Tournicoti

Hello

I hope the comments will help.

Anyway please feel free to ask for precisions.

The application uses a network of 10*8 nodes.

You can paint (left/right buttons) the panel to change the network state.

You press compute to get the next stable state.

I'm a bit lazy, I 've put only '0', '1' and '2' digits in the dataset , but you can add more if you want (max 11 patterns)

Good luck !

The network implementation is divided into 2 classes :

HopfieldUnit.java which defines the behavior of an unit. (output computing and learning)

HopfieldNetwork.java which defines standard Hopfield Network. (asynchronous computing and a Hebbian offline learning method)

The file Patterns.java defines the patterns to be learnt . For now, 3 patterns ('0', '1', '2') are defined, but some others can be added directly.

The others are just UI stuff so let's ignore them

Hello

Ok, I'm doing something in java. Something with digits. The user draws a digit as  input, and the network answers the nearest learned digit from it.

Not sure if I'll be able to post it today , but I do as fast as I can

Not sure if you did this in your initialization, but in the weight matrix  :

Mat _i,i=0

Mat _i,j= Mat _j,i

... is generally used : it permits to be sure the network will reach a stable state.

The capacity of a hopfield network is approximately nbPatterns=0.138*nbNodes, so 4 nodes is too small, maybe try with 10x10 nodes ( about 13 patterns in term of capacity )

I noticed you have implemented a synchronous method to compute the network state. It's totally possible even if initially, it was supposed to be asynchronous and stochastic.

Hope it can help !

If you still have problems with your implementation, I can post some pseudo-code if necessary

Hello

The final output vector will then be –2, 1, –2, 1

You have to pass this to the activation function which is, typically in Hopfield Network :

f(x)=1 if x>0, -1 otherwise

A vector (input/output)  of Hopfield network can only consists in -1 and 1 (At least in discrete model)

Good luck

In the stochastic version, you choose randomly an unit, compute its ouput (integrate inputs + activation function), until you get a stable network state (vector)

instead of

public override void setSomething(boolean something)
{
      throw new Exception("This function is not supported in the sub class");
}

use this :

protected override void setSomething(boolean something)
{
}

or

private override void setSomething(boolean something)
{
}

so that the user of the B's class object can't call void setSomething(boolean something)

Maybe you can look for information on public, protected and private keywords ?

[EDIT]

So apparently illegal ... in C#