Knuth's algorithm S: Difference between revisions
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=={{header|Elena}}== |
=={{header|Elena}}== |
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<lang elena>#import system. |
<lang elena>#import system'dynamic. |
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#import system'dynamic. |
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#import extensions. |
#import extensions. |
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#import system'routines. |
#import system'routines. |
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#import system'collections. |
#import system'collections. |
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extension algorithmOp |
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{ |
{ |
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s_of_n |
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[ |
[ |
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var counter := Integer new. |
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var n := self. |
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^ ArrayList new mix &into: |
^ ArrayList new mix &into: |
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{ |
{ |
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eval : |
eval : i |
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[ |
[ |
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counter += 1. |
counter += 1. |
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( |
if (self length < n) |
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[ self += i. ]; |
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[ |
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(randomGenerator eval:counter < |
((randomGenerator eval:counter < n) bool) |
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? [ |
? [ self@(randomGenerator eval:n) := i. ]. |
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]. |
]. |
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^ |
^ self array. |
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] |
] |
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}. |
}. |
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} |
} |
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program = |
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[ |
[ |
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var bin := Array new:10 set &every:(&index:n) [ Integer new ]. |
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0 till:10000 &doEach: trial |
0 till:10000 &doEach: trial |
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[ |
[ |
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var s_of_n := 3 s_of_n. |
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0 till:10 &doEach:n |
0 till:10 &doEach:n |
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[ |
[ |
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var sample := s_of_n eval:n. |
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(n == 9) |
if (n == 9) |
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[ sample run &each: i [ bin@i += 1. ]. ]. |
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]. |
]. |
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]. |
]. |
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console writeLine:bin. |
console writeLine:bin readChar. |
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].</lang> |
].</lang> |
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{{out}} |
{{out}} |
Revision as of 14:04, 30 March 2017
You are encouraged to solve this task according to the task description, using any language you may know.
This is a method of randomly sampling n items from a set of M items, with equal probability; where M >= n and M, the number of items is unknown until the end. This means that the equal probability sampling should be maintained for all successive items > n as they become available (although the content of successive samples can change).
- The algorithm
- Select the first n items as the sample as they become available;
- For the i-th item where i > n, have a random chance of n/i of keeping it. If failing this chance, the sample remains the same. If not, have it randomly (1/n) replace one of the previously selected n items of the sample.
- Repeat #2 for any subsequent items.
- The Task
- Create a function
s_of_n_creator
that given the maximum sample size, returns a functions_of_n
that takes one parameter,item
. - Function
s_of_n
when called with successive items returns an equi-weighted random sample of up to n of its items so far, each time it is called, calculated using Knuths Algorithm S. - Test your functions by printing and showing the frequency of occurrences of the selected digits from 100,000 repetitions of:
