Lecture #8, Tuesday, February 9th
=================================
- Introducing Racket's `lambda`
- Using Functions as Objects
- Using `define-type` for new "type aliases"
- Currying
- Using Higher-Order & Anonymous Functions
- Side-note: "Point-Free" combinators
- This is not Runtime Code Generation
------------------------------------------------------------------------
# Introducing Racket's `lambda`
`fun` & `lambda`
difference between lambda and simple values
not being able to do recursive functions with `let`
let* as a derived form
let with lambda in Racket --> can be a derived form
how `if` can be used to implement `and` and `or` as derived forms
Newtonian syntax vs. a lambda expression.
Don't be fooled into making a bogus connection between Racket's syntax,
and its `unique` powers... The fact is that it is not the only language
that has this capability. For example, this:
(define (f g) (g 2 3))
(f +) ==> 5
(f *) ==> 6
(f (lambda (x y) (+ (square x) (square y)))) ==> 13
Can be written in JavaScript like this:
function f(g) { return g(2,3); }
function square(x) { return x*x; }
console.log(f(function (x,y) { return square(x) + square(y); }));
or in ES6 JavaScript:
let f = (g) => g(2,3);
let square = (x) => x*x;
console.log(f((x,y) => square(x) + square(y)));
In Perl:
sub f { my ($g) = @_; return $g->(2,3); }
sub square { my ($x) = @_; return $x * $x; }
print f(sub { my ($x, $y) = @_; return square($x) + square($y); });
In Ruby:
def f(g) g.call(2,3) end
def square(x) x*x end
puts f(lambda{|x,y| square(x) + square(y)})
etc. Even [Java has lambda expressions], and recently
[C++ added them too].
[Java has lambda expressions]:
http://www.drdobbs.com/jvm/lambda-expressions-in-java-8/240166764
[C++ added them too]:
http://www.cprogramming.com/c++11/c++11-lambda-closures.html
------------------------------------------------------------------------
# Using Functions as Objects
A very important aspect of Racket --- using "higher order" functions ---
functions that get and return functions. Here is a very simple example:
(define (f x) (lambda () x))
(define a (f 2))
(a) --> 2
(define b (f 3))
(b) --> 3
Note: what we get is actually an object that remembers (by the
substitution we're doing) a number. How about:
(define aa (f a))
(aa) --> # (this is a)
((aa)) --> 2
Take this idea to the next level:
(define (kons x y)
(lambda (b)
(if b x y)))
(define (kar p) (p #t))
(define (kdr p) (p #f))
(define a (kons 1 2))
(define b (kons 3 4))
(list (kar a) (kdr a))
(list (kar b) (kdr b))
Or, with types:
(: kons : (All (A B) A B -> (Boolean -> (U A B))))
(define (kons x y)
(lambda (b)
(if b x y)))
(: kar : (All (T) (Boolean -> T) -> T))
(define (kar p) (p #t))
(: kdr : (All (T) (Boolean -> T) -> T))
(define (kdr p) (p #f))
(define a (kons 1 2))
(define b (kons 3 4))
(list (kar a) (kdr a))
(list (kar b) (kdr b))
Even more --- why should the internal function expect a boolean and
choose what to return? We can simply expect a function that will take
the two values and return one:
(define (kons x y) (lambda (s) (s x y)))
(define (kar p) (p (lambda (x y) x)))
(define (kdr p) (p (lambda (x y) y)))
(define a (kons 1 2))
(define b (kons 3 4))
(list (kar a) (kdr a))
(list (kar b) (kdr b))
And a typed version, using our own constructor to make it a little less
painful:
(define-type (Kons A B) = ((A B -> (U A B)) -> (U A B)))
(: kons : (All (A B) A B -> (Kons A B)))
(define (kons x y) (lambda (s) (s x y)))
(: kar : (All (A B) (Kons A B) -> (U A B)))
(define (kar p) (p (lambda (x y) x)))
(: kdr : (All (A B) (Kons A B) -> (U A B)))
(define (kdr p) (p (lambda (x y) y)))
(define a (kons 1 2))
(define b (kons 3 4))
(list (kar a) (kdr a))
(list (kar b) (kdr b))
Note that the `Kons` type definition is the same as:
(define-type Kons = (All (A B) (A B -> (U A B)) -> (U A B)))
so `All` is to polymorphic type definitions what `lambda` is for
function definitions.
