# Category theory for programmers made easier

I imagine most programmers who develop an interest in category theory do so after hearing about monads. They ask someone what a monad is, and they’re told that if they really want to know, they need to learn category theory.

Unfortunately, there are couple unnecessary difficulties anyone wanting to understand monads etc. is likely to face immediately. One is some deep set theory.

“A category is a collection of objects …”

“You mean like a set?”

“Ah, well, no. You see, Bertrand Russell showed that …”

There are reasons for such logical niceties, but they don’t matter to someone who wants to understand programming patterns.

Another complication is morphisms.

“As I was saying, a category is a collection of objects and morphisms between objects …”

“You mean like functions?”

“Well, they might be functions, but more generally …”

Yes, Virginia, morphisms are functions. It’s true that they might not always be functions, but they will be functions in every example you care about, at least for now.

Category theory is a framework for describing patterns in function composition, and so that’s why things like monads find their ultimate home in category theory. But doing category theory rigorously requires some setup that people eager to get into applications don’t have to be concerned with.

Patrick Honner posted on Twitter recently that his 8-year-old child asked him what area is. My first thought on seeing that was that a completely inappropriate answer would be that this is a deep question that wasn’t satisfactorily settled until the 20th century using measure theory. My joking response to Patrick was

Well, first we have to define σ-algebras. They’re kinda like topologies, but closed under countable union and intersection instead of arbitrarily union and finite intersection. Anyway, a measure is a …

It would be ridiculous to answer a child this way, and it is nearly as ridiculous to burden a programmer with unnecessary logical nuance when they’re trying to find out why something is called a functor, or a monoid, or a monad, etc.

I saw an applied category theory presentation that began with “A category is a graph …” That sweeps a lot under the rug, but it’s not a bad conceptual approximation.

So my advice to programmers learning category theory is to focus on the arrows in the diagrams. Think of them as functions; they probably are in your application . Think of category theory as a framework for describing patterns. The rigorous foundations can be postponed, perhaps indefinitely, just as an 8-year-old child doesn’t need to know measure theory to begin understanding area.

## More category theory posts

 The term “contravariant functor” has unfortunately become deprecated. In more modern presentations, all functors are covariant, but some are covariant in an opposite category. That does make the presentation more slick, but at the cost of turning arrows around that used to represent functions and now don’t really. In my opinion, category theory would be more approachable if we got rid of all “opposite categories” and said that functors come in two flavors, covariant and contravariant, at least in introductory presentations.

# Pretending OOP never happened

I ran across someone recently who says the way to move past object oriented programming (OOP) is to go back to simply telling the computer what to do, to clear OOP from your mind like it never happened. I don’t think that’s a good idea, but I also don’t think it’s possible.

Object oriented programming, for all its later excesses, was a big step forward in software engineering. It made it possible to develop much larger programs than before, maybe 10x larger. Someone might counter by saying that programs had to be 10x larger because of all the overhead of OOP, but that’s not the case. OOP does add some overhead, and the amount of overhead grew over time as tools and frameworks became more complicated, but OOP made it possible to write programs that could not have been written before.

OOP provides a way for programmers to organize their code. It may not be the best way, depending on the problem, but the way to move past OOP is to replace it with another discipline. And I imagine most people who have learned and then rejected OOP do just that, whether they realize it or not. Maybe they retain some organizational patterns that they learned in the context of OOP.

That has been my experience. I hardly ever write classes anymore; I write functions. But I don’t write functions quite the way I did before I spent years writing classes.

And while I don’t often write classes, I do often use classes that come from libraries. Sometimes these objects seem like they’d be better off as bare functions, but I imagine the same libraries would be harder to use if no functions were wrapped in objects.

There are many alternatives to OOP for organizing code. I generally like functional programming, but in my experience there’s a hockey stick effort curve as you try to push the purity to 100%. James Hague said it well:

100% pure functional programming doesn’t work. Even 98% pure functional programming doesn’t work. But if the slider between functional purity and 1980s BASIC-style imperative messiness is kicked down a few notches — say to 85% — then it really does work. You get all the advantages of functional programming, but without the extreme mental effort and unmaintainability that increases as you get closer and closer to perfectly pure.

It’s possible, and a good idea, to develop large parts of a system in purely functional code. But someone has to write the messy parts that interact with the outside world.

