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Manhattan distance weight function
Z = mandist(W,P)
df = mandist('deriv')
D = mandist(pos);
mandist is the Manhattan distance weight function. Weight functions apply weights to an input to get weighted inputs.
mandist(W,P) takes these inputs,
and returns the S x Q matrix of vector distances.
mandist('deriv') returns '' because mandist does not have a derivative function.
mandist is also a layer distance function which can be used to find the distances between neurons in a layer.
mandist(pos) takes one argument,
pos - An S row matrix of neuron positions.
S x S matrix of distances.
Here we 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 we 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)You can create a standard network that uses
mandist as a distance function by calling newsom.
To change a network so an input weight uses mandist, set net.inputWeight{i,j}.weightFcn to 'mandist'. For a layer weight set net.inputWeight{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))
sim, dist, linkdist