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http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Common_Lisp | Common Lisp | (loop for x from 1 to 10
for xx = (* x x)
for n from 1
summing xx into xx-sum
finally (return (sqrt (/ xx-sum n)))) |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Raku | Raku | my $e64 = '
VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY2
9tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g=
';
my @base64map = flat 'A' .. 'Z', 'a' .. 'z', ^10, '+', '/';
my %base64 is default(0) = @base64map.pairs.invert;
sub base64-decode-slow ($enc) {
my $buf = Buf.new;
for $enc.subst(/\s/, '', :g).comb(4) -> $chunck {
$buf.append: |(sprintf "%06d%06d%06d%06d", |$chunck.comb.map:
{%base64{$_}.base(2)}).comb(8).map: {:2($_)};
}
$buf
}
say 'Slow:';
say base64-decode-slow($e64).decode('utf8');
# Of course, the above routine is slow and is only for demonstration purposes.
# For real code you should use a module, which is MUCH faster and heavily tested.
say "\nFast:";
use Base64::Native;
say base64-decode($e64).decode('utf8'); |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Crystal | Crystal | def rms(seq)
Math.sqrt(seq.sum { |x| x*x } / seq.size)
end
puts rms (1..10).to_a |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #D | D | import std.stdio, std.math, std.algorithm, std.range;
real rms(R)(R d) pure {
return sqrt(d.reduce!((a, b) => a + b * b) / real(d.length));
}
void main() {
writefln("%.19f", iota(1, 11).rms);
} |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Red | Red | Red [Source: https://github.com/vazub/rosetta-red]
print to-string debase "VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuCiAgICAtLVBhdWwgUi5FaHJsaWNo"
|
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Ring | Ring |
#======================================#
# Sample: Base64 decode data
# Author: Gal Zsolt, Mansour Ayouni
#======================================#
load "guilib.ring"
oQByteArray = new QByteArray()
oQByteArray.append("Rosetta Code Base64 decode data task")
oba = oQByteArray.toBase64().data()
see oba + nl
oQByteArray = new QByteArray()
oQByteArray.append(oba)
see oQByteArray.fromBase64(oQByteArray).data()
|
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Ruby | Ruby | require 'base64'
raku_example ='
VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY2
9tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g=
'
puts Base64.decode64 raku_example |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Delphi.2FPascal | Delphi/Pascal | program AveragesMeanSquare;
{$APPTYPE CONSOLE}
uses Types;
function MeanSquare(aArray: TDoubleDynArray): Double;
var
lValue: Double;
begin
Result := 0;
for lValue in aArray do
Result := Result + (lValue * lValue);
if Result > 0 then
Result := Sqrt(Result / Length(aArray));
end;
begin
Writeln(MeanSquare(TDoubleDynArray.Create()));
Writeln(MeanSquare(TDoubleDynArray.Create(1,2,3,4,5,6,7,8,9,10)));
end. |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Rust | Rust | use std::str;
const INPUT: &str = "VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuCiAgICAtLVBhdWwgUi5FaHJsaWNo";
const UPPERCASE_OFFSET: i8 = -65;
