The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 15 on langram.org
Thank you
Chris W
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 15 on langram.org
Thank you
Chris W
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 14 on langram.org
Thank you
Chris W
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 13 on langram.org
Thank you
Chris W
As I travel on my path to perhaps what I deem as some sort of enlightenment, back in time via Clojure to one of the great ancestors of language, structure and computational thought (Lisp), I continue to come across a simple theme.
Building Blocks
That theme is the concept of basic building blocks with which vast cathedrals can be constructed. Those building blocks are, in Lisp terms at least, car
, cdr
and cons
.
One of my companions on this path is Daniel Higginbotham’s Clojure for the Brave and True. In Part II, covering Language Fundamentals, Clojure’s abstractions, or interfaces, are discussed. One of the Clojure philosophies is that the abstraction idea allows a simplified collection of functions that work across a range of different data structures. Abstracting action patterns from concrete implementations allows this to happen. This is nicely illustrated with a look the first
, rest
and cons
functions from the sequence (or ‘seq’) abstraction.
There’s a close parallel between first
, rest
& cons
in Clojure and car
, cdr
& cons
in other Lisps such as Scheme. And there’s an inherent and implicit beauty in a collection of constructs so simple yet collectively so powerful. You can read about the origins of the terms car
and cdr
on the Wikipedia page, which have a depth and a degree of venerability of their own. Essentially both sets of functions implement a linked list, which can be simply illustrated, as shown in the book and elsewhere, as a sequence of connected nodes, like this:
node1 node2 node3 ++ ++ ++  value  next > value  next > value  next  ++ ++ ++    V V V "one" "two" "three"
Implementing a linked list
Daniel goes on to show how such a linked list of nodes like this, along with the three functions, can be simply implemented in, say, JavaScript. Given that these nodes could be represented like this in JavaScript:
node3 = { value: "three", next: null } node2 = { value: "two", next: node3 } node1 = { value: "one", next: node2 }
then the first
, rest
and cons
functions could be implemented as follows:
function first(n) { return n.value; } function rest(n) { return n.next; } function cons(newval, n) { return { value: newval, next: n }; }
With those basic building blocks implemented, you can even build the next level, for example, he shows that map
might be implemented thus:
function map(s, f) { if (s === null) { return null; } else { return cons(f(first(s)), map(rest(s), f)); } }
To me, there’s a beauty there that is twofold. It’s implemented using the three core functions we’ve already seen, the core atoms, if you will. Moreover, there’s a beauty in the recursion and the “first and rest pattern” I touched upon earlier in “A meditation on reduction“.
Using the building blocks
Let’s look at another example of how those simple building blocks are put together to form something greater. This time, we’ll take inspiration from a presentation by Marc Feeley: “The 90 minute Scheme to C compiler“. In a slide on tail calls and garbage collection, the sample code, in Scheme (a dialect of Lisp), is shown with a tail call recursion approach thus:
(define f (lambda (n x) (if (= n 0) (car x) (f ( n 1) (cons (cdr x) (+ (car x) (cdr x)))))))
If you stare long enough at this you’ll realise two things: It really only uses the core functions car
(first
), cdr
(rest
) and cons
. And it’s a little generator for finding the Nth term of the Fibonacci sequence:
(f 20 (cons 1 1)) ; => 10946
I love that even the example call uses cons
to construct the second parameter.
I read today, in “Farewell, Marvin Minsky (1927–2016)” by Stephen Wolfram, how Marvin said that “programming languages are the only ones that people are expected to learn to write before they can read”. This is a great observation, and one that I’d like to think about a bit more. But before I do, I’d at least like to consider that studying the building blocks of language helps in reading, as well as writing.
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 12 on langram.org
Thank you
Chris W
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 11 on langram.org
Thank you
Chris W
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 10 on langram.org
Thank you
Chris W
The entire Mindshift blog series has moved to langram.org/mindshiftseries.
