutator of GTree, Integer Range Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
.. versionadded:: 0.6
The *GTreeMutatorIntegerRange* 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 range(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
rand_node = genome.getRandomNode()
random_int = rand_randint(range_min, range_max)
rand_node.setData(random_int)
else:
for it in range(int(round(mutations))):
rand_node = genome.getRandomNode()
random_int = rand_randint(range_min, range_max)
rand_node.setData(random_int)
return int(mutations)
def G1DListMutatorIntegerRange(genome, **args):
""" Simple integer 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_randint(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_randint(genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
return int(mutations)
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
def GTreeMutatorIntegerRange(genome, **args):
""" The mutator of GTree, Integer Range Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
.. versionadded:: 0.6
The *GTreeMutatorIntegerRange* 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_int = rand_randint(range_min, range_max)
rand_node.setData(random_int)
else:
for it in xrange(int(round(mutations))):
rand_node = genome.getRandomNode()
random_int = rand_randint(range_min, range_max)
rand_node.setData(random_int)
return int(mutations)
def G1DListCrossoverTwoPoint(genome, **args):
""" The G1DList crossover, Two Point
.. warning:: You can't use this crossover method for lists with just one or two elements.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) < 3:
Util.raiseException("The 1D List has less than 3 elements, can't use the Two Point Crossover method !", TypeError)
cuts = [0, 0]
# Keep generating random cuts until they are in different places
while cuts[0] == cuts[1]:
cuts = [rand_randint(1, len(gMom)-1), rand_randint(1, len(gMom)-1)]
if cuts[0] > cuts[1]:
Util.listSwapElement(cuts, 0, 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister[cuts[0]:cuts[1]] = gDad[cuts[0]:cuts[1]]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cuts[0]:cuts[1]] = gMom[cuts[0]:cuts[1]]
return (sister, brother)
def G1DListCrossoverOX(genome, **args):
""" The OX Crossover for G1DList (order crossover) """
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
listSize = len(gMom)
c1, c2 = [rand_randint(1, len(gMom) - 1), rand_randint(1, len(gMom) - 1)]
while c1 == c2:
c2 = rand_randint(1, len(gMom) - 1)
if c1 > c2:
h = c1
c1 = c2
c2 = h
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
P1 = [c for c in gMom[c2:] + gMom[:c2] if c not in gDad[c1:c2]]
sister.genomeList = P1[listSize - c2:] + gDad[c1:c2] + P1[:listSize - c2]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
P2 = [c for c in gDad[c2:] + gDad[:c2] if c not in gMom[c1:c2]]
brother.genomeList = P2[listSize - c2:] + gMom[c1:c2] + P2[:listSize - c2]
assert listSize == len(sister)
assert listSize == len(brother)
return (sister, brother)
def G1DListCrossoverOX(genome, **args):
""" The OX Crossover for G1DList (order crossover) """
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
listSize = len(gMom)
c1, c2 = [rand_randint(1, len(gMom)-1), rand_randint(1, len(gMom)-1)]
while c1 == c2:
c2 = rand_randint(1, len(gMom)-1)
if c1 > c2:
h = c1
c1 = c2
c2 = h
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
P1 = [ c for c in gMom[c2:] + gMom[:c2] if c not in gDad[c1:c2] ]
sister.genomeList = P1[listSize - c2:] + gDad[c1:c2] + P1[:listSize-c2]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
P2 = [ c for c in gDad[c2:] + gDad[:c2] if c not in gMom[c1:c2] ]
brother.genomeList = P2[listSize - c2:] + gMom[c1:c2] + P2[:listSize-c2]
assert listSize == len(sister)
assert listSize == len(brother)
return (sister, brother)
def G1DListCrossoverTwoPoint(genome, **args):
""" The G1DList crossover, Two Point
.. warning:: You can't use this crossover method for lists with just one element.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) == 1:
utils.raise_exception("The 1D List have one element, can't use the Two Point Crossover method !", TypeError)
cuts = [rand_randint(1, len(gMom) - 1), rand_randint(1, len(gMom) - 1)]
if cuts[0] > cuts[1]:
utils.list_swap_element(cuts, 0, 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.reset_stats()
sister[cuts[0] : cuts[1]] = gDad[cuts[0] : cuts[1]]
if args["count"] == 2:
brother = gDad.clone()
brother.reset_stats()
brother[cuts[0] : cuts[1]] = gMom[cuts[0] : cuts[1]]
return (sister, brother)
def G1DListMutatorIntegerRange(genome, **args):
""" Simple integer 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 range(listSize):
if Util.randomFlipCoin(args["pmut"]):
genome[it] = rand_randint(genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
mutations += 1
else:
for it in range(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
genome[which_gene] = rand_randint(genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
return int(mutations)
def G2DListMutatorRealGaussian(genome, **args):
""" A gaussian mutator for G2DList of Real
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.