const LOWERCASE_OFFSET: i8 = 26 - 97;
const NUM_OFFSET: i8 = 52 - 48;
fn main() {
println!("Input: {}", INPUT);
let result = INPUT.chars()
.filter(|&ch| ch != '=') //Filter '=' chars
.map(|ch| { //Map char values using Base64 Characters Table
let ascii = ch as i8;
let convert = match ch {
'0' ... '9' => ascii + NUM_OFFSET,
'a' ... 'z' => ascii + LOWERCASE_OFFSET,
'A' ... 'Z' => ascii + UPPERCASE_OFFSET,
'+' => 62,
'/' => 63,
_ => panic!("Not a valid base64 encoded string")
};
format!("{:#08b}", convert)[2..].to_string() //convert indices to binary format and remove the two first digits
})
.collect::<String>() //concatenate the resulting binary values
.chars()
.collect::<Vec<char>>()
.chunks(8) //split into 8 character chunks
.map(|chunk| {
let num_str = chunk.iter().collect::<String>();
usize::from_str_radix(&num_str, 2).unwrap() as u8 //convert the binary string into its u8 value
})
.collect::<Vec<_>>();
let result = str::from_utf8(&result).unwrap(); //convert into UTF-8 string
println!("Output: {}", result);
} |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Scala | Scala | import java.util.Base64
object Base64Decode extends App {
def text2BinaryDecoding(encoded: String): String = {
val decoded = Base64.getDecoder.decode(encoded)
new String(decoded, "UTF-8")
}
def data =
"VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g="
println(text2BinaryDecoding(data))
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Action.21 | Action! | INCLUDE "H6:REALMATH.ACT"
BYTE CH=$02FC,COLOR1=$02C5,COLOR2=$02C6
REAL r0,r4,r15,r16,r20,r22,r23,r24,r26,r28,r44,r85,r160
PROC Init()
ValR("0",r0)
ValR("0.04",r4)
ValR("0.15",r16)
ValR("0.16",r16)
ValR("0.2",r20)
ValR("0.22",r22)
ValR("0.23",r23)
ValR("0.24",r24)
ValR("0.26",r26)
ValR("0.28",r28)
ValR("0.44",r44)
ValR("0.85",r85)
ValR("1.6",r160)
RETURN
PROC Fern(REAL POINTER scale)
BYTE r
REAL x,y,xp,yp,tmp1,tmp2
INT i,ix,iy
RealAssign(r0,x)
RealAssign(r0,y)
DO
RealMult(x,scale,tmp1)
RealMult(y,scale,tmp2)
ix=Round(tmp2) ;fern is rotated to fit the screen size
iy=Round(tmp1)+85
IF (ix>=0) AND (ix<=319) AND (iy>=0) AND (iy<=191) THEN
Plot(ix,iy)
FI
r=Rand(100)
RealAssign(x,xp) ;xp=x
RealAssign(y,yp) ;yp=y
IF r<1 THEN
RealAssign(r0,x) ;x=0
RealMult(r16,yp,y) ;y=0.16*yp
ELSEIF r<86 THEN
RealMult(r85,xp,tmp1) ;tmp1=0.85*xp
RealMult(r4,yp,tmp2) ;tmp2=0.4*yp
RealAdd(tmp1,tmp2,x) ;x=0.85*xp+0.4*yp
RealMult(r4,xp,tmp1) ;tmp1=0.04*xp
RealSub(r160,tmp1,tmp2) ;tmp2=-0.04*xp+1.6
RealMult(r85,yp,tmp1) ;tmp1=0.85*yp
RealAdd(tmp1,tmp2,y) ;y=-0.04*xp+0.85*yp+1.6
ELSEIF r<93 THEN
RealMult(r20,xp,tmp1) ;tmp1=0.2*xp
RealMult(r26,yp,tmp2) ;tmp2=0.26*yp
RealSub(tmp1,tmp2,x) ;x=0.2*xp-0.26*yp
RealMult(r23,xp,tmp1) ;tmp1=0.23*xp
RealAdd(r160,tmp1,tmp2) ;tmp2=0.23*xp+1.6
RealMult(r22,yp,tmp1) ;tmp1=0.22*yp
RealAdd(tmp1,tmp2,y) ;y=0.23*xp+0.22*yp+1.6
ELSE
RealMult(r15,xp,tmp1) ;tmp1=0.15*xp
RealMult(r28,yp,tmp2) ;tmp2=0.28*yp
RealSub(tmp2,tmp1,x) ;x=-0.15*xp+0.28*yp
RealMult(r26,xp,tmp1) ;tmp1=0.26*xp
RealAdd(r44,tmp1,tmp2) ;tmp2=0.26*xp+0.44
RealMult(r24,yp,tmp1) ;tmp1=0.24*yp
RealAdd(tmp1,tmp2,y) ;y=0.26*xp+0.44*yp+0.44
FI
Poke(77,0) ;turn off the attract mode
UNTIL CH#$FF ;until key pressed
