Commit 716eb173 authored by Romain Reuillon's avatar Romain Reuillon
Browse files

[Plugin] fix: update mgo

parent 9a2cbf52
Pipeline #433 passed with stage
in 14 minutes and 48 seconds
......@@ -227,7 +227,7 @@ lazy val squants =
) settings(settings: _*)
lazy val mgoVersion = "3.40"
lazy val mgoVersion = "3.41"
lazy val mgo = OsgiProject(dir, "mgo", exports = Seq("mgo.*", "freestyle.*"), imports = Seq("!better.*", "!javax.xml.*", "!scala.meta.*", "!sun.misc.*", "*"), privatePackages = Seq("!scala.*", "!monocle.*", "!org.apache.commons.math3.*", "!cats.*", "!squants.*", "!scalaz.*", "*")) settings(
libraryDependencies += "org.openmole" %% "mgo" % mgoVersion,
......
......@@ -66,10 +66,13 @@ object NSGA2 {
genomes ++ fitness
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
MGONSGA2.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
MGONSGA2.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(individuals: Vector[I], n: Int, s: S, rng: scala.util.Random) = FromContext { p
import p._
......@@ -141,10 +144,13 @@ object NSGA2 {
genomes ++ fitness ++ Seq(samples)
}
def initialGenomes(n: Int, rng: util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
MGONoisyNSGA2.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
MGONoisyNSGA2.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(individuals: Vector[I], n: Int, s: S, rng: util.Random) = FromContext { p
import p._
......
......@@ -56,8 +56,8 @@ object NichedNSGA2Algorithm {
def gridObjectiveProfile[P](x: Int, intervals: Vector[Double], fitness: P Vector[Double]): Niche[Individual[P], Int] =
mgo.evolution.niche.gridContinuousProfile[Individual[P]](i fitness(i.phenotype), x, intervals)
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, rng)
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome Boolean], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, reject, rng)
def adaptiveBreeding[S, P](lambda: Int, reject: Option[Genome Boolean], operatorExploration: Double, discrete: Vector[D], fitness: P Vector[Double]) =
NSGA2Operations.adaptiveBreeding[S, Individual[P], Genome](
......@@ -166,8 +166,8 @@ object NoisyNichedNSGA2Algorithm {
def expression[P: Manifest](fitness: (util.Random, Vector[Double], Vector[Int]) P, continuous: Vector[C]): (util.Random, Genome) Individual[P] =
NoisyIndividual.expression(fitness, continuous)
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, rng)
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome Boolean], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, reject, rng)
}
......@@ -255,10 +255,13 @@ object NichedNSGA2 {
genomes ++ fitness
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
mgo.evolution.algorithm.Profile.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
mgo.evolution.algorithm.Profile.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(population: Vector[I], n: Int, s: S, rng: scala.util.Random) = FromContext { p
import p._
......@@ -358,10 +361,13 @@ object NichedNSGA2 {
genomes ++ fitness ++ Seq(samples)
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
NoisyNichedNSGA2Algorithm.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
NoisyNichedNSGA2Algorithm.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(individuals: Vector[I], n: Int, s: S, rng: scala.util.Random) = FromContext { p
import p._
......
......@@ -68,10 +68,13 @@ object OSE {
genomes ++ fitness
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
MGOOSE.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
MGOOSE.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(individuals: Vector[I], n: Int, s: S, rng: scala.util.Random) = FromContext { p
import p._
......@@ -152,10 +155,13 @@ object OSE {
genomes ++ fitness ++ Seq(samples)
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
MGONoisyOSE.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
MGONoisyOSE.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(individuals: Vector[I], n: Int, s: S, rng: scala.util.Random) = FromContext { p
import p._
......
......@@ -76,8 +76,8 @@ object PSEAlgorithm {
def buildIndividual[P](g: CDGenome.Genome, p: P) = Individual(g, p)
// def vectorPhenotype = Individual.phenotype composeLens arrayToVectorLens
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, rng)
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome Boolean], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, reject, rng)
def adaptiveBreeding[S, P](
lambda: Int,
......@@ -131,8 +131,8 @@ object NoisyPSEAlgorithm {
def buildIndividual[P: Manifest](genome: CDGenome.Genome, phenotype: P) = Individual(genome, 1, Array(phenotype))
def vectorPhenotype[P: Manifest] = Individual.phenotypeHistory[P] composeLens arrayToVectorLens
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, rng)
def initialGenomes(lambda: Int, continuous: Vector[C], discrete: Vector[D], reject: Option[Genome Boolean], rng: scala.util.Random) =
CDGenome.initialGenomes(lambda, continuous, discrete, reject, rng)
def adaptiveBreeding[S, P: Manifest](
lambda: Int,
......@@ -264,8 +264,13 @@ object PSE {
genomes ++ fitness
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete) PSEAlgorithm.initialGenomes(n, continuous, discrete, rng) }
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
PSEAlgorithm.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
private def pattern(p: Array[Any]) = om.pattern(ExactObjective.toFitnessFunction(om.objectives)(p))
......@@ -349,10 +354,13 @@ object PSE {
genomes ++ fitness ++ Seq(samples)
}
def initialGenomes(n: Int, rng: scala.util.Random) =
(Genome.continuous(om.genome) map2 Genome.discrete(om.genome)) { (continuous, discrete)
NoisyPSEAlgorithm.initialGenomes(n, continuous, discrete, rng)
}
def initialGenomes(n: Int, rng: scala.util.Random) = FromContext { p
import p._
val continuous = Genome.continuous(om.genome).from(context)
val discrete = Genome.discrete(om.genome).from(context)
val rejectValue = om.reject.map(f GAIntegration.rejectValue[G](f, om.genome, _.continuousValues.toVector, _.discreteValues.toVector).from(context))
NoisyPSEAlgorithm.initialGenomes(n, continuous, discrete, rejectValue, rng)
}
def breeding(individuals: Vector[I], n: Int, s: S, rng: scala.util.Random) = FromContext { p
import p._
......
......@@ -9,7 +9,7 @@ object Libraries {
lazy val gridscaleVersion = "2.27"
lazy val sshjVersion = "0.27.0"
lazy val containerVersion = "1.4"
lazy val mgoVersion = "3.40"
lazy val mgoVersion = "3.41"
lazy val bouncyCastleVersion = "1.60"
lazy val d3Version = "3.5.12"
lazy val tooltipserVersion = "3.3.0"
......
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