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CV_Report.m
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CV_Report.m
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Actual = CumulativeActualMulti;
Predict = CumulativePredictMulti;
Act{1} = CumulativeActualClass0;
Act{2} = CumulativeActualClass1;
Act{3} = CumulativeActualClass2;
Act{4} = CumulativeActualClass3;
Act{5} = CumulativeActualClass4;
Pre{1} = CumulativePredictClass0;
Pre{2} = CumulativePredictClass1;
Pre{3} = CumulativePredictClass2;
Pre{4} = CumulativePredictClass3;
Pre{5} = CumulativePredictClass4;
sumspec=0; sumsens=0; %initialize before any runs
for i=1:5,
%Statistics for Binary Matrices
[c,cm,ind,per] = confusion(Act{i},Pre{i});
spec(i) = cm(2,2) / (cm(2,2) + cm(1,2));
sens(i) = cm(1,1) / (cm(1,1) + cm(2,1));
prec(i) = per(1,3);
accu(i) = (cm(1,1) + cm(2,2)) / (cm(1,1) + cm(1,2) + cm(2,1) + cm(2,2));
%I would like to store the values of each binary calculation.
sumspec = spec(i) + sumspec;
sumsens = sens(i) + sumsens;
end
title = input('Please enter a title (CV Confusion Matrices): ', 's');
classifier = input('Please enter classifier type (Random Forest): ', 's');
dataset = input('Please enter Data Set Name (AprilDataSet-10Fold): ', 's');
parameters = input('Please enter any parameters used (mtry=.8, ntrees=100): ', 's');
outfile = input('Please enter an output filename: ', 's');
FH = fopen(outfile, 'w');
fprintf(FH, 'Title, Classifier, Data Set, Parameters, , , , \n');
fprintf(FH, '%s, %s, %s, %s, , , , \n', title, classifier, dataset, parameters);
fprintf(FH, '\n');
%Statistics for Multinomial Matrices
[c,cm,ind,per]=confusion(Actual,Predict);
cm=cm'; %transposed for personal readability
[nr,nc]=size(cm);
specificity=sumspec/nr;
sensitivity=sumsens/nr;
precision=sum(per(:,3))/nr; %average the precisions of each class
tot=0;
sumcor=0; %initialize
for r=1:nr
for c=1:nc
if r==c
sumcor=sumcor+cm(r,c);
end
tot=tot+cm(r,c);
end
end
accuracy=sumcor/tot;
fprintf(FH, 'Class,Multi,,A0,A1,A2,A3,A4\n');
fprintf(FH, 'Accuracy,%f,P0,%f,%f,%f,%f,%f\n', accuracy, cm(1,1), cm(1,2), cm(1,3), cm(1,4), cm(1,5));
fprintf(FH, 'Precision,%f,P1,%f,%f,%f,%f,%f\n', precision, cm(2,1), cm(2,2), cm(2,3), cm(2,4), cm(2,5));
fprintf(FH, 'Sensitivity,%f,P2,%f,%f,%f,%f,%f\n', sensitivity, cm(3,1), cm(3,2), cm(3,3), cm(3,4), cm(3,5));
fprintf(FH, 'Specificity,%f,P3,%f,%f,%f,%f,%f\n', specificity, cm(4,1), cm(4,2), cm(4,3), cm(4,4), cm(4,5));
fprintf(FH, ',,P4,%f,%f,%f,%f,%f\n', cm(5,1), cm(5,2), cm(5,3), cm(5,4), cm(5,5));
fprintf(FH, '\n');
for i=1:5,
%Statistics for Binary Matrices
[c,cm,ind,per] = confusion(Act{i},Pre{i});
% ca{i} = c;
% cma{i} = cm;
% inda{i} = ind;
% pera{i} = per;
spec(i) = cm(2,2) / (cm(2,2) + cm(1,2));
sens(i) = cm(1,1) / (cm(1,1) + cm(2,1));
prec(i) = per(1,3);
accu(i) = (cm(1,1) + cm(2,2)) / (cm(1,1) + cm(1,2) + cm(2,1) + cm(2,2));
fprintf(FH, 'Class, %d, , 0, 1, , , \n', i-1);
fprintf(FH, 'Accuracy, %f, 0, %f, %f, , , \n', accu(i), cm(1,1), cm(1,2));
fprintf(FH, 'Precision, %f, 1, %f, %f, , , \n', prec(i), cm(2,1), cm(2,2));
fprintf(FH, 'Sensitivity,%f, , , , , , \n', sens(i));
fprintf(FH, 'Specificity,%f, , , , , , \n', spec(i));
fprintf(FH, '\n');
end
fclose(FH);
% Title, Classifier, Data Set, Parameters, , , , ,
% CV Confusion Matrices, RF, April_Partitions, ntrees=100, , , , ,
%
% Class, Multi, ,0, 1, 2, 3, 4
% Accuracy, ,0, , , , ,
% Precision, ,1, , , , ,
% Sensitivity, ,2, , , , ,
% Specificity, ,3, , , , ,
% , ,4, , , , ,
%
% Class, 0, ,0, 1, , ,
% Accuracy, ,0, , , , ,
% Precision, ,1, , , , ,
% Sensitivity, , , , , , ,
% Specificity, , , , , , ,
%
% Class, 1, ,0, 1, , ,
% Accuracy, ,0, , , , ,
% Precision, ,1, , , , ,
% Sensitivity, , , , , , ,
% Specificity, , , , , , ,
%
% Class, 2, ,0, 1, , ,
% Accuracy, ,0, , , , ,
% Precision, ,1, , , , ,
% Sensitivity, , , , , , ,
% Specificity, , , , , , ,
%
% Class, 3, ,0, 1, , ,
% Accuracy, ,0, , , , ,
% Precision, ,1, , , , ,
% Sensitivity, , , , , , ,
% Specificity, , , , , , ,
%
% Class, 4, ,0, 1, , ,
% Accuracy, ,0, , , , ,
% Precision, ,1, , , , ,
% Sensitivity, , , , , , ,
% Specificity, , , , , , ,
%