def combined_1D_onehot(DataCenter, folder_paths, samples = 4):
folder_paths = np.array(folder_paths)
channels = folder_paths.shape[0]
plot = research.SubPlot(samples, channels)
update_plot = 1
# Iterate over all samples
for sample in range(samples):
for channel in range(channels):
one_hot_labels = data.load_data(folder_paths[channel] + 'one_hot_labels.csv')
max_curves = data.load_data(folder_paths[channel] + 'max_array_record.csv')
min_curves = data.load_data(folder_paths[channel] + 'min_array_record.csv')
participants = max_curves[:, 0]
max_curves = max_curves[:, 1:]
min_curves = min_curves[:, 1:]
# Randomly Select Input Data
rand_idx = np.random.randint(DataCenter.all_input_data.shape[0])
# Get Output idx
output_idx = np.argmax(DataCenter.all_output_data[rand_idx])
# Get Participant ID from One Hot Labels
participant = one_hot_labels[output_idx]
curve_id = np.array(np.where(participants == participant))[0][0]
max_curve = np.divide(max_curves[curve_id],DataCenter.input_scale[channel])
min_curve = np.divide(min_curves[curve_id], DataCenter.input_scale[channel])
# Plot Max/Min Curves
plot.current_plot = update_plot
plot.add_subplot_data(max_curve, add_data_to=update_plot, color='black')
plot.add_subplot_data(min_curve, add_data_to=update_plot, color='black')
# Plot Input Data
input_data = DataCenter.all_input_data[rand_idx][:,channel]
plot.add_subplot_data(input_data, add_data_to=update_plot, color='blue')
update_plot += 1
plot.show_plow()
def load_all_data_multiple(self, data_folder, data_files):
print('Loading Data from multiple CSV files')
print('Loading from {}. {} Left'.format(data_folder + data_files[0],
len(data_files)))
self.data_location = data_folder
self.all_data = data.load_data(data_folder + data_files[0])
print('Current Samples = {}'.format(self.all_data.shape[0]))
for i in range(1, len(data_files)):
print('Loading from {}. {} Left'.format(
data_folder + data_files[i],
len(data_files) - i))
new_data = data.load_data(data_folder + data_files[i])
self.all_data = np.concatenate([self.all_data, new_data], axis=0)
print('Current Samples = {}'.format(self.all_data.shape[0]))
def load_data(self):
path = self.folder_path + self.file_prefix
print('Loading data from {}'.format(path))
self.train_input_data = np.load(path + 'training_input_data.npy')
self.val_input_data = np.load(path + 'validation_input_data.npy')
self.eval_input_data = np.load(path + 'evaluation_input_data.npy')
self.train_output_data = np.load(path + 'training_output_data.npy')
self.val_output_data = np.load(path + 'validation_output_data.npy')
self.eval_output_data = np.load(path + 'evaluation_output_data.npy')
try:
self.one_hot_labels = data.load_data(self.folder_path +
self.file_prefix +
'one_hot_labels.csv')
except:
'No one-hot labels'
self.print_num_samples()
def load_all_data_single(self, data_folder, data_file):
print('Loading Data from CSV file')
self.data_location = data_folder
self.all_data = data.load_data(data_folder + data_file)
self.all_data = np.nan_to_num(self.all_data)