- Use the s_of_n_creator with n == 3 to generate an s_of_n.
- call s_of_n with each of the digits 0 to 9 in order, keeping the returned three digits of its random sampling from its last call with argument item=9.
Note: A class taking n and generating a callable instance/function might also be used.
- Reference
- The Art of Computer Programming, Vol 2, 3.4.2 p.142
- Cf.
Ada
Instead of defining a function S_of_N_Creator, we define a generic packgage with that name. The generic parameters are N (=Sample_Size) and the type of the items to be sampled:
<lang Ada>generic
Sample_Size: Positive; type Item_Type is private;
package S_Of_N_Creator is
subtype Index_Type is Positive range 1 .. Sample_Size; type Item_Array is array (Index_Type) of Item_Type;
procedure Update(New_Item: Item_Type); function Result return Item_Array;
end S_Of_N_Creator;</lang>
Here is the implementation of that package:
<lang Ada>with Ada.Numerics.Float_Random, Ada.Numerics.Discrete_Random;
package body S_Of_N_Creator is
package F_Rnd renames Ada.Numerics.Float_Random; F_Gen: F_Rnd.Generator;
package D_Rnd is new Ada.Numerics.Discrete_Random(Index_Type); D_Gen: D_Rnd.Generator;
Item_Count: Natural := 0; -- this is a global counter Sample: Item_Array; -- also used globally
procedure Update(New_Item: Item_Type) is begin Item_Count := Item_Count + 1; if Item_Count <= Sample_Size then -- select the first Sample_Size items as the sample Sample(Item_Count) := New_Item; else -- for I-th item, I > Sample_Size: Sample_Size/I chance of keeping it if (Float(Sample_Size)/Float(Item_Count)) > F_Rnd.Random(F_Gen) then -- randomly (1/Sample_Size) replace one of the items of the sample Sample(D_Rnd.Random(D_Gen)) := New_Item; end if; end if; end Update;
function Result return Item_Array is begin Item_Count := 0; -- ready to start another run return Sample; end Result;
begin
D_Rnd.Reset(D_Gen); -- at package instantiation, initialize rnd-generators F_Rnd.Reset(F_Gen);
end S_Of_N_Creator;</lang>
The main program:
<lang Ada>with S_Of_N_Creator, Ada.Text_IO;
procedure Test_S_Of_N is
Repetitions: constant Positive := 100_000; type D_10 is range 0 .. 9;
-- the instantiation of the generic package S_Of_N_Creator generates -- a package with the desired functionality package S_Of_3 is new S_Of_N_Creator(Sample_Size => 3, Item_Type => D_10);
Sample: S_Of_3.Item_Array; Result: array(D_10) of Natural := (others => 0);
begin
for J in 1 .. Repetitions loop -- get Sample for Dig in D_10 loop S_Of_3.Update(Dig); end loop; Sample := S_Of_3.Result;
-- update current Result for Item in Sample'Range loop Result(Sample(Item)) := Result(Sample(Item)) + 1; end loop; end loop;
-- finally: output Result for Dig in Result'Range loop Ada.Text_IO.Put(D_10'Image(Dig) & ":" & Natural'Image(Result(Dig)) & "; "); end loop;
end Test_S_Of_N;</lang>
A sample output:
0: 30008; 1: 30056; 2: 30080; 3: 29633; 4: 29910; 5: 30293; 6: 30105; 7: 29924; 8: 29871; 9: 30120;
BBC BASIC
At each of the 100000 repetitions not only is a new function created but also new copies of its PRIVATE variables index% and samples%(). Creating such a large number of variables at run-time impacts adversely on execution speed and isn't to be recommended, other than to meet the artificial requirements of the task. <lang bbcbasic> HIMEM = PAGE + 20000000
PRINT "Single run samples for n = 3:" SofN% = FNs_of_n_creator(3) FOR I% = 0 TO 9 !^a%() = FN(SofN%)(I%) PRINT " For item " ; I% " sample(s) = " FNshowarray(a%(), I%+1) NEXT DIM cnt%(9) PRINT '"Digit counts after 100000 runs:" FOR rep% = 1 TO 100000 IF (rep% MOD 1000) = 0 PRINT ; rep% ; CHR$(13) ; F% = FNs_of_n_creator(3) FOR I% = 0 TO 9 !^a%() = FN(F%)(I%) NEXT cnt%(a%(1)) += 1 : cnt%(a%(2)) += 1 : cnt%(a%(3)) += 1 NEXT FOR digit% = 0 TO 9 PRINT " " ; digit% " : " ; cnt%(digit%) NEXT END REM Dynamically creates this function: REM DEF FNfunction(item%) : PRIVATE samples%(), index% REM DIM samples%(n%) : = FNs_of_n(item%, samples%(), index%) DEF FNs_of_n_creator(n%) LOCAL p%, f$ f$ = "(item%) : " + CHR$&0E + " samples%(), index% : " + \ \ CHR$&DE + " samples%(" + STR$(n%) + ") : = " + \ \ CHR$&A4 + "s_of_n(item%, samples%(), index%)" DIM p% LEN(f$) + 4 : $(p%+4) = f$ : !p% = p%+4 = p% DEF FNs_of_n(D%, s%(), RETURN I%) LOCAL N% N% = DIM(s%(),1) I% += 1 IF I% <= N% THEN s%(I%) = D% ELSE IF RND(I%) <= N% s%(RND(N%)) = D% ENDIF = !^s%() DEF FNshowarray(a%(), n%) LOCAL i%, a$ a$ = "[" IF n% > DIM(a%(),1) n% = DIM(a%(),1) FOR i% = 1 TO n% a$ += STR$(a%(i%)) + ", " NEXT = LEFT$(LEFT$(a$)) + "]"</lang>
Output:
