def fitness_calc_pole(self,
net,
cart,
s_len,
src_models,
target_model,
sample_size,
mutation_strength,
samples_count,
solution_found=None):
start = time()
if not all(self.genes == self.genes[0]):
termination_mask = self.genes > (1 / (len(src_models) + 1) *
0.01) * 1.0
genes = termination_mask * self.genes
genes = genes / np.sum(genes)
print('genes after normalization: ', genes)
else:
genes = self.genes
print('first iteration!? not neutralized')
# Initializing the weights of the mixture model with
mixModel = MixtureModel(src_models, alpha=genes)
mixModel.add_target_model(target_model)
offsprings, mutation_strength, samples_count, fitness_mean, eval_num = \
mixModel.sample_enhanced(sample_size, cart, mutation_strength,
samples_count, net=net, s_len=s_len, mutation=False,
solution_found=solution_found, problem_type='pole')
self.fitness = fitness_mean
self.fitness_calc_time = time() - start
print('self.fitness_calc_time (m): ', self.fitness_calc_time / 60)
# best_offspring = np.max(offsprings)
return self.fitness, offsprings, mutation_strength, samples_count, eval_num
def fitness_calc_enhanced(self,
problem,
src_models,
target_model,
sample_size,
mutation_strength,
samples_count,
max_sampling_num=None,
mutation=True,
problem_type='knapsack'):
start = time()
if (not all(self.genes == self.genes[0])):
termination_mask = self.genes > (1 / (len(src_models) + 1) *
0.01) * 1.0 # BUG
genes = termination_mask * self.genes
genes = genes / np.sum(genes)
else:
genes = self.genes
print('first iteration!? not neutralized')
# Initializing the weights of the mixture model with
mixModel = MixtureModel(src_models, alpha=genes)
mixModel.add_target_model(target_model)
offsprings, mutation_strength, samples_count, fitness_mean = \
mixModel.sample_enhanced(sample_size, problem, mutation_strength,
samples_count, max_sampling_num, mutation=mutation,
problem_type=problem_type)
self.fitness = fitness_mean
self.fitness_calc_time = time() - start
return self.fitness, offsprings, mutation_strength, samples_count