Please complete it in matlab...

function [rmsvars lowndx rmstrain rmstest] = a_

% [RMSVARS LOWNDX RMSTRAIN RMSTEST]=A3 finds the RMS errors of

% linear regression of the data in the file "GOODS.CSV" by treating

% each column as a vector of dependent observations, using the other

% columns of the data as observations of independent varaibles. The

% individual RMS errors are returned in RMSVARS and the index of the

% smallest RMS error is returned in LOWNDX. For the variable that is

% best explained by the other variables, a 5-fold cross validation is

% computed. 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:

% none

% OUTPUTS:

% RMSVARS - 1xN array of RMS errors of linear regression

% LOWNDX - integer scalar, index into RMSVALS

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

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

filename = 'xxx.csv';

[rmsvars lowndx] = a1(filename);

[rmstrain rmstest] = a2(filename, lowndx)

end

function [rmsvars lowndx] = a1(filename)

% [RMSVARS LOWNDX]=A1(FILENAME) finds the RMS errors of

% linear regression of the data in the file FILENAME by treating

% each column as a vector of dependent observations, using the other

% columns of the data as observations of independent varaibles. The

% individual RMS errors are returned in RMSVARS and the index of the

% smallest RMS error is returned in LOWNDX.

%

% INPUTS:

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

% assume that the first row describes the data variables

% OUTPUTS:

% RMSVARS - 1xN array of RMS errors of linear regression

% LOWNDX - integer scalar, index into RMSVALS

% 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

% %

% Compute the RMS errors for linear regression

% %

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

% % THEN PERFORM THE COMPUTATIONS

rmsvars = 0.1*(1:16);

lowndx = 1;

% Find the regression on your choice of standardized

% or unstandardized variables

% %

% % STUDENT CODE GOES HERE: REMOVE THIS COMMENT

% % THEN PERFORM THE COMPUTATIONS

% %

% Plot the results

% %

% % STUDENT CODE GOES HERE: REMOVE THIS COMMENT

% % THEN PLOT THE RESULTS

% %

end

function [rmstrain rmstest] = a2(filename,lowndx)

% [RMSTRAIN RMSTEST]=A2(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

% Initialize the return variables

rmstrain = zeros(1, k);

rmstest = zeros(1, k);

% Process each fold

for ix=1:k

% %

% % STUDENT CODE GOES HERE: replace the next 5 lines with code to

% % (1) set up the "train" and "test" indexing for "xmat" and "yvec"

% % (2) use the indexing to set up the "train" and "test" data

% % (3) compute "wvec" for the training data

% %

xmat_train = [0 1];

yvec_train = 0;

wvec = [0 0];

xmat_test = [0 1];

yvec_test = 0;

rmstrain(ix) = rms(xmat_train*wvec - yvec_train);

rmstest(ix) = rms(xmat_test*wvec - yvec_test);

end

end