67 lines (53 with data), 2.8 kB

%ttesting

%ttest((round(rtClassValue(:,7,2)) == round(Yaverage(:,7))), (round(wsClassValue(:,7,2)) == round(Yaverage(:,7))))
%Better on the final ensemble
%2 sample t test right? 
%Normality test? [a b c d ] =kstest(round(rtEnsPred(:,i)) == Yaverage(:,i))

%We want to do a one tail test. We only care if the ws is better or equal
%to rt (for now) vs ws being worse than rt. Therefore null hypothesis will
%be that wsAcc >= rtAcc (> comes from the one tail and exclusion).
%Alternative hypothesis will be wsAcc < rtAcc 
%'tail', 'left' Set the alternative hypothesis to be that the population mean of x is less than the population mean of y.
%make x = wsAcc and y = rtAcc
%Accepting the null hypothesis is success (Answer = 0. p >= 0.05)
%Rejecting the null hypothesis is failure (Meaning that wsAcc < rtAcc)


%dirty code. Overwrites the data
%for i = 1: 7
%    pwsEns(i) =  isGreaterorEqualTTest(wsEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i));
%end

%for i = 1: 7
%    pwsSConcatEns(i) =  isGreaterorEqualTTest(wsSConcatEnsPred(:,i), rtEnsPred(:,i), YwsMulti(:,i), Yaverage(:,i));
%end

%for i = 1:7
%    for j = 1:3
%        pws(j,i) =  isGreaterorEqualTTest(wsClassValue(:,i,j), rtClassValue(:,i,j), Yaverage(:,i), Yaverage(:,i));
%    end
%end

%for i = 1:7
%    for j = 1:3
%        pwsSConcat(j,i) =  isGreaterorEqualTTest(wsSConcatClassValue(:,i,j), rtClassValue(:,i,j), YwsMulti(:,i), Yaverage(:,i));
%    end
%end

%for i = 1:7
%    for j = 1:3
%        pwsSConcat(j,i) =  isGreaterorEqualTTest(wsSConcatClassValue(:,i,j), rtClassValue(:,i,j), YwsMulti(:,i), Yaverage(:,i));
%    end
%end

for i = 1:numCategories
    pwsMultiEnsGreater(i) =  isGreaterTTest(wsMultiEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i)); %low p means greater greater (not less)
    
    pwsMultiEnsNotLess(i) = isNotLessTTest(wsMultiEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i)); %high P means not less than
    pwsMultiEnsEqual(i) = isSameTTest(wsMultiEnsPred(:,i), rtEnsPred(:,i), Yaverage(:,i), Yaverage(:,i)); %high p means equal
end


wsMultiEnsImprovement = wsMultiEnsSuccess - rtEnsSuccess;
[wsMultiEnsImprovement;pwsMultiEns;pwsMultiEns<0.05];

%T test notes:
%Three types of t tests
%One Sample - If we already have a known (Iron Clad) value from other experiments and
%want to know if this experiment produced a differing result. - Nope
%Paired Two Sample - if you are testing the same guys but after changing
%something. Is this us? Same photos, different processing.
%Independant Two Sample - Probably us?
%a = Yes or no. uselsess
%b = probability they are the same. Lower is better. Less that 0.05 is good
%c = Confidence Interval. 
%d = tstat, the raw T value. df Degrees of Freedom. sd - standar deviation.