# Currying in calculus, PDEs, programming, and categories Currying is a simple but useful idea. (It’s named after logician Haskell Curry  and has nothing to do with spicy cuisine.) If you have a function of two variables, you can think of it as a function of one variable that returns a function of one variable. So starting with a function f(x, y), we can think of this as a function that takes a number x and returns a function f(x, -) of y. The dash is a placeholder as in this recent post.

## Calculus: Fubini’s theorem

If you’ve taken calculus then you saw this in the context of Fubini’s theorem: To integrate the function of two variables f(x, y), you can temporarily fix y and integrate the remaining function of x. This gives you a number, the value of an integral, for each y, so it’s a function of y. Integrate that function, and you have the value of the original function of two variables.

The first time you see this you may think it’s a definition, but it’s not. You can define the integral on the left directly, and it will equal the result of the two nested integrations on the right. Or at least the two sides will often be equal. The conditions on Fubini’s theorem tell you exactly when the two sides are equal.

## PDEs: Evolution equations

A more sophisticated version of the same trick occurs in partial differential equations. If you have an evolution equation, a PDE for a function on one time variable and several space variables, you can think of it as an ODE via currying. For each time value t, you get a function of the spatial variables. So you can think of your solution as a path in a space of functions. The spatial derivatives specify an operator on that space of functions.

(I’m glossing over details here because spelling everything out would take a lot of writing, and might obscure the big idea, which relevant for this post. If you’d like the full story, you can see, for example, my graduate advisor’s book. It was out of print when I studied it, but now it’s a cheap Dover paperback.)

In the Haskell programming language (also named after Haskell Curry) you get currying for free. In fact, there’s no other way to express a function of two variables. For example, suppose you want to implement the function f(xy) = x² + y.

    Prelude> f x y = x**2 + y

Then Haskell thinks of this as a function of one variable (i.e. x), that returns a function of one variable (i.e. f(x, -)) which itself returns a number (i.e. f(x, y)). You can see this by asking the REPL for the type of f:

    Prelude> :info f
f :: Floating a => a -> a -> a


Technically, Haskell, just like lambda calculus, only has functions of one variable. You could create a product datatype consisting of a pair of variables and have your function take that as an argument, but it’s still a function on one variable, though that variable is not atomic.

## Category theory

The way you’d formalize currying in category theory is to say that the following is a natural isomorphism: For more on what Hom means, see this post.

## Related posts

 In concordance with Stigler’s law of eponymy, currying was not introduced by Curry but Gottlob Frege. It was then developed by Moses Schönfinkel and developed further by Haskell Curry.

# One practical application of functional programming

Arguments in favor of functional programming are often unconvincing. For example, the most common argument is that functional programming makes it easier to “reason about your code.” That’s true to some extent. All other things being equal, it’s easier to understand a function if all its inputs and outputs are explicit. But all other things are not equal. In order to make one function easier to understand, you may have to make something else harder to understand.

Here’s an argument from Brian Beckman for using a functional style of programming in a particular circumstance that I find persuasive. The immediate context is Kalman filtering, but it applies to a broad range of mathematical computation.

By writing a Kalman ﬁlter as a functional fold, we can test code in friendly environments and then deploy identical code with confidence in unfriendly environments. In friendly environments, data are deterministic, static, and present in memory. In unfriendly, real-world environments, data are unpredictable, dynamic, and arrive asynchronously.

If you write the guts of your mathematical processing as a function to be folded over your data, you can isolate the implementation of your algorithm from the things that make code hardest to test, i.e. the data source. You can test your code in a well-controlled “friendly” test environment and deploy exactly the same code into production, i.e. an “unfriendly” environment.

Brian continues:

The flexibility to deploy exactly the code that was tested is especially important for numerical code like filters. Detecting, diagnosing and correcting numerical issues without repeatable data sequences is impractical. Once code is hardened, it can be critical to deploy exactly the same code, to the binary level, in production, because of numerical brittleness. Functional form makes it easy to test and deploy exactly the same code because it minimizes the coupling between code and environment.

I ran into this early on when developing clinical trial methods first for simulation, then for production. Someone would ask whether we were using the same code in production as in simulation.

“Yes we are.”

Exactly the same code?”

“Well, essentially.”

“Essentially” was not good enough. We got to where we would use the exact same binary code for simulation and production, but something analogous to Kalman folding would have gotten us there sooner, and would have made it easier to enforce this separation between numerical code and its environment across applications.

