functional programming / racket translating imperative nested loops - functional-programming

If you have an algorithm that calls for something like (in C):
int pts[length];
for(int i = 0; i < length; ++i){
for(int j = 0; j < length; ++j){
if(pts[i] == pts[j]){
//modify both pts[i] and pts[j] somehow
}
}
}
How would you translate this into a functional style? Meaning that it returns an array or list of points with the modifications without changing the original. Answer can be demonstrated with nested recursion, map/filter,etc, Racket style for loops or something else. While I'm trying this in Racket, I'm open to answers in other languages.

You really don't do it like that in Algol either. You use a hash table
to get rid of the O(n²).
;; finds and replaces duplicates in O(n) time
(define (replace-duplicates replacement-proc lst)
;; this goes through the list once
;; mapping every value into a hash
;; with their frequency
(define h
(foldl (lambda (e h)
(hash-update h e add1 0))
(hash)
lst))
;; goes through the list once more
;; and replacing the elements that has
;; a frequency over 1 with the result of
;; (replacement-proc e)
(map (lambda (e)
(if (> (hash-ref h e) 1)
(replacement-proc e)
e))
lst))
(replace-duplicates (lambda (x) 'duplicate)
'(1 2 3 4 1))
; ==> (duplicate 2 3 4 duplicate)

If you want to use list to do stuff, then:
(define (my-list-update lst idx val) ; older version of Racket doesn't have list-update
(if (= idx 0)
(cons val (rest lst))
(cons (first lst) (my-list-update (rest lst) (- idx 1) val))))
(define LENGTH 10)
(define initial-lst (build-list LENGTH (lambda (_) 0))) ; create list consisting of 0 for LENGTH entries
(define arr
(foldl
(lambda (i lst)
(foldl
(lambda (j lst) ; shadowing, change if you don't like it
(my-list-update lst i (+ (list-ref lst i) (+ i j))))
lst (range LENGTH))) ; for j = 0 -> LENGTH - 1
initial-lst (range LENGTH))) ; for i = 0 -> LENGTH - 1
The above code is almost equivalent to the following pseudocode:
int arr[N] = initial_lst;
for(int i = 0; i < N; ++i){
for(int j = 0; j < N; ++j){
arr[i] += i + j; // not exactly, in Racket, we didn't mutate anything
}
}
Post-Discussion
How would you translate this into a functional style?
Well, what is the definition of "functional style"? In particular, do you really want an array as the output? Because array is a mutable data structure, it is not purely functional. Is this acceptable to you?
returns an array or list of points with the modifications without changing the original.
If you want to output an array without modifying the original array. That's easy: just copy all entries from the original array to a new array. Then, you can mutate the new array, do whatever you want to do with it, and return it. However, this becomes the imperative style instead.
If you really want the program to be purely functional, arrays are not the option for you. Using lists like the above example is not ideal because updating and getting takes O(n), while arrays could do it in O(1). There is a better approach: use Okasaki's random access list. This data structure is purely functional, while letting you update an entry in O(log n). Its implementation is really simple (compared to some data structures like AVL Tree), and you can use it in place of lists easily.

