def new_genome(results_path, **kwargs):
'''
*new_genome : takes a set of alleles [function_alleles, leaves_alleles]
and options that define initializator, mutator, crossover, evaluator
and returns a genome with those options
possible initializator : "grow" : "grow" algorithm of network = recursive and possibly incomplete
possible mutator : "simple" : change genomic alleles with possible alleles with probability pmut
possible crossover :
possible evaluator : "degree_distribution", "2distributions"
possible network-type : "directed_weighted", "directed_unweighted", "undirected_weighted", "undirected_unweighted"
possible tree_type : "with_constants"
'''
evaluation_method = kwargs.get("evaluation_method")
network_type = kwargs.get("network_type")
data_path = kwargs.get("data_path")
name = kwargs.get("name")
dynamic = kwargs.get("dynamic")
extension = kwargs.get("extension")
choices = emo.get_alleles(evaluation_method, network_type)
genome = py.GTree.GTree()
#genome.setParams(nb_nodes=ne.get_number_of_nodes(results_path))
#genome.setParams(nb_edges=ne.get_number_of_edges(results_path))
genome.setParams(data_path=data_path)
genome.setParams(results_path=results_path)
genome.setParams(name=name)
genome.setParams(extension=extension)
#defines alleles : one array containing possible leaves and one containing possible functions
alleles = gall.GAlleles()
lst = gall.GAlleleList(choices)
alleles.add(lst)
genome.setParams(allele=alleles)
#defines the way to construct a random tree
genome.setParams(max_depth=int(kwargs.get("max_depth", "3")))
genome.setParams(max_siblings=int(kwargs.get("max_siblings", "2")))
genome.setParams(tree_type=kwargs.get("tree_type", "default"))
genome.initializator.set(tree_init)
#defines the how to evaluate a genome
genome.setParams(evaluation_method=evaluation_method)
if dynamic:
genome.evaluator.set(eval_func_dynamic)
else:
genome.evaluator.set(eval_func)
#defines the crossover function - default now
#defines the function that mutates trees
genome.mutator.set(mutate_tree)
#defines the network_type
genome.setParams(network_type=network_type)
#tree_init(genome)
return genome
def new_genome(results_path, **kwargs):
"""
*new_genome : takes a set of alleles [function_alleles, leaves_alleles]
and options that define initializator, mutator, crossover, evaluator
and returns a genome with those options
possible initializator : "grow" : "grow" algorithm of network = recursive and possibly incomplete
possible mutator : "simple" : change genomic alleles with possible alleles with probability pmut
possible crossover :
possible evaluator : "degree_distribution", "2distributions"
possible network-type : "directed_weighted", "directed_unweighted", "undirected_weighted", "undirected_unweighted"
possible tree_type : "with_constants"
"""
evaluation_method = kwargs.get("evaluation_method")
network_type = kwargs.get("network_type")
data_path = kwargs.get("data_path")
name = kwargs.get("name")
dynamic = kwargs.get("dynamic")
extension = kwargs.get("extension")
choices = emo.get_alleles(evaluation_method, network_type)
genome = py.GTree.GTree()
# genome.setParams(nb_nodes=ne.get_number_of_nodes(results_path))
# genome.setParams(nb_edges=ne.get_number_of_edges(results_path))
genome.setParams(data_path=data_path)
genome.setParams(results_path=results_path)
genome.setParams(name=name)
genome.setParams(extension=extension)
# defines alleles : one array containing possible leaves and one containing possible functions
alleles = gall.GAlleles()
lst = gall.GAlleleList(choices)
alleles.add(lst)
genome.setParams(allele=alleles)
# defines the way to construct a random tree
genome.setParams(max_depth=int(kwargs.get("max_depth", "3")))
genome.setParams(max_siblings=int(kwargs.get("max_siblings", "2")))
genome.setParams(tree_type=kwargs.get("tree_type", "default"))
genome.initializator.set(tree_init)
# defines the how to evaluate a genome
genome.setParams(evaluation_method=evaluation_method)
if dynamic:
genome.evaluator.set(eval_func_dynamic)
else:
genome.evaluator.set(eval_func)
# defines the crossover function - default now
# defines the function that mutates trees
genome.mutator.set(mutate_tree)
# defines the network_type
genome.setParams(network_type=network_type)
# tree_init(genome)
return genome
def createListOfRules():
choices = emo.get_alleles(evaluation_method,network_type)[1]
list_of_rules = []
for allele in choices :
list_of_rules.append(py.GTree.GTreeNode([5,allele]))
return list_of_rules
def createListOfRules():
choices = emo.get_alleles(evaluation_method, network_type)[1]
list_of_rules = []
for allele in choices:
list_of_rules.append(py.GTree.GTreeNode([5, allele]))
return list_of_rules
