def __init__(self,
num_samples,
population_size,
topology,
train_data,
test_data,
directory,
problem_type='classification',
max_limit=2,
min_limit=-2):
self.num_samples = num_samples
self.pop_size = population_size
self.topology = topology
self.train_data = train_data
self.test_data = test_data
self.problem_type = problem_type
self.directory = directory
self.w_size = (topology[0] * topology[1]) + (
topology[1] * topology[2]) + topology[1] + topology[2]
self.neural_network = Network(topology, train_data, test_data)
self.min_limits = np.repeat(min_limit, self.w_size)
self.max_limits = np.repeat(max_limit, self.w_size)
self.initialize_sampling_parameters()
self.create_directory(directory)
PSO.__init__(self, self.pop_size, self.w_size, self.max_limits,
self.min_limits, self.neural_network.evaluate_fitness)
def __init__(self,
num_samples,
burn_in,
population_size,
topology,
train_data,
test_data,
directory,
temperature,
swap_sample,
parameter_queue,
problem_type,
main_process,
event,
active_chains,
num_accepted,
swap_interval,
max_limit=(10),
min_limit=-10):
# Multiprocessing attributes
multiprocessing.Process.__init__(self)
self.process_id = temperature
self.parameter_queue = parameter_queue
self.signal_main = main_process
self.event = event
self.active_chains = active_chains
self.num_accepted = num_accepted
self.event.clear()
self.signal_main.clear()
# Parallel Tempering attributes
self.temperature = temperature
self.swap_sample = swap_sample
self.swap_interval = swap_interval
self.burn_in = burn_in
# MCMC attributes
self.num_samples = num_samples
self.topology = topology
self.train_data = train_data
self.test_data = test_data
self.problem_type = problem_type
self.directory = directory
self.w_size = (topology[0] * topology[1]) + (
topology[1] * topology[2]) + topology[1] + topology[2]
self.neural_network = Network(topology, train_data, test_data)
self.min_limits = np.repeat(min_limit, self.w_size)
self.max_limits = np.repeat(max_limit, self.w_size)
self.initialize_sampling_parameters()
self.create_directory(directory)
PSO.__init__(self,
pop_size=population_size,
num_params=self.w_size,
max_limits=self.max_limits,
min_limits=self.min_limits,
fitness_function=self.neural_network.evaluate_fitness,
problem_type=self.problem_type)