function [gam_optimization,C_optimization,Yt_svm_train,Yt_svm_test,time_run]=z_lib_gridsearch(gam_array,C_array,X_svm_train,X_svm_test,Y_svm_train,Y_svm_test,P)
%this function is a full grid-search using Lib-SVM

tic; %記錄開始時間
if (nargin == 6) % check  number of input arguments
    P=0.1;	%默認epsilon值為0.1
end

gam_size = size(gam_array,2);
C_size = size(C_array,2);
%以gama的值作為行，C的值作為列,記錄train set的mse誤差
search_array_train = zeros(gam_size,C_size); %儲存train set的mse誤差

a='-s 3 -t 2 -c ';%設置libsvm type為epsilon SVR，核函數為RBF
b='-g ';
e='-p ';

for i=1:gam_size
    gam = gam_array(i);
    for j=1:C_size
        C = C_array(j);
        Parameter=[a,' ',num2str(C),' ',b,' ',num2str(gam),' ',e,' ',num2str(P)];
        model = svmtrain(Y_svm_train, X_svm_train , Parameter);
        Yt_svm_train = svmpredict(Y_svm_train, X_svm_train, model);
        [train_mse,train_mae,train_nmse,train_mape] = w_errcompute(Y_svm_train,Yt_svm_train);
        search_array_train(i,j) = train_mse;
        train_mse=0; %數值清0
    end
end


for i=1:gam_size
    min_mse_train(i)= min(search_array_train(i,:));
end

min_point_train = min(min_mse_train); %記錄最小值

%查找此最小值在原數組中的位置，從而確定對應的gam和C
gam_count_train = 1; %計數作用，防止有超過1個以上的最優參數組合
C_count_train = 1; %與gam_count_train保持一致,形成一對組合

for j=1:size(min_mse_train,2)
    if min_point_train ==  min_mse_train(j)
       gam_data_train(gam_count_train) = j;  %數組gam_data_train用來記錄在gam_array數組中的最優參數所在位置
       gam_count_train = gam_count_train +1;
    end
end

for ii = 1:(gam_count_train-1)
    for jj = 1:C_size
       if search_array_train(gam_data_train(ii),jj) == min_point_train
          C_data_train(C_count_train) = jj;%數組C_data_train用來記錄在C_array數組中的最優參數所在位置
           %只要在此列中找到了一個同最小值一樣的數值，就停止搜索，而不再考慮向后繼續搜索.主要出于對數組存儲位置的考慮，用以實現同gam值的一一對應
          C_count_train = C_count_train +1;
          break; %跳出此內嵌循環，回到外層循環
       end
    end
end

flag = 0; %標識是否存在多于1組的最優參數，0表示沒有，1表示有 %%注意，判斷是否存在多于1組最優參數，是基于比較train set的mse的
%取出最小誤差下的C和gam的值
if gam_count_train >2 & C_count_train >2
   disp('Attention: There are more than one pair of optimization parameters!')
   flag = 1;
end



if flag == 0 %表示只有一組最優值

   time_run = toc; %計算完全搜索所使用時間

   gam_optimization = gam_array(gam_data_train);
   C_optimization = C_array(C_data_train);
   Parameter=[a,' ',num2str(C_optimization),' ',b,' ',num2str(gam_optimization),' ',e,' ',num2str(P)];
   model = svmtrain(Y_svm_train, X_svm_train , Parameter);
   Yt_svm_train = svmpredict(Y_svm_train, X_svm_train, model);
   Yt_svm_test = svmpredict(Y_svm_test, X_svm_test, model);
   %[train_mse,train_mae,train_nmse,train_mape] = w_errcompute(Y_svm_train,Yt_svm_train);
   %[test_mse,test_mae,test_nmse,test_mape] = w_errcompute(Y_svm_test,Yt_svm_test);
   
%    disp(['Using Full_gridsearch'])
    disp(['The optimization parameters are follows: gam is ' num2str(gam_optimization) ', C is ' num2str(C_optimization)])
%    disp(['train_mse is ' num2str(train_mse) ', train_mae is ' num2str(train_mae) ',train_nmse is' num2str(test_nmse) ',train_mape is ' num2str(train_mape)])
%    disp(['test_mse is ' num2str(test_mse) ', test_mae is ' num2str(test_mae) ',test_nmse is ' num2str(test_nmse) ',test_mape is ' num2str(test_mape)])

 end





% 存在多于1組的最優參數! 在這種情況下，再比較具有相同train mse的不同最優參數下面的test set上面的mse
 if flag ==1
   for i = 1:(gam_count_train-1)
	   gam_optimization_array(i) = gam_array(gam_data_train(i));
	   C_optimization_array(i) = C_array(C_data_train(i));
       Parameter=[a,' ',num2str(C_optimization_array(i)),' ',b,' ',num2str(gam_optimization_array(i)),' ',e,' ',num2str(P)];
       model = svmtrain(Y_svm_train, X_svm_train , Parameter);
       Yt_svm_train = svmpredict(Y_svm_train, X_svm_train, model);
       Yt_svm_test = svmpredict(Y_svm_test, X_svm_test, model);
       [train_mse,train_mae,train_nmse,train_mape] = w_errcompute(Y_svm_train,Yt_svm_train);
       [test_mse,test_mae,test_nmse,test_mape] = w_errcompute(Y_svm_test,Yt_svm_test);
	   test_mse_array(i) = test_mse;
	   train_mse_array(i) = train_mse;
   end

   %%%在test_mse_array中找出最小的誤差值，然后找出對應情況下的gam，C,%注意,找到第一個即可，不考慮還存在相同train set mse相同而且test set上面mse還相同的情況
   min_test_mse_array = min(test_mse_array);
   for ii =1:size(test_mse_array,2)
       if test_mse_array(ii) == min_test_mse_array
          min_position = ii;
       end
   end
   %%%得到最優參數如下
   gam_optimization = gam_optimization_array(min_position);
   C_optimization = C_optimization_array(min_position);
   time_run = toc; %計算完全搜索所使用時間

   Parameter=[a,' ',num2str(C_optimization),' ',b,' ',num2str(gam_optimization),' ',e,' ',num2str(P)];
   model = svmtrain(Y_svm_train, X_svm_train , Parameter);
   Yt_svm_train = svmpredict(Y_svm_train, X_svm_train, model);
   Yt_svm_test = svmpredict(Y_svm_test, X_svm_test, model);
   %[train_mse,train_mae,train_nmse,train_mape] = w_errcompute(Y_svm_train,Yt_svm_train);
   %[test_mse,test_mae,test_nmse,test_mape] = w_errcompute(Y_svm_test,Yt_svm_test);
   
%    disp(['Using Full_gridsearch'])
    disp(['The optimization parameters are follows: gam is ' num2str(gam_optimization) ', C is ' num2str(C_optimization)])
%    disp(['train_mse is ' num2str(train_mse) ', train_mae is ' num2str(train_mae) ',train_nmse is' num2str(test_nmse) ',train_mape is ' num2str(train_mape)])
%    disp(['test_mse is ' num2str(test_mse) ', test_mae is ' num2str(test_mae) ',test_nmse is ' num2str(test_nmse) ',test_mape is ' num2str(test_mape)])
end