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I have this rail fence cipher code in Clojure with encryption and decryption. I need to get rid of loop-recur. How to get rid of loop-recur to make it more elegant? Thank you for helping.
(defn rail_fence_cipher [type key text]
(let [counter (* 2 (dec key))
groups (map
(fn [index]
(take-nth
counter
(drop index (range))))
(range counter))
encrypt-text
(take
(count text)
(loop [x (rest groups)
encrypt-text (take-while
#(> (count text) %)
(first groups))]
(if
(= 1 (count x))
(concat encrypt-text (last x))
(recur
(rest (drop-last x))
(concat
encrypt-text
(take-while
#(> (count text) %)
(interleave (first x) (last x))))))))
decrypt-text (->> encrypt-text
(map-indexed vector)
(sort-by second)
(map first))]
(cond
(= type "encrypt")
(apply str
(map #(nth text %) encrypt-text))
(= type "decrypt")
(apply str
(map #(nth text %) decrypt-text))
:else
"Please enter a valid cipher mode.")))
Style notes:
Clojure uses kebab-case, not snake_case.
cond can be replaced with case or condp.
Your function is very long and hard to read- you should split it into more parts.
Names of arguments could be more descriptive- key is actually number of rails.
Entry point can be rewritten as:
(defn rail-fence-cipher [action rails text]
(case action
"encrypt" (encrypt text rails)
"decrypt" (decrypt text rails)
"Unknown action."))
And first part can be rewritten without recur as:
(defn rails-pattern [rails]
(concat (range rails)
(drop 1 (range (dec rails) 0 -1))))
(defn encrypt [text rails]
(->> (clojure.string/replace text #" " "")
(map vector (cycle (rails-pattern rails)))
(sort-by first)
(map second)
(clojure.string/join)))
Example (from Wikipedia):
(encrypt "WE ARE DISCOVERED RUN AT ONCE" 3)
=> "WECRUOERDSOEERNTNEAIVDAC"
Related
Edit: Title updated to reflect what my question should have been, and hopefully lead other users here when they have the same problem.
Little bit of a mess, but this is a work-in-progress common lisp implementation of anydice that should output some ascii art representing a probability density function for a hash-table representing dice rolls. I've been trying to figure out exactly why, but I keep getting the error *** - SYSTEM::READ-EVAL-READER: variable BAR-CHARS has no value when attempting to run the file in clisp. The error is originating from the output function.
The code is messy and convoluted (but was previously working if the inner most loop of output is replaced with something simpler), but this specific error does not make sense to me. Am I not allowed to access the outer let* variables/bindings/whatever from the inner most loop/cond? Even when I substitute bar-chars for the list form directly, I get another error that char-decimal has no value either. I'm sure there's something about the loop macro interacting with the cond macro I'm missing, or the difference between setf, let*, multiple-value-bind, etc. But I've been trying to debug this specific problem for hours with no luck.
