def GenerateOffspring(parents):
offspring = []
while (len(offspring) != len(parents)):
parentA, parentB = Selection.Selection(parents)
childA, childB = Crossover.Crossover(parentA, parentB)
childA = Mutation.Mutation(childA)
childB = Mutation.Mutation(childB)
offspring.append(childA)
offspring.append(childB)
return offspring
def ga_stuff(self):
population = Initialization.initialize(population_size, lower_bound, upper_bound, self.degree + 1)
best_forever = population[0].copy()
for current_generation in range(generations):
self.sort_population(population)
best_of_generation = population[0].copy()
best_forever = best_of_generation.copy() if self.mse(best_of_generation) < self.mse(
best_forever) else best_forever
Crossover.cross(population_size // 2, population, self.x, self.y)
Mutation.mutate(population, current_generation, generations, depending_factor)
return best_forever
def __init__(self, params):
self.epochs = params['epochs']
self.Crossover = Crossover(params['crossover_type'],
params['crossover_prob'])
self.Population = Population(booth_function, params['population_size'],
2, -10, 10,
params['population_precision'])
self.Mutation = Mutation(params['mutation_type'],
params['mutation_prob'])
self.Inversion = Inversion(params['inversion_type'],
params['inversion_prob'])
self.Selection = Selection(params['selection_type'],
params['selection_prob'])
self.EliteStrategy = EliteStrategy(params['elite_prob'])
def create_new_genome(input_size, output_size, fully_connected=False):
nodes_genes = {}
for i in range(0, input_size):
nodes_genes[i] = NodeGene(input_nodes=None,
output_nodes=[],
neuron_type='i')
for j in range(input_size, input_size + output_size):
nodes_genes[j] = NodeGene(input_nodes=[],
output_nodes=[],
neuron_type='o')
cpt = input_size + output_size
connection_genes = dict()
if fully_connected:
for i in range(0, input_size):
for j in range(input_size, input_size + output_size):
connection_genes[i,
j] = ConnectionGene(cpt,
Mutation.get_new_weight(),
False)
nodes_genes[i].output_nodes.append(j)
nodes_genes[j].input_nodes.append(i)
cpt += 1
return Genome(input_size=input_size,
output_size=output_size,
nodes_genes=nodes_genes,
connection_genes=connection_genes,
generation=0,
parents_species_id=[])
def update(self, evt=None):
"""Draw all elements"""
sequence = self.P.DNAseq
#print sequence
self.width=self.winfo_width()
start = self.xstart
end = self.width-20
self.scale = (end-start)/float(len(sequence))
c = self.canvas
c.delete(ALL)
import Mutation
AAseqs3,AAseqs1 = Mutation.translate(sequence)
#print AAseqs1
i=1
for seq in AAseqs1:
row = 40+i*30
c.create_text(start-60,row,
font=self.font,text='Frame '+str(i),
fill='black',anchor='w',tag=('label'))
c.create_line(start,row,end,row,width=2,
fill='darkgreen',tag=('line'))
pos=1
for letter in seq:
if letter=='*':
x = self.getAAPosition(pos)
print pos,x, self.scale, len(sequence)
c.create_line(x,row,x,row-10,fill='red',width=2)
c.create_text(x,row-20,text='%d' %(pos),
font='Arial 7',anchor='n')
pos+=1
i+=1
return
def showAA3Seq(self, sequence=None):
"""Show 3 letter code amino acid sequence"""
if sequence == None:
return
c = self.canvas
seqfont = self.getCurrentFont()
y = (self.baserow+self.orfoffset)*self.yscale
frame = 0
AAseqs3,AAseqs1 = Mutation.translate(sequence)
aaseq = AAseqs3[frame]
#print aaseq
for i in range(len(aaseq)):
pos = self.getAAPosition(i)#,frame+1)
c.create_text(pos,y,
font=seqfont,text=aaseq[i],
fill='black',tag=('aasequence'))
for i in range(len(aaseq)):
if i%5!=0:
continue
pos = self.getAAPosition(i)
c.create_text(pos,y+45,
font='Courier 11',text=i,
fill='black',tag=('aasequence'))
c.create_line(pos,y+35,pos,y+20,fill='red',width=2)
return
def showAA3Seq(self, sequence=None):
"""Show 3 letter code amino acid sequence"""
if sequence == None:
return
c = self.canvas
seqfont = self.getCurrentFont()
y = (self.baserow + self.orfoffset) * self.yscale
frame = 0
AAseqs3, AAseqs1 = Mutation.translate(sequence)
aaseq = AAseqs3[frame]
#print aaseq
for i in range(len(aaseq)):
pos = self.getAAPosition(i) #,frame+1)
c.create_text(pos,
y,
font=seqfont,
text=aaseq[i],
fill='black',
tag=('aasequence'))
for i in range(len(aaseq)):
if i % 5 != 0:
continue
pos = self.getAAPosition(i)
c.create_text(pos,
y + 45,
font='Courier 11',
text=i,
fill='black',
tag=('aasequence'))
c.create_line(pos, y + 35, pos, y + 20, fill='red', width=2)
return
def run(self):
"""
:return:
"""
offspring = []
i = 0
while len(offspring
) < self.mating_pool_size: # 2 parents -> 2 individuals
p1 = self.parents[i]
p2 = self.parents[i + 1]