"""
from . import Consts
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG2DListMutRealMU
if sigma is None:
sigma = Consts.CDefG2DListMutRealSIGMA
if mutations < 1.0:
mutations = 0
for i in range(genome.getHeight()):
for j in range(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] + rand_gauss(mu, sigma)
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in range(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = genome[which_y][which_x] + rand_gauss(mu, sigma)
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def G2DListMutatorIntegerGaussianGradient(genome, **args):
""" A gaussian mutator for G2DList of Integers
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
from . import Consts
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in range(genome.getHeight()):
for j in range(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = int(genome[i][j] *
abs(rand_gauss(mu, sigma)))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in range(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = int(genome[which_y][which_x] *
abs(rand_gauss(mu, sigma)))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def population_init(genome, **args):
# genome will have 1 <= i <= MAX rules within the rule set
MAX_NUMBER_OF_RULES = genome.getParam("initrules")
genomeExamples = genome.getExamplesRef()
number_of_rules_to_add = rand_randint(1, MAX_NUMBER_OF_RULES)
for i in range(0, number_of_rules_to_add):
genome.addRuleAsString(genomeExamples[rand_randint(
0,
len(genomeExamples) - 1)])
def G2DListMutatorRealGaussian(genome, **args):
""" A gaussian mutator for G2DList of Real
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
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG2DListMutRealMU
if sigma is None:
sigma = Consts.CDefG2DListMutRealSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] + rand_gauss(mu, sigma)
final_value = min(final_value, genome.getParam(
"rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam(
"rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
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)
final_value = genome[which_y][which_x] + rand_gauss(mu, sigma)
final_value = min(final_value, genome.getParam(
"rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam(
"rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def G2DListMutatorRealGaussianGradient(genome, **args):
""" A gaussian gradient mutator for G2DList of Real
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
The difference is that this multiplies the gene by gauss(1.0, 0.0333), allowing
for a smooth gradient drift about the value.
"""
from . import Consts
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in range(genome.getHeight()):
for j in range(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] * abs(rand_gauss(mu, sigma))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in range(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = genome[which_y][which_x] * abs(rand_gauss(mu, sigma))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def G2DListMutatorIntegerGaussianGradient(genome, **args):
""" A gaussian mutator for G2DList of Integers
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = int(
genome[i][j] * abs(rand_gauss(mu, sigma)))
final_value = min(final_value, genome.getParam(
"rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam(
"rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
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)
final_value = int(genome[which_y][which_x]
* abs(rand_gauss(mu, sigma)))
final_value = min(final_value, genome.getParam(
"rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam(
"rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def crossover_gabil(genome, **args): #def crossover_gabil(gDad, gMom): sister = None brother = None gMom = args["mom"] gDad = args["dad"] if (len(gMom) < len(gDad)): gMom , gDad = gDad, gMom splitMom = [ rand_randint( 1 , len(gMom) + 1) , rand_randint(1, len(gMom) + 1)] if (splitMom [1] < splitMom[0] ): splitMom[0],splitMom[1] = splitMom[1],splitMom[0] s1 = gMom[0:splitMom[0]] s2 = gMom[splitMom[0]:splitMom[1]] s3 = gMom[splitMom[1]:len(gMom)] nRulesD = len(gDad) / RULE_SIZE; splitDad = [ rand_randint( 1 , nRulesD + 1) , rand_randint(1, nRulesD + 1)] if (splitDad[0] > splitDad[1] ): splitDad[0], splitDad[1] = splitDad[1], splitDad[0] positionDad = splitMom[0] % RULE_SIZE positionDad1 = splitMom[1] % RULE_SIZE n1 = splitDad[0] + positionDad n2 = splitDad[1] + positionDad1 if (n2 < n1): n1,n2 = n2,n1 d1 = gDad[0:n1] d2 = gDad[n1:n2] d3 = gDad[n2:len(gDad)] sister = gMom.clone() sister.resetStats() sister.genomeString = s1 + d2 + s3 sister.stringLength = len(s1 + d2 + s3) brother = gDad.clone() brother.resetStats() brother.genomeString = d1 + s2 + d3 brother.stringLength = len(d1 + s2 + d3) return (sister, brother)
