Euclidean distance weight function
information about this function. For more information, see the code
info = dist(
Calculate the Weighted Input By Using the
This example shows how to calculate the corresponding weighted input
Z, given a random weight matrix
W and input vector
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)
W — Weight matrix
Weight matrix, specified as an
P — Input matrix
Input matrix, specified as an
Q matrix of
Q input (column) vectors.
S — Layer dimension
Layer dimension, specified as a scalar.
R — Input dimension
Input dimension, specified as a scalar.
pos — Neuron positions
Matrix of neuron positions, specified as an
code — Information option
Information you want to retrieve from the function, specified as one of the following:
'name'returns the name of this function.
'deriv'returns the name of the derivative function
'fullderiv'returns 1 for full derivative and 0 for linear derivative.
'pfullderiv'returns 2 for reduced derivative, 1 for full derivative, and 0 for linear derivative.
'fpnames'returns the names of the function parameters.
'fpdefaults'returns the default function parameters.
Z — Vector distances
Vector distances, returned as an
dim — Weight size
Weight size, returned as a row vector.
dw — Derivative of w
Z with respect to
W, returned as a
D — Distances
Distances, returned as an
You can create a standard network that uses
dist by calling
To change a network so an input weight uses
'dist'. For a layer
To change a network so that a layer’s topology uses
In either case, call
sim to simulate the network with
The Euclidean distance
d between two vectors
d = sum((x-y).^2).^0.5
Introduced before R2006a