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: lassoConjGrad.m
    path of file: ./repos/LassoADMM/Dataset2/使用共轭梯度法
    
    the code of the file until where you have to start completion: function [w,v,W_i, V_i, history] = lassoConjGrad(dataAll, lambda, rho, patientNo)

t_start = tic;%设置时间戳
QUIET    = 0;
ABSTOL   = 1e-4;%定义绝对容忍度和相对容忍度
RELTOL   = 1e-2;

%Data preprocessing
[examples,Col] = size(dataAll);

% [examples, feature] = size(dataAll);
trainingNum = floor(examples*0.7);
testExamples = dataAll(trainingNum+1:examples,:);
dataAll = dataAll(1:trainingNum,:);

data = dataAll(:, 1:Col-1);
b0 = dataAll(:, Col);
features = Col-1;

onepatientSample = floor(trainingNum/patientNo);% 每个病人提供样本数

MAX_ITER = 10000;
%初始值赋值
flag = 0;
w = zeros(1,features);        % 特征向量拟合系数
v = 0;
W_i = zeros(patientNo,features);        % 辅助变量
V_i = zeros(patientNo,1);

    
function [w,v,W_i, V_i, history] = lassoConjGrad(dataAll, lambda, rho, patientNo)

t_start = tic;%设置时间戳
QUIET    = 0;
ABSTOL   = 1e-4;%定义绝对容忍度和相对容忍度
RELTOL   = 1e-2;

%Data preprocessing
[examples,Col] = size(dataAll);

% [examples, feature] = size(dataAll);
trainingNum = floor(examples*0.7);
testExamples = dataAll(trainingNum+1:examples,:);
dataAll = dataAll(1:trainingNum,:);

data = dataAll(:, 1:Col-1);
b0 = dataAll(:, Col);
features = Col-1;

onepatientSample = floor(trainingNum/patientNo);% 每个病人提供样本数

MAX_ITER = 10000;
%初始值赋值
flag = 0;
w = zeros(1,features);        % 特征向量拟合系数
v = 0;
W_i = zeros(patientNo,features);        % 辅助变量
V_i = zeros(patientNo,1);

gama_w = zeros(patientNo,features);        % 辅助变量平均值
gama_v = zeros(patientNo,1);

if ~QUIET
    fprintf('%3s\t%10s\t%10s\t%10s\t%10s\t%10s\n', 'iter', ...
      'r norm', 'eps pri', 's norm', 'eps dual', 'objective');
end