argmin_wcf_snn.m

来自「神经网络的工具箱, 神经网络的工具箱,」· M 代码 · 共 49 行

function y = argmin_wcf_snn(net, data)
%ARGMIN_WCF_SNN Zeroth order output estimate given data and network
%               WCF_SNN cost function.
%
%   y_min = argmin_{y} {wcf_snn(Y,T) | Y = {y^1, ..., y^MU}; 
%                                      \forall \mu: y^{\mu} = y}
%

if ~strcmp(net.costFcn.name, 'wcf_snn') 
   error('First argument must be a net_struct with WCF_SNN cost function');
end

g = getg_snn(net, data);
errf = net.costFcn.fn;

[NL, MU] = size(data.T);

if ~(isstr(errf) | (size(errf,1) == NL) )
   error('Sub-sub-field costFcn.fn of first argument incorrect.');
end

y = zeros(NL,1);
for i = 1:NL
    if isstr(errf)
       fi = errf; 
    else
       fi = errf{i};
    end
    MU_rel = find(~isnan(g(i,:)));
    g_rel = g(i,MU_rel);
    t_rel = data.T(i,MU_rel); 
    switch (fi)
      case 'se_snn',
           y(i) = sum((g_rel .* t_rel),2) ./ sum(g_rel, 2); 
      case 'relerr_snn',
           y(i) = sum(g_rel./t_rel, 2) ./ (sum(g_rel./(t_rel.*t_rel), 2));  
      case 'loglikelihood_snn', 
           y(i) = sum((g_rel .* t_rel),2) ./ sum(g_rel, 2); 
      case 'crosslogistic_snn',
           y(i) = sum((g_rel .* t_rel),2) ./ sum(g_rel, 2); 
      case 'crossentropy_snn',
           y(i) = sum((g_rel .* t_rel),2) ./ sum(g_rel, 2); 
      otherwise,
	   err_msg = sprintf('Do not know how to handle error function: %s', fi);
           error(err_msg);
    end
end