Finally in JavaScript:
function kons(x,y) { return function(s) { return s(x, y); } }
function kar(p) { return p(function(x,y){ return x; }); }
function kdr(p) { return p(function(x,y){ return y; }); }
a = kons(1,2);
b = kons(3,4);
console.log('a = <' + kar(a) + ',' + kdr(a) + '>' );
console.log('b = <' + kar(b) + ',' + kdr(b) + '>' );
Or with ES6 *arrow functions*, the function definitionss become:
const kons = (x,y) => s => s(x,y);
const kar = p => p((x,y) => x);
const kdr = p => p((x,y) => y);
------------------------------------------------------------------------
# Using `define-type` for new "type aliases"
As seen in these examples, there is another way to use `define-type`,
using a `=` to create a new type name "alias" for an *existing* type.
For example:
(define-type Strings = (Listof String))
These uses of `define-type` do not define any new kind of type, they are
essentially a convenience tool for making code shorter and more
readable.
(define-type NumericFunction = Number -> Number)
(: square : NumericFunction)
(define (square n) (* n n))
Note in particular that this can also be used to define "alias type
constructors" too: somewhat similar to creating new "type functions".
For example:
(define-type (BinaryFun In Out) = In In -> Out)
(: diagonal : (BinaryFun Natural Number))
(define (diagonal width height)
(sqrt (+ (* width width) (* height height))))
This is something that we will only need in a few rare cases.
------------------------------------------------------------------------
# Currying
A *curried* function is a function that, instead of accepting two (or
more) arguments, accepts only one and returns a function that accepts
the rest. For example:
(: plus : Number -> (Number -> Number))
(define (plus x)
(lambda (y)
(+ x y)))
It's easy to write functions for translating between normal and curried
versions.
(define (currify f)
(lambda (x)
(lambda (y)
(f x y))))
Typed version of that, with examples:
(: currify : (All (A B C) (A B -> C) -> (A -> (B -> C))))
;; convert a double-argument function to a curried one
(define (currify f)
(lambda (x) (lambda (y) (f x y))))
(: add : Number Number -> Number)
(define (add x y) (+ x y))
(: plus : Number -> (Number -> Number))
(define plus (currify add))
(test ((plus 1) 2) => 3)
(test (((currify add) 1) 2) => 3)
(test (map (plus 1) '(1 2 3)) => '(2 3 4))
(test (map ((currify add) 1) '(1 2 3)) => '(2 3 4))
(test (map ((currify +) 1) '(1 2 3)) => '(2 3 4))
Usages --- common with H.O. functions like map, where we want to *fix*
one argument.
When dealing with such higher-order code, the types are very helpful,
since every arrow corresponds to a function:
(: currify : (All (A B C) (A B -> C) -> (A -> (B -> C))))
It is common to make the `->` function type associate to the right, so
you can find this type written as:
currify : (A B -> C) -> (A -> B -> C)
or even as
currify : (A B -> C) -> A -> B -> C
but that can be a little confusing...
------------------------------------------------------------------------
# Using Higher-Order & Anonymous Functions
Say that we have a function for estimating derivatives of a function at
a specific point:
(define dx 0.01)
(: deriv : (Number -> Number) Number -> Number)
;; compute the derivative of `f' at the given point `x'
(define (deriv f x)
(/ (- (f (+ x dx)) (f x)) dx))
(: integrate : (Number -> Number) Number -> Number)
;; compute an integral of `f' at the given point `x'
(define (integrate f x)
(: loop : Number Number -> Number)
(define (loop y acc)
(if (> y x)
(* acc dx)
(loop (+ y dx) (+ acc (f y)))))
(loop 0 0))
And say that we want to try out various functions given some `plot`
function that draws graphs of numeric functions, for example:
(plot sin)
The problem is that `plot` expects a single `(Number -> Number)`
function --- if we want to try it with a derivative, we can do this:
(: sin-deriv : Number -> Number)
;; the derivative of sin
(define sin-deriv (lambda (x) (deriv sin x)))
(plot sin-deriv)
But this will get very tedious very fast --- it is much simpler to use
an anonymous function:
(plot (lambda (x) (deriv sin x)))
we can even verify that our derivative is correct by comparing a known
function to its derivative
(plot (lambda (x) (- (deriv sin x) (cos x))))
But it's still not completely natural to do these things --- you need to
explicitly combine functions, which is not too convenient. Instead of
doing this, we can write H.O. functions that will work with functional
inputs and outputs. For example, we can write a function to subtract
functions:
(: fsub : (Number -> Number) (Number -> Number)
-> (Number -> Number))
;; subtracts two numeric 1-argument functions
(define (fsub f g)
(lambda (x) (- (f x) (g x))))
and the same for the derivative:
(: fderiv : (Number -> Number) -> (Number -> Number))
