Automatic differentiation for Scala
import com.kogecoo.scalaad.graph.Var // always need to import
import com.kogecoo.scalaad.ScalarRule.Implicits._ // when x is a scalar variable
val x = Var(5.0)
val y = 2 * x + 3 * x * y
// forward-mode automatic differentiation
// partial differentiation w.r.t x
println(y.deriv(x))
// reverse-mode automatic differentiation computes a gradient
println(y.grad())
// we can get partial differentiation through `gradient` after running grad()
println(x.gradient)
println(y.gradient)- test
- make it to be multiple package
- exclude Nd4jRule and BreezeRule to other package
- maven repo
- http://d.hatena.ne.jp/Nos/20130811/1376232751
- http://www.win-vector.com/dfiles/ReverseAccumulation.pdf
- http://www.win-vector.com/blog/2010/07/gradients-via-reverse-accumulation/
- http://www.win-vector.com/blog/2010/06/automatic-differentiation-with-scala/
- https://justindomke.wordpress.com/2009/02/17/automatic-differentiation-the-most-criminally-underused-tool-in-the-potential-machine-learning-toolbox/
- https://justindomke.wordpress.com/2009/03/24/a-simple-explanation-of-reverse-mode-automatic-differentiation/
- http://arxiv.org/pdf/1404.7456v1.pdf
- http://arxiv.org/pdf/1502.05767v2.pdf
- https://en.wikipedia.org/wiki/Automatic_differentiation
- http://www.met.reading.ac.uk/clouds/publications/adept.pdf
- http://colah.github.io/posts/2015-08-Backprop/index.html
- http://uhra.herts.ac.uk/bitstream/handle/2299/4335/903836.pdf?sequence=1