Task_1_model1.m

format compact
clear 
clc

%% Load data
data=load('../Datasets/haberman.data');


class2=data(find(data(:,4)==2),:);
class1=data(find(data(:,4)==1),:);

class1=class1(1:length(class2),:);
data=[class1',class2'];
data=data';


%% init arrays
Acc=zeros(2,1);
numOfRules=zeros(2,1);
Perf=zeros(1,6);


%% split the data to 60 training,20 val and 20 test.

preproc=1;
[trnData,chkData,tstData]=split_scale(data,preproc);

%% ANFIS - Scatter Partition
options = genfisOptions('SubtractiveClustering');
options.ClusterInfluenceRange = 0.7;
model = genfis(trnData(:,1:end-1), trnData(:,end), options);
%get the num of rules
numOfRules=length(model.Rules());
[trnFis,trnError,~,valFis,valError]=anfis(trnData,model,[100 0 0.01 0.9 1.1],[],chkData);

for j = 1:length(model.output(1).mf)
    model.output(1).mf(j).type = 'constant';
end
    
%evaluation of model
Y=evalfis(tstData(:,1:end-1),valFis);
Y=round(Y);
diff=tstData(:,end)-Y;
Acc=(length(diff)-nnz(diff))/length(Y)*100;
    
%% Error Matrix
error_matrix = confusionmat(tstData(:,end), Y);
pa = zeros(1, 2);
ua = zeros(1, 2);
%% confusion matrix
figure;
cm = confusionchart(tstData(:,end),Y);
    
%% Producer’s accuracy – User’s accuracy
N = length(tstData);
for i = 1 : 2
    pa(i) = error_matrix(i, i) / sum(error_matrix(:, i));
    ua(i) = error_matrix(i, i) / sum(error_matrix(i, :));
end

%% Overall accuracy
overall_acc = 0;
for i = 1 : 2
    overall_acc = overall_acc + error_matrix(i, i);
end
overall_acc = overall_acc / N;

%% k
p1 = sum(error_matrix(1, :)) * sum(error_matrix(:, 1)) / N ^ 2;
p2 = sum(error_matrix(2, :)) * sum(error_matrix(:, 2)) / N ^ 2;
pe = p1 + p2;
k = (overall_acc - pe) / (1 - pe);

%% Plot some input membership functions
figure;
plotMFs(model,size(trnData,2)-1);
suptitle('TSK model : some membership functions before training');
%% Plot the input membership functions after training
figure;
plotMFs(valFis,size(trnData,2)-1);
suptitle('TSK model : some membership functions after training');

%% Learning curve
figure;
plot(1:length(trnError), trnError, 1:length(valError), valError);
title('TSK model : Learning Curve');
xlabel('iterations');
ylabel('Error');
legend('Training Set', 'Test Set');