This package is unmaintained. Its reliability is not guaranteed.

Discrete and continuous Hopfield nets in Julia.

We'll restore a corrupted representation fo the letter X specified as a vector of -1's and +1's:

using HopfieldNets

include(Pkg.dir("HopfieldNets", "demo", "letters.jl"))

patterns = hcat(X, O)

n = size(patterns, 1)

net = DiscreteHopfieldNet(n)

train!(net, patterns)

settle!(net, 10, true)

Xcorrupt = copy(X)
for i = 2:7
     Xcorrupt[i] = 1
end

Xrestored = associate!(net, Xcorrupt)
all(Xcorrupt .== X)
all(Xrestored .== X)

The idea for the letters demo is taken from an implementation of Hopfield nets in Ruby. The code is based on the presentation of the theory in Mackay's book and in Storkey's learning rule paper.