gendatk.m

这是我找到的一个模式识别工具箱

%GENDATK K-Nearest neighbor data generation
% 
%   B = GENDATK(A,N,K,S)
%
% INPUT
%   A  Dataset
%   N  Number of points (optional; default: 50)
%   K  Number of nearest neighbors (optional; default: 1)
%   S  Standard deviation (optional; default: 1)
%
% OUTPUT
%   B  Generated dataset
%
% DESCRIPTION 
% Generation of N points using the K-nearest neighbors of objects in the 
% dataset A. First, N points of A are chosen in a random order. Next, to each 
% of these points and for each direction (feature), a Gaussian-distributed 
% offset is added with the zero mean and the standard deviation: S * the mean 
% signed difference between the point of A under consideration and its K 
% nearest neighbors in A. 
%
% The result of this procedure is that the generated  points follow the local
% density properties of the point from which they originate.
%
% If A is a multi-class dataset the above procedure is followed class by
% class, neglecting objects of other classes and possibly unlabeled objects.
% 
% If N is a vector of sizes, exactly N(I) objects are generated
% for class I. Default N is 100 objects per class.
%
% SEE ALSO
% DATASETS, GENDATP, GENDATT

% Copyright: R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands

% $Id: gendatk.m,v 1.4 2007/04/23 12:49:29 duin Exp $

function B = gendatk(A,N,k,stdev)

	prtrace(mfilename);

	if (nargin < 4) 		
		prwarning(3,'Standard deviation of the added Gaussian noise is not specified, assuming 1.');
		stdev = 1; 
	end
	if (nargin < 3) 
		prwarning(3,'Number of nearest neighbors to be used is not specified, assuming 1.');
		k = 1; 
	end
	if (nargin < 2)
		prwarning(3,'Number of samples to generate is not specified, assuming 50.');
		N = [];   % This happens some lines below.
	end
	if (nargin < 1)
		error('No dataset found.');
	end

	A = dataset(A);
	A = setlablist(A); % remove empty classes first
	[m,n,c] = getsize(A);
	prior = getprior(A);
	if isempty(N), 
		N = repmat(50,1,c); 				% 50 samples are generated.  		
	end
	N = genclass(N,prior);				% Generate class frequencies according to the priors.			

	lablist = getlablist(A);
	B = [];
	labels = [];
	% Loop over classes.
	for j=1:c
		a = getdata(A,j); 					% The j-th class.
		[D,I] = sort(distm(a)); 
		I = I(2:k+1,:); 						% Indices of the K nearest neighbors.
		alf = randn(k,N(j))*stdev;	% Normally distributed 'noise'.
		nu = ceil(N(j)/size(a,1));	% It is possible that NU > 1 if many objects have to be generated. 
		J = randperm(size(a,1));		
		J = repmat(J,nu,1)';				
		J = J(1:N(j));							% Combine the NU repetitions of J into one column vector.
		b = zeros(N(j),n);

		% Loop over features.
		for f = 1:n
%      Take all objects given by J, consider feature F.
%      Their K nearest neighbors are given by I(:,J)
%      We reshape them as a N(j) by K matrix (N(j) is the length of J)
%      Compute all differences between them and the original objects
%      Multiply these differences by the std dev stored in alf
%      Transpose and sum over the K neighbors, normalize by K
%      Transpose again and add to the original objects 
			 b(:,f) = a(J,f) + sum(( ( a(J,f)*ones(1,k) - ...
								reshape(+a(I(:,J),f),k,N(j))' ) .* alf' )' /k, 1)';
		end
		B = [B;b];
		labels = [labels; repmat(lablist(j,:),N(j),1)];
	end

	B = dataset(B,labels,'prior',A.prior);
	%B = set(B,'featlab',getfeatlab(A),'name',getname(A),'featsize',getfeatsize(A));
	%DXD. Added this exception, because else it's going to complain
	%     that the name is not a string.
	B = set(B,'featlab',getfeatlab(A),'featsize',getfeatsize(A));
	if ~isempty(getname(A))
		B = setname(B,getname(A));
	end

return;