方法参数中具有抽象类型的特征

Question

I am new to Scala and am building tools for statistical estimation. Consider the following: a trait probabilityDistribution is defined, which guarantees that classes which inherit from it will be able to perform certain functions, such as compute a density. Two such examples of probability distributions might be a binomial and beta distribution. The support of these two functions is Int and Double , respectively.

Set Up

trait probabilityDistribution extends Serializable {
  type T
  def density(x: T): Double
}

case class binomial(n: Int, p: Double) extends probabilityDistribution {
  type T = Int
  def density(x: Int): Double = x*p
}

case class beta(alpha: Double, beta: Double) extends probabilityDistribution {
  type T = Double
  def density(x: Double): Double = x*alpha*beta
}

Note that the actual mathematical implementations of the density methods are simplified above. Now, consider a Mixture Model, in which we have several features or variables which come from different distributions. We may choose to create a list of probabilityDistribution s to represent our features.

val p = List(binomial(5, .5), beta(.5,.5))

Suppose that we are now interested in supplying a vector of hypothetical data values, and wish to query the density functions for each respective probability distribution.

val v = List[Any](2, 0.75)

The Problem Of course, we use a zip with map. However, this doesn't work:

p zip v map { case (x,y) => x.density(y) }
### found   : Any
  # required: x.T

Caveat: Choice of Container A valid question is to wonder why I have chosen List[Any] as the container to hold data values, rather than List[Double] , or perhaps List[T <: Double] . Consider the case when some of our probability distributions have a support over vectors or even matrices (eg multivariate normal and inverse Wishart)

An idea to address the caveat might be to instead house our input values in a container that is more representative of our input type. eg something like

class likelihoodSupport
val v = List[likelihoodSupport](...)

where Int , Double , and Array[Double] and even a tuple (Array[Double], Array[Array[Double]]) all inherit from likelihoodSupport . As some of these classes are final, however, this is not possible.

One (Crummy) Fix

Note that this can be handled by using pattern matching and a polymorphic method within each subclass, but as Odersky might say this has a code smell :

trait probabilityDistribution extends Serializable {
  type T
  def density[T](x: T): Double
}

case class binomial(n: Int, p: Double) extends probabilityDistribution {
  type T = Int
  def density[U](x: U): Double = x match {case arg: Int => arg * p }
}

case class beta(alpha: Double, beta: Double) extends probabilityDistribution {
  type T = Double
  def density[U](x: U): Double = x match {case arg: Double => arg * alpha * beta}
}

We can now run

p zip v map { case (x,y) => x.density(y) }

Plea I know what I'm trying to do should be very easily accomplished in such a beautiful and powerful language, but I can't figure out how! Your help is much appreciated.

Note I am not interested in using additional packages/imports, as I feel this problem should be trivially solved in base Scala.

Answer 1

You can't do it given the separate p and v lists (at least without casts, or by writing your own HList library). This should be obvious: if you change the order of elements in one of these lists, the types won't change (unlike for HList ), but distributions will now be paired with values of a wrong type!

The simplest approach is to add a cast:

p zip v map { case (x,y) => x.density(y.asInstanceOf[x.T]) }

Note that this may be a no-op at the runtime and lead to a ClassCastException inside density call instead, thanks to JVM type erasure.

If you want a safer alternative to the cast, something like this should work (see http://docs.scala-lang.org/overviews/reflection/typetags-manifests.html for more information on ClassTags and related types):

// note that generics do buy you some convenience in this case:
// abstract class probabilityDistribution[T](implicit val tag: ClassTag[T]) extends Serializable
// will mean you don't need to set tag explicitly in subtypes
trait probabilityDistribution extends Serializable {
  type T
  implicit val tag: ClassTag[T]
  def density(x: T): Double
}

case class binomial(n: Int, p: Double) extends probabilityDistribution {
  type T = Int
  val tag = classTag[Int]
  def density(x: Int): Double = x*p
}

p zip v map { (x,y) => 
  implicit val tag: ClassTag[x.T] = x.tag
  y match { 
    case y: x.T => ... 
    case _ => ... 
  }
}

Or you can combine distributions and values (or data structures containing values, functions returning values, etc.):

// alternately DistribWithValue(d: probabilityDistribution)(x: d.T)
case class DistribWithValue[A](d: probabilityDistribution { type T = A }, x: A) {
  def density = d.density(x)
}

val pv: List[DistribWithValue[_]] = List(DistribWithValue(binomial(5, .5), 2), DistribWithValue(beta(.5,.5), 0.75))

// if you want p and v on their own
val p = pv.map(_.d)
val v = pv.map(_.x)

Of course, if you want to use a probabilityDistribution as a method argument , as the question title says, it's simple, for example:

def density(d: probabilityDistribution)(xs: List[d.T]) = xs.map(d.density _)

The problems only arise specifically when

The user may wish to make multiple density queries with different x values that are not intrinsically related to the probability distribution itself

and the compiler can't prove that these values have the correct type.