c_confidence_snn.m

神经网络的工具箱, 神经网络的工具箱,

function c_point_conf = c_confidence_snn(elevel, nets, data, alpha)
%C_CONFIDENCE_SNN Estimate c_confidence
%
%  Syntax
%
%   c_point_conf = c_confidence_snn(e_level, nets, wcf_data, alpha)
%
%  Description
%
%   C_CONFIDENCE_SNN takes
%     e_level   - error level.
%     nets      - [1 x M] net_structs of trained networks with cost 
%                 function WCF_SNN.
%     wcf_data  - wcfdata_struct with training data.
%     alpha     - [1 x M] matrix of network weighting factors.
%   and returns 
%     c_point_conf  - c_confidence.
% 
%   (M = #networks in ensemble)
%
%  See also
%
%   CONFIDENCE_SNN, BALANCE_SNN
%

M = size(nets,2);
N = size(nets(1).biases{nets(1).numLayers},1);
[NI, MU] = size(data.P);

g = getg_snn(nets(1), data);
errf = nets(1).costFcn.fn;
if nofieldorempty_snn(data, 'useT')
   data.useT = ones(N, MU);
end

for m = 1:M
    ym(:,:,m) = simff_snn(nets(1,m), data);
end
y_av = average_outputs_snn(ym, alpha, errf);

useTm = reshape(repmat(data.useT,1,M), [N,MU,M]);

mu_indices = cell(N,1);
for i = 1:N
    mu = i:N:(N*MU*M);
    mu_indices{i,1} = mu(find(useTm(mu)));
end
iall = col(find(useTm));

%#function se_snn
%#function relerr_snn
%#function loglikelihood_snn 
%#function crosslogistic_snn
%#function crossentropy_snn
em = zeros(N, MU, M);
if isstr(errf)
   em(iall) = feval(errf, col(ym(iall)), col(repmat(y_av(find(data.useT)),1,M)) ); 
elseif iscell(errf)
   for i = 1:N
       mu = find(data.useT(i,:));
       em(mu_indices{i,1}) = feval(errf{i}, ym(mu_indices{i,1}), ...
                   repmat(y_av(i,mu),1,M) );
   end
else
   error('Incorrect format for output errorfunction');
end
correction = 1/(1-sumsqr_snn(alpha));
em = correction * em;

e_av = reshape(reshape(em, [N*MU, M]) * alpha, [N,MU]);

for i = 1:N
    mu = find(data.useT(i,:));
    d3 = repmat(alpha, 1, size(mu,2)) .* repmat(g(i,mu), M, 1);
    d3 = d3(:)./sum(d3(:));
    [d1, d2] = sort(-em(mu_indices{i,1})./(repmat(e_av(i, mu),1,M)));
    c_point_conf(i,1) = -d1(min(find( cumsum(d3(d2))>elevel )));
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function A = col(A)
A = reshape(A, prod(size(A)), 1);
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