def load_sensor_files(training_data_path, testing_data_path,
num_training_samples=1000000,
num_training_samples_per_file=100,
num_test_samples=10000,
num_test_samples_per_file=10,
history_length=3,
feature_indexes=None,
label_indexes=None,
shared=True):
from os import listdir
from os.path import isdir
from random import sample
from random import shuffle
from numpy import array
file_names = listdir(training_data_path)
training_file_paths = [training_data_path + name for name in file_names]
training_file_paths = [path for path in training_file_paths if not (isdir(path) or "trajectory" in path)]
num_training_files = min([len(training_file_paths), max([1, num_training_samples / num_training_samples_per_file])])
training_file_paths = sample(training_file_paths, num_training_files)
shuffle(training_file_paths)
file_names = listdir(testing_data_path)
test_file_paths = [testing_data_path + name for name in file_names]
test_file_paths = [path for path in test_file_paths if not (isdir(path) or "trajectory" in path)]
num_test_files = min([len(test_file_paths), max([1, num_test_samples / num_test_samples_per_file])])
test_file_paths = sample(test_file_paths, num_test_files)
shuffle(test_file_paths)
print '... loading training data'
training_data, training_labels = load_data_set(training_file_paths, num_training_samples_per_file, history_length, feature_indexes, label_indexes)
print '... loading testing data'
test_data, test_labels = load_data_set(test_file_paths, num_test_samples_per_file, history_length, feature_indexes, label_indexes)
print '... ' + str(len(training_data)) + ' training samples loaded'
print '... ' + str(len(test_data)) + ' test samples loaded'
if shared:
training_data = shared_dataset(training_data)
training_labels = shared_dataset(training_labels)
test_data = shared_dataset(test_data)
test_labels = shared_dataset(test_labels)
return training_data, training_labels, test_data, test_labels
def load_config_data(config_path):
from os import listdir
from data_loader import shared_dataset
from numpy import array
num_files_per_layer = 4
file_names = listdir(config_path)
file_names = sorted(file_names)
autoencoder_file_paths = [config_path + name for name in file_names if name.startswith("al")]
supervised_file_paths = [config_path + name for name in file_names if name.startswith("sl")]
autoencoder_weights = []
for i in range(len(autoencoder_file_paths)/num_files_per_layer):
with open(autoencoder_file_paths[num_files_per_layer*i], 'r') as weights_file:
weights_data = weights_file.read()
weights = array([[float(value) for value in line.split(",")] for line in weights_data.split('\n')]).T
with open(autoencoder_file_paths[num_files_per_layer*i + 1], 'r') as weights_prime_file:
weights_prime_data = weights_prime_file.read()
weights_prime = array([[float(value) for value in line.split(",")] for line in weights_prime_data.split('\n')]).T
with open(autoencoder_file_paths[num_files_per_layer*i + 2], 'r') as bias_file:
bias_data = bias_file.readline()
bias = array([float(value) for value in bias_data.split(",")])
with open(autoencoder_file_paths[num_files_per_layer*i + 3], 'r') as bias_prime_file:
bias_prime_data = bias_prime_file.readline()
bias_prime = array([float(value) for value in bias_prime_data.split(",")])
weights = shared_dataset(weights, name='W')
bias = shared_dataset(bias, name='b')
weights_prime = shared_dataset(weights_prime, name='Whid')
bias_prime = shared_dataset(bias_prime, name='bvis')
autoencoder_weights.append((weights, bias, weights_prime, bias_prime))
with open(supervised_file_paths[0], 'r') as supervised_weights_file:
supervised_weights_data = supervised_weights_file.read()
supervised_weights = shared_dataset(array([[float(value) for value in line.split(",")] for line in supervised_weights_data.split('\n')]).T, name='W')
with open(supervised_file_paths[1], 'r') as supervised_bias_file:
supervised_bias_data = supervised_bias_file.readline()
supervised_bias = shared_dataset(array([float(value) for value in supervised_bias_data.split(",")]), name='b')
config_file_path = config_path + "conf.txt"
with open(config_file_path, 'r') as config_file:
config_data = config_file.readlines()
return autoencoder_weights, (supervised_weights, supervised_bias), config_data