def afpo(self):
envs = ENVIRONMENTS()
print('envs to run:', c.envsToRun, ' pop init:',
c.num_rand_phc + len(self.fit_ids))
parents = POPULATION(c.num_rand_phc + len(self.fit_ids))
parents.InitializeRandomPop()
# parents = self.phc_ga(parents, envs, c.afpo2_phc_gens)
# print('\n############# phc done #############\n')
parents.Evaluate(envs, False, True)
for i in range(0, len(parents.p)):
parents.p[i].age += i
# for i in range(0, len(parents.p)):
# print('p[' + str(i) + '].age =', parents.p[i].age)
print("Initial population age adjusted")
parents.Print()
finalpop = self.af_pareto_ga(parents, envs)
frontinds = self.af_pareto_math(finalpop.GetAges(), finalpop.GetFits())
finalpopfront = POPULATION(len(frontinds))
for i in range(0, len(frontinds)):
finalpopfront.p[i] = finalpop.p[frontinds[i]]
finalpopfront.Pickling618()
self.ShowEvaluatePop(finalpopfront, envs, pp=True, pb=False)
def main(self):
t_end = time.time() + 60 * c.minutes
envs = ENVIRONMENTS()
print('envs to run:', c.envsToRun, ' initial popsize:', c.num_rand_phc)
parents = POPULATION(c.num_rand_phc + len(self.fit_ids))
parents.InitializeRandomPop()
parents.Evaluate(envs, pp=False, pb=True)
parents.Print()
if c.alg == 'afpo2':
self.afpo()
elif c.alg == 'garef':
parents = self.garef(c.numGensDivPop, envs, parents, t_end)
self.ShowEvaluatePop(parents, envs)
def af_pareto_spop(self, front, envs):
target_popsize = 20
pcc = POPULATION(target_popsize)
single = POPULATION(1)
single.InitializeRandomPop()
single.Evaluate(envs, False, True)
num_remain = target_popsize - (len(front.p) + 1)
if num_remain > 0:
pccr = POPULATION(num_remain)
for i in range(0, num_remain):
pccr.p[i] = copy.deepcopy(pcc.p[i % len(front.p)])
pccr.Mutate()
pccr.Evaluate()
for i in range(0, target_popsize):
if i == 0:
pcc.p[i] = single.p[0]
elif i < len(front.p) + 1:
pcc.p[i] = front.p[i - 1]
else:
pcc.p[i] = pccr[i - (len(front) + 1)]
ages = pcc.GetAges()
fits = pcc.GetFits()
print('fits', fits, ' ages', ages)
fits_inv = []
for i in range(0, len(fits)):
if fits[i] != 0:
fits_inv.append(fits[i]**-1)
else:
fits_inv.append(10**6)
dominant_inds = np.array(self.af_pareto_math(ages, fits_inv))
new_pop = POPULATION(len(dominant_inds))
for i in range(0, target_popsize):
dominant_inds_index = i % len(dominant_inds)
new_pop.p[i] = copy.deepcopy(pcc.p[dominant_inds_index])
if i >= len(dominant_inds):
new_pop.p[i].Mutate()
del pcc
new_pop.Print()
return new_pop
if k < num_rand:
first = np.random.random_sample((c.numInputs, c.numHidden)) * 2 - 1
second = np.random.random_sample((c.numHidden, c.numOutputs)) * 2 - 1
genome = [first, second]
else:
filename = 'genome' + str(fit_labels[0])
del fit_labels[0]
pathLoad = c.pathLoad
filename = pathLoad + filename
g = open(filename, 'r')
genome = pickle.load(g)
g.close()
# parents.InitializeUniformPop(genome)
parents.p[k] = INDIVIDUAL(k, genome, 0)
parents.Evaluate(envs, pp=False, pb=True)
parents.Print()
for i in range(0, c.numGensDivPop):
# the POPULATION constructor creates an empty dictionary and stores the variable popSize
children = POPULATION(c.popSize)
# children.Print('bm')
print()
children.FillFrom(parents)
print('seconds remaining', t_end - time.time())