The first thing to say here is that there are algorithms that make a lot more sense in an imperative style, and most functional languages provide you with the mechanisms you need to do this. You use Racket as an example, and it would be totally reasonable to translate this code to nested loops (e.g. using racket's for) that perform mutation.
However, there are also algorithms that make perfect sense in a functional style;
the answer to this is going to depend a lot on the modify both pts[i] and pts[j] somehow.
Let's make something up; suppose I have an array of boys at a party, and each one is wearing a dress, and if two of them are wearing the same dress, their happiness is decreased. This is the first thing that pops into my head, but honestly it generalizes pretty well.
One way to do this would be with a nested loop, as you describe. In order to do it functionally, I think I might just write it like this:
#lang racket
;; a boy is a structure: (make-boy symbol number)
(define-struct boy [dress-color happiness]
#:transparent)
;; given a list of boys,
;; decrease each boy's happiness
;; by 3 for each other boy wearing the same color
;; dress
;; list-of-boys -> list-of-boys
(define (oh-noes boys)
(oh-noes-helper boys (boy-colors boys)))
;; given a list of boys and a list of all the colors
;; decrease each boy's happiness
;; by 3 for each other boy wearing the same color
;; dress
;; list-of-boys list-of-colors -> list-of-boys
(define (oh-noes-helper boys all-colors)
(cond [(empty? boys) empty]
[else (cons (adjust-boy (first boys) all-colors)
(oh-noes-helper (rest boys) all-colors))]))
;; given a boy and a list of all the dress colors,
;; decrease the boy's happiness by three for every
;; *other* boy wearing the same color dress
;; boy list-of-colors -> boy
(define (adjust-boy boy all-colors)
(make-boy (boy-dress-color boy)
(- (boy-happiness boy)
(* 3 (sub1 (num-occurrences (boy-dress-color boy)
all-colors))))))
;; given a list of boys, return a list of colors
;; let's just use map...
(define (boy-colors boys)
(map boy-dress-color boys))
;; given a list of colors, return the number of occurrences of that
;; color in the list
;; too lazy, just using foldl...
(define (num-occurrences element list)
(length (filter (λ (c) (equal? element c)) list)))
(require rackunit)
(check-equal? (boy-colors (list (make-boy 'blue 13)
(make-boy 'green 15)
(make-boy 'orange 9)
(make-boy 'green 2)
(make-boy 'orange 2)
(make-boy 'green 1)))
(list 'blue 'green 'orange 'green 'orange 'green))
(check-equal? (num-occurrences 'green
(list 'blue 'green 'orange
'green 'orange 'green))
3)
(check-equal? (oh-noes (list (make-boy 'blue 13)
(make-boy 'green 15)
(make-boy 'orange 9)
(make-boy 'green 2)
(make-boy 'orange 2)
(make-boy 'green 1)))
(list (make-boy 'blue 13)
(make-boy 'green 9)
(make-boy 'orange 6)
(make-boy 'green -4)
(make-boy 'orange -1)
(make-boy 'green -5)))
Please note that I wrote this in a very HtDP-heavy style; you could do this in just a few lines if you gave yourself free rein of Racket.
How does this compare to the original implementation? Well, they both have n^2
running time. If you want to improve this, you probably want to create a small
hash table mapping colors to number of occurrences, in both the imperative and
functional solutions.
EDIT:
Here's the whole thing again in 5 loc:
;; once again more rackety-small
(define (oh-noes2 boys)
(define colors (map boy-dress-color boys))
(for/list ([b (in-list boys)])
(match-define (struct boy (c h)) b)
(boy c (- h (* 3 (sub1 (length (filter (λ(x)(eq? x c)) colors))))))))

Related

How to remove mutability from this function in scheme (N-queens)

