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This is a package for type safe heterogenous vectors, or HVectors. This library was developed to allow the HLearn library to handle multivariate distributions---each data point is an HVector, and the trained multivariate distribution will also be an HVector. This might be of more general interest, however, and so has been separated into its own library. It is called vector-heterogenous on hackage.


The simplest way to construct an HVector is from an HList with the vec function. For example, we can run:

ghci> import Data.Vector.Heterogenous
ghci> let hvec = vec ShowBox $ "test":::Nothing:::([4,5,6],()):::HNil

This declaration contains two parts. To the right of the $ we have the HList of our data. Notice that any type is allowed. The first argument of vec is the constructor for an "existential box" that we want to put these elements in. We will call this constructor on each element in the HList in order to make them all of one type. We then store these homogenous types into a standard vector from Data.Vector.

Since we used ShowBox in our hvec variable, we can print hvec to the screen:

ghci> hvec
vec ShowBox $ "test":::Nothing:::([4,5,6],()):::HNil

The type of our HVector has two components as well. First is the type of box we use, and second is the list of which type corresponds to which value in the vector.

ghci> :t hvec
(Num b) => HVector ShowBox '[String,Maybe a,([b],())]

Advantages over HLists

The advantage of an HVector over an HList is that we get O(1) indexing anywhere in the list. (Technically the type checker still takes time O(n), but run time takes only O(1).) We use the view function to do this:

ghci> hvec `view` (undefined :: Sing 0)

ghci> hvec `view` (undefined :: Sing 1)

ghci> hvec `view` (undefined :: Sing 2)

Unfortunately, this is slightly awkward because we must make our accessor function polymorphic on the index. Maybe someone with a better knowledge of a lens library's internals could come up with a prettier interface.

Advantages over straight ExistentialQuantification

There are two advantages. First, as we have seen, we can recover the original type for each index with our view function. This would not be possible if we used a type of V.Vector ShowBox.

Second, HVector also has a Monoid instance. That means we can do:

ghci> hvec<>hvec
vec ShowBox $ "testtest":::Nothing:::([4,5,6,4,5,6],())::HNil

With straight existential quantification, it would not be possible to merge the corresponding positions in each vector because they are not guaranteed to be the same type.


Use of the view function above is not ideal for performance critical applications because it prevents fusion. The easiest way to work around this is to directly access the underlying vector of existential boxes. We do this with the getvec function:

ghci> :t getvec
getvec :: HVector box xs -> Vector box

ghci> getvec hvec
fromList [([4,5,6],()),Nothing,"test"]

Notice that elements will now be accessed in reverse order.

Now the compiler can use fusion and everything runs quite zippy. The trick to making this work well is creating a good existential box for your specific application. In the HLearn library, for example, we would use a DatapointBox and a DistributionBox to represent our data points and multivariate distributions.

Based on my tests, a variable of type HVector ShowBox '[Int,Int,Int,Int,Int,...] performs the same as the standard Data.Vector.Vector Int. The HVector has an extra layer of boxing to deal with, but using BangPatterns and -funbox-strict-fields the compiler can remove this from the generated code.