def G2DListMutatorRealGaussianGradient(genome, **args):
""" A gaussian gradient mutator for G2DList of Real
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
The difference is that this multiplies the gene by gauss(1.0, 0.0333), allowing
for a smooth gradient drift about the value.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] * abs(rand_gauss(mu, sigma))
final_value = min(final_value, genome.getParam(
"rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam(
"rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
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)
final_value = genome[which_y][which_x] * abs(rand_gauss(mu, sigma))
final_value = min(final_value, genome.getParam(
"rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam(
"rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
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)
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) - 1
mutations = args["pmut"] * (listSize + 1)
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 + 1):
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)
new_val = allele[which_gene].getRandomAllele()
genome[which_gene] = new_val
return int(mutations)
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)
def G1DBinaryStringXSinglePoint(genome, **args):
""" The crossover of 1D Binary String, Single Point
.. warning:: You can't use this crossover method for binary strings with length of 1.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) == 1:
Util.raiseException(
"The Binary String have one element, can't use the Single Point Crossover method !",
TypeError)
cut = rand_randint(1, len(gMom) - 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister[cut:] = gDad[cut:]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cut:] = gMom[cut:]
return (sister, brother)
def G1DBinaryStringMutatorFlip_GABIL(genome, **args): 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.genomeString[it] == '0': genome.genomeString[it] = '1' else: genome.genomeString[it] = '0' mutations += 1.0 else: for it in xrange(int(round(mutations))): which = rand_randint(0, stringLength - 1) if genome.genomeString[which] == '0': genome.genomeString[which] = '1' else: genome.genomeString[which] = '0' return int(mutations)
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 utils.random_flip_coin(args["pmut"]):
genome[it] = rand_uniform(genome.get_param("rangemin", CDefRangeMin),
genome.get_param("rangemax", CDefRangeMax))
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize-1)
genome[which_gene] = rand_uniform(genome.get_param("rangemin", CDefRangeMin),
genome.get_param("rangemax", CDefRangeMax))
return int(mutations)
def __init__(self, host, port, group_name):
super(WANMigration, self).__init__()
self.setMyself(host, port)
self.setGroupName(group_name)
self.topologyGraph = None
self.serverThread = Network.UDPThreadServer(host, port)
self.clientThread = Network.UDPThreadUnicastClient(self.myself[0], rand_randint(30000, 65534))
def G1DListCrossoverSinglePoint(genome, **args):
""" The crossover of G1DList, Single Point
.. warning:: You can't use this crossover method for lists with just one element.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) == 1:
utils.raise_exception("The 1D List have one element, can't use the Single Point Crossover method !", TypeError)
cut = rand_randint(1, len(gMom) - 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.reset_stats()
sister[cut:] = gDad[cut:]
if args["count"] == 2:
brother = gDad.clone()
brother.reset_stats()
brother[cut:] = gMom[cut:]
return (sister, brother)
def G2DBinaryStringXSingleHPoint(genome, **args):
""" The crossover of G2DBinaryString, Single Horizontal Point
.. versionadded:: 0.6
The *G2DBinaryStringXSingleHPoint* function
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
cut = rand_randint(1, gMom.getHeight()-1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
for i in xrange(cut, sister.getHeight()):
sister[i][:] = gDad[i][:]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
for i in xrange(brother.getHeight()):
