Calculate a confusion matrix given two matrix matlab - matlab

Question

I have two Xval(Predicted values) and Sv(validation test) matrices, one with the classifier output data and the other with the validation data for the same samples. Each column represents the predicted value, eg [0 0 1 0 0 0 0 0 0 0] represents digit 3 (1 in the digit that is). I would like to know if it is possible to calculate the confusion matrix in a vectorized way or with a built in function, the sizes of both matrices are 12000x10. The code who generates both matrices are this
load data;
load test;
[N, m] = size(X);
X = [ones(N, 1) X];
[Nt, mt] = size(Xt);
Xt = [ones(Nt, 1) Xt];
new_order = randperm(N);
X = X(new_order,: );
S = S(new_order,: );
part = 0.8;
Xtr = X(1: (part * N),: );
Xv = X((part * N + 1): N,: );
Str = S(1: (part * N),: );
Sv = S((part * N + 1): N,: );
v_c = [];
v_tx_acerto = [];
tx_acerto_max = 0;
c = 250;
w = (X'*X+c*eye(m+1))\X' * S;
Xval = Xv*w;
for i=1:12000
aux = Xval(i,:);
aux(aux == max(aux)) = 1;
aux(aux<1) = 0;
Xval(i,:) = aux;
end

Answer 1

There are build-in functions confusionmat or plotconfusion. But if you want to have full control, you can just write a simple function yourself, e.g:
function [CMat_rel,CMat_abs] = ConfusionMatrix(Cprd,Cact)
Cprd_uq = unique(Cprd);
Cact_uq = unique(Cact);
NumPrd = length(Cprd_uq);
NumAct = length(Cact_uq);
% assert(NumPrd == NumAct)
% allocate memory
CMat_abs = NaN(NumPrd,NumAct);
CMat_rel = NaN(NumPrd,NumAct);
for j = 1:NumAct
lgAct = Cact == Cact_uq(j);
SumAct = sum(lgAct);
for i = 1:NumAct
lgPrd = Cprd == Cact_uq(i);
Num = sum( lgPrd(lgAct) == true );
CMat_abs(i,j) = Num;
CMat_rel(i,j) = Num/SumAct;
end
end
end