Issue
First approach
If would like to have
trait Distance extends ((SpacePoint, SpacePoint) => Double)
object EuclideanDistance extends Distance {
override def apply(sp1: SpacePoint, sp2: SpacePoint): Double = ???
}
trait Kernel extends (((Distance)(SpacePoint, SpacePoint)) => Double)
object GaussianKernel extends Kernel {
override def apply(distance: Distance)(sp1: SpacePoint, sp2: SpacePoint): Double = ???
}
However the apply
of object GaussianKernel extends Kernel
is not an excepted override
to the apply
of trait Kernel
.
Second approach - EDIT: turns out this works afterall...
Alternatively I could write
trait Kernel extends ((Distance) => ( (SpacePoint, SpacePoint) => Double))
object GaussianKernel extends Kernel {
override def apply(distance: Distance): (SpacePoint, SpacePoint) => Double =
(sp1: SpacePoint, sp2: SpacePoint) =>
math.exp(-math.pow(distance(sp1, sp2), 2) / (2))
}
but am not sure this is currying...
EDIT: Turns out that I can use this second approach in a currying fashion. I think it is exactly what the typical currying is, only without the syntactic sugar.
Explanation of the idea
The idea is this: For my algorithm I need a Kernel. This kernel calculates a metric for two vectors in space - here SpacePoint
s. For that the Kernel requires a way to calculate the distance between the two SpacePoint
s. Both distance and kernel should be exchangeable (open-closed principle), thus I declare them as traits (in Java I had them declared as interfaces). Here I use the Euclidean Distance (not shown) and the Gaussian Kernel. Why the currying? Later when using those things, the distance
is going to be more or less the same for all measurements, while the SpacePoint
s will change all the time. Again, trying to stay true to the open-closed principle. Thus, in a first step I would like the GaussianKernel
to be pre-configured (if you will) with a distance and return a Function
that can be feed later in the program with the SpacePoint
s (I am sure the code is wrong, just to give you an idea what I am aiming at):
val myFirstKernel = GaussianKernel(EuclideanDistance)
val mySecondKernel = GaussianKernel(FancyDistance)
val myThirdKernel = EpanechnikovKernel(EuclideanDistance)
// ... lots lof code ...
val firstOtherClass = new OtherClass(myFirstKernel)
val secondOtherClass = new OtherClass(mySecondKernel)
val thirdOtherClass = new OtherClass(myThirdKernel)
// ... meanwhile in "OtherClass" ...
class OtherClass(kernel: Kernel) {
val thisSpacePoint = ??? // ... fancy stuff going on ...
val thisSpacePoint = ??? // ... fancy stuff going on ...
val calculatedKernel = kernel(thisSpacePoint, thatSpacePoint)
}
Questions
- How do I build my trait?
- Since
distance
can be different for differentGaussianKernel
s - shouldGaussianKernel
be a class instead of an object? - Should I partially apply
GaussianKernel
instead of currying? - Is my approach bad and
GaussianKernel
should be a class that stores thedistance
in a field?