def table_of_contents(self, node):
functions.remove_tag(node, 'div', id='footer')
functions.remove_tag(node, 'div', 'pageSection', 'main-content')
functions.remove_tag(node, 'div', 'pageSectionHeader')
functions.remove_tag(node, 'div', id='main-header')
functions.remove_tag(node, 'img')
return node.find('div', 'pageSection').ul
def load(string) -> Generation:
generation_count, string = remove_tag("generation_count", string)
population_str, string = remove_tag("population", string)
gene_pool_str, string = remove_tag("gene_pool", string)
population = Population.load(population_str)
gene_pool = GenePool.load(gene_pool_str)
return Generation(int(generation_count), population, gene_pool)
def table_of_contents(self, node):
functions.remove_tag(node,'div',id='footer')
functions.remove_tag(node,'div','pageSection','main-content')
functions.remove_tag(node,'div','pageSectionHeader')
functions.remove_tag(node,'div',id='main-header')
functions.remove_tag(node,'img')
return node.find('div','pageSection').ul
def load(self, file_path):
load_file = open(file_path, 'r')
load_string = "\n".join(load_file.readlines())
load_file.close()
current_gen, load_string = remove_tag('current', load_string)
self.current_generation = Generation.load(current_gen)
past, load_string = remove_tag('past', load_string)
past_gen, load_string = remove_tag('generation', load_string)
self.past = []
while past_gen is not None:
self.past.append(Generation.load(past_gen))
past_gen, load_string = remove_tag('generation', load_string)
def load(string) -> Gene:
weight_str, string = remove_tag("weight", string)
in_node_str, string = remove_tag("in_node", string)
out_node_str, string = remove_tag("out_node", string)
innovation_number_str, string = remove_tag("innovation_number", string)
enabled_str, string = remove_tag("enabled", string)
weight = float(weight_str)
in_node = int(in_node_str)
out_node = int(out_node_str)
innovation_number = int(innovation_number_str)
enabled = bool(enabled_str)
return Gene(weight, in_node, out_node, innovation_number, enabled)
def load(string) -> Genome:
input_size_str, string = remove_tag("input_size", string)
output_size_str, string = remove_tag("output_size", string)
raw_fitness_str, string = remove_tag("raw_fitness", string)
genes_str, string = remove_tag("genes", string)
input_size = int(input_size_str)
output_size = int(output_size_str)
raw_fitness = float(raw_fitness_str)
genes = []
while genes_str:
gene_str, genes_str = remove_tag("gene", genes_str)
gene = Gene.load(gene_str)
genes.append(gene)
genome = Genome(genes, input_size, output_size)
genome.raw_fitness = raw_fitness
return genome
def load(string) -> GenePool:
innovation_number_str, string = remove_tag("innovation_number", string)
node_number_str, string = remove_tag("node_number", string)
connection_innovations_str, string = remove_tag("connection_innovations", string)
node_innovations_str, string = remove_tag("node_innovations", string)
node_depths_str, string = remove_tag("node_depths", string)
innovation_number = int(innovation_number_str)
node_number = int(node_number_str)
connection_innovations = load_dict(connection_innovations_str, StructureGene.StructureGene, int)
node_innovations = load_dict(node_innovations_str, Gene.Gene, int)
node_depths = load_dict(node_depths_str, int, int)
gene_pool = GenePool(innovation_number, node_number, node_depths)
gene_pool.connection_innovations = connection_innovations
gene_pool.node_innovations = node_innovations
return gene_pool
def page(node, depth=0, mindepth=0, singlepage=False):
functions.remove_tag(node, 'div', id='footer')
functions.remove_tag(node, 'head')
node = node.find('div', 'wiki-content group', id='main-content')
[
x.unwrap()
for x in node.find_all('span', 'confluence-embedded-file-wrapper')
]
[image(x) for x in node.find_all('img')]
[
header(x, depth, mindepth)
for x in node.find_all(['h1', 'h2', 'h3', 'h4', 'h5', 'h6'])
]
if singlepage:
fix_anchors(node)
else:
fix_links(node)
return node
def load(string) -> Specie:
representative_str, string = remove_tag("representative", string)
age_str, string = remove_tag("age", string)
niche_fitness_str, string = remove_tag("niche_fitness", string)
max_fitness_str, string = remove_tag("max_fitness", string)
genomes_str, string = remove_tag("genomes", string)
representative = Genome.load(representative_str)
age = int(age_str)
niche_fitness = float(niche_fitness_str)
max_fitness = float(max_fitness_str)
genomes = []
while genomes_str:
genome_str, genomes_str = remove_tag("genome", genomes_str)
genome = Genome.load(genome_str)
genomes.append(genome)
specie = Specie(representative, genomes, age, max_fitness)
specie.niche_fitness = niche_fitness
return specie