Single run samples for n = 3: For item 0 sample(s) = [0] For item 1 sample(s) = [0, 1] For item 2 sample(s) = [0, 1, 2] For item 3 sample(s) = [0, 1, 2] For item 4 sample(s) = [0, 1, 4] For item 5 sample(s) = [0, 1, 4] For item 6 sample(s) = [0, 1, 6] For item 7 sample(s) = [0, 1, 6] For item 8 sample(s) = [8, 1, 6] For item 9 sample(s) = [8, 1, 9] Digit counts after 100000 runs: 0 : 30068 1 : 30017 2 : 30378 3 : 29640 4 : 30153 5 : 29994 6 : 29941 7 : 29781 8 : 29918 9 : 30110
C
Instead of returning a closure we set the environment in a structure:
<lang c>#include <stdlib.h>
- include <stdio.h>
- include <string.h>
- include <time.h>
struct s_env {
unsigned int n, i; size_t size; void *sample;
};
void s_of_n_init(struct s_env *s_env, size_t size, unsigned int n) {
s_env->i = 0; s_env->n = n; s_env->size = size; s_env->sample = malloc(n * size);
}
void sample_set_i(struct s_env *s_env, unsigned int i, void *item) {
memcpy(s_env->sample + i * s_env->size, item, s_env->size);
}
void *s_of_n(struct s_env *s_env, void *item) {
s_env->i++; if (s_env->i <= s_env->n) sample_set_i(s_env, s_env->i - 1, item); else if ((rand() % s_env->i) < s_env->n) sample_set_i(s_env, rand() % s_env->n, item); return s_env->sample;
}
int *test(unsigned int n, int *items_set, unsigned int num_items) {
int i; struct s_env s_env; s_of_n_init(&s_env, sizeof(items_set[0]), n); for (i = 0; i < num_items; i++) { s_of_n(&s_env, (void *) &items_set[i]); } return (int *)s_env.sample;
}
int main() {
unsigned int i, j; unsigned int n = 3; unsigned int num_items = 10; unsigned int *frequencies; int *items_set; srand(time(NULL)); items_set = malloc(num_items * sizeof(int)); frequencies = malloc(num_items * sizeof(int)); for (i = 0; i < num_items; i++) { items_set[i] = i; frequencies[i] = 0; } for (i = 0; i < 100000; i++) { int *res = test(n, items_set, num_items); for (j = 0; j < n; j++) { frequencies[res[j]]++; }
free(res);
} for (i = 0; i < num_items; i++) { printf(" %d", frequencies[i]); } puts(""); return 0;
}</lang>
C++
<lang cpp>#include <iostream>
- include <functional>
- include <vector>
- include <cstdlib>
- include <ctime>
template <typename T> std::function<std::vector<T>(T)> s_of_n_creator(int n) {
std::vector<T> sample; int i = 0; return [=](T item) mutable { i++; if (i <= n) { sample.push_back(item); } else if (std::rand() % i < n) { sample[std::rand() % n] = item; } return sample; };
}
int main() {
std::srand(std::time(NULL)); int bin[10] = {0}; for (int trial = 0; trial < 100000; trial++) { auto s_of_n = s_of_n_creator<int>(3); std::vector<int> sample; for (int i = 0; i < 10; i++) sample = s_of_n(i); for (int s : sample) bin[s]++; } for (int x : bin) std::cout << x << std::endl; return 0;
}</lang>
- Output:
30052 29740 30197 30223 29857 29688 30095 29803 30098 30247
Class-based version: <lang cpp>#include <iostream>
- include <vector>
- include <cstdlib>
- include <ctime>
template <typename T> class SOfN {
std::vector<T> sample; int i; const int n; public: SOfN(int _n) : i(0), n(_n) { } std::vector<T> operator()(T item) { i++; if (i <= n) { sample.push_back(item); } else if (std::rand() % i < n) { sample[std::rand() % n] = item; } return sample; }
};
int main() {
std::srand(std::time(NULL)); int bin[10] = {0}; for (int trial = 0; trial < 100000; trial++) { SOfN<int> s_of_n(3); std::vector<int> sample; for (int i = 0; i < 10; i++) sample = s_of_n(i); for (std::vector<int>::const_iterator i = sample.begin(); i != sample.end(); i++) bin[*i]++; } for (int i = 0; i < 10; i++) std::cout << bin[i] << std::endl; return 0;
}</lang>
Clojure
The Clojure approach to problems like this is to define a function which takes an accumulator state and an input item and produces the updated state. Here the accumulator state is the current sample and the number of items processed. This function is then used in a reduce call with an initial state and a list of items. <lang clojure>(defn s-of-n-fn-creator [n]
(fn [[sample iprev] item] (let [i (inc iprev)] (if (<= i n) [(conj sample item) i] (let [r (rand-int i)] (if (< r n) [(assoc sample r item) i] [sample i]))))))
(def s-of-3-fn (s-of-n-fn-creator 3))
(->> #(reduce s-of-3-fn [[] 0] (range 10))
(repeatedly 100000) (map first) flatten frequencies sort println)
</lang> Sample output: <lang>([0 29924] [1 30053] [2 30018] [3 29765] [4 29974] [5 30225] [6 30082] [7 29996] [8 30128] [9 29835])</lang>
If we really need a stateful (thread safe!) function for some reason, we can get it like this: <lang clojure>(defn s-of-n-creator [n]
(let [state (atom [[] 0]) s-of-n-fn (s-of-n-fn-creator n)] (fn [item] (first (swap! state s-of-n-fn item)))))</lang>
CoffeeScript
<lang coffeescript> s_of_n_creator = (n) ->
