def main(self):
self.af_pareto_math(self.xsd, self.ysd, self.ymax)
print('\n############# test graph done #############\n')
num_rand = c.num_rand_phc
envs = ENVIRONMENTS()
print('envs to run:', c.envsToRun, ' pop init:',
num_rand + len(self.fit_ids))
parents = POPULATION(c.num_rand_phc + len(self.fit_ids))
parents.InitializeRandomPop()
parents = self.phc_ga(parents, envs, 1)
print('\n############# phc done #############\n')
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)
# 3rd arg gens=2 by default
parents = self.af_pareto_ga(parents, envs)
# print('parents fits:')
# parents.Print()
for j in range(0, len(parents.p)):
raw_input('press enter to continue...')
for e in c.envsToRun:
# for j in range(0, 1):
parents.p[j].Start_Evaluation(envs.envs[e],
pp=True,
pb=False,
send=True)
def simple_ga(self, envs):
def inner(parents, envs, i):
# children = POPULATION(len(parents.p))
children = copy.deepcopy(parents)
children.FillFrom(parents)
children.Evaluate(envs, False, True)
print("\n*** Generation", i, "***")
print("\n*** Parents ***")
parents.Print()
print("*** Children ***")
children.Print()
print()
parents = children
return parents
envs = ENVIRONMENTS()
parents = POPULATION(c.popsize_simple_ga)
print('envs:', c.envsToRun, ' pop init:', c.popsize_simple_ga)
parents.InitializeRandomPop()
parents.Evaluate(envs, False, True)
# if c.gen_mode == 'time':
# t_end = time.time() + 60 * c.minutes
# i = 0
# while(time.time() < t_end):
# parents = inner(parents, envs, i)
# i += 1
# elif c.gen_mode == 'gens':
# for i in range(0, c.gens):
# parents = inner(parents, envs, i)
for i in range(0, c.numGensDivPop):
parents = inner(parents, envs, i)
return parents.p[0]
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):
envs = ENVIRONMENTS()
parents = POPULATION(c.num_rand_phc + len(self.fit_ids))
print('envs:', c.envsToRun, ' pop init:',
c.num_rand_phc + len(self.fit_ids))
parents.InitializeRandomPop()
parents.Evaluate(envs, False, True)
for i in range(0, len(parents.p)):
parents.p[i].age += i
self.afpo(parents, envs)
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 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')
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)
# 3rd arg gens=2 by default
parents = self.af_pareto_ga(parents, envs, c.afpo2_afpo_gens)
parents.Pickling618()
def combo_simple_phc_ga(self):
envs = ENVIRONMENTS()
# parents = POPULATION(c.num_rand_phc)
# print('envs:', c.envsToRun, ' pop init:', c.num_rand_phc)
# parents.InitializeRandomPop()
# parents.Evaluate(envs, False, True)
pop_phc = POPULATION(c.combo_ga_num_slots)
for k in range(0, c.combo_ga_num_slots):
print("***** Slot number", k, "*****")
pop_phc.p[k] = self.simple_ga(envs)
pop_phc.p[k].ID = k
pop_phc = self.phc_ga(pop_phc, envs)
pop_phc.Pickling618()
self.ShowEvaluatePop(pop_phc, envs, True, False)
def main(self):
num_rand = c.num_rand
envs = ENVIRONMENTS()
print('envs to run', c.envsToRun)
parents = POPULATION(c.num_rand + len(self.fit_ids), 0)
parents.InitializeRandomPop()
for ind in range(0, num_rand + len(self.fit_ids)):
print('ind', ind)
self.af_pareto(self.xs, self.ys, self.popsize, self.ymax)
parents = self.phc_ga(parents, envs, 3) # like phc
for j in range(0, len(parents.p)):
raw_input('press enter to continue...')
for e in c.envsToRun:
# for j in range(0, 1):
parents.p[j].Start_Evaluation(envs.envs[e],
pp=True,
pb=False,
send=True)
import random
import copy
import pickle
### new
# parents0 = POPULATION(100)
# parents0.Initialize()
# parents0.Evaluate(envs, pp=False, pb=True)
# parents0.Print()
# print('********************* done')
### new end
i = 0
t_end = time.time() + 60 * c.minutes
envs = ENVIRONMENTS()
parentsF = POPULATION(len(c.fit_labels))
fit_labels = copy.deepcopy(c.fit_labels)
num_rand = c.num_rand_phc
print('fit labels', fit_labels)
print('envs to run', c.envsToRun)
for k in range(0, len(c.fit_labels) + num_rand):
print('**********************\nindividual num', k,
'begin\n**********************')
parents = POPULATION(c.popSize)
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]