testAccuracy.m

clear all; clc;
load('dataset/processedDataset_raw.mat');

%% Create gaussian EM
Left = GaussianEM(2,size(train_setl,2),'Left');
Right = GaussianEM(2,size(train_setr,2),'Right');

Left.train(train_setl,3,1,0,2,4);
Right.train(train_setr,3,1,0,2,4);

%
conf = zeros(2);
setl = test_same_setl;
setr = test_same_setr;
for i=1:size(setl,1)
    datapoint = setl(i,:);
    lpr = (datapoint-mul)/coeffl';
    rpr = (datapoint-mur)/coeffr';
    lp = Left.getLikelihood(lpr); rp = Right.getLikelihood(rpr);
    choice = datasample(find([lp,rp] == max([lp,rp])),1,'Replace',false);
    conf(1,choice) = conf(1,choice)+1;
end

for i=1:size(setr,1)
    datapoint = setr(i,:);
    lpr = (datapoint-mul)/coeffl';
    rpr = (datapoint-mur)/coeffr';
    lp = Left.getLikelihood(lpr); rp = Right.getLikelihood(rpr);
    choice = datasample(find([lp,rp] == max([lp,rp])),1,'Replace',false);
    conf(2,choice) = conf(2,choice)+1;
end
conf
%% Create multiple gaussians
ust = unique(train_users);
ntr = size(ust,1);
gaussians = cell(ntr,2);
K = 1; maxIter = 3;
for user = 1:ntr
    user
    IND = find(train_users==ust(user));
    user_set = train_set(IND,:); labels = labelstr(IND);
    
    left = user_set(labels==1,:); right = user_set(labels==2,:);
    L = GaussianEM(K,size(train_set,2),'Left');
    R = GaussianEM(K,size(train_set,2),'Right');
    %     L = Gaussian(size(train_set,2),'Left');
    %     R = Gaussian(size(train_set,2),'Right');
    
    L.train(left,maxIter,0,0,2,4);
    R.train(right,maxIter,0,0,2,4);
    gaussians{user,1} = L; gaussians{user,2} = R;
end

%% Test on multiple gaussians
conf = zeros(2);
set = test_same_set;
labels = labelstes;

for j=1:size(labels,1)
    datapoint = test_same_set(j,:);
    l = -Inf; r = -Inf;
    for i=1:ntr
        ll = gaussians{i,1}.getLikelihood(datapoint)+eps; l = l+ll;
        lr = gaussians{i,2}.getLikelihood(datapoint)+eps; r = r+lr;
    end
    [ll,lr]
    
    like = find([ll,lr]==max([ll,lr]));
    [num2str(j),' should be: ',num2str(labels(j)),' and is ',num2str(like)]
    conf(labels(j),like) = conf(labels(j),like)+1;
end
conf
%% Create Gaussian clustering

L = train_set(labelstr==1,:);
R = train_set(labelstr==2,:);
SET = train_set;
LABELS = labelstr;

SET2 = test_same_set;
LABELS2 = labelstes;
Iter = 5:5:10;
Clusters = 5:2:20;

table = zeros(size(Iter,2),size(Clusters,2));
self = zeros(size(Iter,2),size(Clusters,2));
for K = 1:size(Clusters,2)
    for MaxIter = 1:size(Iter,2);
        [K,MaxIter]
        
        Left = GaussianEM(Clusters(K),size(train_set,2),'Left');
        Right = GaussianEM(Clusters(K),size(train_set,2),'Right');
        % Baseline = GaussianEM(K,size(train_set,2),'Nothing');
        %
        Left.train(L,Iter(MaxIter),0,0,2,4);
        Right.train(R,Iter(MaxIter),0,0,2,4);
        % Baseline.train(train_set(labelstr==3,:),MaxIter,1,1,1,10000);
        
        S = size(SET,1);
        conf = zeros(2);
        predictions = zeros(S,1);
        for i=1:S
            probability = [max(Left.getLikelihood(SET(i,:))),max(Right.getLikelihood(SET(i,:)))];
            predictions(i) = datasample(find(probability == max(probability)),1,'Replace',false);
            conf(LABELS(i),predictions(i)) = conf(LABELS(i),predictions(i)) + 1;
        end
        correct = sum(predictions==LABELS)/S;
        self(K,MaxIter) = correct;
        
        % Test on train set EM GAUSSIAN
        
        S = size(SET2,1);
        conf = zeros(2);
        predictions = zeros(S,1);
        for i=1:S
            probability = [max(Left.getLikelihood(SET2(i,:))),max(Right.getLikelihood(SET2(i,:)))];
            predictions(i) = datasample(find(probability == max(probability)),1,'Replace',false);
            conf(LABELS2(i),predictions(i)) = conf(LABELS2(i),predictions(i)) + 1;
        end
        conf
        correct = sum(predictions==LABELS2)/S
        table(MaxIter,K) = correct;
    end
end

%% Create clustering classifiers and train with KMEANS
IND = randperm(size(train_set,1)); %randomize training data
train_set = train_set(IND,:);
labelstr = labelstr(IND);
K = 10;

Left = KMeans(K,size(train_set,2));
Right = KMeans(K,size(train_set,2));

for i=1:size(train_set,1)
    i
    switch labelstr(i)
        case 1, Left.addDataPoint(train_set(i,:));
        case 2, Right.addDataPoint(train_set(i,:));
    end
end

%% Test on train set KMEANS
SET = test_same_set;
LABELS = labelstes;
S = size(SET,1);
conf = zeros(2);
predictions = zeros(S,1);
five = @(x) x(1,1);
for i=1:S
    distance = [min(Left.getEuclideanDistance(SET(i,:))),min(Right.getEuclideanDistance(SET(i,:)))];
    predictions(i) = find(distance == min(distance));
    conf(LABELS(i),predictions(i)) = conf(LABELS(i),predictions(i)) + 1;
end
conf
correct = sum(predictions==LABELS)/S