In Python you can write this:
def firstn(n):
num = 0
while num < n:
yield num
num += 1
What is the lisp equivalent of this?
Existing package
Download, install and load the GENERATORS system with Quicklisp. Then, use package :generators (or preferably, define your own package first).
(ql:quickload :generators)
(use-package :generators)
Define an infinite generator for random values:
(defun dice (n)
(make-generator ()
;; repeatedly return a random value between 1 and N
(loop (yield (1+ (random n))))))
Use the generator:
(loop
with dice = (dice 6)
repeat 20
collect (next dice))
=> (1 2 6 1 1 4 4 2 4 3 6 2 1 5 6 5 1 5 1 2)
Note however what the author of the library says:
This library is more of an interesting toy, though as far as I know it
does work. I dont think I have ever used this in application code,
though I think that with care, it could be.
See also
The ITERATE package provides a way to define generators for use inside its iteration facility.
The SERIES package provide stream-like data structures and operations on them.
The Snakes library (same approach as GENERATORS as far as I know).
Iterators in generic-cl
Closures
In practice, CL does not rely that much on generators as popularized by Python. What happens instead is that when people need lazy sequences, they use closures:
(defun dice (n)
(lambda ()
(1+ (random n))))
Then, the equivalent of next is simply a call to the thunk generated by dice:
(loop
with dice = (dice 6)
repeat 20
collect (funcall dice))
This is the approach that is preferred, in particular because there is no need to rely on delimited continuations like with generators. Your example involves a state, which the dice example does not require (there is a hidden state that influences random, but that's another story) . Here is how your counter is typically implemented:
(defun first-n (n)
(let ((counter -1))
(lambda ()
(when (< counter n)
(incf counter)))))
Higher-order functions
Alternatively, you design a generator that accepts a callback function which is called by your generator for each value. Any funcallable can be used, which allows the caller to retain control over code execution:
(defun repeatedly-throw-dice (n callback)
(loop (funcall callback (1+ (random n)))))
Then, you can use it as follows:
(prog ((counter 0) stack)
(repeatedly-throw-dice 6
(lambda (value)
(if (<= (incf counter) 20)
(push value stack)
(return (nreverse stack))))))
See documentation for PROG.
do-traversal idiom
Instead of building a function, data sources that provides a custom way of generating values (like matches of a regular expressions in a string) also regularly provide a macro that abstracts their control-flow. You would use it as follows:
(let ((counter 0) stack)
(do-repeatedly-throw-dice (value 6)
(if (<= (incf counter) 20)
(push value stack)
(return (nreverse stack))))))
DO-X macros are expected to define a NIL block around their body, which is why the return above is valid.
A possible implementation for the macro is to wrap the body in a lambda form and use the callback-based version defined above:
(defmacro do-repeatedly-throw-dice ((var n) &body body)
`(block nil (repeatedly-throw-dice ,n (lambda (,var) ,#body))))
Directly expanding into a loop would be possible too:
(defmacro do-repeatedly-throw-dice ((var n) &body body)
(let ((max (gensym)) (label (make-symbol "NEXT")))
`(prog ((,max ,n) ,var)
,label
(setf ,var (1+ (random ,max)))
(progn ,#body)
(go ,label))))
One step of macroexpansion for above form:
(prog ((#:g1078 6) value)
#:next
(setf value (1+ (random #:g1078)))
(progn
(if (<= (incf counter) 20)
(push value stack)
(return (nreverse stack))))
(go #:next))
Bindings
Broadly speaking, building a generator with higher-order functions or directly with a do- macro gives the same result. You can implement one with the other (personally, I prefer to define first the macro and then the function using the macro, but doing the opposite is also interesting, since you can redefine the function without recompiling all usages of the macro).
However, there is still a difference: the macro reuses the same variable across iterations, whereas the closure introduces a fresh binding each time. For example:
(let ((list))
(dotimes (i 10) (push (lambda () i) list))
(mapcar #'funcall list))
.... returns:
(10 10 10 10 10 10 10 10 10 10)
Most (if not all) iterators in Common Lisp tend to work like this1, and it should not come as a surprise for experienced users (the opposite would be surprising, in fact). If dotimes was implemented by repeatedly calling a closure, the result would be different:
(defmacro my-dotimes ((var count-form &optional result-form) &body body)
`(block nil
(alexandria:map-iota (lambda (,var) ,#body) ,count-form)
,result-form))
With the above definition, we can see that:
(let ((list))
(my-dotimes (i 10) (push (lambda () i) list))
(mapcar #'funcall list))
... returns:
(9 8 7 6 5 4 3 2 1 0)
In order to have the same result with the standard dotimes, you only need to create a fresh binding before building the closure:
(let ((list))
(dotimes (i 10)
(let ((j i))
(push (lambda () j) list))))
Here j is a fresh binding whose value is the current value of i at closure creation time; j is never mutated so the closure will constantly return the same value.
