findknn

[indxknn, distknn] = findknn(x, y, xpoint, ypoint, numofneighbors, distCalcMethod, dispout)

Description

Find k-nearest neighbors using Euclidean distance

Inputs

x

Coordinate of data in x direction

y

Coordinate of data in y direction

xpoint

Coordinate (in x direction) of point that nearest point to that is disired to be found

ypoint

Coordinate (in y direction) of point that nearest point to that is disired to be found

numofneighbors=1;

Number of nearest neighbors to (xpoint,ypoint) that are desired to be found

distCalcMethod=’1d’;

Distance calculation method

‘1d’: use 1d array

‘pdist2’: Use 2d distance function

‘vector’: Use vectorized distance

dispout=’no’;

Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs

indxknn

Index of nearest neighbors points

returns M*N array where M=length(xpoint) and N=numofneighbors

nth row associated with nth point in (xpoint,ypoint)

distknn

Distance of nearest neighbors points

returns M*N array where M=length(xpoint) and N=numofneighbors

mth row associated with mth point in (xpoint,ypoint)

nth column associated with nth nearest neighbors

Examples

x(:,1)=10.*rand(100,1);
y(:,1)=10.*rand(100,1);
xpoint=mean(x);
ypoint=mean(y);
[indxknn,distknn]=findknn(x,y,xpoint,ypoint,1,'1d','yes');

x(:,1)=10.*rand(100,1);
y(:,1)=10.*rand(100,1);
xpoint=[2.5;5;7.5];
ypoint=[3;6;9];
[indxknn,distknn]=findknn(x,y,xpoint,ypoint,10,'1d','yes');

References