wavenet.m

新的神经网络算法源程序

function [g, xt, xtd2] = wavenet(x, t, d, w, c, b)
%Wavenet:  WaveNet mapping
%
%	g = wavenet(x, THETA) or g = wavenet(x, t, d, w, c, b)
%
%	May be used for batching.

%	By Qinghua Zhang. March, 1994.

if nargin==2, [t,d,w,c,b]=decompth(t);  x=x';
elseif nargin~=6
  error('Wrong number of arguments.');
end

[xl,xc] = size(x);
[tl,tc] = size(t);
[dl,dc] = size(d);
[wl,wc] = size(w);
[cl,cc] = size(c);
[bl,bc] = size(b);

if isempty(t) & isempty(d) & isempty(w) % Linear regression
  g = c*x + b; xt=[]; xtd2=[];
  return
end

if (xl ~= tl) | (xl ~= dl)
  error('x, t, d must have the same number of lines.');
end

if (tc ~= dc) | (tc ~= wc)
  error('t, d, w must have the same number of columns.');
end

if wl ~= 1
  error('w must be a row vector.');
end

if (xl ~= cc) | (cl ~= 1)
  error('c must be a row vector and length(c) = linenb(x).');
end

if (bl ~= 1) | (bc ~= 1)
  error('b must be a scalar.');
end

if nargout == 1
  g = w * wavelon(x,t,d) + c * x + b;        
 
  if nargin==2, g=g'; end

  return;
elseif (nargout > 1) & (xc ~= 1)
  error( ...
    'If there are more than one output arguments, x must be a row vector ');
end

% Complete list of output

[wavelonvalue, xt, xtd2] = wavelon(x,t,d);
g = w * wavelonvalue + c * x + b;