Please write in matlab!

function [rmstrain rmstest] = a(filename,lowndx)

% [RMSTRAIN RMSTEST]=A(LOWNDX) finds the RMS errors of 5-fold

% cross-validation for the variable LOWNDX of the data in the file

% FILENAME. The RMS errors for the training of each fold are returned

% in RMSTEST and the RMS errors for the testing of each fold are

% returned in RMSTEST.

%

% INPUTS:

% FILENAME - character string, name of file to be processed;

% assume that the first row describes the data variables

% LOWNDX - integer scalar, index into the data

% OUTPUTS:

% RMSTRAIN - 1x5 array of RMS errors for 5-fold training

% RMSTEST - 1x5 array of RMS errors for 5-fold testing

% Read the test data from a CSV file; find the size of the data

% %

% % STUDENT CODE GOES HERE: REMOVE THIS COMMENT

% % THEN READ THE FILE SPECIFIED BY THE INPUT ARGUMENT

% %

% Create Xmat and yvec from the data and the input parameter,

% accounting for no standardization of data

% %

% % STUDENT CODE GOES HERE: REMOVE THIS COMMENT

% % THEN ASSIGN THE VARIABLES FROM THE DATASET

% %

% Compute the RMS errors of 5-fold cross-validation

% %

% % STUDENT CODE GOES HERE: REMOVE THE NEXT 2 LINES AND THIS COMMENT

% % THEN PERFORM THE COMPUTATIONS

% %

rmstrain = 0.5*ones(1,5);

rmstest = 0.6*ones(1,5);

end

function [rmstrain,rmstest]=mykfold(Xmat, yvec, k_in)

% [RMSTRAIN,RMSTEST]=MYKFOLD(XMAT,yvec,K) performs a k-fold validation

% of the least-squares linear fit of yvec to XMAT. If K is omitted,

% the default is 5.

%

% INPUTS:

% XMAT - MxN data vector

% yvec - Mx1 data vector

% K - positive integer, number of folds to use

% OUTPUTS:

% RMSTRAIN - 1xK vector of RMS error of the training fits

% RMSTEST - 1xK vector of RMS error of the testing fits

% Problem size

M = size(Xmat, 1);

% Set the number of folds; must be 1

if nargin >= 3 & ~isempty(k_in)

k = max(min(round(k_in), M-1), 2);

else

k = 5;

end