def METHOD_NAME(excitation):
    # returns euclidean distance to nearest trainning
    # vector in desciptor space
    mat, rownames = matrix_loader('train_data.csv', rownames=True)
    train_mat = np.array(mat, dtype='float64')
    min_dist = 1000
    excitation, _, _ = excitation_standardizer(excitation, 'split')
    for i, rows in enumerate(train_mat):
        np.subtract(rows, np.array(excitation))
        this_dist = np.linalg.norm(np.subtract(rows, np.array(excitation)))/3
        if this_dist < min_dist:
            min_dist = this_dist
            best_row = rownames[i]
    # print('min dist is ' +str(min_dist))
    return min_dist, best_row