(defun sides-to-sequence (sides)
(check-type sides integer)
(loop for n from 1 below (1+ sides) by 1 collect n))
(defun sequence-to-distribution (sequence)
(check-type sequence list)
(setf distribution (make-hash-table))
(loop for x in sequence
do (setf (gethash x distribution) (1+ (gethash x distribution 0))))
distribution)
(defun distribution-to-sequence (distribution)
(check-type distribution hash-table)
(loop for key being each hash-key of distribution
using (hash-value value) nconc (loop repeat value collect key)))
(defun combinations (&rest lists)
(if (endp lists)
(list nil)
(mapcan (lambda (inner-val)
(mapcar (lambda (outer-val)
(cons outer-val
inner-val))
(car lists)))
(apply #'combinations (cdr lists)))))
(defun mapcar* (func lists) (mapcar (lambda (args) (apply func args)) lists))
(defun dice (left right)
(setf diceprobhash (make-hash-table))
(cond ((integerp right)
(setf right-distribution
(sequence-to-distribution (sides-to-sequence right))))
((listp right)
(setf right-distribution (sequence-to-distribution right)))
((typep right 'hash-table) (setf right-distribution right))
(t (error (make-condition 'type-error :datum right
:expected-type
(list 'integer 'list 'hash-table)))))
(cond ((integerp left)
(sequence-to-distribution
(mapcar* #'+
(apply 'combinations
(loop repeat left collect
(distribution-to-sequence right-distribution))))))
(t (error (make-condition 'type-error :datum left
:expected-type
(list 'integer))))))
(defmacro d (arg1 &optional arg2)
`(dice ,#(if (null arg2) (list 1 arg1) (list arg1 arg2))))
(defun distribution-to-probability (distribution)
(setf probability-distribution (make-hash-table))
(setf total-outcome-count
(loop for value being the hash-values of distribution sum value))
(loop for key being each hash-key of distribution using (hash-value value)
do (setf (gethash key probability-distribution)
(float (/ (gethash key distribution) total-outcome-count))))
probability-distribution)
(defun output (distribution)
(check-type distribution hash-table)
(format t " # %~%")
(let* ((bar-chars (list 9617 9615 9614 9613 9612 9611 9610 9609 9608))
(bar-width 100)
(bar-width-eighths (* bar-width 8))
(probability-distribution (distribution-to-probability distribution)))
(loop for key being each hash-key of
probability-distribution using (hash-value value)
do (format t "~4d ~5,2f ~{~a~}~%" key (* 100 value)
(loop for i from 0 below bar-width
do (setf (values char-column char-decimal)
(truncate (* value bar-width)))
collect
(cond ((< i char-column)
#.(code-char (car (last bar-chars))))
((> i char-column)
#.(code-char (first bar-chars)))
(t
#.(code-char (nth (truncate
(* 8 (- 1 char-decimal)))
bar-chars)))))))))
(output (d 2 (d 2 6)))
This is my first common lisp program I've hacked together, so I don't really want any criticism about formatting/style/performance/design/etc as I know it could all be better. Just curious what little detail I'm missing in the output function that is causing errors. And felt it necessary to include the whole file for debugging purposes.
loops scoping is perfectly conventional. But as jkiiski says, #. causes the following form to be evaluated at read time: bar-chars is not bound then.
Your code is sufficiently confusing that I can't work out whether there's any purpose to read-time evaluation like this. But almost certainly there is not: the uses for it are fairly rare.
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)
I am trying to implement a dictionary using lists in Common Lisp. The program is supposed to take a list of words and create a word histogram with frequency of each unique word.
This is the program:
(defparameter *histo* '())
(defun scanList (list)
(loop for word in list
do (if (assoc word histo)
((setf pair (assoc word histo))
(remove pair histo)
(setf f (+ 1 (second pair)))
(setf pair ((car pair) f))
(append histo pair))
((setf pair (word '1)) (append histo pair)))))
The error I get is: (SETF PAIR (ASSOC WORD *HISTO*)) should be a lambda expression.
Where is the syntax or semantic error exactly ?
(defun scanList (list the fox jumped over the other fox))
(princ *histo*)
Use hash-table for creating the dictionary and then transform to an association-list (alist) to sort it by key or value.