brother[i][:] = gMom[i][:]
return (sister, brother)
def G2DBinaryStringXSingleHPoint(genome, **args):
""" The crossover of G2DBinaryString, Single Horizontal Point
.. versionadded:: 0.6
The *G2DBinaryStringXSingleHPoint* function
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
cut = rand_randint(1, gMom.getHeight() - 1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
for i in range(cut, sister.getHeight()):
sister[i][:] = gDad[i][:]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
for i in range(brother.getHeight()):
brother[i][:] = gMom[i][:]
return (sister, brother)
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 range(listSize):
if Util.randomFlipCoin(args["pmut"]):
new_val = allele[it].getRandomAllele()
genome[it] = new_val
mutations += 1
else:
for it in range(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
new_val = allele[which_gene].getRandomAllele()
genome[which_gene] = new_val
return int(mutations)
def __init__(self, host, port, group_name):
super(WANMigration, self).__init__()
self.setMyself(host, port)
self.setGroupName(group_name)
self.topologyGraph = None
self.serverThread = Network.UDPThreadServer(host, port)
self.clientThread = Network.UDPThreadUnicastClient(self.myself[0], rand_randint(30000, 65534))
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 range(stringLength):
if Util.randomFlipCoin(args["pmut"]):
if genome[it] == 0:
genome[it] = 1
else:
genome[it] = 0
mutations += 1
else:
for it in range(int(round(mutations))):
which = rand_randint(0, stringLength - 1)
if genome[which] == 0:
genome[which] = 1
else:
genome[which] = 0
return int(mutations)
def G1DBinaryStringXSinglePoint(genome, **args):
""" The crossover of 1D Binary String, Single Point
.. warning:: You can't use this crossover method for binary strings with length of 1.
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
if len(gMom) == 1:
Util.raiseException("The Binary String have one element, can't use the Single Point Crossover method !", TypeError)
cut = rand_randint(1, len(gMom)-1)
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister[cut:] = gDad[cut:]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cut:] = gMom[cut:]
return (sister, brother)
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 range(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 range(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)
def G1DBinaryStringSetXTwoPoint(genome, **args):
"""The 1D Binary String Set crossover, Two Point"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
# the mother will always have the smallest bitstring
if len(args["mom"]) > len(args["dad"]):
gMom = args["dad"] #change roles
gDad = args["mom"]
if len(gMom) == 1:
Util.raiseException(
"The Binary String have one element, can't use the Two Point Crossover method !",
TypeError)
#Generating random crossover points over the mother parent
cutsMom = [rand_randint(1, len(gMom) - 1), rand_randint(1, len(gMom) - 1)]
if cutsMom[0] > cutsMom[1]:
Util.listSwapElement(cutsMom, 0, 1)
# Computing the distance from the cuts to the nearest rule to the left
cutsMomDistance = [
gMom.distanceFromCutToClosestRuleByLeft(cutsMom[0]),
gMom.distanceFromCutToClosestRuleByLeft(cutsMom[1])
]
# Computing factible crossover points for the dad parent
factibleDadCuts = gDad.getCutPointsFromDistances(cutsMomDistance[0],
cutsMomDistance[1])
#picking one random cut pair for the parent from the factible cuts
cutsDad = factibleDadCuts[rand_randint(0, len(factibleDadCuts) - 1)]
# genome crossover
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister.substitute(cutsMom[0], cutsMom[1], gDad[cutsDad[0]:cutsDad[1]])
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother.substitute(cutsDad[0], cutsDad[1], gMom[cutsMom[0]:cutsMom[1]])
return (sister, brother)
def G1DListMutatorSwap(genome, **args):
""" The mutator of G1DList, Swap Mutator """
if args["pmut"] <= 0.0: return 0
listSize = len(genome) - 1
mutations = args["pmut"] * (listSize+1)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize+1):
if Util.randomFlipCoin(args["pmut"]):
Util.listSwapElement(genome, it, rand_randint(0, listSize))
mutations+=1
else:
for it in xrange(int(round(mutations))):
Util.listSwapElement(genome, rand_randint(0, listSize), rand_randint(0, listSize))
return mutations
def G1DListInitializatorInteger(genome, **args):
""" Integer initialization function of G1DList
This initializator accepts the *rangemin* and *rangemax* genome parameters.
