Library to create neural networks.
This is primarily a math project meant to be used to investigate the behavior of different small neural networks.
gem install neuronetThe library is meant to be read, but here is a motivating example:
require 'neuronet'
include Neuronet
ff = FeedForward.new([3,3])
# It can mirror, equivalent to "copy":
ff.last.mirror
values = ff * [-1, 0, 1]
values.map { '%.13g' % _1 } #=> ["-1", "0", "1"]
# It can anti-mirror, equivalent to "not":
ff.last.mirror(-1)
values = ff * [-1, 0, 1]
values.map { '%.13g' % _1 } #=> ["1", "0", "-1"]
# It can "and";
ff = FeedForward.new([2,2,1])
ff[1].mirror(-1)
ff.last.connect(ff.first)
ff.last.average
# Training "and" pairs:
pairs = [
[[1, 1], [1]],
[[-1, 1], [-1]],
[[1, -1], [-1]],
[[-1, -1], [-1]],
]
# Train until values match:
ff.pairs(pairs) do
pairs.any? { |input, target| (ff * input).map { _1.round(1) } != target }
end
(ff * [-1, -1]).map{ _1.round } #=> [-1]
(ff * [-1, 1]).map{ _1.round } #=> [-1]
(ff * [ 1, -1]).map{ _1.round } #=> [-1]
(ff * [ 1, 1]).map{ _1.round } #=> [1]
# It can "or";
ff = FeedForward.new([2,2,1])
ff[1].mirror(-1)
ff.last.connect(ff.first)
ff.last.average
# Training "or" pairs:
pairs = [
[[1, 1], [1]],
[[-1, 1], [1]],
[[1, -1], [1]],
[[-1, -1], [-1]],
]
# Train until values match:
ff.pairs(pairs) do
pairs.any? { |input, target| (ff * input).map { _1.round(1) } != target }
end
(ff * [-1, -1]).map{ _1.round } #=> [-1]
(ff * [-1, 1]).map{ _1.round } #=> [1]
(ff * [ 1, -1]).map{ _1.round } #=> [1]
(ff * [ 1, 1]).map{ _1.round } #=> [1]Mju is a Marklar which value depends on which Marklar is asked.
Other known Marklars are Mu and Kappa.
Hope it's not confusing...
I tried to give related Marklars the same name.
Marshal works with Neuronet to save your networks:
dump = Marshal.dump ff
ff2 = Marshal.load dump
ff2.inspect == ff.inspect #=> trueCopyright (c) 2023 CarlosJHR64
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