Modified & Generalized Dunn's index
12 Jun 2013
18 Jun 2013)
An implementation of Modified and Generalized Dunn's index for internal cluster validation.
|dist_euclidean( X, Y )
% Calculates the Euclidean distance between vectors [FAST].
% Assume X is an m-by-p matrix representing m points in p-dimensional space and Y is an
% n-by-p matrix representing another set of points in the same space. This function
% compute the m-by-n distance matrix D where D(i,j) is the SQUARED Euclidean distance
% between X(i,:) and Y(j,:). Running time is O(m*n*p).
% If x is a single data point, here is a faster, inline version to use:
% D = sum( (Y - ones(size(Y,1),1)*x).^2, 2 )';
% X - m-by-p matrix of m p dimensional vectors
% Y - n-by-p matrix of n p dimensional vectors
% D - m-by-n distance matrix
% X=[randn(100,5)]; Y=randn(40,5)+2;
% D = dist_euclidean( [X; Y], [X; Y] ); im(D)
% 29-Sep-2005 2:00pm
% See also DIST_CHISQUARED, DIST_EMD
% Piotr's Image&Video Toolbox Version 1.03
% Written and maintained by Piotr Dollar pdollar-at-cs.ucsd.edu
% Please email me if you find bugs, or have suggestions or questions!
function D = dist_euclidean( X, Y )
if( ~isa(X,'double') || ~isa(Y,'double'))
error( 'Inputs must be of type double'); end;
m = size(X,1); n = size(Y,1);
Yt = Y';
XX = sum(X.*X,2);
YY = sum(Yt.*Yt,1);
D = XX(:,ones(1,n)) + YY(ones(1,m),:) - 2*X*Yt;