If you wanted to, you could always introduce that inner let from the macro, but this is rarely done.
1: Note that the specification for DOTIMES does not require that bindings are fresh at each iteration, or only mutates the same binding at each step: "It is implementation-dependent whether dotimes establishes a new binding of var on each iteration or whether it establishes a binding for var once at the beginning and then assigns it on any subsequent iterations." In order to write portably, it is necessary to assume the worst-case scenario (i.e. mutation, which happens to be what most (all?) implementations do) and manually rebind iteration variables if they are to be captured and reused at a later point.
Related
Prelude
In Raku there's a notion called infinite list AKA lazy list which is defined and used like:
my #inf = (1,2,3 ... Inf);
for #inf { say $_;
exit if $_ == 7 }
# => OUTPUT
1
2
3
4
5
6
7
I'd like to implement this sort of thing in Common Lisp, specifically an infinite list of consecutive integers like:
(defun inf (n)
("the implementation"))
such that
(inf 5)
=> (5 6 7 8 9 10 .... infinity)
;; hypothetical output just for the demo purposes. It won't be used in reality
Then I'll use it for lazy evaluation like this:
(defun try () ;; catch and dolist
(catch 'foo ;; are just for demo purposes
(dolist (n (inf 1) 'done)
(format t "~A~%" n)
(when (= n 7)
(throw 'foo x)))))
CL-USER> (try)
1
2
3
4
5
6
7
; Evaluation aborted.
How can I implement such an infinite list in CL in the most practical way?
A good pedagogical approach to this is to define things which are sometimes called 'streams'. The single best introduction to doing this that I know of is in Structure and Interpretation of Computer Programs. Streams are introduced in section 3.5, but don't just read that: read the book, seriously: it is a book everyone interested in programming should read.
SICP uses Scheme, and this sort of thing is more natural in Scheme. But it can be done in CL reasonably easily. What I've written below is rather 'Schemy' CL: in particular I just assume tail calls are optimised. That's not a safe assumption in CL, but it's good enough to see how you can build these concepts into a language which does not already have them, if your language is competent.
First of all we need a construct which supports lazy evaluation: we need to be able to 'delay' something to create a 'promise' which will be evaluated only when it needs to be. Well, what functions do is evaluate their body only when they are asked to, so we'll use them:
(defmacro delay (form)
(let ((stashn (make-symbol "STASH"))
(forcedn (make-symbol "FORCED")))
`(let ((,stashn nil)
(,forcedn nil))
(lambda ()
(if ,forcedn
,stashn
(setf ,forcedn t
,stashn ,form))))))
(defun force (thing)
(funcall thing))
delay is mildly fiddly, it wants to make sure that a promise is forced only once, and it also wants to make sure that the form being delayed doesn't get infected by the state it uses to do that. You can trace the expansion of delay to see what it makes:
(delay (print 1))
-> (let ((#:stash nil) (#:forced nil))
(lambda ()
(if #:forced #:stash (setf #:forced t #:stash (print 1)))))
This is fine.
So now, we'll invent streams: streams are like conses (they are conses!) but their cdrs are delayed:
(defmacro cons-stream (car cdr)
`(cons ,car (delay ,cdr)))
(defun stream-car (s)
(car s))
(defun stream-cdr (s)
(force (cdr s)))
OK, let's write a function to get the nth element of a stream:
(defun stream-nth (n s)
(cond ((null s)
nil)
((= n 0) (stream-car s))
(t
(stream-nth (1- n) (stream-cdr s)))))
And we can test this:
> (stream-nth 2
(cons-stream 0 (cons-stream 1 (cons-stream 2 nil))))
2
And now we can write a function to enumerate an interval in the naturals, which by default will be an half-infinite interval:
(defun stream-enumerate-interval (low &optional (high nil))
(if (and high (> low high))
nil
(cons-stream
low
(stream-enumerate-interval (1+ low) high))))
And now:
> (stream-nth 1000 (stream-enumerate-interval 0))
1000
And so on.
