Пример #1
0
from numpy.random import seed, rand, randn, randint
from numpy import array, arange, asfarray, exp, pi, cos, zeros, where, linspace
import matplotlib as mpl
import matplotlib.pyplot as plt
from peabox_individual import Individual
from peabox_population import Population
from peabox_testfuncs import CEC05_test_function_producer as tfp
from peabox_helpers import parentselect_exp as pse
from peabox_helpers import simple_searchspace

#-------------------------------------------------------------------------------
#--- part 1: Ingredients -------------------------------------------------------
#-------------------------------------------------------------------------------

dim = 10
f11 = tfp(11, dim)  # Weierstrass function in 10 dimensions

searchspace = simple_searchspace(10, -3., 1.)

ps = 40  # population size
G = 80  # number of generations to go through
parents = Population(Individual, ps, f11, searchspace)
offspring = Population(Individual, ps, f11, searchspace)

#-------------------------------------------------------------------------------
#--- part 2: initialisation ----------------------------------------------------
#-------------------------------------------------------------------------------

seed(
    1
)  # seeding numpy's random number generator so we get reproducibly the same random numbers
Пример #2
0
        self.traj=[]
        self.trajqual=[]
    def memorize(self):
        self.traj.append(self.get_copy_of_DNA())
        self.trajqual.append(self.score)
    def do_step(self):
        PSO_Individ.do_step(self)
        self.memorize()
    def get_trajectory_components(self,dims):
        output=[]
        for d in dims:
            output.append([self.traj[i][d] for i in range(len(self.traj))])
        return output

dim=2
f1=tfp(1,dim)   # Weierstrass function in 2 dimensions
f11=tfp(11,dim)   # Weierstrass function in 2 dimensions
searchspace=simple_searchspace(dim,-3.,1.)
p=Pswarm(Memorizing_PSO_Individ,12,f1,searchspace,[3,4],1)
p.random_ini()
#p.alpha=0.95


if True:
    dude=p[0]
    #dude.random_influence='1D'
    dude.add_attractor(ones(dim),1.)
    dude.speed[:]=1,0
    
    for i in range(20):
        dude.do_step()
Пример #3
0
        self.sa_beststeps=[]
        self.ga_beststeps=[]


#-------------------------------------------------------------------------------
#--- intermezzo: the colormap used in peabox_plotting.ancestryplot -------------
#-------------------------------------------------------------------------------

show_these_colormaps([ancestcolors],'colormap_used_for_ancestry_coloring.png')

#-------------------------------------------------------------------------------
#--- part 1: Ingredients -------------------------------------------------------
#-------------------------------------------------------------------------------

dim=10
f11=tfp(11,dim)   # Weierstrass function in 10 dimensions

searchspace=simple_searchspace(10,-3.,1.)

ps=80    # population size
G=120     # number of generations to go through
parents=SAGA_Population(Individual,ps,f11,searchspace); parents.objname='CEC05 f11'
offspring=SAGA_Population(Individual,ps,f11,searchspace); offspring.objname='CEC05 f11'

parents.ncase=1   # let this be case 1, plots and pickles will contain the number
offspring.ncase=1

rec=Recorder(parents) # creating this Recorder instance to collect survey data on parent population
rec.snames.append('sa_improverate') # appending a scalar property's name to the survey list
rec.snames.append('sa_toleraterate')
rec.snames.append('ga_improverate')
Пример #4
0
        self.traj.append(self.get_copy_of_DNA())
        self.trajqual.append(self.score)

    def do_step(self):
        PSO_Individ.do_step(self)
        self.memorize()

    def get_trajectory_components(self, dims):
        output = []
        for d in dims:
            output.append([self.traj[i][d] for i in range(len(self.traj))])
        return output


dim = 2
f1 = tfp(1, dim)  # Weierstrass function in 2 dimensions
f11 = tfp(11, dim)  # Weierstrass function in 2 dimensions
searchspace = simple_searchspace(dim, -3., 1.)
p = Pswarm(Memorizing_PSO_Individ, 12, f1, searchspace, [3, 4], 1)
p.random_ini()
#p.alpha=0.95

if True:
    dude = p[0]
    #dude.random_influence='1D'
    dude.add_attractor(ones(dim), 1.)
    dude.speed[:] = 1, 0

    for i in range(20):
        dude.do_step()