(defun build-histo (l)
(let ((dict (make-hash-table :test 'equal)))
(loop for word in l
do (incf (gethash word dict))
finally (return dict))))
;; which was simplification (by #Renzo) of
;; (defun build-histo (l)
;; (let ((dict (make-hash-table :test 'equal)))
;; (loop for word in l
;; for count = (1+ (gethash word dict 0))
;; do (setf (gethash word dict) count)
;; finally (return dict))))
(defparameter *histo* (build-histo '("a" "b" "c" "a" "a" "b" "b" "b")))
(defun hash-table-to-alist (ht)
(maphash #'(lambda (k v) (cons k v)) ht))
;; which is the same like:
;; (defun hash-table-to-alist (ht)
;; (loop for k being each hash-key of ht
;; for v = (gethash k ht)
;; collect (cons k v)))
;; sort the alist ascending by value
(sort (hash-table-to-alist *histo*) #'< :key #'cdr)
;; => (("c" . 1) ("a" . 3) ("b" . 4))
;; sort the alist descending by value
(sort (hash-table-to-alist *histo*) #'> :key #'cdr)
;; => (("b" . 4) ("a" . 3) ("c" . 1))
;; sort the alist ascending by key
(sort (hash-table-to-alist *histo*) #'string< :key #'car)
;; => (("a" . 3) ("b" . 4) ("c" . 1))
;; sort the alist descending by key
(sort (hash-table-to-alist *histo*) #'string> :eky #'car)
;; => (("c" . 1) ("b" . 4) ("a" . 3))
The posted code has a whole lot of problems. The reported error is caused by superfluous parentheses. Parentheses can't be added arbitrarily to expressions in Lisps without causing problems. In this case, these are the offending expressions:
((setf pair (assoc word histo))
(remove pair histo)
(setf f (+ 1 (second pair)))
(setf pair ((car pair) f)
(append histo pair))
((setf pair (word '1)) (append histo pair))
In both of these expressions, the results of the calls to setf are placed in the function position of a list, so the code attempts to call that result as if it is a function, leading to the error.
There are other issues. It looks like OP code is trying to pack expressions into the arms of an if form; this is probably the origin of the extra parentheses noted above. But, if forms can only take a single expression in each arm. You can wrap multiple expressions in a progn form, or use a cond instead (which does allow multiple expressions in each arm). There are some typos: *histo* is mistyped as histo in most of the code; f and pair are not defined anyplace; (setf pair (word '1)) quotes the 1 unnecessarily (which will work, but is semantically wrong).
Altogether, the code looks rather convoluted. This can be made much simpler, still following the same basic idea:
(defparameter *histo* '())
(defun build-histogram (words)
(loop :for word :in words
:if (assoc word *histo*)
:do (incf (cdr (assoc word *histo*)))
:else
:do (push (cons word 1) *histo*)))
This code is almost self-explanatory. If a word has already been added to *histo*, increment its counter. Otherwise add a new entry with the counter initialized to 1. This code isn't ideal, since it uses a global variable to store the frequency counts. A better solution would construct a new list of frequency counts and return that:
(defun build-histogram (words)
(let ((hist '()))
(loop :for word :in words
:if (assoc word hist)
:do (incf (cdr (assoc word hist)))
:else
:do (push (cons word 1) hist))
hist))
Of course, there are all kinds of other ways you might go about solving this.
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I would like to define a list operation that takes a list and 2 functions as its input. Let me be more concise. This is the function I will implement.
This is the list I will use :
(define student-table '(students (name id gpa) (ali 1 3.2) (ayse 2 3.7)))
and I have defined some functions.
(define (get table row field)
(nth (list-index field (cadr student-table)) row)
)
(define (alter table row fields-values)
(cond
((= (length fields-values) 0) row)
((> (length fields-values) 0)
(list-with row (list-index (car (car fields-values)) (cadr student-table)) (cadr (car fields-values)))
(alter table (list-with row (list-index (car (car fields-values)) (cadr student-table)) (cadr (car fields-values))) (cdr fields-values)))))
This is the function I would like to implement
(define (update-rows table predicate change))
So if I call with this, I would expect this result.
> (update-rows student-table
(lambda (table row)
(eq? (get table row 'name) 'ali))
(lambda (table row)
(alter table row '((gpa 3.3)))))
=> '(students (name id gpa) (ali 1 3.3) (ayse 2 3.7)))
Looks like you've got most all of it. Fill out update-rows something like this:
(define (update-rows table predicate change)
;; Look through all the rows
(let looking ((rows (cddr table)))
(unless (null? rows)
;; Handle the next row
(let ((row (car rows)))
(when (predicate table row)
(change table row)))
;; Continue looking at the rest
(looking (cdr rows))))
I'm not able to check if this works since I don't have list-index or list-with. I expect change to evaluate to a element that can replace the current row.