Click here to read Mindshift: Part 9 on langram.org
Thank you
Chris W
In the last exciting episode of the Mindshift blog, we looked at how subtraction can be performed when our counting system only allows us to count upwards from zero.
Now we need to expand our range of predicate functions to perform various equality or equivalence tests, and from these, then build up a wider range of functional tools.
It would be pretty handy if we could determine whether a quantity function is ZERO
or not. To put that in fancier language, how can we tell whether or not a quantity function has zero magnitude?
Lets remind ourselves of the basic definition of ZERO
and ONE
(ignoring the use of the SUCC
function)
// ZERO and ONE 
Notice how the transform
function is used inside the ZERO
function – it isn’t. It’s completely ignored; all that happens inside ZERO
is that the second parameter (start_value
) is returned unmodified.
Aside 

Let’s hold the definition of ZERO up against the definition of another function we’ve seen already – FALSE . 



Ok, the parameter names are different, but the functionality is identical! Both ZERO and FALSE brainlessly return their second parameter unmodified. 

This now provides us with a (possibly unexpected) way to demonstrate that it is far more than simply computing convention to evaluate Boolean false to zero – they are in fact functionally indistinguishable. 
The only circumstance under which a quantity function will ignore its transformation function is when its magnitude is zero, and any nonzero quantity function will always apply its transform function some number of times to the start value.
Therefore, we can get a quantity function to reveal its magnitude by passing it a transformation function that, if called, will always return FALSE
. We can pass a starting value of TRUE
knowing that this will be returned only when the quantity function has a magnitude of zero.
// Define an IS_ZERO function 
So now that we can determine whether a quantity function has a magnitude of zero, we can now define the “less than or equal to” predicate.
You might recall from the previous blog that our counting system cannot handle negative numbers. This means that we will get odd results such as 5  8 = 0
instead of the expected 3
.
Apart from limiting all our calculations to give positive answers, this is in fact a useful feature that allows us to check whether one number is greater than another number.
All we need to do is subtract one number from the other, then pass the result to our new IS_ZERO
function. And there you have it, a definition for the “less than or equal to” predicate.
// Define an IS_ZERO function 
Using the Boolean values of TRUE
and FALSE
, the normal Boolean operators of NOT
, AND
, OR
and XOR
can be defined.
// Define Boolean the values TRUE and FALSE 
Now that we have a “not” and a “less than or equal” operator, it is quite straight forward to adapt this to create a “less than” operator.
// Define an IS_LT function 
Now that we have these tools available to us, we can start to look at arithmetic operations such as division and modulus. What makes these operations harder to implement is not only that they need to perform a SUBTRACT
operation an unknown number of times, they must also be defined recursively.
I know I’ve been cheating slightly by assigning names such as TRUE
, ZERO
and MULTIPLY
to our function definitions, but this is to account for our severely limited human ability to interpret long expressions. Really, names such as ZERO
and MULTIPLY
behave as macros that are substituted at runtime for the functions they represent.
Here, we also run up against another problem. Any recursively defined function must be able to make reference to itself; but if we create a DIV
function that refers to itself directly by name, then we have broken our rule that functions may not directly refer to themselves by name.
At first, you might think this arbitrary restriction requires us to perform some pointless mental gymnastics simply to overcome our own selfimposed hurdles. Whilst this might be true from one particular perspective, from the wider perspective of wanting understanding the nature of computation, overcoming this hurdle leads us to an understanding of one of the most important constructs in functional programming – the Ycombinator.
Chris W
In the previous Mindshift blog, we looked at how minimal JavaScript arrow functions can be used to define firstly a PAIR
of values, and secondly how extracting the first or second item from such a PAIR
yields a definition of the Boolean values of TRUE
and FALSE
.
So now that we have these definitions under our belt, we can look at how subtraction is implemented.
In the same way that the addition of a and b is defined as the b’th successor of a, so subtracting b from a amounts to nothing more than finding the b’th predecessor of a.