# crossover ################################################################################################
# print("crossover...")
if random.random() < self.xover_rate:
# off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1 = Crossover.order_crossover(p1, p2, city_map)
off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
# print("crossover end")
# mutation #################################################################################################
# print("Mutation...")
if random.random() < self.mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
# off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
# off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < self.mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
# off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
# off2 = Mutation.inversion_mutation(p2, city_map)
# print("Mutation end")
offspring.append(off1)
offspring.append(off2)
# print(len(offsprings))
i += 2
q.put(offspring)
print('end')
def main():
Support.log(msg="# Parsing and checking the input arguments\n", level="STEP")
args = Argparser.parse_mascotte_arguments()
logArgs(args)
if args['rndseed'] != None:
random.seed(args['rndseed'])
Support.log(msg="# Setting up for simulating human diploid genome\n", level="STEP")
human = Genomics.HumanGenome(reference=args['reference'], snplist=args['snplist'], snpratio=args['snpratio'], HEHOratio=args['HEHOratio'], ignorelist=args['ignore'])
maternalhuman = os.path.join(args['xdir'], 'human.maternal.fa')
paternalhuman = os.path.join(args['xdir'], 'human.paternal.fa')
Support.log(msg="# Simulating human diploid genome\n", level="STEP")
human.buildGenome(maternalout=maternalhuman, paternalout=paternalhuman)
Support.log('Chromosomes: {}\n'.format(', '.join(human.chromosomes)), level='INFO')
Support.log('Number of simulated SNPs: {}\n'.format(human.numsnps), level='INFO')
Support.log('Number of heterozygous SNPs: {}\n'.format(human.hetsnps), level='INFO')
Support.log('Maternal chromosome of human genome written in {}\n'.format(maternalhuman), level='INFO')
Support.log('Maternal chromosome of human genome written in {}\n'.format(paternalhuman), level='INFO')
Support.log(msg="# Simulating tumor clones and their evolution through specified CNAs\n", level="STEP")
tumor = Mutation.simulateEvolution(numclones=args['numclones'], humanGenome=human, binsize=args['binsize'], mutations=args['mutations'])
Support.log('Simulated tumor clones: {}\n'.format(', '.join([clone.label for clone in tumor.clones])), level='INFO')
Support.log('Founder tumor clone: {}\n'.format(tumor.root.label), level='INFO')
with open(os.path.join(args['xdir'], 'tumor.dot'), 'w') as o: o.write("{}\n".format(tumor.draw()))
Support.log('The resulting tumor evolution of clones and related CNAs have been drawn in {} as dot format\n'.format(os.path.join(args['xdir'], 'tumor.dot')), level='INFO')
Support.log('Genome length of the various tumor clones:\n\t{}\n'.format('\n\t'.join(['{}: {}'.format(clone.label, clone.genomeLength()) for clone in tumor.clones])), level='INFO')
Support.log('Computing and segmenting the copy-number profiles jointly for all tumor clones\n', level='INFO')
segments = segmentation(evolution=tumor)
Support.log('Total number of resulting segments= {}\n'.format(sum(len(segments[chro]) for chro in tumor.human.chromosomes)), level='INFO')
