scientimate.findknn

indxknn, distknn = scientimate.findknn(x, y, xpoint, ypoint, numofneighbors=1, distCalcMethod='1d', dispout='no')

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

import scientimate as sm
import numpy as np

x=10*np.random.rand(100)
y=10*np.random.rand(100)
xpoint=np.mean(x)
ypoint=np.mean(y)
indxknn,distknn=sm.findknn(x,y,xpoint,ypoint,1,'1d','yes')

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

References