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)
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:
Util.raiseException("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.resetStats()
sister[cut:] = gDad[cut:]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cut:] = gMom[cut:]
return (sister, brother)
def compare(self, other):
""" This method will compare the currently tree with another one
:param other: the other GTreeGP to compare
"""
if not isinstance(other, GTreeGP):
Util.raiseException(
"The other tree used to compare is not a GTreeGP class",
TypeError)
stack_self = []
stack_other = []
stack_self.append(self.getRoot())
stack_other.append(other.getRoot())
while len(stack_self) > 0:
if (len(stack_self) <= 0) or (len(stack_other) <= 0):
return -1
tmp_self, tmp_other = stack_self.pop(), stack_other.pop()
if tmp_self.compare(tmp_other) != 0:
return -1
stack_self.extend(tmp_self.getChilds())
stack_other.extend(tmp_other.getChilds())
return 0
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:]
Pruner(sister)
Pruner(brother)
#print "\tsister :\t\t %s\n\n" % (sister.getBinary(),)
#print "\tbrother:\t\t %s\n\n" % (brother.getBinary(),)
return (sister, brother)
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
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)
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 writePopulationDotRaw(ga_engine, filename, start=0, end=0):
""" Writes to a raw dot file using pydot, the population of trees
Example:
>>> GTreeGP.writePopulationDotRaw(ga_engine, "pop.dot", 0, 10)
This example will draw the first ten individuals of the population into
the file called "pop.dot".
:param ga_engine: the GA Engine
:param filename: the filename, ie. population.dot
:param start: the start index of individuals
:param end: the end index of individuals
"""
if not HAVE_PYDOT:
Util.raiseException("You must install Pydot to use this feature !")
pop = ga_engine.getPopulation()
graph = pydot.Dot(graph_type="digraph")
if not isinstance(pop[0], GTreeGP):
Util.raiseException("The population must have individuals of the GTreeGP chromosome !")
n = 0
end_index = len(pop) if end == 0 else end
for i in xrange(start, end_index):
ind = pop[i]
subg = pydot.Cluster(
"cluster_%d" % i,
label="\"Ind. #%d - Score Raw/Fit.: %.4f/%.4f\"" % (i, ind.getRawScore(), ind.getFitnessScore())
)
n = ind.writeDotGraph(subg, n)
graph.add_subgraph(subg)
graph.write(filename, prog='dot', format="raw")
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)
def compare(self, other):
""" This method will compare the currently tree with another one
:param other: the other GTreeGP to compare
"""
if not isinstance(other, GTreeGP):
Util.raiseException("The other tree used to compare is not a GTreeGP class", TypeError)
stack_self = []
stack_other = []
stack_self.append(self.getRoot())
stack_other.append(other.getRoot())
while len(stack_self) > 0:
if (len(stack_self) <= 0) or (len(stack_other) <= 0):
return -1
tmp_self, tmp_other = stack_self.pop(), stack_other.pop()
if tmp_self.compare(tmp_other) != 0:
return -1
stack_self.extend(tmp_self.getChilds())
stack_other.extend(tmp_other.getChilds())
return 0
def insert(self, ga_engine):
""" Insert the stats
:param ga_engine: the GA Engine
"""
Util.raiseException("This method is not implemented on the ABC",
NotImplementedError)
def RawScoreCriteria(ga_engine):
""" Terminate the evolution using the **bestrawscore** and **rounddecimal**
parameter obtained from the individual
Example:
>>> genome.setParams(bestrawscore=0.00, rounddecimal=2)
(...)