(define (update-rows table predicate change)
(cons* (car table) ; keep header
(cadr table) ; column symbols
;; process the rest
(map (lambda (row)
(if (predicate table row)
(change table row)
row))
(cddr table))))
cons* is defined in R6RS. In some Scheme implementations (and Common Lisp) it's called list*
To set some context, I'm in the process of learning Clojure, and Lisp development more generally. On my path to Lisp, I'm currently working through the "Little" series in an effort to solidify a foundation in functional programming and recursive-based solution solving. In "The Little Schemer," I've worked through many of the exercises, however, I'm struggling a bit to convert some of them to Clojure. More specifically, I'm struggling to convert them to use "recur" so as to enable TCO. For example, here is a Clojure-based implementation to the "occurs*" function (from Little Schemer) which counts the number of occurrences of an atom appearing within a list of S-expressions:
(defn atom? [l]
(not (list? l)))
(defn occurs [a lst]
(cond
(empty? lst) 0
(atom? (first lst))
(cond
(= a (first lst)) (inc (occurs a (rest lst)))
true (occurs a (rest lst)))
true (+ (occurs a (first lst))
(occurs a (rest lst)))))
Basically, (occurs 'abc '(abc (def abc) (abc (abc def) (def (((((abc))))))))) will evaluate to 5. The obvious problem is that this definition consumes stack frames and will blow the stack if given a list of S-expressions too deep.
Now, I understand the option of refactoring recursive functions to use an accumulator parameter to enable putting the recursive call into the tail position (to allow for TCO), but I'm struggling if this option is even applicable to situations such as this one.
Here's how far I get if I try to refactor this using "recur" along with using an accumulator parameter:
(defn recur-occurs [a lst]
(letfn [(myoccurs [a lst count]
(cond
(empty? lst) 0
(atom? (first lst))
(cond
(= a (first lst)) (recur a (rest lst) (inc count))
true (recur a (rest lst) count))
true (+ (recur a (first lst) count)
(recur a (rest lst) count))))]
(myoccurs a lst 0)))
So, I feel like I'm almost there, but not quite. The obvious problem is my "else" clause in which the head of the list is not an atom. Conceptually, I want to sum the result of recurring over the first element in the list with the result of recurring over the rest of the list. I'm struggling in my head on how to refactor this such that the recurs can be moved to the tail position.
Are there additional techniques to the "accumulator" pattern to achieving getting your recursive calls put into the tail position that I should be applying here, or, is the issue simply more "fundamental" and that there isn't a clean Clojure-based solution due to the JVM's lack of TCO? If the latter, generally speaking, what should be the general pattern for Clojure programs to use that need to recur over a list of S-expressions? For what it's worth, I've seen the multi method w/lazy-seq technique used (page 151 of Halloway's "Programming Clojure" for reference) to "Replace Recursion with Laziness" - but I'm not sure how to apply that pattern to this example in which I'm not attempting to build a list, but to compute a single integer value.
Thank you in advance for any guidance on this.
Firstly, I must advise you to not worry much about implementation snags like stack overflows as you make your way through The Little Schemer. It is good to be conscientious of issues like the lack of tail call optimization when you're programming in anger, but the main point of the book is to teach you to think recursively. Converting the examples accumulator-passing style is certainly good practice, but it's essentially ditching recursion in favor of iteration.
