Exact numeric integration

This post describes a simple way to integrate a function over an interval and get an exact answer. The question came out of another one, which is how to optimally render a continuous-space image onto a discrete array of pixels.

For anti-aliasing, I’ll make two simplying assumptions (to be revisited):

  • Each pixel is a square area. (With apologies to Alvy Ray Smith.)
  • Since I can choose only one color per pixel, I want exactly the average of the continuous image over pixel’s subregion of 2D space.

The average of a function over a region (here a continuous image over a 2D interval) is equal to the integral of the function across the region divided by the size (area for 2D) of the region. Since our regions are simple squares, the average and the integral can each be defined easily in terms of the other (dividing or multiplying by the size).

To simplify the problem further, I’ll consider one-dimensional integration, i.e., integrating a function of R over a 1D interval. My solution below involves the least upper bound operator I’ve written about (and its specialization unamb).

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Lazier function definitions by merging partial values

This post continues from an idea of Ryan Ingram’s in an email thread How to make code least strict?.

A pretty story

Pattern matching in function definitions is very handy and has a declarative feel. For instance,

sum []     = 0
sum (x:xs) = x + sum xs

Simply replace “=” by “==” to read such a set of pattern clauses (partial definitions) as a collection of properties specifying a sum function:

  • The sum of an empty list equals zero
  • The sum of a (non-empty) list x:xs equals x plus the sum of the xs.

Moreover, these properties define the sum function, in that sum is the least-defined function that satisfies these two properties.

Guards have a similar style and meaning:

abs x | x < 0 = -x
abs x | x >= 0 =  x

Replacing “=” by “==” and guards by logical implication, we again have two properties that define abs:

x < 0 ==> abs x == -x
x >= 0 ==> abs x ==  x

O, the lies!

This pretty story is a lie, as becomes apparent when we look at overlapping clauses. For instance, we’re more likely to write abs without the second guard:

abs x | x < 0 = -x
abs x          =  x

A declarative of the second clause (∀ x. abs x == x) is false.

I’d more likely write

abs x | x < 0     = -x
      | otherwise =  x

which is all the more deceptive, since “otherwise” doesn’t really mean otherwise. It’s just a synonym for “True“.

Another subtle but common problem arises with definitions like the following, as pointed out by ChrisK in How to make code least strict?:

zip :: [a] -> [b] -> [(a,b)]
zip []      _       = []
zip _       []      = []
zip (x:xs') (y:ys') = (x,y) : zip xs' ys'

These three clauses read like independently true properties for zip. The first two clauses overlap, but their values agree, so what could possibly go wrong with a declarative reading?

The problem is that there are really three flavors of lists, not two. This definition explicitly addresses the nil and cons cases, leaving ⊥.

By the definition above, the value of ‘zip [] ⊥‘ is indeed [], which is consistent with each clause. However, the value of ‘zip ⊥ []‘ is ⊥, because Haskell semantics says that each clause is tried in order, and the first clause forces evaluation of when comparing it with []. This ⊥ value is inconsistent with reading the second clause as a property. Swapping the first two clauses fixes the second example but breaks the first one.

Is it possible to fix zip so that its meaning is consistent with these three properties? We seem to be stuck with an arbitrary bias, with strictness in the first or second argument.

Or are we?

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Merging partial values

Last year I stumbled across a simple representation for partial information about values, and wrote about it in two posts, A type for partial values and Implementing a type for partial values. Of particular interest is the ability to combine two partial values into one, combining the information present in each one.

More recently, I played with unambiguous choice, described in the previous post.

This post combines these two ideas. It describes how to work with partial values in Haskell natively, i.e., without using any special representation and without the use restrictions of unambiguous choice. I got inspired to try removing those restrictions during stimulating discussions with Thomas Davie, Russell O’Connor others in the #haskell gang.

You can download and play with the library shown described here. There are links and a bit more info on the lub wiki page.


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Implementing a type for partial values

A type for partial values

In simplifying my Eros implementation, I came across a use for a type that represents partial information about values. I came up with a very simple implementation, though perhaps not quite ideal. In this post, I’ll present the interface and invite ideas for implementation. In the next post, I’ll give the implementation I use.

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