>>> ga_engine.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
"""
ind = ga_engine.bestIndividual()
bestRawScore = ind.getParam("bestrawscore")
roundDecimal = ind.getParam("rounddecimal")
if bestRawScore is None:
Util.raiseException("you must specify the bestrawscore parameter", ValueError)
if ga_engine.getMinimax() == Consts.minimaxType["maximize"]:
if roundDecimal is not None:
return round(bestRawScore, roundDecimal) <= round(ind.score, roundDecimal)
else:
return bestRawScore <= ind.score
else:
if roundDecimal is not None:
return round(bestRawScore, roundDecimal) >= round(ind.score, roundDecimal)
else:
return bestRawScore >= ind.score
def googleQueryMutator(genome, **args):
""" The mutator of GoogleQueryChromosome
"""
print "Mutating genome", genome
if args["pmut"] <= 0.0: return 0
# height, width = genome.getParam('seqlength')
height = 2
width = genome.getParam('seqlength')
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 = (random.randint(0, height - 1),
random.randint(0, width - 1))
list2DSwapElement(genome.genomeList, (i, j), index_b)
mutations += 1
else:
for it in xrange(int(round(mutations))):
index_a = (random.randint(0, height - 1),
random.randint(0, width - 1))
index_b = (random.randint(0, height - 1),
random.randint(0, width - 1))
Util.list2DSwapElement(genome.genomeList, index_a, index_b)
return int(mutations)
def RawScoreCriteria(ga_engine):
""" Terminate the evolution using the **bestrawscore** and **rounddecimal**
parameter obtained from the individual
Example:
>>> genome.setParams(bestrawscore=0.00, rounddecimal=2)
(...)
>>> ga_engine.terminationCriteria.set(GSimpleGA.RawScoreCriteria)
"""
ind = ga_engine.bestIndividual()
bestRawScore = ind.getParam("bestrawscore")
roundDecimal = ind.getParam("rounddecimal")
if bestRawScore is None:
Util.raiseException("you must specify the bestrawscore parameter", ValueError)
if ga_engine.getMinimax() == Consts.minimaxType["maximize"]:
if roundDecimal is not None:
return round(bestRawScore, roundDecimal) <= round(ind.score, roundDecimal)
else:
return bestRawScore <= ind.score
else:
if roundDecimal is not None:
return round(bestRawScore, roundDecimal) >= round(ind.score, roundDecimal)
else:
return bestRawScore >= ind.score
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
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)
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 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)
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 distanceFromCutToClosestRuleByLeft(self,cut):
if ((cut < 0) or cut > self.ruleSetSize) :
Util.raiseException("Crossover cut point %s is out of the bounds of the rule set <%s,%s>" %(cut,0,self.ruleSetSize), ValueError)
shift = 0
for lower,upper in self.rulePartition:
if upper > cut:
return cut - lower
def rule_eval2(genome):
MAX_ALLOWED_RULES = genome.getParam("maxrules")
#genomes that surpass rule threshold are automatically discarded
if len(genome.rulePartition) > MAX_ALLOWED_RULES: return 0
examples = genome.getExamplesRef()
attribute_bits = [2, 5, 4, 4, 3, 14, 9, 4, 2, 2, 5, 2, 3, 3, 4]
if not isinstance(genome,GD1BinaryStringSet):
Util.raiseException("The rule must of type G1DBinaryString", ValueError)
if (sum(attribute_bits) != genome.rule_length -1 ):
Util.raiseException("Example is not consistent with its attributes", ValueError)
rule_binary = genome.getBinary()
rule_length = genome.rule_length
rule_list = [rule_binary[i:i+rule_length] for i in xrange(0,len(rule_binary),rule_length)]
corrects = 0.0
for example in examples:
corrects += match_example(example,rule_list, attribute_bits)
accuracy = corrects/float(len(examples))
genome.setAccuracy(accuracy)
#the final score is the classification accuracy to the power of 2
score = (accuracy)**2
#applying ruleLength penalization. if not specified, decay is 1 and penalization is nonexistent
decay = genome.getParam("decay")
new_score = score*(decay**(len(rule_list) -1))
#print 'correct: %.2f | total: %.2f | size: %.2f | score: %.2f/%.2f' % (corrects, len(examples), len(rule_list), score, new_score)
return new_score
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:
Util.raiseException("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.resetStats()
sister[cut:] = gDad[cut:]
if args["count"] == 2:
brother = gDad.clone()
brother.resetStats()
brother[cut:] = gMom[cut:]
return (sister, brother)
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 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)
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:]
Pruner(sister)
Pruner(brother)
#print "\tsister :\t\t %s\n\n" % (sister.getBinary(),)
#print "\tbrother:\t\t %s\n\n" % (brother.getBinary(),)
return (sister, brother)
def ruleExists(self,rule):
if not (isinstance(rule,str) or isinstance(rule,G1DBinaryString)):
Util.raiseException("BitString expected as input", ValueError)
if isinstance(rule,G1DBinaryString): rule = G1DBinaryString.getBinary()
for lowerCut,upperCut in self.rulePartition:
currentRule = ''.join(map(str,self.ruleSet[lowerCut:upperCut]))
if (currentRule[:-1]==rule): return True
return False
def __typeCheck(self, func):
""" Used internally to check if a function passed to the
function slot is callable. Otherwise raises a TypeError exception.