Well, we'd like some kind of macro which lets us traverse a stream: something like dolist, but for streams. Well we can do this by first writing a function which will call a function for each element in the stream (this is not the way I'd do this in production CL code, but it's fine here):
(defun call/stream-elements (f s)
;; Call f on the elements of s, returning NIL
(if (null s)
nil
(progn
(funcall f (stream-car s))
(call/stream-elements f (stream-cdr s)))))
And now
(defmacro do-stream ((e s &optional (r 'nil)) &body forms)
`(progn
(call/stream-elements (lambda (,e)
,#forms)
,s)
,r))
And now, for instance
(defun look-for (v s)
;; look for an element of S which is EQL to V
(do-stream (e s (values nil nil))
(when (eql e v)
(return-from look-for (values e t)))))
And we can then say
> (look-for 100 (stream-enumerate-interval 0))
100
t
Well, there is a lot more mechanism you need to make streams really useful: you need to be able to combine them, append them and so on. SICP has many of these functions, and they're generally easy to turn into CL, but too long here.
For practical purposes it would be wise to use existing libraries, but since the question is about how to implemented lazy lists, we will do it from scratch.
Closures
Lazy iteration is a matter of producing an object that can generate the new value of a lazy sequence each time it is asked to do so.
A simple approach for this is to return a closure, i.e. a function that closes over variables, which produces values while updating its state by side-effect.
If you evaluate:
(let ((a 0))
(lambda () (incf a)))
You obtain a function object that has a local state, namely here the variable named a.
This is a lexical binding to a location that is exclusive to this function, if you evaluate a second time the same expression, you'll obtain a different anonymous function that has its own local state.
When you call the closure, the value stored in a in incremented and its value is returned.
Let's bind this closure to a variable named counter, call it multiple times and store the successive results in a list:
(let ((counter (let ((a 0))
(lambda () (incf a)))))
(list (funcall counter)
(funcall counter)
(funcall counter)
(funcall counter)))
The resulting list is:
(1 2 3 4)
Simple iterator
In your case, you want to have an iterator that starts counting from 5 when writing:
(inf 5)
This can implemented as follows:
(defun inf (n)
(lambda ()
(shiftf n (1+ n))))
Here is there is no need to add a let, the lexical binding of an argument to n is done when calling the function.
We assign n to a different value within the body over time.
More precisely, SHIFTF assigns n to (1+ n), but returns the previous value of n.
For example:
(let ((it (inf 5)))
(list (funcall it)
(funcall it)
(funcall it)
(funcall it)))
Which gives:
(5 6 7 8)
Generic iterator
The standard dolist expects a proper list as an input, there is no way you can put another kind of data and expect it to work (or maybe in an implementation-specific way).
We need a similar macro to iterate over all the values in an arbitrary iterator.
We also need to specify when iteration stops.
There are multiple possibilities here, let's define a basic iteration protocol as follows:
we can call make-iterator on any object, along with arbitrary arguments, to obtain an iterator
we can call next on an iterator to obtain the next value.
More precisely, if there is a value, next returns the value and T as a secondary value; otherwise, next returns NIL.
Let's define two generic functions:
(defgeneric make-iterator (object &key)
(:documentation "create an iterator for OBJECT and arguments ARGS"))
(defgeneric next (iterator)
(:documentation "returns the next value and T as a secondary value, or NIL"))
Using generic functions allows the user to define custom iterators, as long as they respect the specified behaviour above.
Instead of using dolist, which only works with eager sequences, we define our own macro: for.
It hides calls to make-iterator and next from the user.
In other words, for takes an object and iterates over it.
We can skip iteration with (return v) since for is implemented with loop.
(defmacro for ((value object &rest args) &body body)
(let ((it (gensym)) (exists (gensym)))
`(let ((,it (make-iterator ,object ,#args)))
(loop
(multiple-value-bind (,value ,exists) (next ,it)
(unless ,exists
(return))
,#body)))))
We assume any function object can act as an iterator, so we specialize next for values f of class function, so that the function f gets called:
(defmethod next ((f function))
"A closure is an interator"
(funcall f))
Also, we can also specialize make-iterator to make closures their own iterators (I see no other good default behaviour to provide for closures):
(defmethod make-iterator ((function function) &key)
function)
Vector iterator
For example, we can built an iterator for vectors as follows. We specialize make-iterator for values (here named vec) of class vector.
