You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: RotationFal.m
    path of file: ./repos/malini/Classifiers/Rotation Forest/Classifier_Files/Rotation Forest
    
    the code of the file until where you have to start completion: 
%%% skymss,2008.6.15 (email: skymss@126.com) %%%%

function [R_new,R_coeff]=RotationFal(Xini, Yini, K, Ratio)

% RotationFal: obtaining the new samples by Rotation forest algorithm;
%
% function [R_new,R_coeff]=RotationFal(DataX_old,trainY,K,Ratio)
%
% Input:  Xini: the original samples;
%         Yini: the labels of original samples;
%            K: the number of feature subset of Xini;
%         Ratio: the proportion of resampling new samples from original
%         samples; (usally Ratio=75%)
% Ourput: R_new: the arranged coffecients of features by Rotation forest;
%         R_coeff: the unarranged coffecients of features by Rotation forest;
%
% Author: Mao Shasha (skymss0828@gmail.com),2008.6.15 %


if (nargin<4 || nargin>5)
    help RotationFal
else
    Xinirfnew=[];

    DataX=[];
  
    
function [R_new,R_coeff]=RotationFal(Xini, Yini, K, Ratio)

% RotationFal: obtaining the new samples by Rotation forest algorithm;
%
% function [R_new,R_coeff]=RotationFal(DataX_old,trainY,K,Ratio)
%
% Input:  Xini: the original samples;
%         Yini: the labels of original samples;
%            K: the number of feature subset of Xini;
%         Ratio: the proportion of resampling new samples from original
%         samples; (usally Ratio=75%)
% Ourput: R_new: the arranged coffecients of features by Rotation forest;
%         R_coeff: the unarranged coffecients of features by Rotation forest;
%
% Author: Mao Shasha (skymss0828@gmail.com),2008.6.15 %


if (nargin<4 || nargin>5)
    help RotationFal
else
    Xinirfnew=[];

    DataX=[];
    DataX=Xini;
    [numtrn num_feature]=size(DataX);
    M=floor(num_feature/K);
    class=[];
    class=unique(Yini);
   
    [data_new_lie index_lie_new]=array_lie(DataX);
    trainX_lie=[];
    trainX_lie=data_new_lie;
    R_coeff=zeros(num_feature,num_feature);
    trainX_subset=[];
    trainY=Yini;
    index=[];
    for m=1:length(class)
        index{m,1}=find(trainY==class(m));
    end