unbiased_average_snn.m

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function y_validation = unbiased_average_snn(nets, alpha, datasets)
%UNBIASED_AVERAGE_SNN Unbiased weighted average.
%
% Syntax
%
%  y_uav = unbiased_average_snn(nets, alpha, datasets)
%
% Description
%
%  UNBIASED_AVERAGE_SNN computes an unbiased weigthed average output
%  for the ensemble of 'nets', i.e. for each pattern the averaging is
%  only over the networks in the ensemble which did not use that
%  pattern in training.   
%
%  UNBIASED_AVERAGE_SNN(nets, alpha, datasets) takes
%    nets       - [1 x M] matrix of net_structs trained with 
%                 costFcn = wcf_snn.
%    alpha      - [1 x M] matrix of network weighting factors. 
%    datasets   - [1 x M] matrix of dataset_structs containing
%                 information on the data used in training 'nets'. 
%  and returns
%    y_uav      - unbiased average.
% 

data = datasets(1).data;

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

errf = nets(1).costFcn.fn;

ym = zeros(N, MU, M);
q = sparse(MU, M);
for m = 1:M
    ym(:,:,m) = simff_snn(nets(1,m), data);
    % q(mu, m) is 1 if pattern mu is in the validation set of network m
    q(datasets(m).val_ind, m) = 1;    
end

for mu = 1:MU
    m = find(q(MU,:)); 
    y_validation(:, mu) =  average_outputs_snn(ym(:,mu,m), alpha(m), errf);
end