I'm arduously struggling my way through the N-queens problem in SICP (the book; I spent a few days on it -- last question here: Solving Eight-queens in scheme). Here is what I have for the helper functions:
#lang sicp
; the SICP language in Racket already defines this:
; (define nil '()
; boilerplate: filter function and range functions
(define (filter func lst)
(cond
((null? lst)
nil)
(else
(if (func (car lst))
(cons (car lst) (filter func (cdr lst)))
(filter func (cdr lst))))))
(define (range a b)
(if (> a b)
nil
(cons a (range (+ 1 a) b))))
; Selectors/handlers to avoid confusion on the (col, row) notation:
; representing it a position as (col, row), using 1-based indexing
(define (make-position col row) (cons col (list row)))
(define (col p) (car p))
(define (row p) (cadr p))
; adding a new position to a board
(define (add-new-position existing-positions p)
(append existing-positions
(list (make-position (col p) (row p)))))
; The 'safe' function
(define (any? l proc)
(cond ((null? l) #f)
((proc (car l)) #t)
(else (any? (cdr l) proc))))
(define (none? l proc) (not (any? l proc)))
(define (safe? existing-positions p)
(let ((bool (lambda (x) x)) (r (row p)) (c (col p)))
(and
; is the row safe? i.e., no other queen occupies that row?
(none? (map (lambda (p) (= (row p) r)) existing-positions)
bool)
; safe from the diagonal going up
(none? (map (lambda (p) (= r (+ (row p) (- c (col p)))))
existing-positions)
bool)
; safe from the diagonal going down
(none? (map (lambda (p) (= r (- (row p) (- c (col p)))))
existing-positions)
bool))))
And now, with that boilerplate, the actual/monstrous first working version I have of the queens problem:
(define (positions-for-col col size)
(map (lambda (ri) (make-position col ri))
(range 1 size)))
(define (queens board-size)
(define possible-positions '())
(define safe-positions '())
(define all-new-position-lists '())
(define all-positions-list '())
; existing-positions is a LIST of pairs
(define (queen-cols col existing-positions)
(if (> col board-size)
(begin
(set! all-positions-list
(append all-positions-list (list existing-positions))))
(begin
; for the column, generate all possible positions,
; for example (3 1) (3 2) (3 3) ...
(set! possible-positions (positions-for-col col board-size))
; (display "Possible positions: ") (display possible-positions) (newline)
; filter out the positions that are not safe from existing queens
(set! safe-positions
(filter (lambda (pos) (safe? existing-positions pos))
possible-positions))
; (display "Safe positions: ") (display safe-positions) (newline)
(if (null? safe-positions)
; bail if we don't have any safe positions
'()
; otherwise, build a list of positions for each safe possibility
; and recursively call the function for the next column
(begin
(set! all-new-position-lists
(map (lambda (pos)
(add-new-position existing-positions pos))
safe-positions))
; (display "All positions lists: ") (display all-new-position-lists) (newline)
; call itself for the next column
(map (lambda (positions-list) (queen-cols (+ 1 col)
positions-list))
all-new-position-lists))))))
(queen-cols 1 '())
all-positions-list)
(queens 5)
(((1 1) (2 3) (3 5) (4 2) (5 4))
((1 1) (2 4) (3 2) (4 5) (5 3))
((1 2) (2 4) (3 1) (4 3) (5 5))
((1 2) (2 5) (3 3) (4 1) (5 4))
((1 3) (2 1) (3 4) (4 2) (5 5))
To be honest, I think I did all the set!s so that I could more easily debug things (is that common?) How could I remove the various set!s to make this a proper functional-procedure?
As an update, the most 'terse' I was able to get it is as follows, though it still appends to a list to build the positions:
(define (queens board-size)
(define all-positions-list '())
(define (queen-cols col existing-positions)
(if (> col board-size)
(begin
(set! all-positions-list
(append all-positions-list
(list existing-positions))))
(map (lambda (positions-list)
(queen-cols (+ 1 col) positions-list))
(map (lambda (pos)
(add-new-position existing-positions pos))
(filter (lambda (pos)
(safe? existing-positions pos))
(positions-for-col col board-size))))))
(queen-cols 1 nil)
all-positions-list)
Finally, I think here is the best I can do, making utilization of a 'flatmap' function that helps deal with nested lists:
; flatmap to help with reduction
(define (reduce function sequence initializer)
(let ((elem (if (null? sequence) nil (car sequence)))
(rest (if (null? sequence) nil (cdr sequence))))
(if (null? sequence)
initializer
(function elem
(reduce function rest initializer)))))
(define (flatmap proc seq)
(reduce append (map proc seq) nil))
; actual
(define (queens board-size)
(define (queen-cols col existing-positions)
(if (> col board-size)
(list existing-positions)
(flatmap
(lambda (positions-list)
(queen-cols (+ 1 col) positions-list))
(map
(lambda (pos)
(add-new-position existing-positions
pos))
(filter
(lambda (pos)
(safe? existing-positions pos))
(positions-for-col col board-size))))))
(queen-cols 1 nil))
Are there any advantages of this function over the one using set! or is it more a matter of preference (I find the set! one easier to read and debug).
When you are doing the SICP problems, it would be most beneficial if you strive to adhere to the spirit of the question. You can determine the spirit from the context: the topics covered till the point you are in the book, any helper code given, the terminology used etc. Specifically, avoid using parts of the scheme language that have not yet been introduced; the focus is not on whether you can solve the problem, it is on how you solve it. If you have been provided helper code, try to use it to the extent you can.
SICP has a way of building complexity; it does not introduce a concept unless it has presented enough motivation and justification for it. The underlying theme of the book is simplification through abstraction, and in this particular section you are introduced to various higher order procedures -- abstractions like accumulate, map, filter, flatmap which operate on sequences/lists, to make your code more structured, compact and ultimately easier to reason about.
As illustrated in the opening of this section, you could very well avoid the use of such higher programming constructs and still have programs that run fine, but their (liberal) use results in more structured, readable, top-down style code. It draws parallels from the design of signal processing systems, and shows how we can take inspiration from it to add structure to our code: using procedures like map, filter etc. compartmentalize our code's logic, not only making it look more hygienic but also more comprehensible.
If you prematurely use techniques which don't come until later in the book, you will be missing out on many key learnings which the authors intend for you from the present section. You need to shed the urge to think in an imperative way. Using set! is not a good way to do things in scheme, until it is. SICP forces you down a 'difficult' path by making you think in a functional manner for a reason -- it is for making your thinking (and code) elegant and 'clean'.
Just imagine how much more difficult it would be to reason about code which generates a tree recursive process, wherein each (child) function call is mutating the parameters of the function. Also, as I mentioned in the comments, assignment places additional burden upon the programmers (and on those who read their code) by making the order of the expressions have a bearing on the results of the computation, so it is harder to verify that the code does what is intended.
