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: errperf.m
    path of file: ./repos/Prediction-using-Bayesian-Neural-Network/Time-Series Data/Levian_m
    
    the code of the file until where you have to start completion: function V=errperf(T,P,M)

%ERRPERF Determine various error related performance metrics.
%
% ERRPERF(T,P,M) uses T and P, which are target and prediction vectors
% respectively, and returns the value for M, which is one of several error
% related performance metrics.
%
% T and P can be row or column vectors of the same size. M can be one of
% the following performance metrics:
%
%   mae (mean absolute error)
%   mse (mean squared error)
%   rmse (root mean squared error)
%
%   mare (mean absolute relative error)
%   msre (mean squared relative error)
%   rmsre (root mean squared relative error)
%
%   mape (mean absolute percentage error)
%   mspe (mean squared percentage error)
%   rmspe (root mean squared percentage error)
%
% EXAMPLE:
%
%   rand('state',0)
%
%   T = [0:0.2:1]
%   P = rand(size(T)).*T
%
%   errperf(T,P,'mae') returns 0.1574
%
% To compute the relevant performance metric, the function uses recursion
% to first compute one or more error vectors. The function can therefore
% secondarily be used to compute these error vectors. M can therefore also
% be one of the following:
%
%   e (errors)
%   ae (absolute errors)
%   se (squared errors)
%
%   re (relative errors)
%   are (absolute relative errors)
%   sre (squared relative errors)
%
%   pe (percentage errors)
%   ape (absolute percentage errors)
%   spe (squared percentage errors)
%
% REMARKS:
%
%   - The Neural Network Toolbox also has functions to compute mae and mse.
%     This function does not make use of the toolbox.
%
%   - Percentage error equals relative error times 100.
%
%   - The abbreviations used in the code, and the calculation tree are
%     documented in a comments section within the file.
%
% VERSION: 20070703
% MATLAB VERSION: 7.4.0.287 (R2007a)
% LICENSE: As-is; public domain
%

    
function V=errperf(T,P,M)

%ERRPERF Determine various error related performance metrics.
%
% ERRPERF(T,P,M) uses T and P, which are target and prediction vectors
% respectively, and returns the value for M, which is one of several error
% related performance metrics.
%
% T and P can be row or column vectors of the same size. M can be one of
% the following performance metrics:
%
%   mae (mean absolute error)
%   mse (mean squared error)
%   rmse (root mean squared error)
%
%   mare (mean absolute relative error)
%   msre (mean squared relative error)
%   rmsre (root mean squared relative error)
%
%   mape (mean absolute percentage error)
%   mspe (mean squared percentage error)
%   rmspe (root mean squared percentage error)
%
% EXAMPLE:
%
%   rand('state',0)
%
%   T = [0:0.2:1]
%   P = rand(size(T)).*T
%
%   errperf(T,P,'mae') returns 0.1574
%
% To compute the relevant performance metric, the function uses recursion
% to first compute one or more error vectors. The function can therefore
% secondarily be used to compute these error vectors. M can therefore also
% be one of the following:
%
%   e (errors)
%   ae (absolute errors)
%   se (squared errors)
%
%   re (relative errors)
%   are (absolute relative errors)
%   sre (squared relative errors)
%
%   pe (percentage errors)
%   ape (absolute percentage errors)
%   spe (squared percentage errors)
%
% REMARKS:
%
%   - The Neural Network Toolbox also has functions to compute mae and mse.
%     This function does not make use of the toolbox.
%
%   - Percentage error equals relative error times 100.
%
%   - The abbreviations used in the code, and the calculation tree are
%     documented in a comments section within the file.
%
% VERSION: 20070703
% MATLAB VERSION: 7.4.0.287 (R2007a)
% LICENSE: As-is; public domain
%
% See also MAE, MSE.

%{
VERSION HISTORY:
20070703: - Added MATLAB version check.
20070606: - Added support for metrics MARE, MSRE, and RMSRE.
          - Addressed a possible division by zero condition in calculating
            relative and percentage errors.
20070528: - Original version.

KEYWORDS:
perf, performance, metric, performance measure, machine learning
%}

%% Comments

%{

Abbreviations:

a: absolute
e: error(s)
M: METRIC
m: mean
P: PREDICTIONS
p: percentage
r: relative (if before e)
r: square root (if before m)
s: squared
T: TARGETS
V: VALUE(S)

Calculation tree:

e
|
|-ae-mae
|
|-se-mse-rmse
|
|-re-pe
  |  |
  |  |-ape-mape
  |  |
  |  |-spe-mspe-rmspe
  |
  |-are-mare
  |
  |-sre-msre-rmsre

%}

%% Check MATLAB version

if datenum(version('-date'))<datenum('29-Jan-2007')
    error(['The MATLAB version in use is ',version('-release'),'. ',...
           'This function requires at least version 2007a.'])
end