Knuth's algorithm S: Difference between revisions
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Revision as of 22:51, 8 November 2011
![Task](http://static.miraheze.org/rosettacodewiki/thumb/b/ba/Rcode-button-task-crushed.png/64px-Rcode-button-task-crushed.png)
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.
Common Lisp
<lang lisp>(setf *random-state* (make-random-state t))
(defun make-selector (n)
(let ((i 0) res) (lambda (&optional (x nil x-p)) (if (and x-p (< (random (incf i)) n))
(if (< (length res) n) (push x res) (setf (elt res (random n)) x)))
res)))
- test
(loop repeat 100000
with freq = (make-array 10 :initial-element 0) do (let ((f (make-selector 3)))
(mapc f '(0 1 2 3 4 5 6 7 8 9)) (mapc (lambda (i) (incf (aref freq i))) (funcall f)))
finally (prin1 freq))</lang>output<lang>#(30026 30023 29754 30017 30267 29997 29932 29990 29965 30029)</lang>
D
<lang d>import std.stdio, std.random, std.range, std.algorithm;
auto s_of_n_creator(in int n) {
int[] sample; int i; typeof(sample) s_of_n(in int item) { i++; if (i <= n) { // Keep first n items sample ~= item; } else if (uniform(0.0, 1.0) < (cast(double)n / i)) { // Keep item sample = remove(sample, uniform(0, n)); sample ~= item; } return sample; } return &s_of_n;
}
void main() {
int[10] bin; auto items = iota(bin.length); writeln("Single run samples for n = 3:"); auto s_of_n1 = s_of_n_creator(3); foreach (item; items) { auto sample = s_of_n1(item); writefln(" Item: %d -> sample: %s", item, sample); }
enum nruns = 1_000_000; foreach (trial; 0 .. nruns) { auto s_of_n2 = s_of_n_creator(3); int[] sample; foreach (item; items) sample = s_of_n2(item); foreach (s; sample) bin[s]++; } writefln("\nTest item frequencies for %d runs:", nruns); foreach (i, d; bin) writefln(" %d: %d", i, d);
}</lang> Example 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, 2] Item: 4 -> sample: [0, 1, 4] Item: 5 -> sample: [0, 1, 4] Item: 6 -> sample: [1, 4, 6] Item: 7 -> sample: [1, 6, 7] Item: 8 -> sample: [6, 7, 8] Item: 9 -> sample: [7, 8, 9] Test item frequencies for 1000000 runs: 0: 300033 1: 299914 2: 299505 3: 300033 4: 299704 5: 299893 6: 299871 7: 300297 8: 300292 9: 300458
Go
<lang go>package main
import (
"fmt" "rand" "time"
)
func sOfNCreator(n int) func(byte) []byte {
s := make([]byte, 0, 3) m := n return func(item byte) []byte { if len(s) < 3 { s = append(s, item) } else { m++ if rand.Intn(m) < n { s[rand.Intn(n)] = item } } return s }
}
func main() {
rand.Seed(time.Nanoseconds()) 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]
J
Note that this approach introduces heavy inefficiencies, to achieve information hiding.
<lang j>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=: (((i.#ITEMS)-.?N){ITEMS),y else. ITEMS end. end. )
s_of_n_creator_base_=: 1 :0
ctx=: conew&'inefficient' m s_of_n__ctx
)</lang>
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 30099 1 29973 2 29795 3 29995 4 29996 5 30289 6 29903 7 29993 8 30215 9 29742</lang>
OCaml
<lang ocaml>let s_of_n_creator n =
let i = ref 0 and sample = ref [| |] in function 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
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 random, 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 random() < n / i: # 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 random() < n / i: # Keep item del sample[randrange(n)] sample.append(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>
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 < (Float(n)/i) 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.call(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
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