"""
r=genome.getParam("range_list", None)
if r is None:
range_min = genome.getParam("rangemin", 0)
range_max = genome.getParam("rangemax", 100)
genome.genomeList = [rand_randint(range_min, range_max) for i in xrange(genome.getListSize())]
else:
genome.genomeList = [rand_randint(0, r[i]) for i in xrange(genome.getListSize())]
def separate_dataset(examples):
training_dataset = []
test_dataset = []
#filtering examples where the last bit - the classification bit - is 1
positives = [example for example in examples if example[-1] == '1']
#filtering examples where the last bit- the classification bit- is 0
negatives = [example for example in examples if example[-1] == '0']
training_dataset_ratio = 0.7
test_dataset_ratio = 1 - training_dataset_ratio
training_positives = int(
math.floor(training_dataset_ratio * len(positives)))
training_negatives = int(
math.floor(training_dataset_ratio * len(negatives)))
test_positives = len(positives) - training_positives
test_negatives = len(negatives) - training_negatives
for npositive in xrange(0, training_positives):
training_positive = positives.pop(rand_randint(0, len(positives) - 1))
training_dataset.append(training_positive)
for nnegativee in xrange(0, training_negatives):
training_negative = negatives.pop(rand_randint(0, len(negatives) - 1))
training_dataset.append(training_negative)
for npositive in xrange(0, test_positives):
test_positive = positives.pop(rand_randint(0, len(positives) - 1))
test_dataset.append(test_positive)
for nnegativee in xrange(0, test_negatives):
test_negative = negatives.pop(rand_randint(0, len(negatives) - 1))
test_dataset.append(test_negative)
#checks that all positives and negatives were distributed within the two datasets
assert (
(positives == []) and (negatives == [])
), 'Error while separating dataset: positives or negatives were not well distributed'
shuffle(training_dataset)
shuffle(test_dataset)
return training_dataset, test_dataset
def G2DListMutatorAllele(genome, **args):
""" The mutator of G2DList, Allele Mutator
To use this mutator, you must specify the *allele* genome parameter with the
:class:`GAllele.GAlleles` instance.
.. warning:: the :class:`GAllele.GAlleles` instance must have the homogeneous flag enabled
"""
if args["pmut"] <= 0.0:
return 0
listSize = genome.getHeight() * genome.getWidth() - 1
mutations = args["pmut"] * (listSize + 1)
allele = genome.getParam("allele", None)
if allele is None:
Util.raiseException(
"to use the G2DListMutatorAllele, you must specify the 'allele' parameter",
TypeError)
if not allele.homogeneous:
Util.raiseException(
"to use the G2DListMutatorAllele, the 'allele' must be homogeneous"
)
if mutations < 1.0:
mutations = 0
for i in range(genome.getHeight()):
for j in range(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
new_val = allele[0].getRandomAllele()
genome.setItem(i, j, new_val)
mutations += 1
else:
for it in range(int(round(mutations))):
which_x = rand_randint(0, genome.getHeight() - 1)
which_y = rand_randint(0, genome.getWidth() - 1)
new_val = allele[0].getRandomAllele()
genome.setItem(which_x, which_y, new_val)
return int(mutations)
def G1DListInitializatorInteger(genome, **args):
""" Integer initialization function of G1DList
This initializator accepts the *rangemin* and *rangemax* genome parameters.
"""
range_min = genome.getParam("rangemin", 0)
range_max = genome.getParam("rangemax", 100)
genome.genomeList = [rand_randint(range_min, range_max) for i in xrange(genome.getListSize())]
def G1DListInitializatorInteger(genome, **args):
""" Integer initialization function of G1DList
This initializator accepts the *rangemin* and *rangemax* genome parameters.