Why is it important to use the exact same binary code in test and production, not just a recompile of the same source code? Brian explains:

Numerical issues can substantially complicate code, and being able to move exactly the same code, without even recompiling, between testing and deployment can make the difference to a successful application. We have seen many cases where differences in compiler flags, let alone differences in architectures, even between different versions of the same CPU family, introduce enough differences in the generated code to cause qualitative differences in the output. A filter that behaved well in the lab can fail in practice.

Emphasis added, here and in the first quote above.

Note that this post gives an argument for a functional style of programming, not necessarily for the use of functional programming languages. Whether the numerical core or the application that uses it would best be written in a functional language is a separate discussion.

# Kalman filters and functional programming

A few weeks ago I started a series of posts on various things you could do with a functional fold. In the first post I mentioned that the idea came from a paper by Brian Beckman on Kalman filters and folds:

This post was inspired by a paper by Brian Beckman (in progress) that shows how a Kalman filter can be implemented as a fold. From a Bayesian perspective, the thing that makes the Kalman filter work is that a certain multivariate normal model has a conjugate prior. This post shows that conjugate models more generally can be implemented as folds over the data. That’s interesting, but what does it buy you? Brian’s paper discusses this in detail, but one advantage is that it completely separates the accumulator function from the data, so the former can be tested in isolation.

At the time Brian was working on one big paper in private. This has since been split into several papers and they’re now public.

# New Twitter account for functional programming and categories

I’m starting a new Twitter account @FunctorFact for functional programming and category theory.

These two subjects have a lot of overlap, and some tweets will combine both, but many will be strictly about one or the other. So some content will be strictly about programming, some strictly about math, and some combining ideas from both. # Insertion sort as a fold

I’ve written several posts lately about various algorithms that can be expressed as functional folds:

These have all been numerical algorithms. Insertion sort is an example of a non-numerical algorithm that could be implemented as a fold.

Insertion sort is not the fastest sorting algorithm. It takes O(n2) operations to sort a list of size n while the fastest algorithms take O(n log n) operations. But insertion sort is simple, and as it works its way down a list, the portion it has processed is sorted. If you interrupt it, you have correct results given the input so far. If you interrupt something like a quicksort, you don’t have a sorted list. If you’re receiving a stream of data points and want to sort them as they come, you have to use insertion sort rather than something like quicksort.

The function to fold over a list looks like this:

    f as a = [x | x <- as, x < a] ++ [a] ++ [x | x <- as, x >= a]

Given a sorted list as and a value a, return a new sorted list that has inserted the element a in the proper position. Our function f does this by joining together the list of the elements less than a, the list containing only a, and the list of elements at least as big as a.

Here’s how we could use this to alphabetize the letters in the word “example.”

    foldl f [] "example"

This returns "aeelmpx".

Haskell takes our function f and an empty list of characters [] and returns a new list of characters by folding f over the list of characters making up the string "example".

You can always watch how foldl works by replacing it with scanl to see intermediate results.

    scanl f [] "example"

returns

    ["", "e", "ex", "aex", "aemx", "aempx", "aelmpx", "aeelmpx"]

# Computing higher moments with a fold

Folds in functional programming are often introduced as a way to find the sum or product of items in a list. In this case the fold state has the same type as the list items. But more generally the fold state could have a different type, and this allows more interesting applications of folds. Previous posts look at using folds to update conjugate Bayesian models and numerically solve differential equations.

This post uses a fold to compute mean, variance, skewness, and kurtosis. See this earlier post for an object-oriented approach. The code below seems cryptic out of context. The object-oriented post gives references for where these algorithms are developed. The important point for this post is that we can compute mean, variance, skewness, and kurtosis all in one pass through the data even though textbook definitions appear to require at least two passes. It’s also worth noting that the functional version is less than half as much code as the object-oriented version.

(Algorithms that work in one pass through a stream of data, updating for each new input, are sometimes called “online” algorithms. This is unfortunate now that “online” has come to mean something else.)

