/**
* Copyright (c) 2013-2017 Patrick Nicolas - Scala for Machine Learning - All rights reserved
*
* Licensed under the Apache License, Version 2.0 (the "License") you may not use this file
* except in compliance with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*
* The source code in this file is provided by the author for the sole purpose of illustrating the
* concepts and algorithms presented in "Scala for Machine Learning 2nd edition".
* ISBN: 978-1-783355-874-2 Packt Publishing.
*
* Version 0.99.2
*/
package org.scalaml.supervised.svm.kernel
import org.scalaml.Predef.DblVec
import scala.language.implicitConversions
import org.scalaml.core.functional._Monad
/**
* Experimental monad to support functional definition of kernels
* @since 0.99.1 (rev. 1)
*/
private[scalaml] object KernelMonad {
type F1 = Double => Double
type F2 = (Double, Double) => Double
case class KF[G](g: G, h: F2) {
def metric(v: DblVec, w: DblVec)(implicit gf: G => F1): Double =
g(v.zip(w).map { case (_v, _w) => h(_v, _w) }.sum)
}
implicit def hg2KF[G](hg: (G, F2)): KF[G] = KF(hg._1, hg._2)
val identity = (x: Double, y: Double) => x * y
val kfMonad = new _Monad[KF] {
override def unit[G](g: G): KF[G] = KF[G](g, identity)
override def map[G, H](kf: KF[G])(f: G => H): KF[H] = KF[H](f(kf.g), kf.h)
override def flatMap[G, H](kf: KF[G])(f: G => KF[H]): KF[H] =
KF[H](f(kf.g).g, kf.h)
}
implicit class kF2Monad[G](kf: KF[G]) {
def map[H](f: G => H): KF[H] = kfMonad.map(kf)(f)
def flatMap[H](f: G => KF[H]): KF[H] = kfMonad.flatMap(kf)(f)
}
class RBF(s2: Double) extends KF[F1]((x: Double) => Math.exp(-0.5 * x * x / s2), (x: Double, y: Double) => x - y)
class Polynomial(d: Int) extends KF[F1]((x: Double) => Math.pow(1.0 + x, d), (x: Double, y: Double) => x * y)
}
private[scalaml] object KernelMonadApp extends {
import KernelMonad._
val v = Vector[Double](0.5, 0.2, 0.3)
val w = Vector[Double](0.1, 0.7, 0.2)
val composed = for {
kf1 <- new RBF(0.6)
kf2 <- new Polynomial(6)
} yield kf2
composed.metric(v, w)
}
// ------------------------------- EOF ------------------------------------