:param func: the function object
"""
if not callable(func):
Util.raiseException("The function must be a method or function", TypeError)
def setRoot(self, root):
""" Sets the root of the tree
:param root: the tree root node
"""
if not isinstance(root, GTreeNodeBase):
Util.raiseException("The root must be a node", TypeError)
self.root_node = root
def setRoot(self, root):
""" Sets the root of the tree
:param root: the tree root node
"""
if not isinstance(root, GTreeNodeBase):
Util.raiseException("The root must be a node", TypeError)
self.root_node = root
def getCutPointsFromDistances(self,leftDistance,rightDistance):
if ((leftDistance < 0) or (rightDistance < 0)) :
Util.raiseException("leftDistance and rightDistance must be positive", ValueError)
if (rightDistance > self.ruleSetSize):
Util.raiseException("rightDistance is out of the bounds of the rule set size", ValueError)
rightCutCandidates = [lower+leftDistance for (lower,_) in self.rulePartition]
leftCutCandidates = [lower+rightDistance for (lower,_) in self.rulePartition]
cross_product = itertools.product(rightCutCandidates,leftCutCandidates)
return [(lower,upper) for (lower,upper) in list(cross_product) if lower<=upper]
def addRuleAsString(self,ruleStr):
if not isinstance(ruleStr,str):
Util.raiseException("The rule must of type str", ValueError)
if (len(ruleStr) != self.rule_length):
Util.raiseException("Rules within a rule set must have the same length %s"%(self.rule_length), ValueError)
rule = G1DBinaryString(len(ruleStr))
for bit in ruleStr:
rule.append(int(bit))
self.addRule(rule)
def __typeCheck(self, func):
""" Used internally to check if a function passed to the
function slot is callable. Otherwise raises a TypeError exception.
:param func: the function object
"""
if not callable(func):
Util.raiseException("The function must be a method or function",
TypeError)
def setElitism(self, flag):
""" Sets the elitism option, True or False
:param flag: True or False
"""
if type(flag) != BooleanType:
Util.raiseException("Elitism option must be True or False", TypeError)
self.elitism = flag
def setMutationRate(self, rate):
""" Sets the mutation rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Mutation rate must be >= 0.0 and <= 1.0", ValueError)
self.pMutation = rate
def setGenerations(self, num_gens):
""" Sets the number of generations to evolve
:param num_gens: the number of generations
"""
if num_gens < 1:
Util.raiseException("Number of generations must be >= 1", ValueError)
self.nGenerations = num_gens
def setCrossoverRate(self, rate):
""" Sets the crossover rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Crossover rate must be >= 0.0 and <= 1.0", ValueError)
self.pCrossover = rate
def setMinimax(self, mtype):
""" Sets the minimize/maximize mode, use Consts.minimaxType
:param mtype: the minimax mode, from Consts.minimaxType
"""
if mtype not in Consts.minimaxType.values():
Util.raiseException("Minimax must be maximize or minimize", TypeError)
self.minimax = mtype
def setMutationRate(self, rate):
""" Sets the mutation rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Mutation rate must be >= 0.0 and <= 1.0", ValueError)
self.pMutation = rate
def ruleExists(self, rule):
if not (isinstance(rule, str) or isinstance(rule, G1DBinaryString)):
Util.raiseException("BitString expected as input", ValueError)
if isinstance(rule, G1DBinaryString):
rule = G1DBinaryString.getBinary()
for lowerCut, upperCut in self.rulePartition:
currentRule = ''.join(map(str, self.ruleSet[lowerCut:upperCut]))
if (currentRule[:-1] == rule): return True
return False
