#Импортим генетический алгоритм
from pyeasyga.pyeasyga import GeneticAlgorithm
import random
import numpy as np
#Представление данных
#Данные представляют из себя множество нулей с одной единицей на какой-либо позиции
#Это позиция отражает положение x на оси абсцисс от -4 до 0
seed_data = [0] * 399
seed_data.append(1)
#Иницилизируем генетический алгоритм с заданными данными, а также размером
#популяции, количеством поколений, шансом на кроссинговер и мутацию
ga = GeneticAlgorithm(seed_data, 400, 200, 0.7, 0.05, True, True)
#Представляем отдельную особь как случайное положение х
def create_individual(data):
individual = data[:]
random.shuffle(individual)
return individual
ga.create_individual = create_individual
#Одноточечный кроссинговер
def crossover(parent_1, parent_2):
crossover_index = random.randrange(1, len(parent_1))
child_1a = parent_1[:crossover_index]
child_1b = [i for i in parent_2 if i not in child_1a]
def Genetic_al(model, data, i, stud1):
ga = GeneticAlgorithm(data,
population_size=100,
generations=100,
crossover_probability=0.01,
mutation_probability=0.01,
elitism=False,
maximise_fitness=False)
def create_individual(data):
return [
float(1.0 + 5.0 * random.uniform(0, 1)), # Drop time actual
float(1.0 + 15.0 * random.uniform(0, 1)), # Drop time actual_EWMA
float(1.0 + 15.0 * random.uniform(0, 1)), # Drop time difference
float(1.0 +
15.0 * random.uniform(0, 1)), # Drop time difference_EWMA
# float((random.randint(300, 4000) + random.uniform(0, 1))),# Energy
# float((random.randint(300, 4000) + random.uniform(0, 1))), # Energy_EWMA
float(1.0 + 2.0 * random.uniform(0, 1)), # Lift Height actual
float(1.0 + 2.0 * random.uniform(0, 1)), # Lift Height actual_EWMA
# float((random.randint(0, 2) + random.uniform(0, 1))), # lift height ref
# float((random.randint(0, 2) + random.uniform(0, 1))), # lift height ref_EWMA
float(15.0 + 40.0 *
random.uniform(0, 1)), # Main Weldcurrent voltage actual
float(15.0 + 4.0 * random.uniform(0, 1)
), # Main Weldcurrent voltage actual_EWMA
# ((random.randint(25, 50) + random.uniform(0, 1))),# Main Weldcurrent voltage Max
# ((random.randint(25, 50) + random.uniform(0, 1))), # Main Weldcurrent voltage Max_EWMA
# ((random.randint(8, 28) + random.uniform(0, 1))), # Main Weldcurrent voltage Min
# ((random.randint(8, 28) + random.uniform(0, 1))), # Main Weldcurrent voltage Min_EWMA
# (-(random.randint(1, 3) + random.uniform(0, 1))), # Penetration Max
# (-(random.randint(1, 3) + random.uniform(0, 1))), # Penetration Max_EWMA
# (-(random.randint(0, 3) + random.uniform(0, 1))), # Penetration Min
# (-(random.randint(0, 3) + random.uniform(0, 1))), # Penetration Min_EWMA
# (-(random.randint(0, 3) + random.uniform(0, 1))),# Penetratiom Ref
# (-(random.randint(0, 3) + random.uniform(0, 1))), # Penetratiom Ref_EWMA
float(8.0 + 35.0 *
random.uniform(0, 1)), # Pilot Weldcurrent Arc Voltage Act
float(8.0 + 35.0 * random.uniform(0, 1)
), # Pilot Weldcurrent Arc Voltage Act_EWMA
# ((random.randint(20, 50) + random.uniform(0, 1))),# Pilot Weldcurrent Arc Voltage Max
# ((random.randint(20, 50) + random.uniform(0, 1))), # Pilot Weldcurrent Arc Voltage Max_EWMA
# ((random.randint(5, 20) + random.uniform(0, 1))),# Pilot Weldcurrent Arc Voltage Min
# ((random.randint(5, 20) + random.uniform(0, 1))), # Pilot Weldcurrent Arc Voltage Min_EWMA
float(1.5 + 8.0 * random.uniform(0, 1)), # Stickout
float(1.5 + 8.0 * random.uniform(0, 1)), # Stickout_EWMA
float(500.0 + 1000.0 *
random.uniform(0, 1)), # Weldcurrent actual Positive
float(500.0 + 1000.0 *
