Accelerating the pace of engineering and science

# Documentation Center

• Trial Software
• Product Updates

# mandist

Manhattan distance weight function

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

## Description

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,

 W S-by-R weight matrix P 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,

 pos S row matrix of neuron positions

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

## 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 = 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.inputWeight{i,j}.weightFcn to 'mandist'. For a layer weight, set net.layerWeight{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.

## More About

expand all

### Algorithms

The Manhattan distance D between two vectors X and Y is

```D = sum(abs(x-y))
```

## See Also

Was this topic helpful?