glmerr.m

有关kalman滤波及其一些变形滤波算法

function [e, edata, eprior, y, a, mse] = glmerr(net, x, t)
%GLMERR Evaluate error function for generalized linear model.
%
%   Description
%    E = GLMERR(NET, X, T) takes a generalized linear model data
%   structure NET together with a matrix X of input vectors and a matrix
%   T of target vectors, and evaluates the error function E. The choice
%   of error function corresponds to the output unit activation function.
%   Each row of X corresponds to one input vector and each row of T
%   corresponds to one target vector.
%
%   [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data
%   and prior components of the total error.
%
%   [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y
%   giving the outputs of the models and a matrix A  giving the summed
%   inputs to each output unit, where each row corresponds to one
%   pattern.
%
%   See also
%   GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN
%

%   Copyright (c) Ian T Nabney (1996-2001)

% Check arguments for consistency
errstring = consist(net, 'glm', x, t);
if ~isempty(errstring);
  error(errstring);
end

[y, a] = glmfwd(net, x);

switch net.outfn

  case 'linear'     % Linear outputs
    edata = 0.5*sum(sum((y - t).^2));

  case 'logistic'   % Logistic outputs
    edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));

  case 'softmax'    % Softmax outputs
    edata = - sum(sum(t.*log(y)));

  otherwise
    error(['Unknown activation function ', net.outfn]);
end

mse = (2*edata) / size(t,1);

[e, edata, eprior] = errbayes(net, edata);