The returned iterator is a closure, so we will be able to call next on it.
The method accepts a :start argument defaulting to zero:
(defmethod make-iterator ((vec vector) &key (start 0))
"Vector iterator"
(let ((index start))
(lambda ()
(when (array-in-bounds-p vec index)
(values (aref vec (shiftf index (1+ index))) t)))))
You can now write:
(for (v "abcdefg" :start 2)
(print v))
And this prints the following characters:
#\c
#\d
#\e
#\f
#\g
List iterator
Likewise, we can build a list iterator.
Here to demonstrate other kind of iterators, let's have a custom cursor type.
(defstruct list-cursor head)
The cursor is an object which keeps a reference to the current cons-cell in the list being visited, or NIL.
(defmethod make-iterator ((list list) &key)
"List iterator"
(make-list-cursor :head list))
And we define next as follows, specializeing on list-cursor:
(defmethod next ((cursor list-cursor))
(when (list-cursor-head cursor)
(values (pop (list-cursor-head cursor)) t)))
Ranges
Common Lisp also allows methods to be specialized with EQL specializers, which means the object we give to for might be a specific keyword, for example :range.
(defmethod make-iterator ((_ (eql :range)) &key (from 0) (to :infinity) (by 1))
(check-type from number)
(check-type to (or number (eql :infinity)))
(check-type by number)
(let ((counter from))
(case to
(:infinity
(lambda () (values (incf counter by) t)))
(t
(lambda ()
(when (< counter to)
(values (incf counter by) T)))))))
A possible call for make-iterator would be:
(make-iterator :range :from 0 :to 10 :by 2)
This also returns a closure.
Here, for example, you would iterate over a range as follows:
(for (v :range :from 0 :to 10 :by 2)
(print v))
The above expands as:
(let ((#:g1463 (make-iterator :range :from 0 :to 10 :by 2)))
(loop
(multiple-value-bind (v #:g1464)
(next #:g1463)
(unless #:g1464 (return))
(print v))))
Finally, if we add small modification to inf (adding secondary value):
(defun inf (n)
(lambda ()
(values (shiftf n (1+ n)) T)))
We can write:
(for (v (inf 5))
(print v)
(when (= v 7)
(return)))
Which prints:
5
6
7
I'll show it with a library:
How to create and consume an infinite list of integers with the GTWIWTG generators library
This library, called "Generators The Way I Want Them Generated", allows to do three things:
create generators (iterators)
combine them
consume them (once).
It is not unsimilar to the nearly-classic Series.
Install the lib with (ql:quickload "gtwiwtg"). I will work in its package: (in-package :gtwiwtg).
Create a generator for an infinite list of integers, start from 0:
GTWIWTG> (range)
#<RANGE-BACKED-GENERATOR! {10042B4D83}>
We can also specify its :from, :to, :by and :inclusive parameters.
Combine this generator with others: not needed here.
Iterate over it and stop:
GTWIWTG> (for x *
(print x)
(when (= x 7)
(return)))
0
1
2
3
4
5
6
7
T
This solution is very practical :)
I am working on problem to get the occurence of Prime in a list in lisp.
Input:
Write a function (OccurencesOfPrimes < list >) which counts the number of primes in a (possibly nested) list.
Output: Example: (OccurencesOfPrimes (((1)(2))(5)(3)((8)3)) returns 4.
I am using the below code but getting the error like:
(
defun OccurencesOfPrimes (list)
(loop for i from 2 to 100
do ( setq isPrime t)
(loop for j from 2 to i
never (zerop (mod i j))
(setq isPrime f)
(break)
)
)
(if (setq isPrime t)
(append list i)
)
)
)
LOOP: illegal syntax near (SETQ ISPRIME F) in
(LOOP FOR J FROM 2 TO I NEVER (ZEROP (MOD I J)) (SETQ ISPRIME F) (BREAK)
)
Any help.
It is important to keep the format consistent with the expected conventions of the language. It helps when reading the code (in particular with other programmers), and can help you see errors.
Also, you should use an editor which, at the minimum, keep tracks of parentheses. In Emacs, when you put the cursor in the first opening parenthesis, the matching parenthesis is highlighted. You can spot that you have one additional parenthesis that serves no purpose.
