Matlab codes to display a variable name with its value

Question

Abdulmu'min Musa 2019-10-17

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/485837-matlab-codes-to-display-a-variable-name-with-its-value

评论： Looky 2019-10-17

i want a way in which i can display a variable together with its value. I have a code inwhich i am working on which can display the minimum value from some series of values of variable, but i want a code that can display the variable name of the minimum value together with the value.

here is the complete code below:

faceDatabase = imageSet('T8ATTMSE','recursive');
%Display Query Image and Database  side by side
fprintf('Press Enter to select image')
pause;
[filename,pathname]=uigetfile({'*.jpg'},'Pick an image file');
galleryImage=imread([pathname,filename]);
figure;
for i=1:size(faceDatabase,2)
    imageList(i)=faceDatabase(i).ImageLocation(1);
end
subplot(1,2,1);imshow(galleryImage);title('Selected Image');
subplot(1,2,2);montage(imageList);title('Database Image');
pause(0.002)
%Split Database into Training and Test sets
[training,test]=partition(faceDatabase,[0.6 0.4]);
%Extract and display Histogram of Oriented Gradient (HOG) features for
%single face
[hogFeature,visualization]=......
    extractHOGFeatures(galleryImage);
figure;
imshow(galleryImage);title('Input face');
figure;
plot(visualization);title('HOG Feature');
%Extract HOG Features for Training Set
% trainingFeatures=zeros(size(training,2)*training(1).Count,139284);
trainingFeatures=[];
featureCount=1;
for i=1:size(training,2)
for j=1:training(i).Count
trainingFeatures(featureCount,:)=extractHOGFeatures(read(training(i),j));
trainingLabel{featureCount}=training(i).Description;
featureCount=featureCount+1;
end
personIndex{i}=training(i).Description;
end
queryFeatures = extractHOGFeatures(galleryImage);
A = queryFeatures;
C = double(A);
TF1 = trainingFeatures(1,:)+trainingFeatures(2,:)+trainingFeatures(3,:)+trainingFeatures(4,:);
TF2 = trainingFeatures(5,:)+trainingFeatures(6,:)+trainingFeatures(7,:)+trainingFeatures(8,:);
TF3 = trainingFeatures(9,:)+trainingFeatures(10,:)+trainingFeatures(11,:)+trainingFeatures(12,:);
TF4 = trainingFeatures(13,:)+trainingFeatures(14,:)+trainingFeatures(15,:)+trainingFeatures(16,:);
TF5 = trainingFeatures(17,:)+trainingFeatures(18,:)+trainingFeatures(19,:)+trainingFeatures(20,:);
TF6 = trainingFeatures(21,:)+trainingFeatures(22,:)+trainingFeatures(23,:)+trainingFeatures(24,:);
TF7 = trainingFeatures(25,:)+trainingFeatures(26,:)+trainingFeatures(27,:)+trainingFeatures(28,:);
TF8 = trainingFeatures(29,:)+trainingFeatures(30,:)+trainingFeatures(31,:)+trainingFeatures(32,:);
TF9 = trainingFeatures(33,:)+trainingFeatures(34,:)+trainingFeatures(35,:)+trainingFeatures(36,:);
TF10 = trainingFeatures(37,:)+trainingFeatures(38,:)+trainingFeatures(39,:)+trainingFeatures(40,:);
TF11 = trainingFeatures(41,:)+trainingFeatures(42,:)+trainingFeatures(43,:)+trainingFeatures(44,:);
TF12 = trainingFeatures(45,:)+trainingFeatures(46,:)+trainingFeatures(47,:)+trainingFeatures(48,:);
TF13 = trainingFeatures(49,:)+trainingFeatures(50,:)+trainingFeatures(51,:)+trainingFeatures(52,:);
TF14 = trainingFeatures(53,:)+trainingFeatures(54,:)+trainingFeatures(55,:)+trainingFeatures(56,:);
TF15 = trainingFeatures(57,:)+trainingFeatures(58,:)+trainingFeatures(59,:)+trainingFeatures(60,:);
TF16 = trainingFeatures(61,:)+trainingFeatures(62,:)+trainingFeatures(63,:)+trainingFeatures(64,:);
TF17 = trainingFeatures(65,:)+trainingFeatures(66,:)+trainingFeatures(67,:)+trainingFeatures(68,:);
TF18 = trainingFeatures(69,:)+trainingFeatures(70,:)+trainingFeatures(71,:)+trainingFeatures(72,:);
TF19 = trainingFeatures(73,:)+trainingFeatures(74,:)+trainingFeatures(75,:)+trainingFeatures(76,:);
TF20 = trainingFeatures(77,:)+trainingFeatures(78,:)+trainingFeatures(79,:)+trainingFeatures(80,:);