arr = [] cnt = 0 (elem) -> cnt += 1 if cnt <= n arr.push elem else pos = Math.floor(Math.random() * cnt) if pos < n arr[pos] = elem arr.sort()
sample_size = 3 range = [0..9] num_trials = 100000
counts = {}
for digit in range
counts[digit] = 0
for i in [1..num_trials]
s_of_n = s_of_n_creator(sample_size) for digit in range sample = s_of_n(digit) for digit in sample counts[digit] += 1
for digit in range
console.log digit, counts[digit]
</lang> output <lang> > coffee knuth_sample.coffee 0 29899 1 29841 2 29930 3 30058 4 29932 5 29948 6 30047 7 30114 8 29976 9 30255 </lang>
Common Lisp
<lang lisp>(defun s-n-creator (n)
(let ((sample (make-array n :initial-element nil)) (i 0)) (lambda (item) (if (<= (incf i) n) (setf (aref sample (1- i)) item) (when (< (random i) n) (setf (aref sample (random n)) item))) sample)))
(defun algorithm-s ()
(let ((*random-state* (make-random-state t)) (frequency (make-array '(10) :initial-element 0))) (loop repeat 100000 for s-of-n = (s-n-creator 3) do (flet ((s-of-n (item) (funcall s-of-n item))) (map nil (lambda (i) (incf (aref frequency i))) (loop for i from 0 below 9 do (s-of-n i) finally (return (s-of-n 9)))))) frequency))
(princ (algorithm-s)) </lang>output<lang>#(30026 30023 29754 30017 30267 29997 29932 29990 29965 30029)</lang>
D
<lang d>import std.stdio, std.random;
auto sofN_creator(in int n) {
size_t i; int[] sample;
return (in int item) { i++; if (i <= n) sample ~= item; else if (uniform01 < (double(n) / i)) sample[uniform(0, n)] = item; return sample; };
}
void main() {
enum nRuns = 100_000; size_t[10] bin;
foreach (immutable trial; 0 .. nRuns) { immutable sofn = sofN_creator(3); int[] sample; foreach (immutable item; 0 .. bin.length) sample = sofn(item); foreach (immutable s; sample) bin[s]++; } writefln("Item counts for %d runs:\n%s", nRuns, bin);
}</lang>
- Output:
Item counts for 100000 runs: [30191, 29886, 29988, 30149, 30251, 29997, 29748, 29909, 30041, 29840]
Faster Version
<lang d>import std.stdio, std.random, std.algorithm;
struct SOfN(size_t n) {
size_t i; int[n] sample = void;
int[] next(in size_t item, ref Xorshift rng) { i++; if (i <= n) sample[i - 1] = item; else if (rng.uniform01 < (double(n) / i)) sample[uniform(0, n, rng)] = item; return sample[0 .. min(i, $)]; }
}
void main() {
enum nRuns = 100_000; size_t[10] bin; auto rng = Xorshift(0);
foreach (immutable trial; 0 .. nRuns) { SOfN!3 sofn; foreach (immutable item; 0 .. bin.length - 1) sofn.next(item, rng); foreach (immutable s; sofn.next(bin.length - 1, rng)) bin[s]++; } writefln("Item counts for %d runs:\n%s", nRuns, bin);
}</lang>
Elena
<lang elena>#import system'dynamic.
- import extensions.
- import system'routines.
- import system'collections.
extension algorithmOp {
s_of_n [ var counter := Integer new. var n := self. ^ ArrayList new mix &into: { eval : i [ counter += 1. if (self length < n) [ self += i. ]; [ ((randomGenerator eval:counter < n) bool) ? [ self@(randomGenerator eval:n) := i. ]. ]. ^ self array. ] }. ]
}
program = [
var bin := Array new:10 set &every:(&index:n) [ Integer new ]. 0 till:10000 &doEach: trial [ var s_of_n := 3 s_of_n. 0 till:10 &doEach:n [ var sample := s_of_n eval:n. if (n == 9) [ sample run &each: i [ bin@i += 1. ]. ]. ]. ]. console writeLine:bin readChar.
].</lang>
- Output:
3050,3029,3041,2931,3040,2952,2901,2984,3069,3003
Elixir
<lang elixir> defmodule Knuth do
def s_of_n_creator(n), do: {n, 1, []}
def s_of_n({n, i, ys}, x) do cond do i <= n -> {n, i+1, [x|ys]}
:rand.uniform(i) <= n -> {n, i+1, List.replace_at(ys, :rand.uniform(n)-1, x)}
true -> {n, i+1, ys} end end
end
results = Enum.reduce(1..100000, %{}, fn _, freq ->
{_, _, xs} = Enum.reduce(1..10, Knuth.s_of_n_creator(3), fn x, s -> Knuth.s_of_n(s, x) end) Enum.reduce(xs, freq, fn x, freq -> Map.put(freq, x, (freq[x] || 0) + 1) end)
end)
IO.inspect results </lang> Output:
%{1 => 30138, 2 => 29980, 3 => 29992, 4 => 29975, 5 => 30110, 6 => 29825, 7 => 29896, 8 => 30188, 9 => 29898, 10 => 29998}
Go
<lang go>package main
import (
"fmt" "math/rand" "time"
)
func sOfNCreator(n int) func(byte) []byte {
s := make([]byte, 0, n) m := n return func(item byte) []byte { if len(s) < n { s = append(s, item) } else { m++ if rand.Intn(m) < n { s[rand.Intn(n)] = item } } return s }
}
func main() {
rand.Seed(time.Now().UnixNano()) var freq [10]int for r := 0; r < 1e5; r++ { sOfN := sOfNCreator(3) for d := byte('0'); d < '9'; d++ { sOfN(d) } for _, d := range sOfN('9') { freq[d-'0']++ } } fmt.Println(freq)
}</lang> Output:
[30075 29955 30024 30095 30031 30018 29973 29642 30156 30031]
Icon and Unicon
The following solution makes use of the makeProc procedure defined in the UniLib library and so is Unicon specific. However, the solution can be modified to work in Icon as well.