Edit: I just wanted to add a couple of points which I feel would add a bit more insight:
Your code using set! is not wrong (or even very inelegant), it is just that in doing so, you are being very explicit in telling what you are doing. Iteration also reduces the elegance a bit in addition to being bottom up -- it is generally harder to think bottom up.
I feel that teaching to do things recursively where possible is one of the aims of the book. You will find that recursion is a crucial technique, the use of which is inevitable throughout the book. For instance, in chapter 4, you will be writing evaluators (interpreters) where the authors evaluate the expressions recursively. Even much earlier, in section 2.3, there is the symbolic differentiation problem which is also an exercise in recursive evaluation of expressions. So even though you solved the problem imperatively (using set!, begin) and bottom-up iteration the first time, it is not the right way, as far as the problem statement is concerned.
Having said all this, here is my code for this problem (for all the structure and readability imparted by FP, comments are still indispensable):
; the board is a list of lists - a physical n x n board, where
; empty positions are 0 and filled positions are 1
(define (queens board-size)
(let ((empty-board (empty-board-gen board-size))) ; minor modification - making empty-board available to queen-cols
(define (queen-cols k)
(if (= k 0)
(list empty-board)
(filter (lambda (positions) (safe? k positions))
; the flatmap below generates a list of new positions
; by 'adjoining position'- adding 'board-size' number
; of new positions for each of the positions obtained
; recursively from (queen-cols (- k 1)), which have
; been found to be safe till column k-1. This new
; set (list) of positions is then filtered using the
; safe? function to filter out unsafe positions
(flatmap
(lambda (rest-of-queens)
; the map below adds 'board-size' number of new
; positions to 'rest-of-queens', which is an
; element of (queen-cols (- k 1))
(map (lambda (new-row)
(adjoin-position new-row k rest-of-queens))
(enumerate-interval 1 board-size)))
(queen-cols (- k 1))))))
(queen-cols board-size)) ; end of let block
)
; add a column having a queen placed at position (new-row, col).
(define (adjoin-position new-row col rest-queens)
(let ((board-dim (length rest-queens))) ;length of board
; first create a zero 'vector', put a queen in it at position
; 'new-row', then put (replace) this new vector/column at the
; 'col' position in rest-queens
(replace-elem (replace-elem 1 new-row (gen-zero-vector board-dim)) col rest-queens)))
(define (safe? k positions) ; the safe function
(let ((row-pos-k (non-zero-index (item-at-index k positions)))) ; get the row of the queen in column k
(define (iter-check col rem) ;iteratively check if column 'col' of the board is safe wrt the kth column
(let ((rw-col (non-zero-index (car rem)))) ; get the row of 'col' in which a queen is placed
(cond ((= k 1) #t); 1x1 board is always safe
((= col k) #t); if we reached the kth column, we are done
; some simple coordinate geometry
; checks if the row of the queen in col and kth
; column is same, and also checks if the 'slope' of
; the line connecting the queens of the two columns
; is 1 (i.e. if it's a diagonal), if either is true,
; the kth queen is not safe
((or (= row-pos-k rw-col) (= (- k col) (abs (- row-pos-k rw-col)))) #f)
(else (iter-check (+ col 1) (cdr rem)))))) ; check the next column
(iter-check 1 positions))) ; start checking from the first column
; helper functions follow
(define (item-at-index n items) ; given a list, return the nth element
(define (iter idx rem)
(if (= idx n)
(car rem)
(iter (+ idx 1) (cdr rem))))
(iter 1 items))
(define (non-zero-index items)
; gives the first non-zero element from items - used for
; determining the row at which a queen is placed
(define (iter a rem)
(if (> (car rem) 0)
a
(iter (+ a 1) (cdr rem))))
(iter 1 items))
(define (empty-board-gen n) ; the empty board is n lists, each list with n zeros
(map (lambda (x) (map (lambda (y) 0) (enumerate-interval 1 n))) (enumerate-interval 1 n)))
(define (replace-elem new-elem pos items) ; replace item at position pos in items by new-elem, ultimately used for replacing an empty column with a column which has a queen
(define (iter i res rem)
(if (= i pos)
(append res (list new-elem) (cdr rem))
(iter (+ i 1) (append res (list(car rem))) (cdr rem)))) (iter 1 '() items))
(define (gen-zero-vector n) ; generate a list of length n with only zeros as elements
(define (iter a res)
(if (> a n)
res
(iter (+ a 1) (append res (list 0))))) (iter 1 '()))
(define (flatmap proc seq)
(accumulate append '() (map proc seq)))
(define (length items) ; not particularly efficient way for length of a list
(accumulate + 0 (map (lambda (x) 1) items)))
(define (accumulate op null-value seq)
(if (null? seq)
null-value
(op (car seq) (accumulate op null-value (cdr seq)))))
(define (enumerate-interval low high) ; a list of integers from low to hi
(define (iter a b res)
(if (> a b)
res
(iter (+ a 1) b (append res (cons a '())))))
(iter low high '()))
There are many ways to tackle this problem. I'll attempt to write a short and concise solution using Racket-specific procedures, explaining each step of the way. A solution using only the Scheme procedures explained in SICP is also possible, but it'll be more verbose and I'd argue, more difficult to understand.
My aim is to write a functional-programming style solution reusing as many built-in procedures as possible, and avoiding mutation at all costs - this is the style that SICP encourages you to learn. I'll deviate from the template solution in SICP if I think we can get a clearer solution by reusing existing Racket procedures (it follows then, that this code must be executed using the #lang racket language), but I've provided another answer that fits exactly exercise 2.42 in the book, implemented in standard Scheme and compatible with #lang sicp.
First things first. Let's agree on how are we going to represent the board - this is a key point, the way we represent our data will have a big influence on how easy (or hard) is to implement our solution. I'll use a simple representation, with only the minimum necessary information.
Let's say a "board" is a list of row indexes. My origin of coordinates is the position (0, 0), on the top-left corner of the board. For the purpose of this exercise we only need to keep track of the row a queen is in, the column is implicitly represented by its index in the list and there can only be one queen per column. Using my representation, the list '(2 0 3 1) encodes the following board, notice how the queens' position is uniquely represented by its row number and its index:
0 1 2 3
0 . Q . .
1 . . . Q
2 Q . . .
3 . . Q .
Next, let's see how are we going to check if a new queen added at the end of the board is "safe" with respect to the previously existing queens. For this, we need to check if there are any other queens in the same row, or if there are queens in the diagonal lines starting from the new queen's position. We don't need to check for queens in the same column, we're trying to set a single new queen and there aren't any others in this row. Let's split this task in multiple procedures.
; main procedure for checking if a queen in the given
; column is "safe" in the board; there are no more
; queens to the "right" or in the same column
