Neuronet

• VERSION 7.0.230416
• github
• rubygems

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 neuronet

The 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]

• Neuronet wiki

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 #=> true

• Requires and autoloads
• Constants and lambdas
• Connection
• Neuron
• Layer
• FeedForward

• Scale
• Gaussian
• LogNormal
• ScaledNetwork

Copyright (c) 2023 CarlosJHR64

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