(
defun OccurencesOfPrimes (list)
(loop for i from 2 to 100
do ( setq isPrime t)
(loop for j from 2 to i
never (zerop (mod i j))
(setq isPrime f)
(break)
)
)
(if (setq isPrime t)
(append list i)
)
) ;; <- end of defun
) ;; <- closes nothing
In Lisp, parentheses are for the computer, whereas indentation is for humans. Tools can automatically indent the code according to the structure (the parenthesis), and any discrepancy between what indentation you expect and the one being computed is a hint that your code is badly formed. If you look at the indentation of your expressions, you can see how deep you are in the form, and that alone helps you understand the code.
Symbol names are dash-separated, not camlCased.
Your code, with remarks:
(defun occurences-of-primes (list)
;; You argument is likely to be a LIST, given its name and the way
;; you call APPEND below. But you never iterate over the list. This
;; is suspicious.
(loop
for i from 2 to 100
do
(setq is-prime t) ;; setting an undeclared variable
(loop
for j from 2 to i
never (zerop (mod i j))
;; the following two forms are not expected here according
;; to LOOP's grammar; setting IS-PRIME to F, but F is not
;; an existing variable. If you want to set to false, use
;; NIL instead.
(setq is-prime f)
;; BREAK enters the debugger, maybe you wanted to use
;; LOOP-FINISH instead, but the NEVER clause above should
;; already be enough to exit the loop as soon as its
;; sub-expression evaluates to NIL.
(break)))
;; The return value of (SETQ X V) is V, so here your test would
;; always succeed.
(if (setq is-prime t)
;; Append RETURNS a new list, without modifying its
;; arguments. In particular, LIST is not modified. Note that "I"
;; is unknown at this point, because the bindings effective
;; inside the LOOP are not visible in this scope. Besides, "I"
;; is a number, not a list.
(append list i)))
Original question
Write one function which counts all the occurrences of a prime number in a (possibly nested) list.
Even though the homework questions says "write one function", it does not say that you should write one big function that compute everything at once. You could write one such big function, but if you split your problem into sub-problems, you will end with different auxiliary functions, which:
are simpler to understand (they do one thing)
can be reused to build other functions
The sub-problems are, for example: how to determine if a number is a prime? how to iterate over a tree (a.k.a. a possibly nested list)? how to count
the occurrences?
The basic idea is to write an "is-prime" function, iterate over the tree and call "is-prime" on each element; if the element is prime and was never seen before, add 1 to a counter, local to your function.
You can also flatten the input tree, to obtain a list, then sort the resulting
list; you iterate over the list while keeping track of the last
value seen: if the value is the same as the previous one, you
already know if the number is prime; if the previous number differs, then
you have to test if the number is prime first.
You could also abstract things a little more, and define a higher-order tree-walker function, which calls a function on each leaf of the tree. And write another higher-order function which "memoizes" calls: it wraps around a
function F so that if you call F with the same arguments as before,
it returns the result that was stored instead of recomputing it.
Example
I'll combine the above ideas because if you give that answer to a teacher you are likely to have to carefully explain what each part does (and if you can, great for you); this is not necessarily the "best" answer, but it covers a lot of things.
(defun tree-walk-leaves (tree function)
(typecase tree
(null nil)
(cons
(tree-walk-leaves (car tree) function)
(tree-walk-leaves (cdr tree) function))
(t (funcall function tree))))
(defun flatten (tree &optional keep-order-p)
(let ((flat nil))
(tree-walk-leaves tree (lambda (leaf) (push leaf flat)))
(if keep-order-p
(nreverse flat)
flat)))
(defun prime-p (n)
(or (= n 2)
(and (> n 2)
(oddp n)
(loop
for d from 3 upto (isqrt n) by 2
never (zerop (mod n d))))))
(defun count-occurences-of-prime (tree)
(count-if #'prime-p (remove-duplicates (flatten tree))))
(count-occurences-of-prime '(((1)(2))(5)(3)((8)3)))
=> 4
If, instead, you don't want to remove duplicates but count the multiple times a prime number occurs, you can do:
(count-if (memoize #'prime-p) (flatten tree))
... where memoize is:
(defun memoize (function &key (test #'equalp) (key #'identity))
(let ((hash (make-hash-table :test test)))
(lambda (&rest args)
(let ((args (funcall key args)))
(multiple-value-bind (result exists-p) (gethash args hash)
(values-list
(if exists-p
result
(setf (gethash args hash)
(multiple-value-list (apply function args))))))))))
(memoize is useless if there are no duplicates)
What exactly is different between these implementations of 'when'?
