Code Optimization & simplification: BSXFUN & Confusion Matrix Calculation

Dear all

The main objective of this thread is toward codes optimization & simplification.

There are two objectives 1) To compare an array A to array B. If array A is larger than array S then return logical 1, or otherwise. Array A is compromise element by element division between each row at EasyComp(:,1) and EasyComp(:,2). Array S is a vector of similar threshold value. In this example, the array A will be compared with different threshold values. The possible threshold value are -3:0.1:3.

2) The second part is to evaluate the density estimation by calculating element in the confusion matrix.

The ultimate question, is there any ways I can improve this code, specifically avoid the use of to many FOR Loops. The drafted code is as below. The MAT file is attached together in this thread.

load ('EasyComp')
ThrshldResult=EvalThershld (EasyComp);
ConfResult=ConfMatrix_Ev (EasyComp,ThrshldResult);

function ResThRat=EvalThershld (EasyComp)

txt =1;
x=-3:0.1:3;  % Threshold value
for k=1:numel(x)
    threshold=x(k);
    S = repmat(threshold, (length (EasyComp(:,1))),1 );
    ResThRat {txt} = bsxfun(@ge, S, (EasyComp(:,1)./EasyComp(:,2)));
    txt =txt+1;
end
end

function ConfResult=ConfMatrix_Ev (EasyComp,ThrshldResult)

txc=1;

ActClss=EasyComp(:,3);          % Actual class

for kok =1:length(ThrshldResult) % Evaluate every logical in each the cell
    PredClss=ThrshldResult{kok};  % Predicted class by varying diff threshold

      % In the following lines a comparison between elements in the column THREE
      % of the EASYCOMP and Predicted Class.
      %           0 0 = True Negetive
      %           0 1 = False Negetive
      %           1 0 = False Positive
      %           1 1 = True Positive
      % The all this value store as a CATEGORICAL array name CONFUSION MATRIX: CONFMAT

      ConfMat = categorical (repmat({'TN'}, [length(EasyComp) 1]));       % True Negative  (TN)  | Predicted NS, actual NS
      ConfMat (logical (((ActClss==0) .*(PredClss==1))))='FN'; % False Negative (FN)  | Predicted NS, actual S
      ConfMat (logical (((ActClss==1) .*(PredClss==0))))='FP'; % False Positive (FP)  | Predicted S, actual NS
      ConfMat (logical (((ActClss==1) .*(PredClss==1))))='TP'; % True Positive (TP)   | Predicted S, actual S

      % % Calculate the True Positive Rate % False Positive Rate which can be used to Plot the Receiver operating characteristic
      perf.TPR  = (sum(ConfMat(:) == 'TP'))/((sum(ConfMat(:) == 'TP'))+ (sum(ConfMat(:) == 'FN')));   % TP/ (TP+FN)  || Sensetivity,  SENT is very high if FN is low
      perf.FPR  = (sum(ConfMat(:) == 'FP'))/((sum(ConfMat(:) == 'FP'))+ (sum(ConfMat(:) == 'TN')));   % FP/ (FP+TN)  || Specificity,  SPEC is very high is TN is low

      result.TPR {txc} = perf.TPR;
      result.FPR {txc} = perf.FPR;
      %
      txc=txc+1;
  end
  end