"""
range_min = genome.get_param("rangemin", 0)
range_max = genome.get_param("rangemax", 100)
genome.genomeList = [rand_randint(range_min, range_max) for i in xrange(genome.get_list_size())]
def G1DListMutatorRealGaussianGradient(genome, **args):
""" The mutator of G1DList, Gaussian Gradient Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional. The
random distribution is set with mu=1.0 and std=0.0333
The difference between this routine and the normal Gaussian Real is that the
other function generates a gaussian value and adds it to the value. If the
mu is 0, and the std is 1, a typical value could be 1.8 or -0.5. These small
values are fine if your range is 0-10, but if your range is much larger, like
0-100,000, a relative gradient makes sense.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
from . import Consts
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for it in range(listSize):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[it] * abs(rand_gauss(mu, sigma))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[it] = final_value
mutations += 1
else:
for it in range(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = genome[which_gene] * abs(rand_gauss(mu, sigma))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[which_gene] = final_value
return int(mutations)
def G1DBinaryStringSetXTwoPoint(genome, **args):
"""The 1D Binary String Set crossover, Two Point"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
# the mother will always have the smallest bitstring
if len(args["mom"]) > len(args["dad"]):
gMom = args["dad"] #change roles
gDad = args["mom"]
if len(gMom) == 1:
Util.raiseException("The Binary String have one element, can't use the Two Point Crossover method !", TypeError)
#Generating random crossover points over the mother parent
cutsMom = [rand_randint(1, len(gMom)-1), rand_randint(1, len(gMom)-1)]
if cutsMom[0] > cutsMom[1]:
Util.listSwapElement(cutsMom, 0, 1)
# Computing the distance from the cuts to the nearest rule to the left
cutsMomDistance = [gMom.distanceFromCutToClosestRuleByLeft(cutsMom[0]),
gMom.distanceFromCutToClosestRuleByLeft(cutsMom[1])]
# Computing factible crossover points for the dad parent
factibleDadCuts = gDad.getCutPointsFromDistances(cutsMomDistance[0],cutsMomDistance[1])
#picking one random cut pair for the parent from the factible cuts
cutsDad = factibleDadCuts[rand_randint(0,len(factibleDadCuts)-1)]
# genome crossover
if args["count"] >= 1:
sister = gMom.clone()
sister.resetStats()
sister.substitute(cutsMom[0],cutsMom[1],gDad[cutsDad[0]:cutsDad[1]])
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother.substitute(cutsDad[0],cutsDad[1], gMom[cutsMom[0]:cutsMom[1]])
return (sister, brother)
def G1DBinaryStringMutatorSwap(genome, **args):
""" The 1D Binary String Swap Mutator """
if args["pmut"] <= 0.0: return 0
stringLength = len(genome)
#mutations = args["pmut"] * (stringLength)
mutations = 0.5 * (stringLength)
if mutations < 1.0:
mutations = 0
for it in xrange(stringLength):
if Util.randomFlipCoin(args["pmut"]):
Util.listSwapElement(genome, it, rand_randint(0, stringLength-1))
mutations+=1
else:
for it in xrange(int(round(mutations))):
Util.listSwapElement(genome, rand_randint(0, stringLength-1),
rand_randint(0, stringLength-1))
return int(mutations)
def __init__(self, ruleLength=5, numRules=0):
""" The initializator of G1DList representation """
if numRules == 0:
numRules = rand_randint(1, MAX_RULES)
super(G1DVariableBinaryString, self).__init__(ruleLength * numRules)
self.genomeList = []
self.stringLength = ruleLength * numRules
self.ruleLength = ruleLength
self.initializator.set(Consts.CDefG1DVariableBinaryStringInit)
self.mutator.set(Consts.CDefG1DVariableBinaryStringMutator)
self.crossover.set(Consts.CDefG1DVariableBinaryStringCrossover)
def __init__(self, ruleLength=5, numRules=0):
""" The initializator of G1DList representation """
if numRules == 0:
numRules = rand_randint(1,MAX_RULES)
super(G1DVariableBinaryString, self).__init__(ruleLength*numRules)
self.genomeList = []
self.stringLength = ruleLength*numRules
self.ruleLength = ruleLength
self.initializator.set(Consts.CDefG1DVariableBinaryStringInit)
self.mutator.set(Consts.CDefG1DVariableBinaryStringMutator)
self.crossover.set(Consts.CDefG1DVariableBinaryStringCrossover)
def G2DListMutatorAllele(genome, **args):
""" The mutator of G2DList, Allele Mutator
To use this mutator, you must specify the *allele* genome parameter with the
:class:`GAllele.GAlleles` instance.
