% -*- texinfo -*-
% @deftypefn {Function File} {}[@var{mTrain}, @var{mTest}, @var{mVali}] = subset (@var{mData},@var{nTargets},@var{iOpti},@var{fTest},@var{fVali})
% @code{subset} splits the main data matrix which contains inputs and targets into 2 or 3 subsets
% depending on the parameters.
%
% The first parameter @var{mData} must be in row order. This means if the network
% contains three inputs, the matrix must be have 3 rows and x columns to define the
% data for the inputs. And some more rows for the outputs (targets), e.g. a neural network
% with three inputs and two outputs must have 5 rows with x columns~
% The second parameter @var{nTargets} defines the number or rows which contains the target values~
% The third argument @code{iOpti} is optional and can have three status:
% 0: no optimization
% 1: will randomise the column order and order the columns containing min and max values to be in the train set
% 2: will NOT randomise the column order, but order the columns containing min and max values to be in the train set
% default value is @code{1}
% The fourth argument @code{fTest} is also optional and defines how
% much data sets will be in the test set. Default value is @code{1/3}
% The fifth parameter @code{fTrain} is also optional and defines how
% much data sets will be in the train set. Default value is @code{1/6}
% So we have 50% of all data sets which are for training with the default values.
%
% @example
% [mTrain, mTest] = subset(mData,1)
% returns three subsets of the complete matrix
% with randomized and optimized columns~
% @end example
% @example
% [mTrain, mTest] = subset(mData,1,)
% returns two subsets
% @end example
%
% @end deftypefn