OD
CH=$FF
RETURN
PROC Main()
REAL scale
Graphics(8+16)
Color=1
COLOR1=$BA
COLOR2=$B2
Init()
ValR("30",scale)
Fern(scale)
RETURN |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #E | E | def makeMean(base, include, finish) {
return def mean(numbers) {
var count := 0
var acc := base
for x in numbers {
acc := include(acc, x)
count += 1
}
return finish(acc, count)
}
}
def RMS := makeMean(0, fn b,x { b+x**2 }, fn acc,n { (acc/n).sqrt() }) |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #EchoLisp | EchoLisp |
(define (rms xs)
(sqrt (// (for/sum ((x xs)) (* x x)) (length xs))))
(rms (range 1 11))
→ 6.2048368229954285
|
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Seed7 | Seed7 | $ include "seed7_05.s7i";
include "gethttp.s7i";
include "encoding.s7i";
const proc: main is func
local
var string: original is "";
var string: encoded is "";
begin
original := getHttp("rosettacode.org/favicon.ico");
encoded := toBase64(original);
writeln("Is the Rosetta Code icon the same (byte for byte) encoded then decoded: " <&
fromBase64(encoded) = original);
end func; |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #SenseTalk | SenseTalk |
put base64Decode ("VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuIC0tUGF1bCBSLkVocmxpY2g=")
|
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Ada | Ada | with Ada.Numerics.Discrete_Random;
with SDL.Video.Windows.Makers;
with SDL.Video.Renderers.Makers;
with SDL.Events.Events;
procedure Barnsley_Fern is
Iterations : constant := 1_000_000;
Width : constant := 500;
Height : constant := 750;
Scale : constant := 70.0;
type Percentage is range 1 .. 100;
package Random_Percentages is
new Ada.Numerics.Discrete_Random (Percentage);
Gen : Random_Percentages.Generator;
Window : SDL.Video.Windows.Window;
Renderer : SDL.Video.Renderers.Renderer;
Event : SDL.Events.Events.Events;
procedure Draw_Barnsley_Fern is
use type SDL.C.int;
subtype F1_Range is Percentage range Percentage'First .. Percentage'First;
subtype F2_Range is Percentage range F1_Range'Last + 1 .. F1_Range'Last + 85;
subtype F3_Range is Percentage range F2_Range'Last + 1 .. F2_Range'Last + 7;
subtype F4_Range is Percentage range F3_Range'Last + 1 .. F3_Range'Last + 7;
X0, Y0 : Float := 0.00;
X1, Y1 : Float;
begin
for I in 1 .. Iterations loop
case Random_Percentages.Random (Gen) is
when F1_Range =>
X1 := 0.00;
Y1 := 0.16 * Y0;
when F2_Range =>
X1 := 0.85 * X0 + 0.04 * Y0;
Y1 := -0.04 * X0 + 0.85 * Y0 + 1.60;
when F3_Range =>
X1 := 0.20 * X0 - 0.26 * Y0;
Y1 := 0.23 * X0 + 0.22 * Y0 + 1.60;
when F4_Range =>
X1 := -0.15 * X0 + 0.28 * Y0;
Y1 := 0.26 * X0 + 0.24 * Y0 + 0.44;
end case;
Renderer.Draw (Point => (X => Width / 2 + SDL.C.int (Scale * X1),
Y => Height - SDL.C.int (Scale * Y1)));
X0 := X1; Y0 := Y1;
end loop;
end Draw_Barnsley_Fern;
procedure Wait is
use type SDL.Events.Event_Types;
begin
loop
while SDL.Events.Events.Poll (Event) loop
if Event.Common.Event_Type = SDL.Events.Quit then
return;
end if;
end loop;
end loop;
end Wait;
begin
if not SDL.Initialise (Flags => SDL.Enable_Screen) then
return;
end if;
SDL.Video.Windows.Makers.Create (Win => Window,
Title => "Barnsley Fern",