random.uniform(0, 1)), # Weldcurrent actual Positive_EWMA
float(-1500.0 + 1500.0 *
random.uniform(0, 1)), # Weldcurrent actual Negative
float(-1500.0 + 1500.0 *
random.uniform(0, 1)), # Weldcurrent actual Negative_EWMA
float(10.0 + 100.0 * random.uniform(0, 1)), # Weld time actual
float(10.0 + 100.0 * random.uniform(0, 1))
] # Weld time actual_EWMA
# ((random.randint(10, 100) + random.uniform(0, 1))), # Weldtime ref
# ((random.randint(10, 100) + random.uniform(0, 1)))] # Weldtime ref_EWMA
ga.create_individual = create_individual
def eval_fitness(individual, data):
array = np.array(individual)[np.newaxis]
error_array = []
error = (model.predict(array, batch_size=1) + 2)**2
error_array.append(individual)
print('Evaluating... error: ' + str(error))
return error
ga.fitness_function = eval_fitness
ga.run()
Gen1 = pd.DataFrame(ga.last_generation())
filepath = "C:\\Users\PHLEGRA\Desktop\MASTER\Data_intersection\Prescribed_parameters\\new"
filename = str(stud1) + "_" + str(i) + '_predictions.csv'
Gen1.to_csv(filepath + '\\' + filename, index=False)
print('Please see file. Process Complete')
def generate(target_params,
insert_aa_seq,
population_size=100,
mutation_probability=0.3,
max_gens_since_improvement=50,
genetic_code=11,
verbose=False):
# back translate to an initial seq
insert = ""
for aa in insert_aa_seq:
try:
insert += Bio.Data.CodonTable.unambiguous_dna_by_id[
genetic_code].back_table[aa]
except:
if aa == "*":
insert += Bio.Data.CodonTable.unambiguous_dna_by_id[
genetic_code].back_table[None]
# create the genetic algorithm instance
ga = GeneticAlgorithm(dna_to_vector(insert),
crossover_probability=0,
maximise_fitness=False,
population_size=population_size,
mutation_probability=mutation_probability)
# get the target values of k
k = list(target_params.keys())
# generate the target vector from the input dict
target = np.array([])
for _k in sorted([x for x in k if x != "codons"]):
target = np.concatenate((target, [
x[1] for x in sorted(target_params[_k].items(), key=lambda x: x[0])
]))
if "codons" in k:
target = np.concatenate((target, [
x[1] for x in sorted(target_params["codons"].items(),
key=lambda x: x[0])
]))
def vector(seq):
output = k_mer_frequencies(seq, [x for x in k if x != "codons"],
include_missing=True,
vector=True)
if "codons" in k:
output = np.concatenate((output, [
x[1]
for x in sorted(codon_frequencies(seq, genetic_code).items(),
key=lambda x: x[0])
]))
return output
def fitness(individual, data):
individual = vector_to_dna(individual)
# fitness = np.linalg.norm(target - vector(individual))
fitness = jensen_shannon_divergence([
dit.ScalarDistribution(target),
dit.ScalarDistribution(vector(individual))
])
return fitness
ga.fitness_function = fitness
synonymous_codons = _synonymous_codons(genetic_codes[genetic_code])
def mutate(individual):
while True:
# choose a random codon
codon_idx = np.random.randint(len(individual) / 6) * 6
# figure out which codon it is
codon = vector_to_dna(individual[codon_idx:codon_idx + 6])
# ensure that mutations actually change the sequence
if len(synonymous_codons[codon]) != 1:
break
# choose a new one at random for the AA
new_codon = dna_to_vector(
np.random.choice(
[x for x in synonymous_codons[codon] if x != codon]))
# replace it in the individual
individual[codon_idx:codon_idx + 6] = new_codon
return individual
ga.mutate_function = mutate
def create_individual(seed_data):
individual = vector_to_dna(seed_data)
new = ""
for codon in [
individual[i:i + 3] for i in range(0, len(individual), 3)
]:
if len(synonymous_codons[codon]) == 1:
new += codon
continue
new += np.random.choice(
[x for x in synonymous_codons[codon] if x != codon])
return dna_to_vector(new)
ga.create_individual = create_individual
# set up for GA run
ga.create_first_generation()
gens_since_improvement = 0
best_indv_fitness = ga.best_individual()[0]
counter = 1
# run the GA
while gens_since_improvement < max_gens_since_improvement:
ga.create_next_generation()
if ga.best_individual()[0] < best_indv_fitness:
best_indv_fitness = ga.best_individual()[0]
gens_since_improvement = 0
else:
gens_since_improvement += 1
if verbose:
print(
"Gen: %s\tSince Improvement: %s/%s\tFitness: %s".expandtabs(15)
% (counter, gens_since_improvement, max_gens_since_improvement,
ga.best_individual()[0]),
end="\r")
counter += 1
if verbose: print()
best_seq = vector_to_dna(ga.best_individual()[1])
best_freqs = vector(best_seq)
return best_seq
if not existenDatosInvalidos(individual) and not existenDatosDuplicados(
individual):
fitness += britanicoCasaRoja(individual)
fitness += suecoTienePerro(individual)
fitness += danesTomaTe(individual)
fitness += casaVerdeIzqACasaBlanca(individual)
fitness += casaVerdeTomaTe(individual)
fitness += fumaPallMallTienePajaro(individual)
fitness += casaAmarillaFumaDunhill(individual)
fitness += casaDelCentroTomaLeche(individual)
fitness += noruegoEnPrimeraCasa(individual)
fitness += fumaBrendsYVecinoTieneGato(individual)
fitness += tieneCaballoYVecinoFumaDunhill(individual)
fitness += fumaBluemastersYBebeCerveza(individual)
fitness += alemanFumaPrince(individual)
fitness += noruegoJuntoACasaAzul(individual)
fitness += fumaBrendsYVecinoTomaAgua(individual)
resultantes.append({'fitness': fitness, 'individual': individual})
return fitness
ga = GeneticAlgorithm(datos, population_size=2000)
ga.create_individual = create_individual
ga.fitness_function = fitness
ga.run()
last = resultantes.pop()
print(last.get('individual'))
parser.add_argument('--adaptive_mutation_factor', default=1.3, type=float, help='enter a decimal value for how much to multiply mutation rate per generation')
parser.add_argument('--clipping_threshold', default=0.9, type=float, help='enter a decimal between 0 and 1 at which clipping is performed')
parser.add_argument('--turbines', default=2, type=int, help='enter an integer number of turbines to start with')
parser.add_argument('--grid_size', default=36, type=int, help='enter an perfect square integer representing grid size (e.g. 6x6 is 36)')
args = parser.parse_args()
given_crossover_probability = args.crossover_probability
given_mutation_probability = args.starting_mutation_probability
given_adaptive_mutation_factor = args.adaptive_mutation_factor
given_clipping_threshold = args.clipping_threshold
given_turbines = args.turbines
grid_size= args.grid_size
input_data = [0]*grid_size # length of this list is the # of grid cells in layout
easyga = GeneticAlgorithm(input_data, crossover_probability =given_crossover_probability , mutation_probability=given_mutation_probability, adaptive_mutation_factor = given_adaptive_mutation_factor, clipping_threshold = given_clipping_threshold, num_turbines = given_turbines)
easyga.fitness_function = fitness
easyga.run()
print(easyga.best_individual())
def fitness (individual, data):
freestream_velocity = 15
individual_2D_list = [[individual[0], individual[1]],[individual[2], individual[3]]]
individual_np = np.asarray(individual_2D_list)
velocities = np.zeros(individual_np.shape)
alpha = .9 # entrainment constant
x = 150 # distance between perpendicular turbine centers