def setCrossoverRate(self, rate):
""" Sets the crossover rate, between 0.0 and 1.0
:param rate: the rate, between 0.0 and 1.0
"""
if (rate > 1.0) or (rate < 0.0):
Util.raiseException("Crossover rate must be >= 0.0 and <= 1.0", ValueError)
self.pCrossover = rate
def setGenerations(self, num_gens):
""" Sets the number of generations to evolve
:param num_gens: the number of generations
"""
if num_gens < 1:
Util.raiseException("Number of generations must be >= 1", ValueError)
self.nGenerations = num_gens
def distanceFromCutToClosestRuleByLeft(self, cut):
if ((cut < 0) or cut > self.ruleSetSize):
Util.raiseException(
"Crossover cut point %s is out of the bounds of the rule set <%s,%s>"
% (cut, 0, self.ruleSetSize), ValueError)
shift = 0
for lower, upper in self.rulePartition:
if upper > cut:
return cut - lower
def setElitism(self, flag):
""" Sets the elitism option, True or False
:param flag: True or False
"""
if type(flag) != bool:
Util.raiseException("Elitism option must be True or False", TypeError)
self.elitism = flag
def setMinimax(self, mtype):
""" Sets the minimize/maximize mode, use Consts.minimaxType
:param mtype: the minimax mode, from Consts.minimaxType
"""
if mtype not in Consts.minimaxType.values():
Util.raiseException("Minimax must be maximize or minimize", TypeError)
self.minimax = mtype
def getClassificationForRule(self,rule):
if not (isinstance(rule,str) or isinstance(rule,G1DBinaryString)):
Util.raiseException("BitString expected as input", ValueError)
if isinstance(rule,G1DBinaryString): rule = G1DBinaryString.getBinary()
for lowerCut,upperCut in self.rulePartition:
fullRule = ''.join(map(str,self.ruleSet[lowerCut:upperCut]))
#current rule is obtained by ignoring the last classification bit
currentRule = fullRule[:-1]
if (currentRule==rule): return fullRule[-1] #last bit corresponds to the classification
return None
def __init__(self, genome, neural_net, train_data, seed=None, interactiveMode=True):
GSimpleGA.GSimpleGA.__init__(self, genome, seed, interactiveMode)
if not isinstance(neural_net, libfann.neural_net):
Util.raiseException("The second argument of the GAnnGA should be an instance of libfann.neural_net",neural_net)
self.internalPop = GAnnPopulation(genome)
self.neuralNet = neural_net
if not isinstance(train_data, libfann.training_data):
Util.raiseException("The second argument of the GAnnGA should be an instance of libfann.neural_net",train_data)
self.trainData = train_data
self.initializationFlag = False
def setPopulationSize(self, size):
""" Sets the population size, calls setPopulationSize() of GPopulation
:param size: the population size
.. note:: the population size must be >= 2
"""
if size < 2:
Util.raiseException("population size must be >= 2", ValueError)
self.internalPop.setPopulationSize(size)
def addChild(self, child):
""" Adds a child to the node
:param child: the node to be added
"""
if type(child) == list:
self.childs.extend(child)
else:
if not isinstance(child, GTreeNodeBase):
Util.raiseException("The child must be a node", TypeError)
self.childs.append(child)
def __init__(self, parent, childs=None):
self.parent = parent
self.childs = []
if childs is not None:
if type(childs) != list:
Util.raiseException("Childs must be a list of nodes", TypeError)
typecheck_list = filter(lambda x: not isinstance(x, GTreeNodeBase), childs)
if len(typecheck_list) > 0:
Util.raiseException("Childs must be a list of nodes", TypeError)
self.childs += childs
def mutate_subset(subset):
mutations = 0
for idx in xrange(len(subset)):
if Util.randomFlipCoin(args["pmut"]):
Util.listSwapElement(