Technically, s_of_n_creator returns a co-expression, not a function. In Unicon, the calling syntax for this co-expression is indistinguishable from that of a function. <lang unicon>import Utils
procedure main(A)
freq := table(0) every 1 to (\A[2] | 100000)\1 do { s_of_n := s_of_n_creator(\A[1] | 3) every sample := s_of_n(0 to 9) every freq[!sample] +:= 1 } every write(i := 0 to 9,": ",right(freq[i],6))
end
procedure s_of_n_creator(n)
items := [] itemCnt := 0.0 return makeProc { repeat { item := (items@&source)[1] itemCnt +:= 1 if *items < n then put(items, item) else if ?0 < (n/itemCnt) then ?items := item } }
end</lang> and a sample run:
->kas 0: 29941 1: 29963 2: 29941 3: 30005 4: 30087 5: 29895 6: 30075 7: 30059 8: 29962 9: 30072 ->
J
Note that this approach introduces heavy inefficiencies, to achieve information hiding.
<lang j>s_of_n_creator=: 1 :0
ctx=: conew&'inefficient' m s_of_n__ctx
)
coclass'inefficient'
create=:3 :0 N=: y ITEMS=: K=:0 )
s_of_n=:3 :0 K=: K+1 if. N>:#ITEMS do. ITEMS=: ITEMS,y else. if. (N%K)>?0 do. ITEMS=: ((<<<?N){ITEMS),y else. ITEMS end. end. )
</lang>
Explanation: create
is the constructor for the class named inefficient
and it initializes three properties: N
(our initial value), ITEMS
(an initially empty list) and K
(a counter which is initially 0).
Also, we have s_of_n
which is a method of that class. It increments K and appends to the list, respecting the random value replacement requirement, once the list has reached the required length.
Finally, we have s_of_n_creator
which is not a method of that class, but which will create an object of that class and return the resulting s_of_n method.
Required example:
<lang j>run=:3 :0
nl=. conl 1 s3_of_n=. 3 s_of_n_creator r=. {: s3_of_n"0 i.10 coerase (conl 1)-.nl r
)
(~.,._1 + #/.~) (i.10),,D=:run"0 i.1e5
0 40119 1 40050 2 40163 3 57996 4 42546 5 40990 6 38680 7 36416 8 33172 9 29868</lang>
Here, we have each of our digits along with how many times each appeared in a result from run
.
Explanation of run
:
First, we get a snapshot of the existing objects in nl
.
Then, we get our s3_of_n which is a method in a new object.
Then we run that method on each of the values 0, 1, 2, 3, 4, 5, 6, 7, 8 and 9, keeping only the values from the last run, this will be the result of the run.
Then we delete any objects which did not previously exist.
Finally return our result.
Java
A class-based solution: <lang java>import java.util.*;
class SOfN<T> {
private static final Random rand = new Random();
private List<T> sample; private int i = 0; private int n; public SOfN(int _n) {
n = _n; sample = new ArrayList<T>(n);
} public List<T> process(T item) {
i++; if (i <= n) {
sample.add(item);
} else if (rand.nextInt(i) < n) { sample.set(rand.nextInt(n), item); } return sample;
}
}
public class AlgorithmS {
public static void main(String[] args) {
int[] bin = new int[10]; for (int trial = 0; trial < 100000; trial++) { SOfN<Integer> s_of_n = new SOfN<Integer>(3); List<Integer> sample = null; for (int i = 0; i < 10; i++) sample = s_of_n.process(i); for (int s : sample) bin[s]++; } System.out.println(Arrays.toString(bin));
}
}</lang>
Output:
[30115, 30141, 30050, 29887, 29765, 30132, 29767, 30114, 30079, 29950]
Alternative solution without using an explicitly named type; instead using an anonymous class implementing a generic "function" interface: <lang java>import java.util.*;
interface Function<S, T> {
public T call(S x);
}
public class AlgorithmS {
private static final Random rand = new Random(); public static <T> Function<T, List<T>> s_of_n_creator(final int n) {
return new Function<T, List<T>>() { private List<T> sample = new ArrayList<T>(n); private int i = 0; public List<T> call(T item) { i++; if (i <= n) { sample.add(item); } else if (rand.nextInt(i) < n) { sample.set(rand.nextInt(n), item); } return sample; } };
}
public static void main(String[] args) {
int[] bin = new int[10]; for (int trial = 0; trial < 100000; trial++) { Function<Integer, List<Integer>> s_of_n = s_of_n_creator(3); List<Integer> sample = null; for (int i = 0; i < 10; i++) sample = s_of_n.call(i); for (int s : sample) bin[s]++; } System.out.println(Arrays.toString(bin));
}
}</lang>
