import breeze.linalg.DenseVector
import breeze.stats.distributions.{RandBasis, ThreadLocalRandomGenerator}
import com.github.nmasahiro.asap.algorithm.StrategyDriver
import com.github.nmasahiro.asap.algorithm.cmaes.{CMAES, CMAWeightNormal}
import com.github.nmasahiro.asap.util.ParallelObjectiveFunction
import com.github.nmasahiro.asap.util._
import org.apache.commons.math3.random.MersenneTwister

object SampleMain extends App {

  val ktablet = ParallelObjectiveFunction ({
    case x: DenseVector[Double] =>
      val dim = x.length
      (for (i <- 0 until dim) yield {
        if (i < (dim / 4.0).toInt) x(i) * x(i) else math.pow(100 * x(i), 2.0)
      }).sum
  })

  val driver = StrategyDriver(ktablet)

  val seed = 10
  implicit val randBasis: RandBasis = new RandBasis(new ThreadLocalRandomGenerator(new MersenneTwister(seed)))

  val dim = 40
  val lambda = 8
  val initialM = 3.0 * DenseVector.ones[Double](dim)
  val initialSigma = 2.0
  val cmaes = CMAES(lambda, initialM, initialSigma, CMAWeightNormal())

  val successFval = 1e-12
  val finishEvalCnt = (5 * dim * 1e4).toInt

  val (evalCnt, bestX) = driver.optimize(
    cmaes,
    fvalBestReached(successFval) orElse
      evalCntReached(finishEvalCnt) orElse
      proceed
  )

  println(s"evalCnt:$evalCnt, bestX:$bestX")

}