(define-syntax when
(syntax-rules ()
((_ pred b1 ...)
(if pred (begin b1 ...)))))
vs.
(define (my-when pred b1 ...)
(if pred (begin b1 ...)))
For example, when 'my-when' is used in this for loop macro:
(define-syntax for
(syntax-rules ()
((_ (i from to) b1 ...)
(let loop((i from))
(my-when (< i to)
b1 ...
(loop (+ i 1)))))))
an error occurs:
(for (i 0 10) (display i))
; Aborting!: maximum recursion depth exceeded
I do not think 'when' can be implemented as a function, but I do not know why...
Scheme has strict semantics.
This means that all of a function's parameters are evaluated before the function is applied to them.
Macros take source code and produce source code - they don't evaluate any of their parameters.
(Or, well, I suppose they do, but their parameters are syntax - language elements - rather than what you normally think of as values, such as numbers or strings. Macro programming is meta-programming. It's important to be aware of which level you're programming at.)
In your example this means that when my-when is a function, (loop (+ i 1)) must be evaluated before my-when can be applied to it.
This leads to an infinite recursion.
When it's a macro, the my-when form is first replaced with the equivalent if-form
(if (< i to)
(begin
b1 ...
(loop (+ i 1))))
and then the whole thing is evaluated, which means that (loop (+ i 1)) only gets evaluated when the condition is true.
If you implement when as a procedure like you did, then all arguments are evaluated. In your for implementation, the evaluation would be processed like this:
evaluate (< i to)
evaluate expansion result of b1 ...
evaluate (loop (+ i 1)) <- here goes into infinite loop!
evaluate my-when
Item 1-3 can be reverse or undefined order depending on your implementation but the point is nr. 4. If my-when is implemented as a macro, then the macro is the first one to be evaluated.
If you really need to implement with a procedure, then you need to use sort of delaying trick such as thunk. For example:
(define (my-when pred body) (if (pred) (body)))
(my-when (lambda () (< i 10)) (lambda () (display i) (loop (+ i 1))))
The code below server to show the number of integer in a list.
(defun isNum (N)
(and (<= N 9) (>= N 0)))
(defun count-numbers (list)
(let ((count 0))
(dolist (item list count)
(cond
((null list) nil)
(((and (<= N 9) (>= N 0))) item)(incf count))
(setq(0 + count))))))
I get the error A' is not of the expected typeREAL' when I run the command
(count-numbers '(3 4 5 6 a 7 b) )
I'm surprised it runs at all, given that your cond is improperly constructed, you switch to infix notation in the unnecessarily side-effect-generating bit of your code and you're using unbound variables in count-numbers. Hypothetically, if it did run, that error sounds about right. You're doing numeric comparisons on a parameter (and those error on non-numeric input).
I've got my codereview hat on today, so lets go through this a bit more in-depth.
Lisp (it actually doesn't matter which, afaik this applies to CL, Scheme and all the mongrels) uses lower-case-snake-case-with-dashes, and not lowerCamelCase for variable and function names.
(defun is-num (n)
(and (<= n 9) (>= n 0)))
Common Lisp convention is to end a predicate with p or -p rather than begin them with is-. Scheme has the (IMO better) convention of ending predicates with ? instead
(defun num-p (n)
(and (<= n 9) (>= n 0)))
((and (<= N 9) (>= N 0))) is not how you call a function. You actually need to use its name, not just attempt to call its body. This is the source of one of the many errors you'd get if you tried to run this code.
(defun count-numbers (list)
(let ((count 0))
(dolist (item list count)
(cond
((null list) nil)
((num-p item) item)(incf count))
(setq(0 + count))))))
numberp already exists, and does a type check on its input rather than attempting numeric comparisons. You should probably use that instead.
(defun count-numbers (list)
(let ((count 0))
(dolist (item list count)
(cond
((null list) nil)
((numberp item) item)(incf count))
(setq(0 + count))))))
((numberp item) item) (incf count)) probably doesn't do what you think it does as a cond clause. It actually gets treated as two separate clauses; one checks whether item is a number, and returns it if it is. The second tries to check the variable incf and returns count if it evaluates to t (which it doesn't, and won't). What you seem to want is to increment the counter count when you find a number in your list, which means you should put that incf clause in with the item.