segout = os.path.join(args['xdir'], 'copynumbers.csv')
with open(segout, 'w') as o:
o.write('\t'.join(['#CHR', 'START', 'END'] + [clone.label for clone in tumor.clones]) + '\n')
o.write('\n'.join(['\t'.join(map(str, [chro, seg[0], seg[1]]+['{}|{}'.format(segments[chro][seg][clone.idx]['m'], segments[chro][seg][clone.idx]['p']) for clone in tumor.clones])) for chro in tumor.human.chromosomes for seg in sorted(segments[chro], key=(lambda x : x[0]))]))
o.write('\n')
Support.log('The allele-specific copy number profiles for every tumor clone has been written in {}\n'.format(segout), level='INFO')
Support.log('Writing the FASTA-format genomes of tumor clones\n', level='INFO')
if args['jobs'] == 1:
for clone in tumor.clones:
maternalout = os.path.join(args['xdir'], '{}.maternal.fa'.format(clone.label))
paternalout = os.path.join(args['xdir'], '{}.paternal.fa'.format(clone.label))
clone.buildGenome(maternalout, paternalout)
else:
builder = Builder.CloneGenomeBuilder(tumor, args['xdir'])
builder.parallelbuild(args['jobs'])
Support.log('Tumor-clone genomes wrote in:\n{}\n'.format('\n'.join(['\t{}: maternal > {} and paternal > {}'.format(clone.label, os.path.join(args['xdir'], '{}.maternal.fa'.format(clone.label)), os.path.join(args['xdir'], '{}.paternal.fa'.format(clone.label))) for clone in tumor.clones])), level='INFO')
Support.log('KTHXBY!\n', level='STEP')
def update(self, evt=None):
"""Draw all elements"""
sequence = self.P.DNAseq
#print sequence
self.width = self.winfo_width()
start = self.xstart
end = self.width - 20
self.scale = (end - start) / float(len(sequence))
c = self.canvas
c.delete(ALL)
import Mutation
AAseqs3, AAseqs1 = Mutation.translate(sequence)
#print AAseqs1
i = 1
for seq in AAseqs1:
row = 40 + i * 30
c.create_text(start - 60,
row,
font=self.font,
text='Frame ' + str(i),
fill='black',
anchor='w',
tag=('label'))
c.create_line(start,
row,
end,
row,
width=2,
fill='darkgreen',
tag=('line'))
pos = 1
for letter in seq:
if letter == '*':
x = self.getAAPosition(pos)
print pos, x, self.scale, len(sequence)
c.create_line(x, row, x, row - 10, fill='red', width=2)
c.create_text(x,
row - 20,
text='%d' % (pos),
font='Arial 7',
anchor='n')
pos += 1
i += 1
return
def loop_over_mutations(db):
# pdb=db['#Pdb'].str.slice(start=0, stop=4, step=1)
i = 0
m = Mutation.MutationMethods()
while i < len(db):
db.loc[db.index[i],
'ITSM'] = m.getSMScore([db['Mutation(s)_cleaned'].iloc[i]],
'ITSM')
db.loc[db.index[i],
'Blosum'] = m.getSMScore([db['Mutation(s)_cleaned'].iloc[i]],
'Blosum')
db.loc[db.index[i],
'Proline'] = m.HasProline([db['Mutation(s)_cleaned'].iloc[i]])
db.loc[db.index[i],
'Glycine'] = m.HasGlycine([db['Mutation(s)_cleaned'].iloc[i]])
#dummy placeholder command
# print (str(db.index[i])+"_"+pdb.iloc[i]+"_"+db['Mutation(s)_cleaned'].iloc[i])
i += 1
def run(self, gp, lp, gdir, mutype, cnt):
gname = os.path.splitext(os.path.basename(gp))[0]
i = 1
while i <= cnt:
cfg = Mutation.mutate_cfg(gp, lp, mutype)
tp = tempfile.mktemp()
tf = open(tp, "w")
tf.write(self.header)
tf.write("%s\n" % str(cfg))
tf.close()
if ValidGrammar.valid(tp, lp):
if not self.check_duplicate_cfg(gdir, tp, lp):
_gp = '%s/%s_%s.acc' % (gdir, gname, i)
r = subprocess.call(["cp", tp, _gp])
if r != 0:
Utils.error("Copy failed.\n", r)
i += 1
sys.stderr.write('.')