The Haskell function moments below returns the number of samples and the mean, but does not directly return variance, skewness and kurtosis. Instead it returns moments from which these statistics can easily be calculated using the mvks function.

    moments (n, m1, m2, m3, m4) x = (n', m1', m2', m3', m4')
where
n' = n + 1
delta = x - m1
delta_n = delta / n'
delta_n2 = delta_n**2
t = delta*delta_n*n
m1' = m1 + delta_n
m4' = m4 + t*delta_n2*(n'*n' - 3*n' + 3) + 6*delta_n2*m2 - 4*delta_n*m3
m3' = m3 + t*delta_n*(n' - 2) - 3*delta_n*m2
m2' = m2 + t

mvsk (n, m1, m2, m3, m4) = (m1, m2/(n-1.0), (sqrt n)*m3/m2**1.5, n*m4/m2**2 - 3.0)


Here’s an example of how you would use this Haskell code to compute statistics for the list [2, 30, 51, 72]:

    ghci>  mvsk \$ foldl moments (0,0,0,0,0) [2, 30, 51, 72]
(38.75, 894.25,-0.1685, -1.2912)


The foldl applies moments first to its initial value, the 5-tuple of zeros. Then it iterates over the data, taking data points one at a time and visiting each point only once, returning a new state from moments each time. Another way to say this is that after processing each data point, moments returns the 5-tuple that it would have returned if that data only consisted of the values up to that point.

For a non-numerical example of folds, see my post on sorting.

# ODE solver as a functional fold

One of the most widely used numerical algorithms for solving differential equation is the 4th order Runge-Kutta method. This post shows how the Runge-Kutta method can be written naturally as a fold over the set of points where the solution is needed. These points do not need to be evenly spaced.

Given a differential equation of the form with initial condition y(t0) = y0, the 4th order Runge-Kutta method advances the solution by an amount of time h by where The Haskell code for implementing the accumulator function for foldl looks very much like the mathematical description above. This is a nice feature of the where syntax.

    rk (t, y) t' = (t', y + h*(k1 + 2.0*k2 + 2.0*k3 + k4)/6.0)
where
h  = t' - t
k1 = f t y
k2 = f (t + 0.5*h) (y + 0.5*h*k1)
k3 = f (t + 0.5*h) (y + 0.5*h*k2)
k4 = f (t + 1.0*h) (y + 1.0*h*k3)


Suppose we want to solve the differential equation y ‘ = (t2y2) sin(y) with initial condition y(0) = -1, and we want to approximate the solution at [0.01, 0.03, 0.04, 0.06]. We would implement the right side of the equation as

     f t y = (t**2 - y**2)*sin(y)

and fold the function rk over our time steps with

    foldl rk (0, -1) [0.01, 0.03, 0.04, 0.06]

This returns (0.06, -0.9527). The first part, 0.06, is no surprise since obviously we asked for the solution up to 0.06. The second part, -0.9527, is the part we’re more interested in.

If you want to see the solution at all the specified points and not just the last one, replace foldl with scanl.

    scanl rk (0, -1) [0.01, 0.03, 0.04, 0.06]

This returns

    [(0.0, -1.0), (0.01, -0.9917), (0.03, -0.9756), (0.042, -0.9678), (0.06, -0.9527)]

As pointed out in the previous post, writing algorithms as folds separates the core of the algorithm from data access. This would allow us, for example, to change rk independently, such as using a different order Runge-Kutta method. (Which hardly anyone does. Fourth order is a kind of sweet spot.) Or we could swap out Runge-Kutta for a different ODE solver entirely just by passing a different function into the fold.

Next see how you could use a fold to compute mean, variance, skewness, and kurtosis in one pass through a stream of data using a fold.

# The success of OOP

Allen Wirfs-Brock gave the following defense of OOP a few days ago in a series of six posts on Twitter:

A young developer approached me after a conf talk and said, “You must feel really bad about the failure of object-oriented programming.” I was confused. I said, “What do you mean that object-orient programming was a failure. Why do you think that?”

He said, “OOP was supposed to fix all of our software engineering problems and it clearly hasn’t. Building software today is just as hard as it was before OOP. came along.”

“Have you ever look at the programs we were building in the early 1980s? At how limited their functionality and UIs were? OOP has been an incredible success. It enabled us to manage complexity as we grew from 100KB applications to today’s 100MB applications.”

Of course OOP hasn’t solved all software engineering problems. Neither has anything else. But OOP has been enormously successful in allowing ordinary programmers to write much larger applications. It has become so pervasive that few programmers consciously think about it; it’s simply how you write software.

I’ve written several posts poking fun at the excesses of OOP and expressing moderate enthusiasm for functional programming, but I appreciate OOP. I believe functional programming will influence object oriented programming, but not replace it.

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