You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: get_likelihood_ratio.m
    path of file: ./repos/audio_steganalysis_ml/application
    
    the code of the file until where you have to start completion: %% steganalysis for person (train step)
% - get_likelihood_ratio(bitrate, algorithm, feature_type, width, RER, is_known, feature_cover_mat_dir, feature_stego_mat_dir, model_files_path)
% - Variable:
% ------------------------------------------input
% bitrate               bitrate of audio for test
% algorithm             algorithm type, (option: 'MP3Stego', 'HCM', 'EECS', 'ACS')
% feature_type          feature type, (option: 'jin', 'ren', 'wang')
% width                 relative payload for 'EECS' and 'ACS' (option: 2, 4, 6)
% is_known              whether algorithm is known or not (option: 'True', 'False')
% model_files_path      path of model files
% feature_cover_mat_dir directory of cover feature
% feature_stego_mat_dir directory of stego feature
% -----------------------------------------output
% Null

function get_likelihood_ratio(bitrate, algorithm, feature_type, width, is_known, feature_cover_mat_dir, feature_stego_mat_dir, model_files_path)

% load feature data
cover_feature_path = fullfile(feature_cover_mat_dir, feature_type, num2str(bitrate));
stego_feature_path = fullfile(feature_stego_mat_dir, algorithm, feature_type, [algorithm, '_B_', num2str(bitrate), '_W_', num2str(width), '_H_7_ER_10']);

cover_feature_file = load(cover_feature_path);
stego_feature_file = load(stego_feature_path);

cover_feature = cover_feature_file.features;cover_feature = cover_feature(801:10
    
function multi_likelihood_ratio_stego = get_likelihood_ratio_stego(cover_feature, stego_feature, model_file_path)

sample_num_cover = size(cover_feature, 1);                                  % the number of cover samples
sample_num_stego = size(stego_feature, 1);                                  % the number of stego samples
feature_dimension = size(cover_feature, 2);                                 % the dimension of feature
cover_label = -ones(sample_num_cover, 1);                                   % cover label
stego_label = ones(sample_num_stego, 1);                                    % steog label
cover_data = [cover_feature, cover_label];                                  % cover data [feature, label]
stego_data = [stego_feature, stego_label];                                  % stego data [feature, label]
merge = [cover_data; stego_data];                                           % data and label pair
merge = shuffle(merge);                                                     % data shuffle

data  = merge(:, 1:feature_dimension);                                      % training data
label = merge(:, feature_dimension+1);                                      % training label

number = 50;                                                                % number in a group
sample_num = sample_num_cover + sample_num_stego;                           % total number for experiments

times = 1000;
multi_likelihood_ratio_stego = zeros(1, 500);

for i = 1:times
    index = randperm(sample_num, number);index = index';                    % generate random number
    predict_data = data(index, :);predict_label = label(index, :);
    model = load(model_file_path);model = model.model;                      % load svm model
    [~, ~, prob] = libsvmpredict(predict_label, predict_data, model, '-b 1');
    prob_cover = prob(:,2);prob_steog = prob(:,1);                          % get prob estimation
    single_likelihood_ratio = prob_cover ./ prob_steog;                     % calculate likelihood ratio of single sample
    multi_likelihood_ratio = 1;
    for j = 1:number
        multi_likelihood_ratio = multi_likelihood_ratio * single_likelihood_ratio(j);
    end