if os.path.exists(tp):
os.remove(tp)
def create(old_population):
# for x in range(len(old_population)):
# print(old_population[x])
new_population = [[-1 for x in range(9)] for y in range(20)]
matrix_aux = [[-1 for x in range(9)] for y in range(2)]
new_population[0] = old_population[0].copy()
for x in range(5):
y = x * 2
matrix_aux[0] = old_population[y].copy()
matrix_aux[1] = old_population[y + 1].copy()
matrix_aux = CrossOver.do(matrix_aux).copy()
new_population[y + 1] = matrix_aux[0].copy()
new_population[y + 2] = matrix_aux[1].copy()
for x in range(9):
y = x + 11
new_population[y] = Mutation.do(old_population[y]).copy()
return new_population
def compileMutations(data, type):
mutationList = MutationList()
for drug in data['viewer']['mutationsAnalysis']['drugResistance'][0][
'drugScores']:
for mutations in drug['partialScores']:
try:
x = mutations['mutations'][1]['text']
muts = []
for mutation in mutations['mutations']:
muts.append(mutation['text'])
temp = mutations['mutations'][0]['text']
mutationList.add_multimutant(
MultipleMutant(muts, mutations['score']))
except:
temp = mutations['mutations'][0]['text']
if temp not in mutationList.get_names():
newMutation = Mutation(temp, type, mutations['score'])
mutationList.add_mutation(newMutation)
else:
mutationList.add_score(temp, mutations['score'])
mutationList.fill_blanks()
mutationList.get_averages()
return mutationList
'''
for x in range (0,10):
Mutation.Mutate(PerfectPSNP)
graph.graphPSNP(PerfectPSNP,'mutated'+'['+str(x)+']','mutated')
#completeName = os.path.join(save_path, name_of_file+".txt")
#out = open(completeName, 'w')
#out.write(' '.join(PerfectPSNP))
'''
save_path = os.getcwd() + '\mutated_txt'
access_rights = 0o755
try:
os.mkdir(save_path, access_rights)
except OSError:
print("Creation of the directory %s failed" % save_path)
else:
print("Successfully created the directory %s " % save_path)
for x in range(0, 10):
Mutation.Mutate(PerfectPSNP)
graph.graphPSNP(PerfectPSNP, 'mutated' + '[' + str(x) + ']',
'mutated_graph')
completePath = os.path.join(
save_path, 'mutated' + '[' + str(x) + ']' + ".txt")
out = open(completePath, 'w')
str_PSNP = [str(i) for i in PerfectPSNP]
out.write(' '.join(str_PSNP))
break
except FileNotFoundError:
print("Enter a valid filename...")
p2 = parents[i + 1]
# crossover ################################################################################################
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1, off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation #################################################################################################
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.WGWRGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.WGWRGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
ruleCount+=1
ruleStartList.append(rulestart)
rulestart = rulestart + 6
#loopCount+=1
return (ruleStartList)
while True:
try:
fileName = input("Enter PSNP filename to modify: ")
fileName = fileName+'.txt'
File = open(fileName, "r+")
PerfectPSNP = [int(string) for string in File.readline().replace('\n','').split(' ')]
Mutation.PrintPSNP(PerfectPSNP)
break
except FileNotFoundError:
print("Enter a valid filename...")
