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main_single_knapsack_transfer.py
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main_single_knapsack_transfer.py
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import numpy as np
import argparse
import os
from copy import deepcopy
from time import time
from pprint import pprint
from utils.data_manipulators import *
from evolution.operators import *
from to.probabilistic_model import ProbabilisticModel
from to.mixture_model import MixtureModel
from evolution.chromosome import *
from utils.ea import evolutionary_algorithm
def transfer_ea(problem,
dims,
reps,
trans,
psize=50,
gen=100,
src_models=[],
time_limits=None,
sample_size=None):
if time_limits is not None:
assert len(
time_limits
) == reps, "time_limits length does not match the repetition numbers"
else:
time_limits = [float('inf')] * reps
if sample_size is None:
sample_size = psize
if trans['transfer'] and (not src_models):
raise ValueError(
'No probabilistic models stored for transfer optimization.')
init_func = lambda n: np.round(np.random.rand(n))
fitness_hist = np.zeros([reps, gen, psize])
fitness_time = np.zeros((
reps,
gen,
))
alpha = list()
time_passed = 0
for rep in range(reps):
print('-------------------- rep: {} -------------------'.format(rep))
alpha_rep = []
start = time()
pop = get_pop_init(psize, dims, init_func)
for i in range(psize):
pop[i].fitness_calc(problem)
bestfitness = np.max(pop).fitness
fitness = Chromosome.fitness_to_numpy(pop)
fitness_hist[rep, 0, :] = fitness
fitness_time[rep, 0] = time() - start
time_passed = fitness_time[rep, 0]
print('Generation 0 best fitness = %f' % bestfitness)
for i in range(1, gen):
start = time()
if trans['transfer'] and i % trans['delta'] == 0:
mixModel = MixtureModel(src_models)
mixModel.createTable(Chromosome.genes_to_numpy(pop), True,
'umd')
mixModel.EMstacking()
alpha_rep.append(mixModel.alpha)
mixModel.mutate()
offsprings = mixModel.sample(sample_size)
offsprings = np.array(
[Chromosome(offspring) for offspring in offsprings])
print('Mixture coefficients: %s' % np.array(mixModel.alpha))
else:
# Crossover & Mutation
offsprings = total_crossover(pop)
for j in range(psize):
offsprings[j].mutation(1 / dims)
# Fitness Calculation
cfitness = np.zeros(psize)
for j in range(psize):
cfitness[j] = offsprings[j].fitness_calc(problem)
# Selection
pop, fitness = total_selection(np.concatenate((pop, offsprings)),
np.concatenate((fitness, cfitness)),
psize)
bestfitness = fitness[0]
fitness_hist[rep, i, :] = fitness
fitness_time[rep, i] = time() - start
print('Generation %d best fitness = %f' % (i, bestfitness))
time_passed += fitness_time[rep, i]
if time_limits[rep] < time_passed:
break
alpha.append(alpha_rep)
return fitness_hist, alpha, fitness_time
def get_args():
pass
def check_args(args):
pass
def main(args=False):
################# Preconfigurations ##############
if args is False:
args = get_args()
check_args(args)
models_path = 'models'
source_models_path = os.path.join(models_path, 'knapsack_source_models')
knapsack_problem_path = 'problems/knapsack'
src_models = None
gen = args.gen
psize = args.psize
src_models, target_problem = source_generator(args.src_version,
knapsack_problem_path,
source_models_path,
args.buildmodel,
args.stop_condition)
# AMTEA solving KP_wc_ak
reps = args.reps
trans = {}
trans['transfer'] = args.transfer
trans['delta'] = args.delta
if args.version == 'ea_time_scale':
ea_fitness_hist = np.zeros((reps, gen, psize))
ea_fitness_time = np.zeros((reps, gen))
for i in range(reps):
_, _, ea_fitness_hist[i, ...], ea_fitness_time[i, ...] = \
evolutionary_algorithm(target_problem, 1000, src_models=src_models,
gen=gen, psize=psize, stop_condition=False,
create_model=False)
return ea_fitness_hist, ea_fitness_time
elif args.version == 'to':
return transfer_ea(target_problem,
1000,
reps,
trans,
psize=psize,
gen=gen,
src_models=src_models,
time_limits=args.time_limits,
sample_size=args.sample_size)
elif args.version == 'ea':
return evolutionary_algorithm(target_problem,
1000,
src_models=src_models,
stop_condition=args.stop_condition)
else:
raise ValueError('Version which you entered is not right')
if __name__ == '__main__':
main()