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Timing_SC_KMeans.m
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Timing_SC_KMeans.m
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clear all
X_read = readtable('C:\Users\mhashemi\OneDrive - Worcester Polytechnic Institute (wpi.edu)\Desktop\PHD\Paper\Crypto\GT_Updated_MULT_half_256.csv','ReadVariableNames', false);
X2 = table2array(X_read);
size = length(X2(1,:)) - 1;
Bin = X2(:,1);
X3 = X2(2:length(X2()),2:1:size+1)
X_trans = X3';
% Y = pdist(X_trans(:,3));
% T = linkage(Y, 'complete');
% dendrogram(T)
% for i = 1:length(X_trans)
% f1(:,i) = X_trans(1:2:size,i);
% f2(:,i) = X_trans(2:2:size,i);
% end
%half gate with or
for i = 1:length(X_trans)
f1(:,i) = X_trans(1:3:size,i);
f2(:,i) = X_trans(2:3:size,i);
f3(:,i) = X_trans(3:3:size,i);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%% mean y=ax+b
for i = 1:length(X_trans)
a(i) = (max(f1(:,i)) - mean(f1(:,i)))/(max(f2(:,i)) - mean(f2(:,i)));
b(i) = mean(f1(:,i)) - (a(i) * mean(f2(:,i)));
for j = 1:length(f3(:,i))
middle = f2(j,i);
f4(j,i) = (a(i) * middle) + b(i);
end
end
for i = 1:length(X_trans)
n_n(1:3:size,i) = normalize(f1(:,i),'zscore','std');%f1(:,i);
n_n(2:3:size,i) = normalize(f4(:,i),'zscore','std');%f2(:,i);
n_n(3:3:size,i) = normalize(f3(:,i),'zscore','std');%f3(:,i);
end
% m = 1;
% % for i = length(X_trans)
% % for j = length(f2)
% % if(X_trans(3:3:size,i) == f2(:,j))
% % ind_f2(m) = i;
% % m = m + 1;
% % end
% % end
% % end
% for i = cast((length(X_trans)/3),"int8")
% ind_f2(i) = i*3;
% end
% f1 = X_trans;
% f1(:,ind_f2(i))=[];
% % for i = 1:length(X_trans)
% % n(1:2:5,i) = (X_trans(1:2:5,i)-min(reshape(f1,[64*3,1])))./(max(reshape(f1,[64*3,1]))-min(reshape(f1,[64*3,1])));
% % n(2:2:6,i) = (X_trans(2:2:6,i)-min(reshape(f2,[64*3,1])))./(max(reshape(f2,[64*3,1]))-min(reshape(f2,[64*3,1])));
% % end
% %normalization
% for i = 1:length(X_trans)
% n_n(1:3:size,i) = normalize(f1(:,i),'scale','std');
% n_n(2:3:size,i) = normalize(f2(:,i),'scale','std');
% n_n(3:3:size,i) = normalize(f3(:,i),'scale','std');
% end
% boxcox
% for i = 1:length(X_trans)
% [transdat1,lambda1] = boxcox(f1(:,i));
% [transdat2,lambda2] = boxcox(f2(:,i));
% [transdat3,lambda3] = boxcox(f3(:,i));
% n_n(1:3:size,i) = transdat1;
% n_n(2:3:size,i) = transdat2;
% n_n(3:3:size,i) = transdat3;
% end
%
% for i = 1:length(X_trans)
% [transdat1,lambda1] = boxcox(X_trans(:,i));
% n_all(1:size,i) = transdat1;
% end
% for i = 1:length(X_trans)
% n(1+1:2:size-1,i) = (f1(:,i)-min(f1(:,i)))./(max(f1(:,i))-min(f1(:,i)));
% n(2+1:2:size,i) = (f2(:,i)-min(f2(:,i)))./(max(f2(:,i))-min(f2(:,i)));
% end
% for i = 1:length(X_trans)
% n_all(1:size,i) = normalize(X_trans(:,i),'norm',1);