neurons = int(PerfectPSNP[0])
rules = int(PerfectPSNP[1])
rulestart = 2+((neurons-1)*neurons)
#print ("Neurons: {}".format(neurons))
#print ("Rules: {}".format(rules))
#print ("Rule Start: {}".format(rulestart))
loopCount = 1
ruleList = []
finRule = []
if (soup.findAll('a').__len__() == 2):
#m.EvTypeID = ev.getEvidenceDetailsfromSoup(soup.select('a')[0].text+" "+soup.select('a')[1].text,)
m.EvUrl1 = soup.select('a')[0].get('href')
m.EvUrl2 = soup.select('a')[0].get('href')
m.EvTypeID = ev.getEvidenceDetailsfromSoup(
soup.select('a')[0].text + " " + soup.select('a')[1].text,
m.EvUrl1, m.EvUrl2)
else:
m.EvTypeID = ev.getEvidenceDetailsfromSoup(soup.text,
soup.a['href'])
m.EvUrl1 = soup.a['href']
m.EvUrl2 = ""
#This is where Mutation Extra Details will be extracted
m.MtypeID = mut.getMutationTypeIDDetailsfromSoup(m.EvUrl1)
# print m.SeqId.encode("utf8")
try:
m.MSub = vr.getREF(directory, m.Pos,
m.SeqId.replace(u'\u2011', ""))
m.MObj = vr.getALT(directory, m.Pos, m.SeqId)
except ValueError:
m.MSub = 0
m.MObj = 0
#print evidence
# print str(m.GeneType)
#Evidence URL
#print soup.findAll('a')[]
#otp+=str(m.EvTypeID)+u","+str(+m.EvUrl1)+u","+str(m.EvUrl2)+u","+m.SeqId+u","+str(m.Pos)+u","+str(m.MtypeID)+u","+str(m.MObj)+u","+str(m.MSub)+u","+str(m.Mutation)+u","+str(m.Coverage)+u","+str(m.AnnotationTypeID)+u","+str(m.AnnotationCodon1)+u","+str(m.AnnotationCodon2)+","+str(m.AnnotationCodonPosition)+u","+str(m.AnnotationBracket1)+","+str(m.AnnotationBracket2)+","+str(m.Annotation)+","+str(m.GeneParameter1)+","+str(m.GeneParameter2)+","+str(m.Gene)+","+str(m.Description)+",\n"
# # print("COMBINED IS")
# # print(combined)
# if (combined == target):
# print("Its Done!")
# sys.exit()
f = Fitness.Fitness(population)
fitness = f.getFitness()
s = Selection.Selection(fitness)
firstChoice, secondChoice, maximum = s.selectFromPop()
c = Crossover.Crossover(population, firstChoice, secondChoice)
population = c.crossover()
m = Mutation.Mutation(population, 10)
population = m.mutate()
if (catch == 1):
print(
"--------------------------------------------------------------------"
)
print("Program is finished!")
print(
"--------------------------------------------------------------------"
)
print("Final fitness: ")
print(fitness)
print(
"--------------------------------------------------------------------"
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 2000
mating_pool_size = 300
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 100
advanced_Canada = {
"description": {
"instance_name": "none",
"algorithm": "none",
"city_all": 4663,
"generation_all": gen_limit
},
"evolutions": [
# nothing yet
]
}
advanced_Uruguay = {
"description": {
"instance_name": "none",
"algorithm": "none",
"city_all": 734,
"generation_all": gen_limit
},
"evolutions": [
# nothing yet
]
}
advanced_WesternSahara = {
"description": {
"instance_name": "none",
"algorithm": "none",
"city_all": 29,
"evolution_all": 10,
"generation_all": gen_limit,
"generation_size": mating_pool_size
},
"evolutions": [
# nothing yet
]
}
print(json.dumps(advanced_WesternSahara))
for evo in range(10): # Experiment
# construct a trial #
trial = [[{"time_cost": 0}]]
print("trial", evo + 1)
# t = time.time()
print("Preparing for the information...")
c = CityMap.CityMap(western29)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
init.evalPopulation()
print("Initialization end.")
# Evolution ########################################################################################################
while gen < gen_limit:
# parent selection ---------------------------------------------------------------------------------------------
#print("parent selection...")
parents = Selection.tournament_selection(init.population[1:],
mating_pool_size,
tournament_size)
#print("parent selection end.")
