simff_snn.m

来自「神经网络的工具箱, 神经网络的工具箱,」· M 代码 · 共 76 行

function a = simff_snn (X, net, data)
%SIMFF_SNN Simulate a feed forward neural network.
%
%  Syntax
%
%    y = simff_snn(net, P)
%
%  Description
%
%   SIMFF_SNN takes
%      net   -  a net_struct with the neural network
%      P     -  [N0 x MU] matrix with inputs
%   and returns 
%      y     -  [NL x MU] matrix with outputs
%
%   (N0 = #inputs; NL = #outputs; MU = #patterns)
%
%  Example
%
%   MU = 60;
%   data.P = 2*pi*rand(1,MU);
%   data.T = sin(data.P) + 0.1*randn(size(data.P));
%   net = net_struct_snn([1 8 1], {'tansigtf_snn', 'lintf_snn'}, 'trainlm_snn');
%   [net, tr_info] = train_snn(net, data)
%
%   P = [0:0.1:6]; 
%   y = simff_snn(net, P)
%
%
%  See also
%
%   NET_STRUCT_SNN, TRAIN_SNN
%

%
%  Additional syntax (also supported; not documented)
%
%    y = simff_snn(net, data)
%    y = simff_snn(X, net, P)
%    y = simff_snn(X, net, data)
%

if (nargin == 2)           % simff_snn(net, P) or simff_snn(net,data)
   if (isfield(net, 'P'))  % simff_snn(net, data)
      a = sim_all(X, net.P);
   else                    % simff_snn(net, P)
      a = sim_all(X, net); 
   end
   return;
elseif (nargin == 3)       % simff_snn(X, net, P) or simff_snn(X, net, data)
   net = setx_snn(net,X);
   if (isfield(data, 'P')) % simff_snn(X, net, data)
      a = sim_all(net, data.P);
   else                    % simff_snn(X, net, P)
      a = sim_all(net, data); 
   end
else
   error('Incorrect number of input arguments');
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function a = sim_all(net, P)

q = size(P,2);
a = feval(net.transferFcn{1}, net.weights{1}*P ...
                              + repmat(net.biases{1}, 1, q));
if (net.numLayers > 1)
   for i = 2:net.numLayers
      a = feval(net.transferFcn{i}, net.weights{i}*a ...
                              + repmat(net.biases{i},1, q));
   end
   return;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%