(define (safe? col board)
; we're only interested in the queen's row for the given column
(let ([row (list-ref board (sub1 col))])
; the queen must be safe on the row and on the diagonals
(and (safe-row? row board)
(safe-diagonals? row board))))
; check if there are any other queens in the same row,
; do this by counting how many times `row` appears in `board`
(define (safe-row? row board)
; only the queen we want to add can be in this row
; `curry` is a shorthand for writing a lambda that
; compares `row` to each element in `board`
(= (count (curry equal? row) board) 1))
; check if there are any other queens in either the "upper"
; or the "lower" diagonals starting from the current queen's
; position and going to the "left" of it
(define (safe-diagonals? row board)
; we want to traverse the row list from right-to-left so we
; reverse it, and remove the current queen from it; upper and
; lower positions are calculated starting from the current queen
(let loop ([lst (rest (reverse board))]
[upper (sub1 row)]
[lower (add1 row)])
; the queen is safe after checking all the list
(or (null? lst)
; the queen is not safe if we find another queen in
; the same row, either on the upper or lower diagonal
(and (not (= (first lst) upper))
(not (= (first lst) lower))
; check the next position, updating upper and lower
(loop (rest lst) (sub1 upper) (add1 lower))))))
Some optimizations could be done, for example stopping early if there's more than one queen in the same row or stopping when the diagonals' rows fall outside of the board, but they'll make the code harder to understand and I'll leave them as an exercise for the reader.
In the book they suggest we use an adjoin-position procedure that receives both row and column parameters; with my representation we only need the row so I'm renaming it to add-queen, it simply adds a new queen at the end of a board:
; add a new queen's row to the end of the board
(define (add-queen queen-row board)
(append board (list queen-row)))
Now for the fun part. With all of the above procedures in place, we need to try out different combinations of queens and filter out those that are not safe. We'll use higher-order procedures and recursion for implementing this backtracking solution, there's no need to use set! at all as long as we're in the right mindset.
This will be easier to understand if you read if from the "inside out", try to grok what the inner parts do before going to the outer parts, and always remember that we're unwinding our way in a recursive process: the first case that will get executed is when we have an empty board, the next case is when we have a board with only one queen in position and so on, until we finally have a full board.
; main procedure: returns a list of all safe boards of the given
; size using our previously defined board representation
(define (queens board-size)
; we need two values to perform our computation:
; `queen-col`: current row of the queen we're attempting to set
; `board-size`: the full size of the board we're trying to fill
; I implemented this with a named let instead of the book's
; `queen-cols` nested procedure
(let loop ([queen-col board-size])
; if there are no more columns to try exit the recursion
(if (zero? queen-col)
; base case: return a list with an empty list as its only
; element; remember that the output is a list of lists
; the book's `empty-board` is just the empty list '()
(list '())
; we'll generate queen combinations below, but only the
; safe ones will survive for the next recursive call
(filter (λ (board) (safe? queen-col board))
; append-map will flatten the results as we go, we want
; a list of lists, not a list of lists of lists of...
; this is equivalent to the book's flatmap implementation
(append-map
(λ (previous-boards)
(map (λ (new-queen-row)
; add a new queen row to each one of
; the previous valid boards we found
(add-queen new-queen-row previous-boards))
; generate all possible queen row values for this
; board size, this is similar to the book's
; `enumerate-interval` but starting from zero
(range board-size)))
; advance the recursion, try a smaller column
; position, as the recursion unwinds this will
; return only previous valid boards
(loop (sub1 queen-col)))))))
And that's all there is to it! I'll provide a couple of printing procedures (useful for testing) which should be self-explanatory; they take my compact board representation and print it in a more readable way. Queens are represented by 'o and empty spaces by 'x:
(define (print-board board)
(for-each (λ (row) (printf "~a~n" row))
(map (λ (row)
(map (λ (col) (if (= row col) 'o 'x))
board))
(range (length board)))))
(define (print-all-boards boards)
(for-each (λ (board) (print-board board) (newline))
boards))
We can verify that things work and that the number of solutions for the 8-queens problem is as expected:
(length (queens 8))
=> 92
(print-all-boards (queens 4))
(x x o x)
(o x x x)
(x x x o)
(x o x x)
(x o x x)
(x x x o)
(o x x x)
(x x o x)
As a bonus, here's another solution that works with the exact definition of queens as provided in the SICP book. I won't go into details because it uses the same board representation (except that here the indexes start in 1 not in 0) and safe? implementation of my previous answer, and the explanation for the queens procedure is essentially the same. I did some minor changes to favor standard Scheme procedures, so hopefully it'll be more portable.
#lang racket
; redefine procedures already explained in the book with
; Racket equivalents, delete them and use your own
; implementation to be able to run this under #lang sicp
(define flatmap append-map)
(define (enumerate-interval start end)
(range start (+ end 1)))
; new definitions required for this exercise
(define empty-board '())
(define (adjoin-position row col board)
; `col` is unused
(append board (list row)))
; same `safe?` implementation as before
(define (safe? col board)
(let ((row (list-ref board (- col 1))))
(and (safe-row? row board)
(safe-diagonals? row board))))
(define (safe-row? row board)
; reimplemented to use standard Scheme procedures
(= (length (filter (lambda (r) (equal? r row)) board)) 1))
(define (safe-diagonals? row board)
(let loop ((lst (cdr (reverse board)))
(upper (- row 1))
(lower (+ row 1)))
(or (null? lst)
(and (not (= (car lst) upper))
(not (= (car lst) lower))
(loop (cdr lst) (- upper 1) (+ lower 1))))))
; exact same implementation of `queens` as in the book
(define (queens board-size)
(define (queen-cols k)
(if (= k 0)
(list empty-board)
(filter
(lambda (positions) (safe? k positions))
(flatmap
(lambda (rest-of-queens)
(map (lambda (new-row)
(adjoin-position new-row k rest-of-queens))
(enumerate-interval 1 board-size)))
(queen-cols (- k 1))))))
(queen-cols board-size))
; debugging
(define (print-board board)
(for-each (lambda (row) (display row) (newline))
(map (lambda (row)
(map (lambda (col) (if (= row col) 'o 'x))
board))
(enumerate-interval 1 (length board)))))
(define (print-all-boards boards)
(for-each (lambda (board) (print-board board) (newline))
boards))
The above code is more in spirit with the original exercise, which asked you to implement just three definitions: empty-board, adjoin-position and safe?, thus this was more of a question about data representation. Unsurprisingly, the results are the same:
(length (queens 8))
=> 92
(print-all-boards (queens 4))
(x x o x)
(o x x x)
(x x x o)
(x o x x)
(x o x x)
(x x x o)
(o x x x)
(x x o x)