However, and I must preface this with a spoiler warning, there is a way to keep the same recursive algorithm without being subject to the whims of the JVM stack. We can use continuation-passing style to make our own stack in the form of an extra anonymous function argument k:
(defn occurs-cps [a lst k]
(cond
(empty? lst) (k 0)
(atom? (first lst))
(cond
(= a (first lst)) (occurs-cps a (rest lst)
(fn [v] (k (inc v))))
:else (occurs-cps a (rest lst) k))
:else (occurs-cps a (first lst)
(fn [fst]
(occurs-cps a (rest lst)
(fn [rst] (k (+ fst rst))))))))
Instead of the stack being created implicitly by our non-tail function calls, we bundle up "what's left to do" after each call to occurs, and pass it along as the next continuation k. When we invoke it, we start off with a k that represents nothing left to do, the identity function:
scratch.core=> (occurs-cps 'abc
'(abc (def abc) (abc (abc def) (def (((((abc))))))))
(fn [v] v))
5
I won't go further into the details of how to do CPS, as that's for a later chapter of TLS. However, I will note that this of course doesn't yet work completely:
scratch.core=> (def ls (repeat 20000 'foo))
#'scratch.core/ls
scratch.core=> (occurs-cps 'foo ls (fn [v] v))
java.lang.StackOverflowError (NO_SOURCE_FILE:0)
CPS lets us move all of our non-trivial, stack-building calls to tail position, but in Clojure we need to take the extra step of replacing them with recur:
(defn occurs-cps-recur [a lst k]
(cond
(empty? lst) (k 0)
(atom? (first lst))
(cond
(= a (first lst)) (recur a (rest lst)
(fn [v] (k (inc v))))
:else (recur a (rest lst) k))
:else (recur a (first lst)
(fn [fst]
(recur a (rest lst) ;; Problem
(fn [rst] (k (+ fst rst))))))))
Alas, this goes wrong: java.lang.IllegalArgumentException: Mismatched argument count to recur, expected: 1 args, got: 3 (core.clj:39). The very last recur actually refers to the fn right above it, the one we're using to represent our continuations! We can get good behavior most of the time by changing just that recur to a call to occurs-cps-recur, but pathologically-nested input will still overflow the stack:
scratch.core=> (occurs-cps-recur 'foo ls (fn [v] v))
20000
scratch.core=> (def nested (reduce (fn [onion _] (list onion))
'foo (range 20000)))
#'scratch.core/nested
scratch.core=> (occurs-cps-recur 'foo nested (fn [v] v))
Java.lang.StackOverflowError (NO_SOURCE_FILE:0)
Instead of making the call to occurs-* and expecting it to give back an answer, we can have it return a thunk immediately. When we invoke that thunk, it'll go off and do some work right up until it does a recursive call, which in turn will return another thunk. This is trampolined style, and the function that "bounces" our thunks is trampoline. Returning a thunk each time we make a recursive call bounds our stack size to one call at a time, so our only limit is the heap:
(defn occurs-cps-tramp [a lst k]
(fn []
(cond
(empty? lst) (k 0)
(atom? (first lst))
(cond
(= a (first lst)) (occurs-cps-tramp a (rest lst)
(fn [v] (k (inc v))))
:else (occurs-cps-tramp a (rest lst) k))
:else (occurs-cps-tramp a (first lst)
(fn [fst]
(occurs-cps-tramp a (rest lst)
(fn [rst] (k (+ fst rst)))))))))
(declare done answer)
(defn my-trampoline [th]
(if done
answer
(recur (th))))
(defn empty-k [v]
(set! answer v)
(set! done true))
(defn run []
(binding [done false answer 'whocares]
(my-trampoline (occurs-cps-tramp 'foo nested empty-k))))
;; scratch.core=> (run)
;; 1
Note that Clojure has a built-in trampoline (with some limitations on the return type). Using that instead, we don't need a specialized empty-k:
scratch.core=> (trampoline (occurs-cps-tramp 'foo nested (fn [v] v)))
1
Trampolining is certainly a cool technique, but the prerequisite to trampoline a program is that it must contain only tail calls; CPS is the real star here. It lets you define your algorithm with the clarity of natural recursion, and through correctness-preserving transformations, express it efficiently on any host that has a single loop and a heap.
You can't do this with a fixed amount of memory. You can consume stack, or heap; that's the decision you get to make. If I were writing this in Clojure I would do it with map and reduce rather than with manual recursion:
(defn occurs [x coll]
(if (coll? coll)
(reduce + (map #(occurs x %) coll))
(if (= x coll)
1, 0)))
Note that shorter solutions exist if you use tree-seq or flatten, but at that point most of the problem is gone so there's not much to learn.
Edit
Here's a version that doesn't use any stack, instead letting its queue get larger and larger (using up heap).
(defn heap-occurs [item coll]
(loop [count 0, queue coll]
(if-let [[x & xs] (seq queue)]
(if (coll? x)
(recur count (concat x xs))
(recur (+ (if (= item x) 1, 0)
count)
xs))
count)))