Learning functions of sensor weights and thresholds in the MATLAB Neural Network Toolbox learnp

I will post the code first! Study it slowly!

function [out1,out2] = learnp(varargin)%LEARNP Perceptron weight/bias learning function.%%  <a href="matlab:doc learnp">learnp</a> is the perceptron weight/bias learning function.%%  <a href="matlab:doc learnp">learnp</a>(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,%    W  - SxR weight matrix (or b, an Sx1 bias vector).%    P  - RxQ input vectors (or ones(1,Q)).%    Z  - SxQ weighted input vectors.%    N  - SxQ net input vectors.%    A  - SxQ output vectors.%    T  - SxQ layer target vectors.%    E  - SxQ layer error vectors.%    gW - SxR gradient with respect to performance.%    gA - SxQ output gradient with respect to performance.%    D  - SxS neuron distances.%    LP - Learning parameters, none, LP = [].%    LS - Learning state, initially should be = [].%  and returns,%    dW - SxR weight (or bias) change matrix.%    LS - New learning state.%%  <a href="matlab:doc learnp">learnp</a>(CODE) returns useful information for each CODE string:%    'pnames'    - Returns names of learning parameters.%    'pdefaults' - Returns default learning parameters.%    'needg'     - Returns 1 if this function uses gW or gA.%%  Here we define a random input P and error E to a layer%  with a 2-element input and 3 neurons.%%    p = rand(2,1);%    e = rand(3,1);%%  LEARNP only needs these values to calculate a weight change.%%    dW = <a href="matlab:doc learnp">learnp</a>([],p,[],[],[],[],e,[],[],[],[],[])%%  See also LEARNPN, NEWP, ADAPT, TRAIN.% Mark Beale, 1-31-92% Revised 11-31-97, MB% Copyright 1992-2010 The MathWorks, Inc.% $Revision: 1.1.6.8 $  $Date: 2010/04/24 18:09:27 $%% =======================================================%  BOILERPLATE_START%  This code is the same for all Learning Functions.  persistent INFO;  if isempty(INFO), INFO = get_info; end  if (nargin < 1), nnerr.throw('Not enough arguments.'); end  in1 = varargin{1};  if ischar(in1)    switch in1      case 'info'        out1 = INFO;      case 'check_param'        out1 = check_param(varargin{2});      otherwise,        try          out1 = eval(['INFO.' in1]);        catch me          nnerr.throw(['Unrecognized first argument: ''' in1 ''''])        end    end  else    [out1,out2] = apply(varargin{:});  endendfunction sf = subfunctions  sf.apply = @apply;endfunction v = fcnversion  v = 7;end%  BOILERPLATE_END%% =======================================================function info = get_info  info = nnfcnLearning(mfilename,'Perceptron',...    fcnversion,subfunctions,false,true,true,false,[]);  % TODO - Indicate that it requires errorendfunction err = check_param(param)  err = '';endfunction [dw,ls] = apply(w,p,z,n,a,t,e,gW,gA,d,lp,ls)  dw = e*p';end