Making current function of list recursive Clojure

Hi i am looking for a bit of help with some Clojure code. I have written a function that will take in a list and calculate the qty*price for a list eg. '(pid3 6 9)
What i am looking for is to expand my current function so that it recursively does the qty*price calculation until it reaches the end of the list.
My current function is written like this:
(defn pid-calc [list] (* (nth list 1) (nth list 2)))
I have tried implementing it into a recursive function but have had no luck at all, i want to be able to call something like this:
(pid-calcc '( (pid1 8 5) (pid2 5 6))
return==> 70
Thats as close as i have came to an answer and cannot seem to find one. If anyone can help me find a solution i that would be great. As so far i am yet to find anything that will compile.
​(defn pid-calc [list]
(if(empty? list)
nil
(* (nth list 1) (nth list 2)(+(pid-calc (rest list))))))
You don't need a recursive function. Just use + and map:
(defn pid-calc [list]
(letfn [(mul [[_ a b]] (* a b))]
(apply + (map mul list))))
#sloth's answer, suitably corrected, is a concise and fast enough way to solve your problem. It shows you a lot.
Your attempt at a recursive solution can be (a)mended to
(defn pid-calc [list]
(if (empty? list)
0
(let [x (first list)]
(+ (* (nth x 1) (nth x 2)) (pid-calc (next list))))))
This works on the example, but - being properly recursive - will run out of stack space on a long enough list. The limit is usually about 10K items.
We can get over this without being so concise as #sloth. You might find the following easier to understand:
(defn pid-calc [list]
(let [line-total (fn [item] (* (nth item 1) (nth item 2)))]
(apply + (map line-total list))))
reduce fits your scenario quite well:
(def your-list [[:x 1 2] [:x 1 3]])
(reduce #(+ %1 (* (nth %2 1) (nth %2 2))) 0 your-list)
(reduce #(+ %1 (let [[_ a b] %2] (* a b)) 0 your-list)

lisp functions ( count numbers in common lisp)

