This is my code. While i run this code it gives me an normalization error.
% Load the Training Set
TrSet = load('TrainingSet.txt');
[m,n] = size(TrSet); % (m samples) x (n dimensions)
for i = 1:m % the output (last column) values (0,1,2,3) are mapped to (0,1)
if TrSet(i,end)>=1
TrSet(i,end)=1;
end
end
% find the range of each attribute (for normalization later)
for i = 1:n
range(1,i) = min(TrSet(:,i));
range(2,i) = max(TrSet(:,i));
end
x = Normalize(TrSet, range); % normalize the data set to a hypercube
x(:,end) = []; % get rid of the output column
[m,n] = size(x);
nc = 2; % number of clusters = 2
% Initialize cluster centers to random points
c = zeros(nc,n);
for i = 1:nc
rnd = int16(rand*m + 1); % select a random vector from the input set
c(i,:) = x(rnd,:); % assign this vector value to cluster (i)
end
% Clustering Loop
delta = 1e-5;
n = 1000;
iter = 1;
while (iter < n)
% Determine the membership matrix U
% u(i,j) = 1 if euc_dist(x(j),c(i)) <= euc_dist(x(j),c(k)) for each k ~= i
% u(i,j) = 0 otherwise
for i = 1:nc
for j = 1:m
d = euc_dist(x(j,:),c(i,:));
u(i,j) = 1;
for k = 1:nc
if k~=i
if euc_dist(x(j,:),c(k,:)) < d
u(i,j) = 0;
end
end
end
end
end
% Compute the cost function J
J(iter) = 0;
for i = 1:nc
JJ(i) = 0;
for k = 1:m
if u(i,k)==1
JJ(i) = JJ(i) + euc_dist(x(k,:),c(i,:));
end
end
J(iter) = J(iter) + JJ(i);
end
% Stop if either J is below a certain tolerance value,
% or its improvement over previous iteration is below a certain threshold
str = sprintf('iteration: %.0d, J=%d', iter, J(iter));
disp(str);
if (iter~=1) & (abs(J(iter-1) - J(iter)) < delta)
break;
end
% Update the cluster centers
% c(i) = mean of all vectors belonging to cluster (i)
for i = 1:nc
sum_x = 0;
G(i) = sum(u(i,:));
for k = 1:m
if u(i,k)==1
sum_x = sum_x + x(k,:);
end
end
c(i,:) = sum_x ./ G(i);
end
iter = iter + 1;
end % while
disp('Clustering Done.');
% ----------------- TESTING PHASE --------------------------
% Load the evaluation data set
EvalSet = load('EvaluationSet.txt');
[m,n] = size(EvalSet);
for i = 1:m
if EvalSet(i,end)>=1
EvalSet(i,end)=1;
end
end
x = Normalize(EvalSet, range);
x(:,end) = [];
[m,n] = size(x);
% Assign evaluation vectors to their respective clusters according
% to their distance from the cluster centers
for i = 1:nc
for j = 1:m
d = euc_dist(x(j,:),c(i,:));
evu(i,j) = 1;
for k = 1:nc
if k~=i
if euc_dist(x(j,:),c(k,:)) < d
evu(i,j) = 0;
end
end
end
end
end
% Analyze results
ev = EvalSet(:,end)';
rmse(1) = norm(evu(1,:)-ev)/sqrt(length(evu(1,:)));
rmse(2) = norm(evu(2,:)-ev)/sqrt(length(evu(2,:)));
subplot(2,1,1);
if rmse(1) < rmse(2)
r = 1;
else
r = 2;
end
str = sprintf('Testing Set RMSE: %f', rmse(r));
disp(str);
ctr = 0;
for i = 1:m
if evu(r,i)==ev(i)
ctr = ctr + 1;
end
end
str = sprintf('Testing Set accuracy: %.2f%%', ctr*100/m);
disp(str);
[m,b,r] = postreg(evu(r,:),ev); % Regression Analysis
disp(sprintf('r = %.3f', r));