Julia
<lang Julia> function makesofn(n::Int)
buf = Any[] i = 0 function sofn(item) i += 1 if i <= n push!(buf, item) else j = rand(1:i) if j <= n buf[j] = item end end return buf end return sofn
end
nhist = zeros(Int, 10)
for i in 1:10^5
kas = makesofn(3) for j in 0:8 kas(j) end for k in kas(9) nhist[k+1] += 1 end
end
println("Simulating sof3(0:9) 100000 times:") for (i, c) in enumerate(nhist)
println(@sprintf " %2d => %5d" i-1 c)
end </lang>
- Output:
Simulating sof3(0:9) 100000 times: 0 => 29994 1 => 30026 2 => 30173 3 => 29590 4 => 29967 5 => 30104 6 => 30185 7 => 29761 8 => 30147 9 => 30053
Objective-C
Uses blocks <lang objc>#import <Foundation/Foundation.h>
typedef NSArray *(^SOfN)(id);
SOfN s_of_n_creator(int n) {
NSMutableArray *sample = [[NSMutableArray alloc] initWithCapacity:n]; __block int i = 0; return [^(id item) { i++; if (i <= n) { [sample addObject:item]; } else if (rand() % i < n) { sample[rand() % n] = item; } return sample; } copy];
}
int main(int argc, const char *argv[]) {
@autoreleasepool {
NSCountedSet *bin = [[NSCountedSet alloc] init]; for (int trial = 0; trial < 100000; trial++) { SOfN s_of_n = s_of_n_creator(3); NSArray *sample; for (int i = 0; i < 10; i++) sample = s_of_n(@(i)); [bin addObjectsFromArray:sample]; } NSLog(@"%@", bin); } return 0;
}</lang>
Log:
<NSCountedSet: 0x100114120> (0 [29934], 9 [30211], 5 [29926], 1 [30067], 6 [30001], 2 [29972], 7 [30126], 3 [29944], 8 [29910], 4 [29909])
OCaml
<lang ocaml>let s_of_n_creator n =
let i = ref 0 and sample = ref [| |] in fun item -> incr i; if !i <= n then sample := Array.append [| item |] !sample else if Random.int !i < n then !sample.(Random.int n) <- item; !sample
let test n items_set =
let s_of_n = s_of_n_creator n in Array.fold_left (fun _ v -> s_of_n v) [| |] items_set
let () =
Random.self_init(); let n = 3 in let num_items = 10 in let items_set = Array.init num_items (fun i -> i) in let results = Array.create num_items 0 in for i = 1 to 100_000 do let res = test n items_set in Array.iter (fun j -> results.(j) <- succ results.(j)) res done; Array.iter (Printf.printf " %d") results; print_newline()</lang>
Output:
30051 29899 30249 30058 30012 29836 29998 29882 30148 29867
PARI/GP
<lang parigp>KnuthS(v,n)={
my(u=vector(n,i,i)); for(i=n+1,#v, if(random(i)<n,u[random(n)+1]=i) ); vecextract(v,u)
}; test()={
my(v=vector(10),t); for(i=1,1e5, t=KnuthS([0,1,2,3,4,5,6,7,8,9],3); v[t[1]+1]++;v[t[2]+1]++;v[t[3]+1]++ ); v
};</lang>
Output:
%1 = [30067, 30053, 29888, 30161, 30204, 29990, 30175, 29980, 29622, 29860]
Perl
<lang perl>use strict;
sub s_of_n_creator {
my $n = shift; my @sample; my $i = 0; sub { my $item = shift; $i++; if ($i <= $n) { # Keep first n items push @sample, $item; } elsif (rand() < $n / $i) { # Keep item @sample[rand $n] = $item; } @sample }
}
my @items = (0..9); my @bin;
foreach my $trial (1 .. 100000) {
my $s_of_n = s_of_n_creator(3); my @sample; foreach my $item (@items) { @sample = $s_of_n->($item); } foreach my $s (@sample) { $bin[$s]++; }
} print "@bin\n"; </lang>
- Sample output
30003 29923 30192 30164 29994 29976 29935 29860 30040 29913
Perl 6
<lang perl6>sub s_of_n_creator($n) {
my @sample; my $i = 0; -> $item { if ++$i <= $n { push @sample, $item; } elsif $i.rand < $n { @sample[$n.rand] = $item; } @sample; }
}
my @items = 0..9; my @bin;
for ^100000 {
my &s_of_n = s_of_n_creator(3); my @sample; for @items -> $item { @sample = s_of_n($item); } for @sample -> $s { @bin[$s]++; }
} say @bin;</lang> Output:
29975 30028 30246 30056 30004 29983 29836 29967 29924 29981
PHP
<lang php><?php function s_of_n_creator($n) {
$sample = array(); $i = 0; return function($item) use (&$sample, &$i, $n) { $i++; if ($i <= $n) { // Keep first n items $sample[] = $item; } else if (rand(0, $i-1) < $n) { // Keep item $sample[rand(0, $n-1)] = $item; } return $sample; };
}
$items = range(0, 9);
for ($trial = 0; $trial < 100000; $trial++) {
$s_of_n = s_of_n_creator(3); foreach ($items as $item) $sample = $s_of_n($item); foreach ($sample as $s) $bin[$s]++;
} print_r($bin); ?></lang>
- Sample output
Array ( [3] => 30158 [8] => 29859 [9] => 29984 [6] => 29937 [7] => 30361 [4] => 29994 [5] => 29849 [0] => 29724 [1] => 29997 [2] => 30137 )
