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• Algorithm.scala

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mgo.tools.execution.Algorithm.scala Maven / Gradle / Ivy

/*
 * Copyright (C) 2015 Romain Reuillon
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */
package mgo.tools.execution

import scala.language.higherKinds
import scala.util.Random
import cats.data._

/**
 * Example:
 * Let type C[A] = (SomeState,A)
 *
 * // Initialisation
 * val (initialState, initialGs) = unwrap(initialGenomes)
 * val initialPop = initialGs.map(express)
 *
 * // First step:
 * val (s11, genomes1) = run((initialState,initialPop), breeding)
 * val indivs1 = genomes1.map(express)
 * val (s12, selected1) = run((s11,indivs1), elitism)
 *
 * // Second step:
 * val (s21, genomes2) = run((s12, selected1), breeding)
 * val indivs2 = genomes2.map(express)
 * val (s22, selected2) = run((s21, indivs2), elitism)
 */
trait Algorithm[T, I, G, S] {
  def initialState(t: T, rng: Random): S
  def initialPopulation(t: T, rng: Random): Vector[I]
  def step(t: T): (S, Vector[I], Random) => (S, Vector[I])
}