You can try the function: pdist2().
help pdist2
PDIST2 Pairwise distance between two sets of observations.
D = PDIST2(X,Y) returns a matrix D containing the Euclidean distances
between each pair of observations in the MX-by-N data matrix X and
MY-by-N data matrix Y. Rows of X and Y correspond to observations,
and columns correspond to variables. D is an MX-by-MY matrix, with the
(I,J) entry equal to distance between observation I in X and
observation J in Y.
D = PDIST2(X,Y,DISTANCE) computes D using DISTANCE. Choices are:
'euclidean' - Euclidean distance (default)
'squaredeuclidean' - Squared Euclidean distance
'seuclidean' - Standardized Euclidean distance. Each
coordinate difference between rows in X and Y
is scaled by dividing by the corresponding
element of the standard deviation computed
from X, S=NANSTD(X). To specify another value
for S, use
D = PDIST2(X,Y,'seuclidean',S).
'cityblock' - City Block distance
'minkowski' - Minkowski distance. The default exponent is 2.
To specify a different exponent, use
D = PDIST2(X,Y,'minkowski',P), where the
exponent P is a scalar positive value.
'chebychev' - Chebychev distance (maximum coordinate
difference)
'mahalanobis' - Mahalanobis distance, using the sample
covariance of X as computed by NANCOV. To
compute the distance with a different
covariance, use
D = PDIST2(X,Y,'mahalanobis',C), where the
matrix C is symmetric and positive definite.
'cosine' - One minus the cosine of the included angle
between observations (treated as vectors)
'correlation' - One minus the sample linear correlation
between observations (treated as sequences of
values).
'spearman' - One minus the sample Spearman's rank
correlation between observations (treated as
sequences of values)
'hamming' - Hamming distance, percentage of coordinates
that differ
'jaccard' - One minus the Jaccard coefficient, the
percentage of nonzero coordinates that differ
function - A distance function specified using @, for
example @DISTFUN
A distance function must be of the form
function D2 = DISTFUN(ZI,ZJ),
taking as arguments a 1-by-N vector ZI containing a single observation
from X or Y, an M2-by-N matrix ZJ containing multiple observations from
X or Y, and returning an M2-by-1 vector of distances D2, whose Jth
element is the distance between the observations ZI and ZJ(J,:).
For built-in distance metrics, the distance between observation I in X
and observation J in Y will be NaN if observation I in X or observation
J in Y contains NaNs.
D = PDIST2(X,Y,DISTANCE,'Smallest',K) returns a K-by-MY matrix D
containing the K smallest pairwise distances to observations in X for
each observation in Y. PDIST2 sorts the distances in each column of D
in ascending order. D = PDIST2(X,Y,DISTANCE, 'Largest',K) returns the K
largest pairwise distances sorted in descending order. If K is greater
than MX, PDIST2 returns an MX-by-MY distance matrix. For each
observation in Y, PDIST2 finds the K smallest or largest distances by
computing and comparing the distance values to all the observations in
X.
[D,I] = PDIST2(X,Y,DISTANCE,'Smallest',K) returns a K-by-MY matrix I
containing indices of the observations in X corresponding to the K
smallest pairwise distances in D. [D,I] = PDIST2(X,Y,DISTANCE,
'Largest',K) returns indices corresponding to the K largest pairwise
distances.
Example:
% Compute the ordinary Euclidean distance
X = randn(100, 5);
Y = randn(25, 5);
D = pdist2(X,Y,'euclidean'); % euclidean distance
% Compute the Euclidean distance with each coordinate difference
% scaled by the standard deviation
Dstd = pdist2(X,Y,'seuclidean');
% Use a function handle to compute a distance that weights each
% coordinate contribution differently.
Wgts = [.1 .3 .3 .2 .1]; % coordinate weights
weuc = @(XI,XJ,W)(sqrt((XI - XJ).^2 * W'));
Dwgt = pdist2(X,Y, @(Xi,Xj) weuc(Xi,Xj,Wgts));
See also PDIST, KNNSEARCH, CREATENS, KDTreeSearcher,
ExhaustiveSearcher.
Documentation for pdist2
doc pdist2
Other uses of pdist2
gpuArray/pdist2 tall/pdist2