TF21 = trainingFeatures(81,:)+trainingFeatures(82,:)+trainingFeatures(83,:)+trainingFeatures(84,:);
TF22 = trainingFeatures(85,:)+trainingFeatures(86,:)+trainingFeatures(87,:)+trainingFeatures(88,:);
TF23 = trainingFeatures(89,:)+trainingFeatures(90,:)+trainingFeatures(91,:)+trainingFeatures(92,:);
TF24 = trainingFeatures(93,:)+trainingFeatures(94,:)+trainingFeatures(95,:)+trainingFeatures(96,:);
TF25 = trainingFeatures(96,:)+trainingFeatures(97,:)+trainingFeatures(99,:)+trainingFeatures(100,:);
TF26 = trainingFeatures(101,:)+trainingFeatures(102,:)+trainingFeatures(103,:)+trainingFeatures(104,:);
TF27 = trainingFeatures(105,:)+trainingFeatures(106,:)+trainingFeatures(107,:)+trainingFeatures(108,:);
TF28 = trainingFeatures(109,:)+trainingFeatures(110,:)+trainingFeatures(111,:)+trainingFeatures(112,:);
TF29 = trainingFeatures(113,:)+trainingFeatures(114,:)+trainingFeatures(115,:)+trainingFeatures(116,:);
TF30 = trainingFeatures(117,:)+trainingFeatures(118,:)+trainingFeatures(119,:)+trainingFeatures(120,:);
TF31 = trainingFeatures(121,:)+trainingFeatures(122,:)+trainingFeatures(123,:)+trainingFeatures(124,:);
TF32 = trainingFeatures(125,:)+trainingFeatures(126,:)+trainingFeatures(127,:)+trainingFeatures(128,:);
TF33 = trainingFeatures(129,:)+trainingFeatures(130,:)+trainingFeatures(131,:)+trainingFeatures(132,:);
TF34 = trainingFeatures(133,:)+trainingFeatures(134,:)+trainingFeatures(135,:)+trainingFeatures(136,:);
TF35 = trainingFeatures(137,:)+trainingFeatures(138,:)+trainingFeatures(139,:)+trainingFeatures(140,:);
TF36 = trainingFeatures(141,:)+trainingFeatures(142,:)+trainingFeatures(143,:)+trainingFeatures(144,:);
TF37 = trainingFeatures(145,:)+trainingFeatures(146,:)+trainingFeatures(147,:)+trainingFeatures(148,:);
TF38 = trainingFeatures(149,:)+trainingFeatures(150,:)+trainingFeatures(151,:)+trainingFeatures(152,:);
TF39 = trainingFeatures(153,:)+trainingFeatures(154,:)+trainingFeatures(155,:)+trainingFeatures(156,:);
TF40 = trainingFeatures(157,:)+trainingFeatures(158,:)+trainingFeatures(159,:)+trainingFeatures(160,:);
error1=immse(C,TF1);
error2=immse(C,TF2);
error3=immse(C,TF3);
error4=immse(C,TF4);
error5=immse(C,TF5);
error6=immse(C,TF6);
error7=immse(C,TF7);
error8=immse(C,TF8);
error9=immse(C,TF9);
error10=immse(C,TF10);
error11=immse(C,TF11);
error12=immse(C,TF12);
error13=immse(C,TF13);
error14=immse(C,TF14);
error15=immse(C,TF15);
error16=immse(C,TF16);
error17=immse(C,TF17);
error18=immse(C,TF18);
error19=immse(C,TF19);
error20=immse(C,TF20);
error21=immse(C,TF21);
error22=immse(C,TF22);
error23=immse(C,TF23);
error24=immse(C,TF24);
error25=immse(C,TF25);
error26=immse(C,TF26);
error27=immse(C,TF27);
error28=immse(C,TF28);
error29=immse(C,TF29);
error30=immse(C,TF30);
error31=immse(C,TF31);
error32=immse(C,TF32);
error33=immse(C,TF33);
error34=immse(C,TF34);
error35=immse(C,TF35);
error36=immse(C,TF36);
error37=immse(C,TF37);
error38=immse(C,TF38);
error39=immse(C,TF39);
error40=immse(C,TF40);
Z=min(error1:error40);

4 个评论
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Stephen23 2019-10-17

在 MATLAB Online 中打开

Numbering variables like that is a sign that you are doing something wrong.

Copy-and-pasting code like that is a sign that you are doing something wrong.

The very inadvisable use of numbered variables means that this will not work:

Z=min(error1:error40);

and while it could be made to work, it would be complex and inefficient.

You should follow Looky's advice and keep your data in one array, then your task is easy.

Looky 2019-10-17

在 MATLAB Online 中打开

Since I don't have a clear idea how the data from extractHOGFeatures() looks, I can only make some guesses. Here is my idea:

lastIndex=40;
for k=1:lastIndex
   trFe_Index=4*(k-1)+1;   
   TF(k,:) = sum(trainingFeatures(trFe_Index:trFe_Index+3,:),1);
   errors(k)=immse(C,TF(k,:)); 
end
[Z,index]=min(errors);
sprintf('error%d', index)