When I am running following K-means code ... it gives me an error as "Normalization....." Please help me how to run this code.

% K-means clustering % ------------------- CLUSTERING PHASE ------------------- % 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));

[Asrar Ahmad]

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));

Answer 1

[Brendan Hamm]

Please format your code. This is unreadable.

http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer