function [b,blck] = make_outliers(a,n,scale)% MAKE_OUTLIERS%%   [B,BLCK] = MAKE_OUTLIERS(A,N,SCALE)%% Add an uniform block-shaped outlier distribution to the given OC% dataset A and create a new dataset B. Dataset should contain a target% class (with the label 'target').  All other data will be deleted. The% N new outliers will be drawn from a block defined around dataset A.% The size of the box can be adjusted by scale (default 2, which means% the edge of the box is twice the maximum distance in the respective% feature direction).%% When N=0 is given, no outliers are created, but the dataset with% only the first class is created and the box is computed.%%   [B,BLCK] = MAKE_OUTLIERS(A,N,BLCK)% % The user now defines the block where the data should be created. The% format of the block is   BLCK=[minx max; miny maxy ] %%   [B,BLCK] = MAKE_OUTLIERS(A)%% Now the same number of target objects and outlier objects are% created.%% In the case of 2D data is also possible to use a grid of objects. In% that case N should be <0.%% See also: gendatoc, gendatgrid%%@article{Tax2001,%	author = {Tax, D.M.J. and Duin, R.P.W.},%	title = {Uniform object generation for optimizing one-class classifiers},%	journal = {Journal for Machine Learning Research},%	year = {2001},%	pages = {155-173}%}% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlandsif (nargin<3)  scale = 2.0;endif (nargin<2)  % if no required number is given, copy from a:  n = size(a,1);end% Well, find the target class:a = target_class(a);[m,k] = size(a);% Create the outlier box:if (max(size(scale))>1)         % the block is known	blck = scale;else	blck = [min(a)' max(a)'];     % the block has to be defined	df = diff(blck,1,2);	meanval = mean(blck,2);	newscale = scale/2;	blck = [meanval-newscale*df, meanval+newscale*df];end%DXD this can be made more efficient using gendatoc, I think...% Combine it to a dataset:if (n>0)  % we randomly draw uniform data:	b = [+a; (ones(n,1)*blck(:,1)') + ...	     (ones(n,1)*diff(blck,1,2)').*rand(n,k)];else      % we make a nice 2D grid:	if k==2		grid = gendatgrid([],[30 30],blck(:,1),blck(:,2));		b = [+a; grid];		n = size(grid,1);	else		error('Data should be 2D');	endend% Now create the new dataset:lablist = str2mat('target','outlier');labb = [ones(m,1); 2*ones(n,1)];b = dataset(b,lablist(labb,:));b = setfeatlab(b,getfeatlab(a));b = setname(b, 'Data with artificial box-distr. outliers');return