from copy import deepcopy
from environments import ENVIRONMENTS
from geneticAlgorithm import population as p
import constants as C
envs = ENVIRONMENTS()
parents = p.POPULATION(10)
parents.evaluate(True)
parents.print(' 0')
for g in range(C.numGens):
children = p.POPULATION(initialize=False)
children.fill_from(parents)
children.evaluate(True)
children.print('%3i' % (g + 1))
parents = children
parents.evaluate(best=True)
import random
import copy
import logging
logging.basicConfig(level=logging.INFO)
import pickle
import pyrosim
import matplotlib.pyplot as plt
import numpy as np
from robot import ROBOT
from individual import INDIVIDUAL
from population import POPULATION
import constants
from environments import ENVIRONMENTS
# create environment with "light source"
envs = ENVIRONMENTS(numEnvs=constants.numEnvs, eval_time=constants.eval_time)
# create initial population
parents = POPULATION(envs,
popSize=constants.popSize,
eval_time=constants.eval_time)
parents.initialize()
parents.evaluate(play_blind=True, play_paused=False)
print(0, parents)
# evolve:
for g in range(constants.numGen):
children = POPULATION(envs, parents=parents)
children.fillFrom(parents)
children.evaluate(play_blind=True, play_paused=False)
parents.replaceWith(children)
from population import POPULATION
import constants as c
from environments import ENVIRONMENTS
import pickle
import csv
import os
import glob
finalData = []
for n in range(0, c.sampleSize):
envs = ENVIRONMENTS()
envs.Initialize()
parents = POPULATION(c.popSize)
parents.Initialize()
parents.Evaluate(envs, pp=False, pb=True, dt=0.05)
parents.Print()
data = []
# for loop that creates n iterations
for g in range(1, c.numGens):
# create the child population
children = POPULATION(c.popSize)
# fill the children population from the parents keeping best in first place and
# filling the rest with mutations
children.Fill_From(parents)
# evaluate the children
children.Evaluate(envs, pp=False, pb=True)
from environments import ENVIRONMENTS
from swarm import SWARM
import constants as c
import pickle
envs = ENVIRONMENTS(c.numEnvs)
parents = pickle.load(open("save.txt", "rb"))
parents.evaluateSwarms(envs, pp=True, pb=False)
def initialize_population(numSpecies, speciesSize, mutationRate, evalTime, numEnvs): parents = SWARM(numSpecies, speciesSize, mutationRate, evalTime, numEnvs) parents.Initialize() envs = ENVIRONMENTS(numEnvs) parents.evaluateSwarms(envs, pp=True, pb=True) return (parents)
if play_result:
parents.evaluate(envs, best=True)
if store_fitness is not None:
parents.p[0].print(to=store_fitness)
def test_loop(pop_range: list, t_fraction: float, m_fraction: float):
with open('data_out3.csv', 'w') as output:
output.write('pop_size,t_size,m_rate,score')
for pop_size in range(*pop_range):
for t_size in range(1,
int(t_fraction * pop_size) + 1,
int(pop_size / 10)):
C.set_cons('t_size', t_size)
for m_rate in range(1, int(m_fraction * 72) + 1, int(72 / 10)):
C.set_cons('m_rate', m_rate)
output.write(f'\n{pop_size},{t_size},{m_rate}')
genetic_algorithm(
pop_size, f'playbacks2/p{pop_size}t{t_size}m{m_rate}',
False, output)
envs = ENVIRONMENTS() if C.numEnvs > 0 else None
# test_loop([25, 26, 5], 0.7, 0.5)
genetic_algorithm()