# print('\n0', end=' ')
parents.Print()
children.Evaluate(envs, pp=False, pb=True)
# print(i, end=' ')
children.Print(i)
children.PrintDists(i)
pathLoad = c.pathLoad
filename = pathLoad + filename
g = open(filename, 'r')
genome = pickle.load(g)
g.close()
# first = np.random.random_sample((c.numInputs, c.numHidden)) * 2 - 1
# second = np.random.random_sample((c.numHidden, c.numOutputs)) * 2 - 1
# genome = [first, second]
envs = ENVIRONMENTS()
parents = POPULATION(1, -1)
parents.InitializeUniformPop(genome)
parents.Evaluate(envs, pp=False, pb=True)
parents.Print()
# # i = 0
# # t_end = time.time() + 60 * c.minutes
# for i in range(0, 1):
# # while time.time() < t_end:
# # the POPULATION constructor creates an empty dictionary and stores the variable popSize
# children = POPULATION(c.popSizeGA1, i)
# children.Print()
# children.FillFrom(parents)
#
# # print('seconds remaining', t_end - time.time())
#
# print('\n0', end=' ')
# parents.Print()
# children.Evaluate(envs, pp=False, pb=True)
from pop import POPULATION
from afpo5t2 import GENETIC_ALGORITHM
import const as c
import time
import pyrosim
import matplotlib.pyplot as plt
import numpy as np
import random
import copy
import pickle
import os
alg = GENETIC_ALGORITHM()
mode = 't'
mode = 'r'
if mode == 't':
envs = ENVIRONMENTS()
pop = POPULATION(10)
pop.InitializeRandomPop()
pop.Evaluate(envs, False, True)
for i in range(0, len(pop.p)):
pop.p[i].age += i
pop.Print()
frontpop = alg.gen_par_front_pop(pop, envs)
frontpop.Print()
else:
# alg.main()
alg.combo_simple_phc_ga()
filename = 'genome' + str(fit_labels[0])
del fit_labels[0]
pathLoad = c.pathLoad
filename = pathLoad + filename
g = open(filename, 'r')
genome = pickle.load(g)
g.close()
parentsF.p[k] = INDIVIDUAL(k, genome)
for e in envs.envs:
parentsF.p[k].Start_Evaluation(envs.envs[e],
pp=False,
pb=True,
send=True)
parentsF.Evaluate(envs, pp=False, pb=True)
parentsF.Print('population size', k)
print('################ Begin parallel hill climber ######################')
while time.time() < t_end:
# print('############# i', i)
print('seconds remaining', t_end - time.time())
print('\nF', end='')
parentsF.Print()
childrenF = copy.deepcopy(parentsF)
childrenF.Mutate()
childrenF.Evaluate(envs, pp=False, pb=True)
print('F', end='')
childrenF.Print(i)
parentsF.ReplaceWith(childrenF)
print('W', end='')
if k < num_rand:
first = np.random.random_sample((c.numInputs, c.numHidden)) * 2 - 1
second = np.random.random_sample((c.numHidden, c.numOutputs)) * 2 - 1
genome = [first, second]
else:
filename = 'genome' + str(fit_labels[0])
del fit_labels[0]
pathLoad = c.pathLoad
filename = pathLoad + filename
g = open(filename, 'r')
genome = pickle.load(g)
g.close()
parents.InitializeUniformPop(genome)
parents.Evaluate(envs, pp=False, pb=True)
parents.Print(-1)
dists = []
fits = []
for i in range(0, c.numGensDivPop):
# the POPULATION constructor creates an empty dictionary and stores the variable popSize
children = POPULATION(c.popSize, i)
# children.Print('bm')
print()
children.FillFrom(parents)
print('seconds remaining', t_end - time.time())
# print('\n0', end=' ')
parents.Print()
children.Evaluate(envs, pp=False, pb=True)
# print(i, end=' ')