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))