def online_test(path, gesture_id):
results_folder_name = os.path.basename(os.path.splitext(path)[0])
full_path = 'online_results/' + results_folder_name
if not os.path.exists(full_path):
os.makedirs(full_path)
data_set = FilesUtil.generate_data_set_from_file(path)
start = 0
end = 149
errors = list()
values = list()
predictions = list()
for i in range(len(data_set)):
if end < len(data_set):
current = data_set[start:end]
x_new, y_pre = evaluate_rnn_model(
current.values, str(gesture_id),
CONFIG.get_neurons_dim(gesture_id))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
errors.append(
np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
values.append(x_new)
predictions.append(y_pre)
current, start, end = FilesUtil.get_next_window(current,
dimension=150,
start=start,
end=end)
print(
"done " + str(i) + " of " + len(data_set).__str__() +
" error " +
str(np.sqrt(mse_x * mse_x + mse_y * mse_y +
mse_z * mse_z)) + " gesture " +
str(gesture_id))
FilesUtil.save_results_to_file(
errors, os.path.basename(os.path.splitext(path)[0]), gesture_id)
return errors, values, predictions
def evaluate_model(gesture_id):
outer_list = list()
for data_set in enumerate(CONFIG.UNI_GE_DATA_SETS):
inner_list = list()
for i in range(CONFIG.evaluation_runs):
train_set, test_set, validation = FilesUtil.split_data_set(
CONFIG.get_path(data_set[1]), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(gesture_id),
CONFIG.get_neurons_dim(gesture_id))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
inner_list.append(
np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
# main_index = data_set[1]
# result_dictionary[main_index][i] =
outer_list.append(inner_list)
return DataFrame(outer_list, index=[1, 2, 5, 6, 7, 8])
def test():
for i in range(CONFIG.evaluation_runs):
train_set, test_set, valid = FilesUtil.split_data_set(
CONFIG.get_path(1), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(1),
CONFIG.get_neurons_dim(1))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
print(np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
print("----------------------------------------------------")
for i in range(CONFIG.evaluation_runs):
train_set, test_set, valid = FilesUtil.split_data_set(
CONFIG.get_path(2), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(1),
CONFIG.get_neurons_dim(1))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
print(np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
print("----------------------------------------------------")
for i in range(CONFIG.evaluation_runs):
train_set, test_set, valid = FilesUtil.split_data_set(
CONFIG.get_path(5), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(1),
CONFIG.get_neurons_dim(1))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
print(np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
print("----------------------------------------------------")
for i in range(CONFIG.evaluation_runs):
train_set, test_set, valid = FilesUtil.split_data_set(
CONFIG.get_path(6), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(1),
CONFIG.get_neurons_dim(1))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
print(np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
print("----------------------------------------------------")
for i in range(CONFIG.evaluation_runs):
train_set, test_set, valid = FilesUtil.split_data_set(
CONFIG.get_path(7), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(1),
CONFIG.get_neurons_dim(1))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
print(np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))
print("----------------------------------------------------")
for i in range(CONFIG.evaluation_runs):
train_set, test_set, valid = FilesUtil.split_data_set(
CONFIG.get_path(8), CONFIG.train_space)
x_new, y_pre = evaluate_rnn_model(
FilesUtil.get_random_file(test_set), str(1),
CONFIG.get_neurons_dim(1))
mse_x = get_mse(x_new[0][1:, 0], y_pre[0][:-1, 0])
mse_y = get_mse(x_new[0][1:, 1], y_pre[0][:-1, 1])
mse_z = get_mse(x_new[0][1:, 2], y_pre[0][:-1, 2])
print(np.sqrt(mse_x * mse_x + mse_y * mse_y + mse_z * mse_z))