class GENEALG:
def __init__(self, play_back=False):
if play_back:
f = open('robot.p', 'rb')
self.parent = pickle.load(f)
f.close()
else:
self.parent = POPULATION(c.popSize)
self.parent.Initialize()
self.parent.Evaluate()
def Evolve(self):
for g in range(c.numGens):
print(g, end=' ')
self.parent.Print()
child = POPULATION(c.popSize)
child.Fill_From(self.parent)
child.Evaluate()
self.parent.ReplaceWith(child)
def Show_Best(self):
bestIndividual = self.parent.Best_Individual()
self.parent.p[bestIndividual].Start_Evaluation(pb=False, pp=True)
self.parent.p[bestIndividual].Compute_Fitness()
print(self.parent.p[bestIndividual].fitness)
def Save(self):
f = open('robot.p', 'wb')
pickle.dump(self.parent, f)
f.close()
from population import POPULATION
import copy
parents = POPULATION(10)
parents.Initialize() # 09
parents.Evaluate(pp=False, pb=True)
parents.Print()
for g in range(1, 200):
children = copy.deepcopy(parents)
children.Mutate()
children.Evaluate(pp=False, pb=True)
parents.ReplaceWith(children)
#parents.Sort()
print g,
parents.Print()
parents.Evaluate(pp=True, pb=False)
import pyrosim
import matplotlib.pyplot as plt
import random
import numpy
import copy
import pickle
from individual import INDIVIDUAL
from robot import ROBOT
from population import POPULATION
parents = POPULATION(
10) # creates a empty population but stores the population size
parents.Initialize() #start a population of individuals of size 'i'
parents.Evaluate(True) # Evaluates the fitness of each individual
#exit() # exit the program immediately
parents.Print() #print ID and fitness of each individual
for i in range(1, 200):
children = POPULATION(5)
#children.Print()
children.Fill_From(parents)
#children.Print()
children.Evaluate(True)
children.Print()
parents = children
parents.Evaluate(False)
parents.Print()
from archive import Archive
envs = PLATFORMS()
# f = open('saved_files/bestJumpEver2019-04-27.p', 'rb')
# f = open('saved_files/4hidden_500gen_apfo_thruwall2019-05-02.p', 'rb')
# f = open('saved_files/2019-05-02.p', 'rb')
# f = open('saved_files/samplesave2019-05-01.p', 'rb')
# f = open('saved_files/4hidden_6legs_apfo_500gen_ind.p', 'rb')
f = open('saved_files/1env_500gens_genetic_6legs_4hidden_trial32019-05-05.p',
'rb')
best_genome = pickle.load(f)
f.close()
best_pop = POPULATION(1)
if isinstance(best_genome, Archive):
best_pop.Initialize(best_genome.genome, best_genome.cppn)
elif isinstance(best_genome, list):
best_pop.Initialize(best_genome)
else:
print("Could not determine class")
exit()
# best_pop.Initialize(best_genome)
best_pop.Evaluate_Best(envs, pb=False, pp=True, retain_data=True)
recorder = Recorder()
recorder.plot_touch_values(best_pop.pop[0])
newSave = input("New Save? (y/n)")
if (newSave == 'y'):
label = input("New Label: ") + '.p'
# ENVIRONMENTS #
################
envs_1 = ENVIRONMENTS_1()
envs_2 = ENVIRONMENTS_2()
envs_3 = ENVIRONMENTS_3()
###########
# PARENTS #
###########
#---NEW POPULATION---#
if (c.newMode):
# initialize parents
parents_1 = POPULATION(c.popSize)
parents_1.Initialize()
#--------------------#
parents_2 = POPULATION(c.popSize)
parents_2.Initialize()
#--------------------#
parents_3 = POPULATION(c.popSize)
parents_3.Initialize()
#---LOAD POPULATION---#
if (c.loadMode):
# load in parents
parents_1 = pickle.load(file_1)
parents_2 = pickle.load(file_2)
parents_3 = pickle.load(file_3)
# close files
file_1.close()
import pickle
import constants as c
from population import POPULATION
# import matplotlib.pyplot as plt
parent = POPULATION(c.popSize)
parent.Initialize()
parent.Evaluate(True)
for g in range(c.numGens):
print(g, end=' ')
parent.Print()
child = POPULATION(c.popSize)
child.Fill_From(parent)
child.Evaluate()
parent.ReplaceWith(child)
bestIndividual = parent.Best_Individual()
parent.p[bestIndividual].Start_Evaluation(pb=False, pp=True)
parent.p[bestIndividual].Compute_Fitness()
f = open('robot.p', 'wb')
pickle.dump(parent, f)
f.close()
import pickle
from population import POPULATION
from platforms import PLATFORMS
import constants as c
import datetime
import time
from recorder import Recorder
import os
import pprint
from archive import Archive
import matplotlib.pyplot as plt
envs = PLATFORMS()
parents = POPULATION(c.popSize)
parents.Initialize()
parents.Evaluate(envs, pb=True, pp=False)
while not parents.pop[0].test_pass:
print("test failure")
parents = POPULATION(c.popSize)
parents.Initialize()
parents.Evaluate(envs, pb=True, pp=False)
parents.Print()
startTimes = []
copyTimes = []
evalTimes = []
generations = []
fillTimes = []
entire_fill = []
pop_size = c.popSize