.. warning:: the :class:`GAllele.GAlleles` instance must have the homogeneous flag enabled
"""
if args["pmut"] <= 0.0:
return 0
listSize = genome.getHeight() * genome.getWidth() - 1
mutations = args["pmut"] * (listSize + 1)
allele = genome.getParam("allele", None)
if allele is None:
Util.raiseException(
"to use the G2DListMutatorAllele, you must specify the 'allele' parameter", TypeError)
if not allele.homogeneous:
Util.raiseException(
"to use the G2DListMutatorAllele, the 'allele' must be homogeneous")
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
new_val = allele[0].getRandomAllele()
genome.setItem(i, j, new_val)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getHeight() - 1)
which_y = rand_randint(0, genome.getWidth() - 1)
new_val = allele[0].getRandomAllele()
genome.setItem(which_x, which_y, new_val)
return int(mutations)
def G1DBinaryStringMutatorSwap(genome, **args):
""" The 1D Binary String Swap Mutator """
if args["pmut"] <= 0.0:
return 0
stringLength = len(genome)
mutations = args["pmut"] * (stringLength)
if mutations < 1.0:
mutations = 0
for it in range(stringLength):
if Util.randomFlipCoin(args["pmut"]):
Util.listSwapElement(genome, it, rand_randint(0, stringLength - 1))
mutations += 1
else:
for it in range(int(round(mutations))):
Util.listSwapElement(genome, rand_randint(0, stringLength - 1),
rand_randint(0, stringLength - 1))
return int(mutations)
def P1DListInitializatorInteger(vector, **args):
""" Integer initialization function of Particle1D
This initializator accepts the *rangemin* and *rangemax* particle parameters.
"""
vector.clearList()
for i in xrange(vector.listSize):
randomInteger = rand_randint(vector.getParam("rangemin", 0),
vector.getParam("rangemax", 100))
vector.append(randomInteger)
def G1DListMutatorSwap(genome, **args):
""" The mutator of G1DList, Swap Mutator
.. note:: this mutator is :term:`Data Type Independent`
"""
if args["pmut"] <= 0.0: return 0
listSize = len(genome) - 1
mutations = args["pmut"] * (listSize+1)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize+1):
if utils.random_flip_coin(args["pmut"]):
utils.list_swap_element(genome, it, rand_randint(0, listSize))
mutations+=1
else:
for it in xrange(int(round(mutations))):
utils.list_swap_element(genome, rand_randint(0, listSize), rand_randint(0, listSize))
return int(mutations)
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 range(height):
for j in range(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 range(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)
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 range(height):
for j in range(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 range(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)
def G1DListMutatorRealGaussian(genome, **args):
""" The mutator of G1DList, Gaussian Mutator
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.
"""
from . import Consts
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG1DListMutRealMU
if sigma is None:
sigma = Consts.CDefG1DListMutRealSIGMA
if mutations < 1.0:
mutations = 0
for it in range(listSize):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[it] + rand_gauss(mu, sigma)
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[it] = final_value
mutations += 1
else:
for it in range(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = genome[which_gene] + rand_gauss(mu, sigma)
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[which_gene] = final_value
return int(mutations)
def G2DListInitializatorInteger(genome, **args):
""" Integer initialization function of G2DList
This initializator accepts the *rangemin* and *rangemax* genome parameters.
"""
genome.clearList()
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
randomInteger = rand_randint(genome.getParam("rangemin", 0),
genome.getParam("rangemax", 100))
genome.setItem(i, j, randomInteger)