I gonna use the following Matlab code but I can't understand what does mean options in this code I mean I don't know what should I use from options set do I use all set or choose one thing?

the code is

function [eigvector, eigvalue] = LPP(W, options, data)

% LPP: Locality Preserving Projections

%

% [eigvector, eigvalue] = LPP(W, options, data)

%

% Input:

% data - Data matrix. Each row vector of fea is a data point.

% W - Affinity matrix. You can either call "constructW"

% to construct the W, or construct it by yourself.

% options - Struct value in Matlab. The fields in options

% that can be set:

%

% Please see LGE.m for other options.

%

% Output:

% eigvector - Each column is an embedding function, for a new

% data point (row vector) x, y = x*eigvector

% will be the embedding result of x.

% eigvalue - The sorted eigvalue of LPP eigen-problem.

%

%

% Examples:

%

% fea = rand(50,70);

% options = [];

% options.Metric = 'Euclidean';

% options.NeighborMode = 'KNN';

% options.k = 5;

% options.WeightMode = 'HeatKernel';

% options.t = 5;

% W = constructW(fea,options);

% options.PCARatio = 0.99

% [eigvector, eigvalue] = LPP(W, options, fea);

% Y = fea*eigvector;

%

%

% fea = rand(50,70);

% gnd = [ones(10,1);ones(15,1)*2;ones(10,1)*3;ones(15,1)*4];

% options = [];

% options.Metric = 'Euclidean';

% options.NeighborMode = 'Supervised';

% options.gnd = gnd;

% options.bLDA = 1;

% W = constructW(fea,options);

% options.PCARatio = 1;

% [eigvector, eigvalue] = LPP(W, options, fea);

% Y = fea*eigvector;

%

%

% Note: After applying some simple algebra, the smallest eigenvalue problem:

% data^T*L*data = \lemda data^T*D*data

% is equivalent to the largest eigenvalue problem:

% data^T*W*data = \beta data^T*D*data

% where L=D-W; \lemda= 1 - \beta.

% Thus, the smallest eigenvalue problem can be transformed to a largest

% eigenvalue problem. Such tricks are adopted in this code for the

% consideration of calculation precision of Matlab.

%

%

% See also constructW, LGE

%

%Reference:

% Xiaofei He, and Partha Niyogi, "Locality Preserving Projections"

% Advances in Neural Information Processing Systems 16 (NIPS 2003),

% Vancouver, Canada, 2003.

%

% Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong-Jiang

% Zhang, "Face Recognition Using Laplacianfaces", IEEE PAMI, Vol. 27, No.

% 3, Mar. 2005.

%

% Deng Cai, Xiaofei He and Jiawei Han, "Document Clustering Using

% Locality Preserving Indexing" IEEE TKDE, Dec. 2005.

%

% Deng Cai, Xiaofei He and Jiawei Han, "Using Graph Model for Face Analysis",

% Technical Report, UIUCDCS-R-2005-2636, UIUC, Sept. 2005

%

% Xiaofei He, "Locality Preserving Projections"

% PhD's thesis, Computer Science Department, The University of Chicago,

% 2005.

%

% version 2.1 --June/2007

% version 2.0 --May/2007

% version 1.1 --Feb/2006

% version 1.0 --April/2004

%

% Written by Deng Cai (dengcai2 AT cs.uiuc.edu)

%

if (~exist('options','var'))

options = [];

end

[nSmp,nFea] = size(data);

if size(W,1) ~= nSmp

error('W and data mismatch!');

end

%==========================

% If data is too large, the following centering codes can be commented

% options.keepMean = 1;

%==========================

if isfield(options,'keepMean') && options.keepMean

;

else

if issparse(data)

data = full(data);

end

sampleMean = mean(data);

data = (data - repmat(sampleMean,nSmp,1));

end

%==========================

D = full(sum(W,2));

if ~isfield(options,'Regu') || ~options.Regu

DToPowerHalf = D.^.5;

D_mhalf = DToPowerHalf.^-1;

if nSmp < 5000

tmpD_mhalf = repmat(D_mhalf,1,nSmp);

W = (tmpD_mhalf.*W).*tmpD_mhalf';

clear tmpD_mhalf;

else

[i_idx,j_idx,v_idx] = find(W);

v1_idx = zeros(size(v_idx));

for i=1:length(v_idx)

v1_idx(i) = v_idx(i)*D_mhalf(i_idx(i))*D_mhalf(j_idx(i));

end

W = sparse(i_idx,j_idx,v1_idx);

clear i_idx j_idx v_idx v1_idx

end

W = max(W,W');

data = repmat(DToPowerHalf,1,nFea).*data;

[eigvector, eigvalue] = LGE(W, [], options, data);

else

options.ReguAlpha = options.ReguAlpha*sum(D)/length(D);

D = sparse(1:nSmp,1:nSmp,D,nSmp,nSmp);

[eigvector, eigvalue] = LGE(W, D, options, data);

end

eigIdx = find(eigvalue < 1e-3);

eigvalue (eigIdx) = [];

eigvector(:,eigIdx) = [];

http://www.cad.zju.edu.cn/home/dengcai/Data/code/LPP.m