r = 40 # radius of turbine
from pyeasyga.pyeasyga import GeneticAlgorithm
import random
import numpy as np
# Формат задания значений (y(x), [x])
data = (4.0, [-2.0])
# Параметры работы генетического алгоритма
# (начальные данные, кол-во особей в популяции, кол-во генераций, вероятность применения оператора скрещивания, вероятность применения мутации к гену, вкл. выбор лучшей особи, максимизация целевой функции)
ga = GeneticAlgorithm(data, 20, 30, 0.7, 0.01, True, False)
# Функция создания начальной популяции
def create_individual(data):
# Вещественное кодирование
ind = [random.uniform(-4.0, 4.0)]
# Вывод начальной популяции на экран
print(ind[0] * ind[0] + 4, ind)
return ind #[random.uniform(-4.0, 4.0) for _ in range(len(data))]
ga.create_individual = create_individual
# Функция кроссинговера ГА (арифметический кроссинговер, lambda = 0.2)
def crossover(parent_1, parent_2):
child_1 = [parent_1[0] * 0.2 + parent_2[0] * 0.8]
child_2 = [parent_2[0] * 0.2 + parent_1[0] * 0.8]
return child_1, child_2
from pyeasyga.pyeasyga import GeneticAlgorithm
import random
data = [('pear', 50), ('apple', 35), ('banana', 40)]
ga = GeneticAlgorithm(data, 20, 50, 0.8, 0.2, True, True)
def create_individual(data):
print("***CREATE INDIVIDUAL***")
ind = [random.randint(0, 1) for _ in xrange(len(data))]
print(ind)
return ind
ga.create_individual = create_individual
def crossover(parent_1, parent_2):
crossover_index = random.randrange(1, len(parent_1))
child_1 = parent_1[:crossover_index] + parent_2[crossover_index:]
child_2 = parent_2[:crossover_index] + parent_1[crossover_index:]
print("***HERENCIA***")
print(child_1)
print(child_2)
return child_1, child_2
ga.crossover_function = crossover
def mutate(individual):
from pyeasyga.pyeasyga import GeneticAlgorithm
from random import random
data = [('pear', 50), ('apple', 35), ('banana', 40)]
ga = GeneticAlgorithm(data, population_size=40,
generations=20,
crossover_probability=0.8,
mutation_probability=0.9,
elitism=True,
maximise_fitness=True)
def fitness (individual, data):
fitness = 0
if individual.count(1) == 3:
for (selected, (fruit, profit)) in zip(individual, data):
if selected:
fitness += profit
return fitness
ga.fitness_function = fitness
ga.run()
print (ga.best_individual())
for individual in ga.last_generation():
print (individual)
def generate(target_params, insert_aa_seq, population_size=100, mutation_probability=0.3, crossover_probability=0.8, max_gens_since_improvement=50, genetic_code=11, verbose=False):
'''Generate a sequence matching :math:`k`-mer usage.
Args:
target_params (dict): The parameters to optimize towards. Should be of the format {:math:`k_n`: {:math:`k_{n1}`: 0.2, :math:`k_{n2}`: 0.3,...}...}
insert_aa_seq (str): The amino acid sequence for the optimized sequence.
population_size (int, optional): The size of the population for the genetic algorithm. Defaults to 100.
mutation_probability (float, optional): The likelihood of changing each member of each generation. Defaults to 0.3.
crossover_probability (float, optional): The likelihood of each member of the population undergoing crossover. Defaults to 0.8.
max_gens_since_improvement (int, optional): The number of generations of no improvement after which to stop optimization. Defaults to 50.
genetic_code (int, optional): The genetic code to use. Defaults to 11, the standard genetic code.
verbose (bool, optional): Whether to print the generation number, generations since improvement, and fitness. Defaults to false.
Returns:
str: The generated sequence.