I am working on program related to the different of dealing with even numbers in C and lisp , finished my c program but still having troubles with lisp
isprime function is defined and I need help in:
define function primesinlist that returns unique prime numbers in a lis
here what i got so far ,
any help with that please?
(defun comprimento (lista)
(if (null lista)
0
(1+ (comprimento (rest lista)))))
(defun primesinlist (number-list)
(let ((result ()))
(dolist (number number-list)
(when (isprime number)
( number result)))
(nreverse result)))
You need to either flatten the argument before processing:
(defun primesinlist (number-list)
(let ((result ()))
(dolist (number (flatten number-list))
(when (isprime number)
(push number result)))
(delete-duplicates (nreverse result))))
or, if you want to avoid consing up a fresh list, flatten it as you go:
(defun primesinlist (number-list)
(let ((result ()))
(labels ((f (l)
(dolist (x l)
(etypecase x
(integer (when (isprime x)
(push x result)))
(list (f x))))))
(f number-list))
(delete-duplicates (nreverse result))))
To count distinct primes, take the length of the list returned by primesinlist.
Alternatively, you can use count-if:
(count-if #'isprime (delete-duplicates (flatten number-list)))
It sounds like you've already got a primality test implemented, but for sake of completeness, lets add a very simple one that just tries to divide a number by the numbers less than it up to its square root:
(defun primep (x)
"Very simple implementation of a primality test. Checks
for each n above 1 and below (sqrt x) whether n divides x.
Example:
(mapcar 'primep '(2 3 4 5 6 7 8 9 10 11 12 13))
;=> (T T NIL T NIL T NIL NIL NIL T NIL T)
"
(do ((sqrt-x (sqrt x))
(i 2 (1+ i)))
((> i sqrt-x) t)
(when (zerop (mod x i))
(return nil))))
Now, you need a way to flatten a potentially nested list of lists into a single list. When approaching this problem, I usually find it a bit easier to think in terms of trees built of cons-cells. Here's an efficient flattening function that returns a completely new list. That is, it doesn't share any structure with the original tree. That can be useful, especially if we want to modify the resulting structure later, without modifying the original input.
(defun flatten-tree (x &optional (tail '()))
"Efficiently flatten a tree of cons cells into
a list of all the non-NIL leafs of the tree. A completely
fresh list is returned.
Examples:
(flatten-tree nil) ;=> ()
(flatten-tree 1) ;=> (1)
(flatten-tree '(1 (2 (3)) (4) 5)) ;=> (1 2 3 4 5)
(flatten-tree '(1 () () 5)) ;=> (1 5)
"
(cond
((null x) tail)
((atom x) (list* x tail))
((consp x) (flatten-tree (car x)
(flatten-tree (cdr x) tail)))))
Now it's just a matter of flatting a list, removing the number that are not prime, and removing duplicates from that list. Common Lisp includes functions for doing these things, namely remove-if-not and remove-duplicates. Those are the "safe" versions that don't modify their input arguments. Since we know that the flattened list is freshly generated, we can use their (potentially) destructive counterparts, delete-if-not and delete-duplicates.
There's a caveat when you're removing duplicate elements, though. If you have a list like (1 3 5 3), there are two possible results that could be returned (assuming you keep all the other elements in order): (1 3 5) and (1 5 3). That is, you can either remove the the later duplicate or the earlier duplicate. In general, you have the question of "which one should be left behind?" Common Lisp, by default, removes the earlier duplicate and leaves the last occurrence. That behavior can be customized by the :from-end keyword argument. It can be nice to duplicate that behavior in your own API.
So, here's a function that puts all those considerations together.
(defun primes-in-tree (tree &key from-end)
"Flatten the tree, remove elements which are not prime numbers,
using FROM-END to determine whether earlier or later occurrences
are kept in the list.
Examples:
(primes-in-list '(2 (7 4) ((3 3) 5) 6 7))
;;=> (2 3 5 7)
(primes-in-list '(2 (7 4) ((3 3) 5) 6 7) :from-end t)
;;=> (2 7 3 5)"
;; Because FLATTEN-TREE returns a fresh list, it's OK
;; to use the destructive functions DELETE-IF-NOT and
;; DELETE-DUPLICATES.
(delete-duplicates
(delete-if-not 'primep (flatten-tree list))
:from-end from-end))

