Using Minimal Functions To Build Predicates And Boolean Operators
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.
There’s Nothing There, Or Is There?
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.
Less Is More (Than a Negative Number)
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 
Boolean Operators
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
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