genome, subset[idx],
subset[rand_randint(0,
len(subset) - 1)])
mutations += 1
return mutations
def setPopulationSize(self, size):
""" Sets the population size, calls setPopulationSize() of GPopulation
:param size: the population size
.. note:: the population size must be >= 2
"""
if size < 2:
Util.raiseException("population size must be >= 2", ValueError)
self.internalPop.setPopulationSize(size)
def addChild(self, child):
""" Adds a child to the node
:param child: the node to be added
"""
if type(child) == list:
self.childs.extend(child)
else:
if not isinstance(child, GTreeNodeBase):
Util.raiseException("The child must be a node", TypeError)
self.childs.append(child)
def setRandomApply(self, flag=True):
""" Sets the random function application, in this mode, the
function will randomly choose one slot to apply
:param flag: True or False
"""
if type(flag) != bool:
Util.raiseException("Random option must be True or False", TypeError)
self.rand_apply = flag
def setDBAdapter(self, dbadapter=None):
""" Sets the DB Adapter of the GA Engine
:param dbadapter: one of the :mod:`DBAdapters` classes instance
.. warning:: the use the of a DB Adapter can reduce the speed performance of the
Genetic Algorithm.
"""
if (dbadapter is not None) and (not isinstance(dbadapter, DBBaseAdapter)):
Util.raiseException("The DB Adapter must be a DBBaseAdapter subclass", TypeError)
self.dbAdapter = dbadapter
def setDBAdapter(self, dbadapter=None):
""" Sets the DB Adapter of the GA Engine
:param dbadapter: one of the :mod:`DBAdapters` classes instance
.. warning:: the use the of a DB Adapter can reduce the speed performance of the
Genetic Algorithm.
"""
if (dbadapter is not None) and (not isinstance(dbadapter, DBBaseAdapter)):
Util.raiseException("The DB Adapter must be a DBBaseAdapter subclass", TypeError)
self.dbAdapter = dbadapter
def addRuleAsString(self, ruleStr):
if not isinstance(ruleStr, str):
Util.raiseException("The rule must of type str", ValueError)
if (len(ruleStr) != self.rule_length):
Util.raiseException(
"Rules within a rule set must have the same length %s" %
(self.rule_length), ValueError)
rule = G1DBinaryString(len(ruleStr))
for bit in ruleStr:
rule.append(int(bit))
self.addRule(rule)
def writeDotImage(self, filename):
""" Writes a image representation of the individual
:param filename: the output file image
"""
if not HAVE_PYDOT:
Util.raiseException("You must install Pydot to use this feature !")
graph = pydot.Dot()
self.writeDotGraph(graph)
graph.write_jpeg(filename, prog='dot')
def add(self, begin, end):
""" Add a new range
:param begin: the begin of range
:param end: the end of the range
"""
if begin > end:
Util.raiseException('Wrong value, the end of the range (%s) is greater than the begin (%s) !' % (end, begin), ValueError)
self.beginEnd.append((begin, end))
self.__processMinMax()
def __getitem__(self, index):
""" Returns the index allele of the alleles list """
if self.homogeneous:
return self.allele_list[0]
try:
val = self.allele_list[index]
except IndexError:
Util.raiseException(
"""An error was occurred while finding allele for the %d position of chromosome.
You may consider use the 'homogeneous' parameter of the GAlleles class.
""" % (index,))
return val
def setSortType(self, sort_type):
""" Sets the sort type, Consts.sortType["raw"]/Consts.sortType["scaled"]
Example:
>>> ga_engine.setSortType(Consts.sortType["scaled"])
:param sort_type: the Sort Type
"""
if sort_type not in Consts.sortType.values():
Util.raiseException("sort type must be a Consts.sortType type", TypeError)
self.internalPop.sortType = sort_type