'''
# back translate to an initial seq
insert = ""
for aa in insert_aa_seq:
try:
insert += Bio.Data.CodonTable.unambiguous_dna_by_id[genetic_code].back_table[aa]
except:
if aa == "*":
insert += Bio.Data.CodonTable.unambiguous_dna_by_id[genetic_code].back_table[None]
# create the genetic algorithm instance
ga = GeneticAlgorithm(dna_to_vector(insert),
crossover_probability=crossover_probability,
maximise_fitness=False,
population_size=population_size,
mutation_probability=mutation_probability)
# get the target values of k
k = list(target_params.keys())
# generate the target vector from the input dict
target = np.array([])
for _k in sorted([x for x in k if x != "codons"]):
target = np.concatenate((target, [x[1] for x in sorted(target_params[_k].items(), key=lambda x: x[0])]))
if "codons" in k:
target = np.concatenate((target, [x[1] for x in sorted(target_params["codons"].items(), key=lambda x: x[0])]))
def vector(seq):
output = k_mer_frequencies(seq, [x for x in k if x != "codons"], include_missing=True, vector=True)
if "codons" in k:
output = np.concatenate((output, [x[1] for x in sorted(codon_frequencies(seq, genetic_code).items(), key=lambda x: x[0])]))
return output
def fitness(individual, data):
individual = vector_to_dna(individual)
# fitness = np.linalg.norm(target - vector(individual))
fitness = jensen_shannon_divergence([dit.ScalarDistribution(target), dit.ScalarDistribution(vector(individual))])
return fitness
ga.fitness_function = fitness
synonymous_codons = _synonymous_codons(genetic_codes[genetic_code])
def mutate(individual):
while True:
# choose a random codon
codon_idx = np.random.randint(len(individual) / 6) * 6
# figure out which codon it is
codon = vector_to_dna(individual[codon_idx:codon_idx+6])
# ensure that mutations actually change the sequence
if len(synonymous_codons[codon]) != 1:
break
# choose a new one at random for the AA
new_codon = dna_to_vector(np.random.choice([x for x in synonymous_codons[codon] if x != codon]))
# replace it in the individual
individual[codon_idx:codon_idx+6] = new_codon
return individual
ga.mutate_function = mutate
def crossover(parent_1, parent_2):
parent_1, parent_2 = list(parent_1), list(parent_2)
index = random.randrange(1, len(parent_1) / 6) * 6
child_1 = parent_1[:index] + parent_2[index:]
child_2 = parent_2[:index] + parent_1[index:]
return child_1, child_2
ga.crossover_function = crossover
def create_individual(seed_data):
individual = vector_to_dna(seed_data)
new = ""
for codon in [individual[i:i+3] for i in range(0, len(individual), 3)]:
if len(synonymous_codons[codon]) == 1:
new += codon
continue
new += np.random.choice([x for x in synonymous_codons[codon] if x != codon])
return dna_to_vector(new)
ga.create_individual = create_individual
# set up for GA run
ga.create_first_generation()
gens_since_improvement = 0
best_indv_fitness = ga.best_individual()[0]
counter = 1
# run the GA
try:
while gens_since_improvement < max_gens_since_improvement:
ga.create_next_generation()
if ga.best_individual()[0] < best_indv_fitness:
best_indv_fitness = ga.best_individual()[0]
gens_since_improvement = 0
else:
gens_since_improvement += 1
if verbose:
print("Gen: %s\tSince Improvement: %s/%s\tFitness: %s".expandtabs(15) % (counter, gens_since_improvement, max_gens_since_improvement, ga.best_individual()[0]), end="\r")
counter += 1
except KeyboardInterrupt:
print("\nStopping early...")
if verbose: print()
best_seq = vector_to_dna(ga.best_individual()[1])
best_freqs = vector(best_seq)
assert Seq(best_seq).translate(genetic_code) == Seq(insert).translate(genetic_code)
return best_seq
print(iterations)
matriz_gerada = list()
i = 0
while (i < len(iterations)):
matriz_gerada.insert(i, iterations[i]['node_count'])
i += 1
# Ready CSV
arq = open("casos_sj.csv")
sirSjCsv = csv.DictReader(arq, fieldnames=["R", "I"])
sirSj = list()
sirI = list()
sirR = list()
i = 0
for row in sirSjCsv:
sirSj.insert(i, {"I": row['I'], "R": row['R']})
sirI.append(row['I'])
sirR.append(row['R'])
i += 1
print(sirSj)
data = sirSj
ga = GeneticAlgorithm(data)
print(data[1, "R"])
print(type(data))
i = np.random.randint(N)
j = np.random.randint(N)
if i != j:
adj_mat[i, j] = 0
adj_mat[j, i] = 0
return adj_mat
N = 10
adj_mat = generate_adj_mat(N)
print("Szombszédsági mátrix:")
print(adj_mat)
ga = GeneticAlgorithm(list(range(N)), 500, 200)
def create_individual(data):
return np.random.randint(0, N, len(data))
ga.create_individual = create_individual
def crossover(parent1, parent2):
c_idx = random.randrange(1, parent1.shape[0])
child_1 = np.append(parent1[:c_idx], parent2[c_idx:])
child_2 = np.append(parent2[:c_idx], parent1[c_idx:])
return child_1, child_2