%NMC Nearest Mean Classifier
%
% W = NMC(A)
% W = A*NMC
%
% INPUT
% A Dataset
%
% OUTPUT
% W Nearest Mean Classifier
%
% DESCRIPTION
% Computation of the nearest mean classifier between the classes in the
% dataset A. The use of soft labels is supported. Prior probabilities are
% not used.
%
% The difference with NMSC is that NMSC is based on an assumption of normal
% distributions and thereby automatically scales the features and is
% sensitive to class priors. NMC is a plain nearest mean classifier for
% which the assigned classes are are sensitive to feature scaling and
% unsensitive to class priors.
%
% The estimated class confidences by B*NMC(A), however, are based on
% assumed spherical gaussian distributions of the same size.
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, MAPPINGS, NMSC, LDC, FISHERC, QDC, UDC
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
% $Id: nmc.m,v 1.14 2009/12/09 15:49:54 duin Exp $
function W = nmc(varargin)
mapname = 'NearestMean';
argin = setdefaults(varargin,[],false);
if mapping_task(argin,'definition')
W = define_mapping(argin,'untrained',mapname);
elseif mapping_task(argin,'training') % Train a mapping.
[a,flag] = deal(argin{:});
% flag = true forces execution by knn_map instead of normal_map
% flag = false (defaults) finds posteriors by spherical normal
% distributions
if nargin < 2, flag = false; end
% flag=true forces to skip computation of posteriors based on normal
% densities
if nargin < 1 | isempty(a)
W = prmapping(mfilename);
W = setname(W,'NearestMean');
return
end
islabtype(a,'crisp','soft');
isvaldfile(a,1,2); % at least 1 object per class, 2 classes
[m,k,c] = getsize(a);
if isdatafile(a), a = setfeatsize(a,k); end
if ~flag & c == 2 % 2-class case: store linear classifier
u = meancov(a);
u1 = +u(1,:);
u2 = +u(2,:);
R = [u1-u2]';
offset =(u2*u2' - u1*u1')/2;
W = affine([R -R],[offset -offset],a,getlablist(a));
W = cnormc(W,a);
W = setname(W,'Nearest Mean');
else
if ~flag & all(classsizes(a) > 1)
a = setprior(a,0); % NMC should be independent of priors: make them equal
p = getprior(a);
U = zeros(c,k);
V = zeros(c,k);
s = sprintf('NMC: Processing %i classes: ',c);
prwaitbar(c,s);
for j=1:c
prwaitbar(c,j,[s int2str(j)]);
b = seldat(a,j);
[v,u] = var(b);
U(j,:) = +u;
V(j,:) = +v;
end
prwaitbar(0);
%G = mean(V'*p') * eye(k);
G = mean(V(:));
w.mean = +U;
w.cov = G;
w.prior =p;
W = normal_map(w,getlablist(a),k,c);
W = setname(W,'Nearest Mean');
W = setcost(W,a);
else
u = meancov(a);
W = knnc(u,1);
W = setname(W,mapname);
end
end
W = setcost(W,a);
end
return