offsprings = []
i = 0
while len(offsprings
) < mating_pool_size: # 2 parents -> 2 individuals
p1 = parents[i]
p2 = parents[i + 1]
# crossover ------------------------------------------------------------------------------------------------
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1 = Crossover.order_crossover(p1, p2, city_map)
off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation -------------------------------------------------------------------------------------------------
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
# survial selection --------------------------------------------------------------------------------------------
#print("survival selection")
init.population[1:] = Selection.mu_plus_lambda(
init.population[1:], offsprings)
#print("survival selection end")
init.evalPopulation()
print("generation:", gen, " Average length:", init.AverageLength,
" Longest length: ", init.worstTour.length,
" shortest length:", init.bestTour.length)
# construct a generation #
generation = {
"best-individual-sequence":
init.bestTour.tour,
"best-individual-distance":
init.bestTour.length,
"worst-individual-sequence":
init.worstTour.tour,
"worst-individual-distance":
init.worstTour.length,
"average-distance":
init.AverageLength,
"individual-distances":
[round(distance.length) for distance in init.population
] # distance.length for distance in init.population
}
trial.append(generation)
gen += 1
advanced_WesternSahara["evolutions"].append(trial)
print("time cost:", time.time())
# here processing generating json file #############################################################################
output_file = open("advanced_WesternSahara.js", "w")
output_file.write("let advanced_WesternSahara = ")
output_file.write(json.dumps(advanced_WesternSahara))
output_file.close()
mutation=mutation,
sizeOfPopulation=10,
genPopulation=populationGen,
numberChromosome=NN, epoche=100,
data=data)
ans = bga.fit()
print(ans)"""
data = GenMtrx_NN() # инициализация условий задачи (считывание матрицы расстояний)
NN = len(data) # считываем количество городов
fitness = Fitness.Fitness('fitness_population_NN')
crossover = Crossover.Crossover('crossover_NN')
# Можно выбрать: inbreeding_NN,
# outbreeding_NN
# panmixia_NN
# tournament_selection_NN
# roulette_selection_NN
selection = Selection.Selection('inbreeding_NN')
mutation = Mutation.Mutation('insertion_deleting_mutation_NN')
populationGen = Population.Population('elite_selection')
bga = BasicGeneticAlgorithm.BasicGeneticAlgorithm(generator=genPopulation_NN,
fitness=fitness,
crossover=crossover,
selection=selection,
mutation=mutation,
sizeOfPopulation=100,
genPopulation=populationGen,
numberChromosome=100, epoche=100,
data=data)
ans = bga.fit()
print(ans)
print('----------------------------------')
caminhos = []
for item in demanda:
caminhos.append(dijkstra_distances[item[0]][item[1]])
todas_possibilidades = itertools.product(*caminhos)
fitness_cromossomos = []
for cromossomo in todas_possibilidades:
fitness_cromossomos.append((fitness.calc_fitness(g, cromossomo), cromossomo))
result = roulletweel_selection.roullet_wheel(fitness_cromossomos)
print('ESCOLHA')
print(result)
print('MUTAÇÃO')
chromosome = Mutation.mutation(g, result[1])
print('----------------------------------')
print("POPULAR DEMANDA")
demand_generator.populate_demand(g, chromosome, [d[2] for d in demanda])
paths = []
for edge in g.edges:
print(edge[0], edge[1], g[edge[0]][edge[1]]['LinkSpeedUsed'])
paths.append([edge[0], edge[1], g[edge[0]][edge[1]]['LinkSpeedUsed']])
print('----------------------------------')
import os
os.environ['PYTHONINSPECT'] = 'True'
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 1000
mating_pool_size = 800
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 100
# choose the instance you are using ################################################################################
instance_name = "WesternSahara"
evolution_all = 10
source = None
city_all = 0
if instance_name == "WesternSahara":
city_all = 29
source = western29
if instance_name == "Uruguay":
city_all = 734
source = uruguay734
if instance_name == "Canada":
city_all = 4663
source = canada4663
experiment = {
"description": {
"instance_name": instance_name,
"algorithm": "advanced",
"city_all": city_all,
"evolution_all": evolution_all,
"generation_all": gen_limit,
"generation_size": popsize
},
"evolutions": [
# nothing yet
]
}
print(json.dumps(experiment))
for evo in range(evolution_all): # Experiment
# construct a trial #
trial = [{"time_cost": 0}]
print("trial", evo + 1)
start_time = time.time()
print("Preparing for the information...")
c = CityMap.CityMap(source)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
init.evalPopulation()
print("Initialization end.")
