def Funcao(self, dimensao, execucao):
sc = movingpeaks.SCENARIO_2
sc["npeaks"] = 30
sc["period"] = 5000 #500, 1000, 2500, 5000, 10000
sc["height_severity"] = 7
sc["width_severity"] = 1
sc["move_severity"] = 1 #1,2,3,5
sc["lambda_"] = 0
sc["max_coord"] = 100
sc["min_coord"] = 0
sc["max_height"] = 70
sc["min_height"] = 30
sc["max_width"] = 12
sc["min_width"] = 1
rnd = random.Random()
rnd.seed(execucao)
mp = movingpeaks.MovingPeaks(dim=self.numero_dimensoes,
random=rnd,
**sc)
fitness = mp.__call__(dimensao)
melhor = mp.globalMaximum()
resultado = fitness[0] - melhor[0]
return np.abs(resultado)
import operator
import random
import numpy
from deap import base
from deap.benchmarks import movingpeaks
from deap import creator
from deap import tools
scenario = movingpeaks.SCENARIO_2
NDIM = 5
BOUNDS = [scenario["min_coord"], scenario["max_coord"]]
mpb = movingpeaks.MovingPeaks(dim=NDIM, **scenario)
def brown_ind(iclass, best, sigma):
return iclass(random.gauss(x, sigma) for x in best)
creator.create("FitnessMax", base.Fitness, weights=(1.0, ))
creator.create("Individual",
array.array,
typecode='d',
fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_float", random.uniform, BOUNDS[0], BOUNDS[1])
toolbox.register("individual", tools.initRepeat, creator.Individual,
import numpy as np
except:
exit()
import random
rnd = random.Random()
rnd.seed(128)
from deap.benchmarks import movingpeaks
sc = movingpeaks.SCENARIO_1
sc["uniform_height"] = 0
sc["uniform_width"] = 0
mp = movingpeaks.MovingPeaks(dim=2, random=rnd, **sc)
fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(0, 100, 1.0)
Y = np.arange(0, 100, 1.0)
X, Y = np.meshgrid(X, Y)
Z = np.fromiter([mp(x)[0] for x in zip(X.flat, Y.flat)],
dtype=np.float,
count=X.shape[0] * X.shape[1]).reshape(X.shape)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2)
plt.xlabel("x")
plt.ylabel("y")