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Manhattan distance weight function


Z = mandist(W,P)
D = mandist(pos)


mandist is the Manhattan distance weight function. Weight functions apply weights to an input to get weighted inputs.

Z = mandist(W,P) takes these inputs,


S-by-R weight matrix


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

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

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

D = mandist(pos) takes one argument,


S row matrix of neuron positions

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


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 = mandist(W,P)

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

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

Network Use

To change a network so an input weight uses mandist, set net.inputWeights{i,j}.weightFcn to 'mandist'. For a layer weight, set net.layerWeights{i,j}.weightFcn to 'mandist'.

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

In either case, call sim to simulate the network with dist. See newpnn or newgrnn for simulation examples.


The Manhattan distance D between two vectors X and Y is

D = sum(abs(x-y))

See Also

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Introduced before R2006a

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