PicoLisp
<lang PicoLisp>(de s_of_n_creator (@N)
(curry (@N (I . 0) (Res)) (Item) (cond ((>= @N (inc 'I)) (push 'Res Item)) ((>= @N (rand 1 I)) (set (nth Res (rand 1 @N)) Item)) ) Res ) )
(let Freq (need 10 0)
(do 100000 (let S_of_n (s_of_n_creator 3) (for I (mapc S_of_n (0 1 2 3 4 5 6 7 8 9)) (inc (nth Freq (inc I))) ) ) ) Freq )</lang>
Output:
-> (30003 29941 29918 30255 29848 29875 30056 29839 30174 30091)
Python
<lang python>from random import randrange
def s_of_n_creator(n):
sample, i = [], 0 def s_of_n(item): nonlocal i
i += 1 if i <= n: # Keep first n items sample.append(item) elif randrange(i) < n: # Keep item sample[randrange(n)] = item return sample return s_of_n
if __name__ == '__main__':
bin = [0]* 10 items = range(10) print("Single run samples for n = 3:") s_of_n = s_of_n_creator(3) for item in items: sample = s_of_n(item) print(" Item: %i -> sample: %s" % (item, sample)) # for trial in range(100000): s_of_n = s_of_n_creator(3) for item in items: sample = s_of_n(item) for s in sample: bin[s] += 1 print("\nTest item frequencies for 100000 runs:\n ", '\n '.join("%i:%i" % x for x in enumerate(bin)))</lang>
- Sample output
Single run samples for n = 3: Item: 0 -> sample: [0] Item: 1 -> sample: [0, 1] Item: 2 -> sample: [0, 1, 2] Item: 3 -> sample: [0, 1, 3] Item: 4 -> sample: [0, 1, 3] Item: 5 -> sample: [0, 1, 3] Item: 6 -> sample: [0, 1, 3] Item: 7 -> sample: [0, 3, 7] Item: 8 -> sample: [0, 3, 7] Item: 9 -> sample: [0, 3, 7] Test item frequencies for 100000 runs: 0:29983 1:30240 2:29779 3:29921 4:30224 5:29967 6:30036 7:30050 8:29758 9:30042
Python Class based version
Only a slight change creates the following class-based implementation: <lang python>class S_of_n_creator():
def __init__(self, n): self.n = n self.i = 0 self.sample = [] def __call__(self, item): self.i += 1 n, i, sample = self.n, self.i, self.sample if i <= n: # Keep first n items sample.append(item) elif randrange(i) < n: # Keep item sample[randrange(n)] = item return sample</lang>
The above can be instantiated as follows after which s_of_n
can be called in the same way as it is in the first example where it is a function instead of an instance.
<lang python>s_of_n = S_of_n_creator(3)</lang>
Racket
<lang racket>#lang racket/base
(define (s-of-n-creator n)
(define i 0) (define sample (make-vector n)) ; the sample of n items (lambda (item) (set! i (add1 i)) (cond [(<= i n) ; we're not full, so kind of boring (vector-set! sample (sub1 i) item)] [(< (random i) n) ; we've already seen n items; swap one? (vector-set! sample (random n) item)]) sample))
(define counts (make-vector 10 0))
(for ([i 100000])
(define s-of-n (s-of-n-creator 3)) (define sample (for/last ([digit 10]) (s-of-n digit))) (for ([d sample]) (vector-set! counts d (add1 (vector-ref counts d)))))
(for ([d 10]) (printf "~a ~a\n" d (vector-ref counts d)))</lang> Output:
0 30117 1 29955 2 30020 3 29906 4 30146 5 29871 6 30045 7 30223 8 29940 9 29777
REXX
<lang rexx>/*REXX program using Knuth's algorithm S (a random sampling N of M items).*/ parse arg trials size . /*obtain optional arguments from the CL*/ if trials== then trials=100000 /*Not specified? Then use the default.*/ if size== then size= 3 /* " " " " " " */
- .=0 /*initialize frequency counter array. */
do trials /*OK, now let's light this candle. */ call s_of_n_creator size /*create an initial list of N items. */
do gener=0 for 10 call s_of_n gener /*call s_of_n with a single decimal dig*/ end /*gener*/
do count=1 for size /*let's examine what SofN generated. */ _=!.count /*get a decimal digit from the Nth */ #._=#._+1 /* ··· item, and count it, of course.*/ end /*count*/ end /*trials*/ @='trials, and with size='
say "Using Knuth's algorithm S for" commas(trials) @ || commas(size)":" say
do dig=0 to 9 /* [↓] display the frequency of a dig.*/ say left(,20) "frequency of the" dig 'digit is:' commas(#.dig) end /*dig*/
exit /*stick a fork in it, we're all done. */ /*────────────────────────────────────────────────────────────────────────────*/ commas: procedure; parse arg _; n=_'.9'; #=123456789; b=verify(n,#,"M")
e=verify(n, #'0', , verify(n, #"0.", 'M')) - 4 do j=e to b by -3; _=insert(',',_,j); end /*j*/; return _