(defun count-numbers (list)
(let ((count 0))
(dolist (item list count)
(cond ((null list) nil)
((numberp item)
(incf count)
item))
(setq (0 + count)))))
(setq (0 + count)) is the wrong thing for three reasons
You seem to have tripped back into infix notation, which means that the second bit there is actually attempting to call the function 0 with the variables + and count as arguments.
You don't have a second part to the setq, which means you're trying to set the above to NIL implicitly.
You don't actually need to set anything in order to return a value
At this point, we finally have a piece of code that will evaluate and run properly (and it doesn't throw the error you mention above).
(defun count-numbers (list)
(let ((count 0))
(dolist (item list count)
(cond ((null list) nil)
((numberp item)
(incf count)
item))
count)))
dolist is an iteration construct that does something for each element in a given list. That means you don't actually need to test for list termination manually with that cond. Also, because dolist doesn't collect results, there's no reason to return item to it. You're also unnecessarily shadowing the local count you declare in the let.
(defun count-numbers (list)
(let ((count 0))
(dolist (item list)
(when (numberp item) (incf count)))
count))
As usual, you can do all this with a simpler loop call.
(defun count-numbers (list)
(loop for item in list
when (numberp item) sum 1))
which makes the counter implicit and saves you from needing to return it manually. In fact, unless this was specifically an exercise to write your own iteration function, Common Lisp has a built in count-if, which takes predicate sequence [some other options] and returns the count of items in sequence that match predicate. If you wanted to name count-numbers specifically, for stylistic reasons, you could just
(defun count-numbers (list) (count-if #'numberp list))
and be done with it.
In conclusion, good try, but please try reading up on the language family for realzies before asking further questions.
Yet another way to do it would be:
(reduce
#'(lambda (a b)
(if (numberp b) (1+ a) a))
'(3 4 5 6 a 7 b) :initial-value 0) ; 5
I.e. process the sequence in a way that you are given at each iteration the result of the previous iteration + the next member of the sequence. Start with zero and increment the result each time the element in the sequence is a number.
EDIT
Sorry, I haven't seen Inaimathi mentioned count-if. That would be probably better.
I'm a Lisp beginner. I'm trying to memoize a recursive function for calculating the number of terms in a Collatz sequence (for problem 14 in Project Euler). My code as of yet is:
(defun collatz-steps (n)
(if (= 1 n) 0
(if (evenp n)
(1+ (collatz-steps (/ n 2)))
(1+ (collatz-steps (1+ (* 3 n)))))))
(defun p14 ()
(defvar m-collatz-steps (memoize #'collatz-steps))
(let
((maxsteps (funcall m-collatz-steps 2))
(n 2)
(steps))
(loop for i from 1 to 1000000
do
(setq steps (funcall m-collatz-steps i))
(cond
((> steps maxsteps)
(setq maxsteps steps)
(setq n i))
(t ())))
n))
(defun memoize (fn)
(let ((cache (make-hash-table :test #'equal)))
#'(lambda (&rest args)
(multiple-value-bind
(result exists)
(gethash args cache)
(if exists
result
(setf (gethash args cache)
(apply fn args)))))))
The memoize function is the same as the one given in the On Lisp book.
This code doesn't actually give any speedup compared to the non-memoized version. I believe it's due to the recursive calls calling the non-memoized version of the function, which sort of defeats the purpose. In that case, what is the correct way to do the memoization here? Is there any way to have all calls to the original function call the memoized version itself, removing the need for the special m-collatz-steps symbol?
EDIT: Corrected the code to have
(defvar m-collatz-steps (memoize #'collatz-steps))
which is what I had in my code.
Before the edit I had erroneously put:
(defvar collatz-steps (memoize #'collatz-steps))
Seeing that error gave me another idea, and I tried using this last defvar itself and changing the recursive calls to
(1+ (funcall collatz-steps (/ n 2)))
(1+ (funcall collatz-steps (1+ (* 3 n))))
This does seem to perform the memoization (speedup from about 60 seconds to 1.5 seconds), but requires changing the original function. Is there a cleaner solution which doesn't involve changing the original function?