# Evolution ########################################################################################################
while gen < gen_limit:
# parent selection ---------------------------------------------------------------------------------------------
#print("parent selection...")
parents = Selection.tournament_selection(init.population[1:],
mating_pool_size,
tournament_size)
#print("parent selection end.")
offsprings = []
i = 0
while len(offsprings
) < mating_pool_size: # 2 parents -> 2 individuals
p1 = parents[i]
p2 = parents[i + 1]
# crossover ------------------------------------------------------------------------------------------------
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1, off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation ------------------------------------------------------------------------------------------------
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.WGWRGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.WGWRGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
# survial selection --------------------------------------------------------------------------------------------
#print("survival selection")
init.population[1:] = Selection.mu_plus_lambda(
init.population[1:], offsprings)
#print("survival selection end")
init.evalPopulation()
print("generation:", gen, " Average length:", init.AverageLength,
" Longest length: ", init.worstTour.length,
" shortest length:", init.bestTour.length)
# construct a generation #
generation = {
"best-individual-sequence":
init.bestTour.tour,
"best-individual-distance":
init.bestTour.length,
"worst-individual-sequence":
init.worstTour.tour,
"worst-individual-distance":
init.worstTour.length,
"average-distance":
init.AverageLength,
"individual-distances":
[round(distance.length) for distance in init.population
] # distance.length for distance in init.population
}
trial.append(generation)
gen += 1
trial[0]["time_cost"] = time.time() - start_time
experiment["evolutions"].append(trial)
# here processing generating json file #############################################################################
output_file = None
variable_declaration = "none"
if instance_name == "WesternSahara":
output_file = open("advanced_WesternSahara.js", "w")
variable_declaration = "let advanced_WesternSahara = "
if instance_name == "Uruguay":
output_file = open("advanced_Uruguay.js", "w")
variable_declaration = "let advanced_Uruguay = "
if instance_name == "Canada":
output_file = open("advanced_Canada.js", "w")
variable_declaration = "let advanced_Canada = "
output_file.write(variable_declaration)
output_file.write(json.dumps(experiment))
output_file.close()
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 10000
mating_pool_size = 8000
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 10000
print("Preparing for the information...")
c = CityMap.CityMap(uruguay734)
city_map = c.city_map
print("preparation end.")
gen = 0
print("Initialize population...")
init = Initialization.Population(popsize, city_map)
init.evalPopulation()
print("Initialization end.")
#EA algorithm
while gen < gen_limit:
#parent selection
#print("parent selection...")
parents = Selection.tournament_selection(init.population[1:],
mating_pool_size,
tournament_size)
#parents = Selection.random_selection(init.population[1:], mating_pool_size)
#parents = Selection.MPS(init.population[1:], mating_pool_size)
offsprings = []
i = 0
while len(
offsprings) < mating_pool_size: # 2 parents -> 2 individuals
p1 = parents[i]
p2 = parents[i + 1]
# crossover ################################################################################################
#print("crossover...")
if random.random() < xover_rate:
#off1, off2 = Crossover.COWGC(p1, p2, city_map)
off1, off2 = Crossover.order_crossover(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
# mutation #################################################################################################
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
#off1 = Mutation.WGWRGM(p1, city_map)
#off1 = Mutation.IRGIBNNM_mutation(p1, city_map)
#off1 = Mutation.inversion_mutation(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#off2 = Mutation.WGWRGM(p2, city_map)
#off2 = Mutation.IRGIBNNM_mutation(p2, city_map)
#off2 = Mutation.inversion_mutation(p2, city_map)
#print("Mutation end")
offsprings.append(off1)
offsprings.append(off2)
#print(len(offsprings))
i += 2
# survial selection ############################################################################################
#print("survival selection")