/*────────────────────────────────────────────────────────────────────────────*/ s_of_n: parse arg item; items=items+1 /*get "item", bump the items counter.*/
c=random(1, items) /* [↓] should replace a previous item?*/ if c>size then return /*probability isn't good, so skip it. */ _=random(1, size); !._=item /*now, figure out which previous ··· */ return /* ··· item to replace with ITEM.*/
/*────────────────────────────────────────────────────────────────────────────*/ s_of_n_creator: parse arg item 1 items /*generate ITEM number of items. */
do k=1 for item /*traipse through the first N items. */ !.k=random(0, 9) /*set the Kth item with random digit.*/ end /*k*/ return /*the piddly stuff is done (for now). */</lang>
output when using the default input of: 100000 2
Using Knuth's algorithm S for 100,000 trials, and with size=3: frequency of the 0 digit is: 29,843 frequency of the 1 digit is: 30,083 frequency of the 2 digit is: 29,966 frequency of the 3 digit is: 30,006 frequency of the 4 digit is: 30,137 frequency of the 5 digit is: 29,833 frequency of the 6 digit is: 30,160 frequency of the 7 digit is: 30,182 frequency of the 8 digit is: 29,941 frequency of the 9 digit is: 29,849
Ruby
Using a closure <lang ruby>def s_of_n_creator(n)
sample = [] i = 0 Proc.new do |item| i += 1 if i <= n sample << item elsif rand(i) < n sample[rand(n)] = item end sample end
end
frequency = Array.new(10,0) 100_000.times do
s_of_n = s_of_n_creator(3) sample = nil (0..9).each {|digit| sample = s_of_n[digit]} sample.each {|digit| frequency[digit] += 1}
end
(0..9).each {|digit| puts "#{digit}\t#{frequency[digit]}"}</lang>
Example
0 29850 1 30015 2 29970 3 29789 4 29841 5 30075 6 30281 7 30374 8 29953 9 29852
Sidef
<lang ruby>func s_of_n_creator(n) {
var i = 0 var sample = [] { |item| if (++i <= n) { sample << item; } elsif (i.rand < n) { sample[n.rand] = item; } sample; }
}
var items = 0..9; var bin = [];
100000.times {
var s_of_n = s_of_n_creator(3); var sample = [] for item in items { sample = s_of_n(item); } for s in sample { bin[s] := 0 ++; }
}
say bin;</lang>
- Output:
[30056, 29906, 30058, 29986, 30062, 29748, 29989, 29985, 30126, 30084]
Swift
<lang swift>import Darwin
func s_of_n_creator<T>(n: Int) -> T -> [T] {
var sample = [T]() var i = 0 return {(item: T) in i++ if (i <= n) { sample.append(item) } else if (Int(arc4random_uniform(UInt32(i))) < n) { sample[Int(arc4random_uniform(UInt32(n)))] = item } return sample }
}
var bin = [Int](count:10, repeatedValue:0) for trial in 0..<100000 {
let s_of_n: Int -> [Int] = s_of_n_creator(3) var sample: [Int] = [] for i in 0..<10 { sample = s_of_n(i) } for s in sample { bin[s]++ }
} println(bin)</lang>
- Output:
[30038, 29913, 30047, 30069, 30159, 30079, 29773, 29962, 30000, 29960]
Tcl
<lang tcl>package require Tcl 8.6
oo::class create SofN {
variable items size count constructor {n} {
set size $n
} method item {item} {
if {[incr count] <= $size} { lappend items $item } elseif {rand()*$count < $size} { lset items [expr {int($size * rand())}] $item } return $items
}
}
- Test code
for {set i 0} {$i < 100000} {incr i} {
set sOf3 [SofN new 3] foreach digit {0 1 2 3 4 5 6 7 8 9} {
set digs [$sOf3 item $digit]
} $sOf3 destroy foreach digit $digs {
incr freq($digit)
}
} parray freq</lang>
Sample output:
freq(0) = 29812 freq(1) = 30099 freq(2) = 29927 freq(3) = 30106 freq(4) = 30048 freq(5) = 29993 freq(6) = 29912 freq(7) = 30219 freq(8) = 30060 freq(9) = 29824
zkl
<lang zkl>fcn s_of_n_creator(n){
fcn(item,ri,N,samples){ i:=ri.inc(); // 1,2,3,4,... if(i<=N) samples.append(item); else if ((0).random(i) < N) samples[(0).random(N)] = item; samples }.fp1(Ref(1),n,L())
}</lang> One run: <lang zkl>s3:=s_of_n_creator(3); [0..9].pump(List,s3,"copy").println();</lang>
- Output:
L(L(0),L(0,1),L(0,1,2),L(0,1,2),L(0,4,2),L(5,4,2),L(5,6,2),L(5,6,2),L(5,6,2),L(9,6,2))
100,000 runs: <lang zkl>dist:=L(0,0,0,0,0,0,0,0,0,0); do(0d100_000){
(0).pump(10,Void,s_of_n_creator(3)).apply2('wrap(n){dist[n]=dist[n]+1})
} N:=dist.sum(); dist.apply('wrap(n){"%.2f%%".fmt(n.toFloat()/N*100)}).println();</lang>
- Output:
L("10.00%","9.98%","10.00%","9.99%","10.00%","9.98%","10.01%","10.04%","9.98%","10.02%")