So the task for this blog is to develop a predecessor function^{1} and then from that, develop a SUBTRACT
function.
First however, we must understand the constraints of our current counting system.
This does restrict the functionality of our SUBTRACT
function, but that’s not an issue here for two reasons:
So given that our implementation cannot actually count backwards, we will have to think of the predecessor of a number in a different way.
The way we do can this is to think of numbers as pairs such as (n1,n)
. Then, to derive the predecessor of a number, we start counting up from zero – but always keeping track of the numbers as pairs. This means that when we’ve finished counting up to n
(the second number in our pair), the required predecessor will be the first number of the pair.
In the previous blog, we saw how the PAIR
function operates, so now we need to apply that function to our situation.
What we need now is a function that creates not just the successor of a single quantity function, but creates the successors of a PAIR
of quantity functions. So what we need here is a SUCC_PAIR
function.
Here, we will implement the “shift and increment” principle. A new pair of numbers is constructed as follows:
// Abstract function to represent any two things and a function to apply 
Now let’s use this function to create the next two successor pairs to PAIR(ZERO)(ZERO)
– which we expect to be PAIR(ZERO)(ONE)
and PAIR(ONE)(TWO)
.
// Starting from PAIR(ZERO)(ZERO), create the next two successive pairs. 
Lastly, to find the predecessor of any particular number n
, we need to invoke the SUCC_PAIR
function n
times on the PAIR(ZERO)(ZERO)
, and the required predecessor will then be the first number of the resulting pair.
Do you remember how a quantity function works? A quantity function applies the transformation function to the starting value as many times as is represented by that quantity function.
Q: What’s our transformation function going to be?
A: The SUCC_PAIR
function.
Q: What’s our starting value going to be?
A: We will define our starting point for counting to be PAIR(ZERO)(ZERO)
.
This means that the predecessor of any particular quantity function can be obtained by a twostep process: first, pass SUCC_PAIR
and PAIR(ZERO)(ZERO)
to the quantity function as the transformation function and starting value, and second, extract the first value from the resulting pair.
// Create a PREDecessor function by using SUCC_PAIR as the transformation 
Phew! That was a lot of hard work just to create a predecessor function, but finally we’re in a position to define a SUBTRACT
function.
Since subtraction is noncommutative, we must stipulate that SUBTRACT
always takes the second quantity away from the first; but apart from that, we’re using exactly the same principle as has been used before. In other words, to subtract b from a, apply the PRED
function b times starting from a.
// Create a SUBTRACT function by applying the PRED function n times to the 
Now that we have the ability to perform any subtraction whose answer is greater than or equal to zero^{2}, we can start to look at other arithmetic operations such as modulus and division; but, as always, there’s a catch – in fact there are two catches caused by the fact that both the modulus and division operators are implemented using repeated subtraction:
SUBTRACT
operation until the divisor becomes bigger than the remainder – yet, we have no functions such as IS_ZERO
or IS_LTE
(is less than or equal to).SUBTRACT
function will need to be called, it must called recursively.Teeny weeny problem… Our ground rules state that all functions must be anonymous. Therefore, how can the function SUBTRACT
call itself without referring to it by name?
This is where something called the YCombinator comes in; but more of that later.
Chris W
1) The definition of the predecessor function shown in this blog is not the only way this functionality could be implemented, but it is the simplest to understand. The following definition of the predecessor function is also widely used:
var PRED = n => f => x => n(g => h => h(g(f)))(y => x)(y => y);
Whilst this definition is equally valid, it is much harder to understand because it involves the use of nested wrapper functions – so we won’t look at this implementation here.
2) It might seem ridiculous to you that we have created a SUBTRACT
function that yields zero when asked what 5  8
is. But this is not so wacky as it might seem. For more information on this type of operation, look up Monus – but be warned – you’ll be venturing into the lands of Abstract Algebra and Group Theory. Here be dragons.