Ejemplos de load_theo en Python

Ejemplo n.º 1

from multilayer_perceptron import MLPSurrogate
from ea import *
from benchmarks import zdt2, load_theo
import matplotlib.pyplot as plt
from matplotlib import rc


# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

# Load theoritical values
theo = load_theo('./ZDT/ZDT2.pf')


# ZDT2
PM_FUN = zdt2
DIMENSION = 30
POP_SIZE = 64
MAX_GENERATION = 25
MAX_EPISODE = 30
MUTATION_RATE = 0.08
MUTATION_U = 0.
MUTATION_ST = 0.2
REF=[1., 1.]


pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,

Ejemplo n.º 2

Archivo: test_kursawe_kriging.py Proyecto: NikoZHAI/samolib

from premade import GOMORS
from surrogates import heuristic_optimizer
import matplotlib.pyplot as plt
from matplotlib import rc

from sklearn.gaussian_process.kernels import RBF, WhiteKernel as W,\
                                             ConstantKernel as C

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)


theo = load_theo('pareto_Kursawe.txt').as_matrix()


# Kursawe
PM_FUN = kursawe
MINIMIZE = True
DIMENSION = 3
N_OBJS = 2
POP_SIZE = 64
MAX_GENERATION = 40
MAX_EPISODE = 60
STOPPING_RULE = 'max_eval'
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.3
REF = [-14., 0.1]

Ejemplo n.º 3

Archivo: test_zdt1_rbfn.py Proyecto: NikoZHAI/samolib

from _utils import gaussian_mutator, nsga_crossover, random_crossover
from premade import GOMORS
import matplotlib.pyplot as plt
from matplotlib import rc

from pySOT.kernels import CubicKernel
from pySOT.tails import LinearTail

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

# Load theoritical values
theo = load_theo('./ZDT/ZDT1.pf').as_matrix()

# ZDT1
PM_FUN = zdt1
MINIMIZE = True
DIMENSION = 30
POP_SIZE = 64
N_OBJS = 2
MAX_GENERATION = 30
MAX_EPISODE = 120
STOPPING_RULE = 'max_eval'
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]

Ejemplo n.º 4

from multilayer_perceptron import MLPSurrogate
# from multioutput import MultiOutputRegressor
from _utils import *
from ea import Population
from benchmarks import load_theo, kursawe
import matplotlib.pyplot as plt
from matplotlib import rc

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

theo = load_theo('pareto_Kursawe.txt')

# Kursawe
PM_FUN = kursawe
DIMENSION = 3
POP_SIZE = 64
MAX_GENERATION = 25
MAX_EPISODE = 30
MUTATION_RATE = 0.07
MUTATION_U = 0.
MUTATION_ST = 0.3
REF = [-14., 1.]

pop = Population(dim=DIMENSION,
                 size=POP_SIZE,
                 fitness_fun=PM_FUN,

Ejemplo n.º 5

Archivo: surrogate_zdt1.py Proyecto: NikoZHAI/meta_model

import numpy as np
import pandas as pd
from multilayer_perceptron import MLPSurrogate
from neuro_surrogate import *
from benchmarks import zdt1, load_theo
import matplotlib.pyplot as plt
from matplotlib import rc

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

# Load theoritical values
theo = load_theo('./ZDT/ZDT1.pf')

# ZDT1
PM_FUN = zdt1
DIMENSION = 30
POP_SIZE = 64
MAX_GENERATION = 25
MAX_EPISODE = 30
MUTATION_RATE = 0.08
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]

pop = Population(dim=DIMENSION,
                 size=POP_SIZE,
                 fitness_fun=PM_FUN,

Ejemplo n.º 6

Archivo: surrogate_zdt6.py Proyecto: NikoZHAI/samolib

sys.path.append('..')

from multilayer_perceptron import MLPSurrogate
# from multioutput import MultiOutputRegressor
from ea import *
from benchmarks import load_theo, zdt6
import matplotlib.pyplot as plt
from matplotlib import rc

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

theo = load_theo('./ZDT/ZDT6.pf')

# ZDT6
PM_FUN = zdt6
DIMENSION = 10
POP_SIZE = 64
MAX_GENERATION = 25
MAX_EPISODE = 30
MUTATION_RATE = 0.08
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [1., 1.]

pop = Population(dim=DIMENSION,
                 size=POP_SIZE,
                 fitness_fun=PM_FUN,

Ejemplo n.º 7

Archivo: surrogate_zdt3.py Proyecto: NikoZHAI/meta_model

import numpy as np
import pandas as pd
from multilayer_perceptron import MLPSurrogate
from neuro_surrogate import *
from benchmarks import zdt3, load_theo
import matplotlib.pyplot as plt
from matplotlib import rc

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

# Load theoritical values
theo = load_theo('./ZDT/ZDT3.pf')

# ZDT3
PM_FUN = zdt3
DIMENSION = 30
POP_SIZE = 64
MAX_GENERATION = 25
MAX_EPISODE = 30
MUTATION_RATE = 0.08
MUTATION_U = 0.
MUTATION_ST = 0.2
REF = [0.9, 1.]

pop = Population(dim=DIMENSION,
                 size=POP_SIZE,
                 fitness_fun=PM_FUN,

Ejemplo n.º 8

Archivo: surrogate_zdt4.py Proyecto: NikoZHAI/meta_model

import numpy as np
import pandas as pd
from multilayer_perceptron import MLPSurrogate
from neuro_surrogate import *
from benchmarks import zdt4, load_theo
import matplotlib.pyplot as plt
from matplotlib import rc

# Matplotlib
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
# for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)

# Load theoritical values
theo = load_theo('./ZDT/ZDT4.pf')

# ZDT4
PM_FUN = zdt4
DIMENSION = 10
POP_SIZE = 32
MAX_GENERATION = 25
MAX_EPISODE = 128
MUTATION_RATE = 0.1
MUTATION_U = 0.
MUTATION_ST = 0.02
REF = [1., 1.]

pop = Population(dim=DIMENSION,
                 size=POP_SIZE,
                 fitness_fun=PM_FUN,