% end
% for i = 1:length(X_trans)
% n_mm_all(1:size,i) = (X_trans(:,i)-min(X_trans(:,i)))./(max(X_trans(:,i))-min(X_trans(:,i)));;
% end
% for i = 1:length(n)
% % test = reshape(X_trans_3d(i,:,:),[6,2])
% % Y = pdist(n(:,i));
% % T = linkage(Y, 'complete');
% % % idx(i,:) = cluster(T,'cutoff',0.24)';
% % idx(:,i) = cluster(T,'maxclust',2)';
% idx_n_n(:,i) = kmeans(n_n(:,i),2);
% % idx = reshape(idx,[64,6])
% % idx = cluster(T,"cutoff",10);
% % idx1(:,i) = knnsearch(X_trans(:,i),Y);
% end
% Y = pdist(n(:,4));
% T = linkage(Y, 'complete');
% dendrogram(T)
% for i = 1:length(X_trans)
% n(:,i) = (X_trans(:,i)-min(X_trans(:,i)))./(max(X_trans(:,i))-min(X_trans(:,i)));
% end
% Y = pdist(n(:,3));
% T = linkage(Y, 'complete');
% dendrogram(T)
% for i = 1:length(n)
% idx(:,i) = kmeans(n,2);
% end
% X = reshape(X_trans,[64*6,1])
% for i = 1:length(X)
% % for j = 1:length(X(1,:))
% % % if(mod(j,2)==1)
% % % X_trans_3d(i,j,1) = 1;
% % % end
% % % if(mod(j,2)==0)
% % % X_trans_3d(i,j,1) = 2;
% % % end
% % X_trans_3d(i,j,1) = X(i,j);
% % X_trans_3d(i,j,2) = X(i,j);
% % end
% X(i,2) = X(i,1);
% end
%'Options',opts,
opts = statset('Display','final');
for i = 1:length(X_trans)
n_ind(:,i) = n_n(1:size,i);
[idx(:,i),C] = kmeans(n_ind(:,i),2,'Distance','sqeuclidean','Replicates',100,'OnlinePhase', 'on','Options',opts);
% n_ind(:,i) = n_all(1:size,i);
% [idx(:,i),C] = kmeans(n_ind(:,i),2,'Distance','sqeuclidean','Replicates',100,'OnlinePhase', 'on','Options',opts);
end
binar = zeros(length(X_trans),size);
for i = 1:length(X_trans)
bb = 1;
in = Bin(i);
while in >= 1
binar(i,bb) = mod(in,2);
in = in - mod(in,2);
in = in / 2;
bb = bb + 1;
end
end
binar = binar';
zero_clust = 0;
one_clust = 0;
for i = 1:length(X_trans)
if (X_trans(1,i)<55000)
zero_clust = idx(1,i);
if(zero_clust == 1)
one_clust = 2;
else
one_clust = 1;
end
else
one_clust = idx(1,i);
if(one_clust == 1)
zero_clust = 2;
else
zero_clust = 1;
end
end
for j = 1 :size
if (binar(j,i) == 0)
binar_clusters(j,i) = zero_clust;
else
binar_clusters(j,i) = one_clust;
end
end
end
for i = 1 :length(X_trans)
correct_clust = 0;
for j = 1 : size
if(binar_clusters(j,i) == idx(j,i))
correct_clust = correct_clust + 1;
end
end
SR(i) = correct_clust/size;
end
SR_avg = mean(SR);
SR_min = min(SR);
% eva = evalclusters(n_ind,'kmeans','Silhouette','klist',[1:4]);
% noclusters=eva.OptimalK;
%
% n_ind = [n_ind,n_ind]
% figure;
% plot(n_ind(idx==1,1),n_ind(idx==1,2),'r.','MarkerSize',12)
% hold on
% plot(n_ind(idx==2,1),n_ind(idx==2,2),'b.','MarkerSize',12)
% plot(C(:,1),C(:,1),'kx',...
% 'MarkerSize',15,'LineWidth',3)
% legend('Cluster 1','Cluster 2','Centroids',...
% 'Location','NW')
% title 'Cluster Assignments and Centroids'
% hold off