Recursion Vs. Tail Recursion

I'm quite new to functional programming, especially Scheme as used below. I'm trying to make the following function that is recursive, tail recursive.
Basically, what the function does, is scores the alignment of two strings. When given two strings as input, it compares each "column" of characters and accumulates a score for that alignment, based on a scoring scheme that is implemented in a function called scorer that is called by the function in the code below.
I sort of have an idea of using a helper function to accumulate the score, but I'm not too sure how to do that, hence how would I go about making the function below tail-recursive?
(define (alignment-score string_one string_two)
(if (and (not (= (string-length string_one) 0))
(not (=(string-length string_two) 0)))
(+ (scorer (string-ref string_one 0)
(string-ref string_two 0))
(alignment-score-not-tail
(substring string_one 1 (string-length string_one))
(substring string_two 1 (string-length string_two))
)
)
0)
)
Just wanted to make an variant of Chris' answer that uses lists of chars:
(define (alignment-score s1 s2)
(let loop ((score 0)
(l1 (string->list s1))
(l2 (string->list s2)))
(if (or (null? l1) (null? l2))
score
(loop (+ score (scorer (car l1)
(car l2)))
(cdr l1)
(cdr l2)))))
No use stopping there. Since this now have become list iteration we can use higher order procedure. Typically we want a fold-left or foldl and SRFI-1 fold is an implementation of that that doesn't require the lists to be of the same length:
; (import (scheme) (only (srfi :1) fold)) ; r7rs
; (import (rnrs) (only (srfi :1) fold)) ; r6rs
; (require srfi/1) ; racket
(define (alignment-score s1 s2)
(fold (lambda (a b acc)
(+ acc (scorer a b)))
0
(string->list s1)
(string->list s2)))
If you accumulating and the order doesn't matter always choose a left fold since it's always tail recursive in Scheme.
Here's how it would look like with accumulator:
(define (alignment-score s1 s2)
(define min-length (min (string-length s1) (string-length s2)))
(let loop ((score 0)
(index 0))
(if (= index min-length)
score
(loop (+ score (scorer (string-ref s1 index)
(string-ref s2 index)))
(+ index 1)))))
In this case, score is the accumulator, which starts as 0. We also have an index (also starting as 0) that keeps track of which position in the string to grab. The base case, when we reach the end of either string, is to return the accumulated score so far.

Functional Programming - Implementing Scan (Prefix Sum) using Fold

I've been teaching myself functional programming, and I'm currently writing different higher order functions using folds. I'm stuck implementing scan (also known as prefix sum). My map implementation using fold looks like:
(define (map op sequence)
(fold-right (lambda (x l) (cons (op x) l)) nil sequence))
And my shot at scan looks like:
(define (scan sequence)
(fold-left (lambda (x y) (append x (list (+ y (car (reverse x)))))) (list 0) sequence))
My observation being that the "x" is the resulting array so far, and "y" is the next element in the incoming list. This produces:
(scan (list 1 4 8 3 7 9)) -> (0 1 5 13 16 23 32)
But this looks pretty ugly, with the reversing of the resulting list going on inside the lambda. I'd much prefer to not do global operations on the resulting list, since my next attempt is to try and parallelize much of this (that's a different story, I'm looking at several CUDA papers).
Does anyone have a more elegant solution for scan?
BTW my implementation of fold-left and fold-right is:
(define (fold-left op initial sequence)
(define (iter result rest)
(if (null? rest)
result
(iter (op result (car rest)) (cdr rest))))
(iter initial sequence))
(define (fold-right op initial sequence)
(if (null? sequence)
initial
(op (car sequence) (fold-right op initial (cdr sequence)))))
Imho scan is very well expressible in terms of fold.
Haskell example:
scan func list = reverse $ foldl (\l e -> (func e (head l)) : l) [head list] (tail list)
Should translate into something like this
(define scan
(lambda (func seq)
(reverse
(fold-left
(lambda (l e) (cons (func e (car l)) l))
(list (car seq))
(cdr seq)))))
I wouldn’t do this. fold can actually be implemented in terms of scan (last element of the scanned list). But scan and fold are in fact orthogonal operations. If you’ve read the CUDA papers you’ll notice that a scan consists of two phases: the first yields the fold result as a by-product. The second phase is only used for the scan (of course, this only counts for parallel implementations; a sequential implementation of fold is more efficient if it doesn’t rely on scan at all).
imho Dario cheated by using reverse since the exercise was about expressing in terms of fold not a reverse fold. This, of course, is a horrible way to express scan but it is a fun exercise of jamming a square peg into a round hole.
Here it is in haskell, I don't know lisp
let scan f list = foldl (\ xs next -> xs++[f (last xs) next]) [0] list
scan (+) [1, 4, 8, 3, 7, 9]
[0,1,5,13,16,23,32]
of course, using teh same trick as Dario one can get rid of that leading 0:
let scan f list = foldl (\ xs next -> xs++[f (last xs) next]) [head list] (tail list)
scan (+) [1, 4, 8, 3, 7, 9]
[1,5,13,16,23,32]

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