Linear discriminant analysis -test data

Hello, Please I would like to check my new point to see if my algorithm works well. I don't have complete understanding on : if a new data is tested, what code I should write or equation I should use to test the new point? Here is the code.

% This example deals with 2 classes
c1=[1 2;2 3;3 3;4 5;5 5]  % the first class 5 observations
c2=[1 0;2 1;3 1;3 2;5 3;6 5] % the second class 6 observations
scatter(c1(:,1),c1(:,2),6,'r'),hold on;
scatter(c2(:,1),c2(:,2),6,'b'),  

% Number of observations of each class
n1=size(c1,1)
n2=size(c2,1)

%Mean of each class
mu1=mean(c1)
mu2=mean(c2)

% Average of the mean of all classes
mu=(mu1+mu2)/2

% Center the data (data-mean)
d1=c1-repmat(mu1,5,1)
d2=c2-repmat(mu2,6,1)

% Calculate the within class variance (SW)
s1=d1'*d1
s2=d2'*d2
sw=s1+s2
invsw=inv(sw)

   % in case of two classes only use v-direction of projection
    v=invsw*(mu1-mu2)'

   % if more than 2 classes calculate between class variance (SB)
   sb1=n1*(mu1-mu)'*(mu1-mu)
   sb2=n2*(mu2-mu)'*(mu2-mu)
   SB=sb1+sb2
   v=invsw*SB

   % find eigne values and eigen vectors of the (v)
   [evec,eval]=eig(v)

% Sort eigen vectors according to eigen values (descending order) and
% neglect eigen vectors according to small eigen values
 % v=evec(greater eigen value)
 % or use all the eigen vectors

   % project the data of the first and second class respectively
   y2=c2*v
   y1=c1*v
   unique(c1)