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dist

Euclidean distance weight function

Syntax

Z = dist(W,P,FP)
dim = dist('size',S,R,FP)
dw = dist('dw',W,P,Z,FP)
D = dist(pos)
info = dist('code')

Description

Weight functions apply weights to an input to get weighted inputs.

Z = dist(W,P,FP) takes these inputs,

W

S-by-R weight matrix

P

R-by-Q matrix of Q input (column) vectors

FP

Struct of function parameters (optional, ignored)

and returns the S-by-Q matrix of vector distances.

dim = dist('size',S,R,FP) takes the layer dimension S, input dimension R, and function parameters, and returns the weight size [S-by-R].

dw = dist('dw',W,P,Z,FP) returns the derivative of Z with respect to W.

dist is also a layer distance function which can be used to find the distances between neurons in a layer.

D = dist(pos) takes one argument,

pos

N-by-S matrix of neuron positions

and returns the S-by-S matrix of distances.

info = dist('code') returns information about this function. The following codes are supported:

'deriv'

Name of derivative function

'fullderiv'

Full derivative = 1, linear derivative = 0

'pfullderiv'

Input: reduced derivative = 2, full derivative = 1, linear derivative = 0

'name'

Full name

'fpnames'

Returns names of function parameters

'fpdefaults'

Returns default function parameters

Examples

Here you define a random weight matrix W and input vector P and calculate the corresponding weighted input Z.

W = rand(4,3);
P = rand(3,1);
Z = dist(W,P)

Here you define a random matrix of positions for 10 neurons arranged in three-dimensional space and find their distances.

pos = rand(3,10);
D = dist(pos)

Network Use

You can create a standard network that uses dist by calling newpnn or newgrnn.

To change a network so an input weight uses dist, set net.inputWeight{i,j}.weightFcn to 'dist'. For a layer weight, set net.layerWeight{i,j}.weightFcn to 'dist'.

To change a network so that a layer's topology uses dist, set net.layers{i}.distanceFcn to 'dist'.

In either case, call sim to simulate the network with dist.

See newpnn or newgrnn for simulation examples.

More About

expand all

Algorithms

The Euclidean distance d between two vectors X and Y is

d = sum((x-y).^2).^0.5

See Also

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