A basic memory model for Thoughtless

gistfile1.scala

import org.hashtree.stringmetric.similarity._

case class Memory[T <: String](val items: Set[Mem[T]]) {

  def rough(precision: Double) = precision - precision * 0.1
  def increased(precision: Double) = precision + precision / 2

  def compare(item1: T, item2: T, precision: Double): Boolean =
    JaroWinklerMetric.compare(item1, item2) match {
      case Some(similarity: Double) => (similarity > rough(precision))
      case None => false
    }

  def scan(depth: Int, ioi: T, precision: Double): Set[Mem[T]] =
    if (depth < 1) Set[Mem[T]]()
    else items
      .filter(item => compare(item.ioi, ioi, precision))
      .flatMap(item =>
        item.related.scan(depth - 1, ioi, math.min(1, increased(precision))) + item)

  def add(mem: Mem[T], precision: Double): Memory[T] = {
    val (related, unrelated) = items.partition(item => compare(item.ioi, mem.ioi, precision))
    if (related.isEmpty == true) Memory(unrelated + mem)
    else Memory(unrelated ++ {
      for (item <- related)
        yield Mem[T](item.ioi, item.emotion, Memory[T](item.related.add(mem, increased(precision)).items + mem))
    }.toSet[Mem[T]])
  }
}

case class Mem[T <: String](val ioi: T, val emotion: Emotion, val related: Memory[T] = Memory[T](Set()))