;; compute the derivative function of `f'
(define (fderiv f)
(lambda (x) (deriv f x)))
Now we can try the same in a much easier way:
(plot (fsub (fderiv sin) cos))
More than that --- our `fderiv` could be created from `deriv`
automatically:
(: currify : (All (A B C) (A B -> C) -> (A -> B -> C)))
;; convert a double-argument function to a curried one
(define (currify f)
(lambda (x) (lambda (y) (f x y))))
(: fderiv : (Number -> Number) -> (Number -> Number))
;; compute the derivative function of `f'
(define fderiv (currify deriv))
Same principle with `fsub`: we can write a function that converts a
binary arithmetical function into a function that operates on unary
numeric function. But to make things more readable we can define new
types for unary and binary numeric functions:
(define-type UnaryFun = (Number -> Number))
(define-type BinaryFun = (Number Number -> Number))
(: binop->fbinop : BinaryFun -> (UnaryFun UnaryFun -> UnaryFun))
;; turns an arithmetic binary operator to a function operator
(define (binop->fbinop op)
(lambda (f g)
(lambda (x) (op (f x) (g x)))))
(: fsub : UnaryFun UnaryFun -> UnaryFun)
;; functional pointwise subtraction
(define fsub (binop->fbinop -))
We can do this with anything --- developing a rich library of functions
and functionals (functions over functions) is extremely easy... Here's
a pretty extensive yet very short library of functions:
#lang pl untyped
(define (currify f)
(lambda (x) (lambda (y) (f x y))))
(define (binop->fbinop op)
(lambda (f g)
(lambda (x) (op (f x) (g x)))))
(define (compose f g)
(lambda (x) (f (g x))))
(define dx 0.01)
(define (deriv f x)
(/ (- (f (+ x dx)) (f x)) dx))
(define (integrate f x)
(define over? (if (< x 0) < >))
(define step (if (< x 0) - +))
(define add (if (< x 0) - +))
(define (loop y acc)
(if (over? y x)
(* acc dx)
(loop (step y dx) (add acc (f y)))))
(loop 0 0))
(define fadd (binop->fbinop +))
(define fsub (binop->fbinop -))
(define fmul (binop->fbinop *))
(define fdiv (binop->fbinop /))
(define fderiv (currify deriv))
(define fintegrate (currify integrate))
;; ...
This is written in the "untyped dialect" of the class language, but it
should be easy now to add the types.
Examples:
;; want to verify that `integrate' is the opposite of `deriv':
;; take a function, subtract it from its derivative's integral
(plot (fsub sin (fintegrate (fderiv sin))))
;; want to magnify the errors? -- here's how you magnify:
(plot (compose ((currify *) 5) sin))
;; so:
(plot (compose ((currify *) 20)
(fsub sin (fintegrate (fderiv sin)))))
------------------------------------------------------------------------
## Side-note: "Point-Free" combinators
> Forming functions without using `lambda` (or an implicit `lambda`
> using a `define` syntactic sugar) is called *point-free style*. It's
> especially popular in Haskell, where it is easier to form functions
> this way because of implicit currying and a large number of higher
> level function combinators. If used too much, it can easily lead to
> obfuscated code.
------------------------------------------------------------------------
## This is not Runtime Code Generation
All of this is similar to run-time code generation, but not really. The
only thing that `fderiv` does is take a function and store it somewhere
in the returned function, then when that function receives a number, it
uses the stored function and send it to deriv with the number. We could
simply write deriv as what `fderiv` is --- which is the *real*
derivative function:
(define (deriv f)
(lambda (x)
(/ (- (f (+ x dx)) (f x)) dx)))
but again, this is not faster or slower than the plain `deriv`.
However, there are some situations where we can do some of the
computation on the first-stage argument, saving work from the second
stage. Here is a cooked-to-exaggeration example --- we want a function
that receives two inputs `x`, `y` and returns `fib(x)*y`, but we must
use a stupid `fib`:
(define (fib n)
(if (<= n 1)
n
(+ (fib (- n 1)) (fib (- n 2)))))
The function we want is:
(define (bogus x y)
(* (fib x) y))
If we currify it as usual (or just use `currify`), we get:
(define (bogus x)
(lambda (y)
(* (fib x) y)))
And try this several times:
(define bogus36 (bogus 36))
(map bogus36 '(1 2 3 4 5))
But in the definition of `bogus`, notice that `(fib x)` does not depend
on `y` --- so we can rewrite it a little differently:
(define (bogus x)
(let ([fibx (fib x)])
(lambda (y)
(* fibx y))))
and trying the above again is much faster now:
(define bogus36 (bogus 36))
(map bogus36 '(1 2 3 4 5))
This is therefore not doing any kind of runtime code generation, but it
*enables* doing similar optimizations in our code. A proper RTCG
facility would recompile the curried function for a given first input,
and (hopefully) automatically achieve the optimization that we did in a
manual way.