init.population = Selection.mu_plus_lambda(init.population, offsprings)
init.evalPopulation()
print("generation:", gen, " Average length:", init.AverageLength,
" Longest length: ", init.worstTour.length, " shortest length:",
init.bestTour.length)
gen += 1
class Algorithm:
def __init__(self, params):
self.epochs = params['epochs']
self.Crossover = Crossover(params['crossover_type'],
params['crossover_prob'])
self.Population = Population(booth_function, params['population_size'],
2, -10, 10,
params['population_precision'])
self.Mutation = Mutation(params['mutation_type'],
params['mutation_prob'])
self.Inversion = Inversion(params['inversion_type'],
params['inversion_prob'])
self.Selection = Selection(params['selection_type'],
params['selection_prob'])
self.EliteStrategy = EliteStrategy(params['elite_prob'])
def fill_selected_population(self, selected):
new_pop = []
pop_size = self.Population.population_size
for _ in range(pop_size):
new_pop.append(random.choice(selected))
return np.array(new_pop)
def run(self):
best_value = []
avg_value = []
std_avg_value = []
best_individual = []
pop = self.Population.generate_population()
time_start = time.time()
for i in range(self.epochs):
# print(i)
# dla kazdej epoki
# ocen populacje (evaluate)
fitness = self.Population.evaluate_population(pop)
# zapisz wartosci do wygenerowania wykresow
best_value.append(fitness.min())
avg_value.append(fitness.mean())
std_avg_value.append(fitness.std())
index_best = fitness.argsort()[0]
best_individual.append(
self.Population.decode_population(pop)[index_best])
# zapisz % najlepszych (strategia elitarna)
elite = self.EliteStrategy.elite(pop, fitness)
# wybierz gatunki do crossowania (selection)
selected, not_selected = self.Selection.select(pop, fitness)
# krzyzowanie gatunków (cross)
if self.Selection.decision != 'roulette_wheel':
selected = self.fill_selected_population(selected)
pop = self.Crossover.cross(selected)
# mutacja i/lub inversja
pop = self.Mutation.mutate(pop)
pop = self.Inversion.inverse(pop)
# jezeli strategia elitarna jest uzywana
if elite.shape[0] != 0:
# polacz najlepszy % z populacja
pop = np.concatenate(
(pop[:-elite.shape[0]], elite)) # , axis=0
time_execution = time.time() - time_start
# print(f"Algorytm zajął {time_execution:.3f} sekund")
Plot.draw_save_plot(
best_value, 'Numer epoki', 'Wartość funkcji',
'Wykres wartości funkcji dla najlepszych osobników',
'best_individual')
Plot.draw_save_plot(avg_value, 'Numer epoki', 'Wartość funkcji',
'Wykres średniej wartości funkcji dla populacji',
'avg_pop')
Plot.draw_save_plot(std_avg_value, 'Numer epoki',
'Wartość odchylenia standardowego',
'Wykres odchylenia standardowego dla populacji',
'avg_std_pop')
Files.numpy_to_csv(best_individual, 'best_individual.csv')
return time_execution, best_value[-1]
def main():
western29 = "Cities/TSP_WesternSahara_29.txt"
uruguay734 = "Cities/TSP_Uruguay_734.txt"
canada4663 = "Cities/TSP_Canada_4663.txt"
popsize = 500
mating_pool_size = 100
tournament_size = 3
mut_rate = 0.2
xover_rate = 0.9
gen_limit = 15
print("Preparing information...")
c = CityMap.CityMap(western29 )
city_map = c.city_map
print("preparation end.")
gen = 0
init = Initialization.Population(popsize, city_map)
population = init.population
#EA algorithm
while gen < gen_limit:
#parent selection
#print("parent selection...")
parents = Selection.tournament_selection(population, mating_pool_size, tournament_size)
#print("parent selection end.")
offsprings = []
i = 0
while len(offsprings) < mating_pool_size:
p1 = parents[i][0]
p2 = parents[i + 1][0]
#crossover
#print("crossover...")
if random.random() < xover_rate:
off1, off2 = Crossover.COWGC(p1, p2, city_map)
else:
off1 = copy.copy(p1)
off2 = copy.copy(p2)
#print("crossover end")
#mutation
#print("Mutation...")
if random.random() < mut_rate:
off1 = Mutation.WGWWGM(p1, city_map)
if random.random() < mut_rate:
off2 = Mutation.WGWWGM(p2, city_map)
#print("Mutation end")
offsprings.append([off1, off1.fitness])
offsprings.append([off2, off2.fitness])
i += 2
#survial selection
#print("survival selection")
population = Selection.mu_plus_lambda(population, offsprings)
#print("survival selection end")
best_fitness = population[0][1]
best_tour = population[0][0].tour
tour_length = population[0][0].length
for i in population:
if i[0].length < tour_length:
best_fitness = i[1]
best_tour = i[0].tour
tour_length = i[0].length
print("generation: ", gen, " best fitness: ", best_fitness, " length: ", tour_length)
gen += 1