def test_it_without_bp():
pop, stats = main()
stringh = "_without_bp"
fronts = tools.sortNondominated(pop, len(pop))
if len(fronts[0]) < 30:
pareto_front = fronts[0]
else:
pareto_front = random.sample(fronts[0], 30)
print("Pareto Front: ")
for i in range(len(pareto_front)):
print(pareto_front[i].fitness.values)
neter = Neterr(indim, outdim, n_hidden, random)
print("\ntest: test on one with min validation error",
neter.test_err(min(pop, key=lambda x: x.fitness.values[1])))
tup = neter.test_on_pareto_patch(pareto_front)
print("\n test: avg on sampled pareto set", tup[0], "least found avg",
tup[1])
st = str(neter.test_err(min(
pop, key=lambda x: x.fitness.values[1]))) + " " + str(
tup[0]) + " " + str(tup[1])
print(note_this_string(st, stringh))
def test_it_with_bp(play=1, NGEN=100, MU=4 * 25):
pop, stats = main(play=play, NGEN=NGEN, MU=MU)
stringh = "_with_bp_without_clustring" + str(play) + "_" + str(NGEN)
fronts = tools.sortNondominated(pop, len(pop))
if len(fronts[0]) < 30:
pareto_front = fronts[0]
else:
pareto_front = random.sample(fronts[0], 30)
print("Pareto Front: ")
for i in range(len(pareto_front)):
print(pareto_front[i].fitness.values)
neter = Neterr(indim, outdim, n_hidden, random)
print("\ntest: test on one with min validation error",
neter.test_err(min(pop, key=lambda x: x.fitness.values[1])))
tup = neter.test_on_pareto_patch_correctone(pareto_front)
print("\n test: avg on sampled pareto set", tup)
st = str(neter.test_err(min(
pop, key=lambda x: x.fitness.values[1]))) + " " + str(tup)
print(
note_this_string(st, stringh)
) ##################################################################################
from deap.benchmarks.tools import diversity, convergence
from deap import creator
from deap import tools
import os
from population import *
from network import Neterr
from chromosome import Chromosome, crossover
import traceback
n_hidden = 100
indim = 32
outdim = 5
network_obj_src = Neterr(indim,
outdim,
n_hidden,
change_to_target=0,
rng=random)
network_obj_tar = Neterr(indim,
outdim,
n_hidden,
change_to_target=2,
rng=random)
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0, -1.0))
creator.create("Individual", Chromosome, fitness=creator.FitnessMin)
print("here network object created")
toolbox = base.Toolbox()
def minimize_src(individual):
outputarr = network_obj_src.feedforward_ne(
import cluster
from deap import algorithms
from deap import base
from deap import benchmarks
from deap.benchmarks.tools import diversity, convergence
from deap import creator
from deap import tools
import os
from population import *
from network import Neterr
from chromosome import Chromosome, crossover
n_hidden = 100
indim = 8
outdim = 2
network_obj = Neterr(indim, outdim, n_hidden, random)
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, 0.0, 0.0))
creator.create("Individual", Chromosome, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
def minimize(individual):
outputarr = network_obj.feedforward_ne(individual,
final_activation=network.softmax)
neg_log_likelihood_val = give_neg_log_likelihood(outputarr,
network_obj.resty)
mean_square_error_val = give_mse(outputarr, network_obj.resty)
false_positve_rat = give_false_positive_ratio(outputarr, network_obj.resty)
false_negative_rat = give_false_negative_ratio(outputarr,