I assume you're using Common-Lisp, which has separate namespaces for variable and function names. In order to memoize the function named by a symbol, you need to change its function binding, through the accessor `fdefinition':
(setf (fdefinition 'collatz-steps) (memoize #'collatz-steps))
(defun p14 ()
(let ((mx 0) (my 0))
(loop for x from 1 to 1000000
for y = (collatz-steps x)
when (< my y) do (setf my y mx x))
mx))
Here is a memoize function that rebinds the symbol function:
(defun memoize-function (function-name)
(setf (symbol-function function-name)
(let ((cache (make-hash-table :test #'equal)))
#'(lambda (&rest args)
(multiple-value-bind
(result exists)
(gethash args cache)
(if exists
result
(setf (gethash args cache)
(apply fn args)))))))
You would then do something like this:
(defun collatz-steps (n)
(if (= 1 n) 0
(if (evenp n)
(1+ (collatz-steps (/ n 2)))
(1+ (collatz-steps (1+ (* 3 n)))))))
(memoize-function 'collatz-steps)
I'll leave it up to you to make an unmemoize-function.
something like this:
(setf collatz-steps (memoize lambda (n)
(if (= 1 n) 0
(if (evenp n)
(1+ (collatz-steps (/ n 2)))
(1+ (collatz-steps (1+ (* 3 n))))))))
IOW: your original (non-memoized) function is anonymous, and you only give a name to the result of memoizing it.
Note a few things:
(defun foo (bar)
... (foo 3) ...)
Above is a function that has a call to itself.
In Common Lisp the file compiler can assume that FOO does not change. It will NOT call an updated FOO later. If you change the function binding of FOO, then the call of the original function will still go to the old function.
So memoizing a self recursive function will NOT work in the general case. Especially not if you are using a good compiler.
You can work around it to go always through the symbol for example: (funcall 'foo 3)
(DEFVAR ...) is a top-level form. Don't use it inside functions. If you have declared a variable, set it with SETQ or SETF later.
For your problem, I'd just use a hash table to store the intermediate results.
Changing the "original" function is necessary, because, as you say, there's no other way for the recursive call(s) to be updated to call the memoized version.
Fortunately, the way lisp works is to find the function by name each time it needs to be called. This means that it is sufficient to replace the function binding with the memoized version of the function, so that recursive calls will automatically look up and reenter through the memoization.
huaiyuan's code shows the key step:
(setf (fdefinition 'collatz-steps) (memoize #'collatz-steps))
This trick also works in Perl. In a language like C, however, a memoized version of a function must be coded separately.
Some lisp implementations provide a system called "advice", which provides a standardized structure for replacing functions with enhanced versions of themselves. In addition to functional upgrades like memoization, this can be extremely useful in debugging by inserting debug prints (or completely stopping and giving a continuable prompt) without modifying the original code.
This function is exactly the one Peter Norvig gives as an example of a function that seems like a good candidate for memoization, but which is not.
See figure 3 (the function 'Hailstone') of his original paper on memoization ("Using Automatic Memoization as a Software Engineering Tool in Real-World AI Systems").
So I'm guessing, even if you get the mechanics of memoization working, it won't really speed it up in this case.
A while ago I wrote a little memoization routine for Scheme that used a chain of closures to keep track of the memoized state:
(define (memoize op)
(letrec ((get (lambda (key) (list #f)))
(set (lambda (key item)
(let ((old-get get))
(set! get (lambda (new-key)
(if (equal? key new-key) (cons #t item)
(old-get new-key))))))))
(lambda args
(let ((ans (get args)))
(if (car ans) (cdr ans)
(let ((new-ans (apply op args)))
(set args new-ans)
new-ans))))))
This needs to be used like so:
(define fib (memoize (lambda (x)
(if (< x 2) x
(+ (fib (- x 1)) (fib (- x 2)))))))
I'm sure that this can be ported to your favorite lexically scoped Lisp flavor with ease.
I'd probably do something like:
(let ((memo (make-hash-table :test #'equal)))
(defun collatz-steps (n)
(or (gethash n memo)
(setf (gethash n memo)
(cond ((= n 1) 0)
((oddp n) (1+ (collatz-steps (+ 1 n n n))))
(t (1+ (collatz-steps (/ n 2)))))))))
It's not Nice and Functional, but, then, it's not much hassle and it does work. Downside is that you don't get a handy unmemoized version to test with and clearing the cache is bordering on "very difficult".