Position => SDL.Natural_Coordinates'(X => 10, Y => 10),
Size => SDL.Positive_Sizes'(Width, Height),
Flags => 0);
SDL.Video.Renderers.Makers.Create (Renderer, Window.Get_Surface);
Renderer.Set_Draw_Colour ((0, 0, 0, 255));
Renderer.Fill (Rectangle => (0, 0, Width, Height));
Renderer.Set_Draw_Colour ((0, 220, 0, 255));
Random_Percentages.Reset (Gen);
Draw_Barnsley_Fern;
Window.Update_Surface;
Wait;
Window.Finalize;
SDL.Finalise;
end Barnsley_Fern; |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Elena | Elena | import extensions;
import system'routines;
import system'math;
extension op
{
get RootMeanSquare()
= (self.selectBy:(x => x * x).summarize(Real.new()) / self.Length).sqrt();
}
public program()
{
console.printLine(new Range(1, 10).RootMeanSquare)
} |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Sidef | Sidef | var data = <<'EOT'
VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY2
9tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g=
EOT
say data.decode_base64 |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Smalltalk | Smalltalk | data := '
VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY2
9tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g=
'.
data base64Decoded asString printCR. |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #ALGOL_68 | ALGOL 68 |
BEGIN
INT iterations = 300000;
LONG REAL scale x = 40, scale y = 40;
[0:400,-200:200]CHAR canvas;
LONG REAL x := 0, y := 0;
FOR i FROM 1 LWB canvas TO 1 UPB canvas DO
FOR j FROM 2 LWB canvas TO 2 UPB canvas DO
canvas[i,j] := "0"
OD OD;
canvas[0, 0] := "1";
TO iterations DO
REAL choice := random;
LONG REAL xn = x, yn = y;
IF choice < 0.01 THEN
x := 0;
y := 0.16 * yn
ELIF (choice -:= 0.01) < 0.85 THEN
x := 0.85 * xn + 0.04 * yn;
y := -0.04 * xn + 0.85 * yn + 1.6
ELIF (choice -:= 0.85) < 0.07 THEN
x := 0.2 * xn - 0.26 * yn;
y := 0.23 * xn + 0.22 * yn + 1.6
ELSE
x := -0.15 * xn + 0.28 * yn;
y := 0.26 * xn + 0.24 * yn + 0.44
FI;
INT px = SHORTEN ROUND (x * scale x),
py = SHORTEN ROUND (y * scale y);
IF px < 2 LWB canvas OR px > 2 UPB canvas OR
py < 1 LWB canvas OR py > 1 UPB canvas
THEN
print(("resize canvas. px=", px, ", py=", py, new line));
leave
FI;
canvas[py, px] := "1"
OD;
FILE f;
IF establish(f, "fern.pbm", stand out channel) /= 0 THEN
print("error creating file!"); leave
FI;
put(f, "P1"); new line(f);
put(f, (whole((2 UPB canvas) - (2 LWB canvas) + 1, 0), " ",
whole((1 UPB canvas) - (1 LWB canvas) + 1, 0), new line));
FOR i FROM 1 UPB canvas BY -1 TO 1 LWB canvas DO
put(f, canvas[i,]); new line(f)
OD;
close(f);
leave: SKIP
END
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Elixir | Elixir |
defmodule RC do
def root_mean_square(enum) do
enum
|> square
|> mean
|> :math.sqrt
end
defp mean(enum), do: Enum.sum(enum) / Enum.count(enum)
defp square(enum), do: (for x <- enum, do: x * x)
end
IO.puts RC.root_mean_square(1..10)
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Emacs_Lisp | Emacs Lisp | (defun rms (nums)
(sqrt (/ (apply '+ (mapcar (lambda (x) (* x x)) nums))
(float (length nums)))))
(rms (number-sequence 1 10)) |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Standard_ML | Standard ML | val debase64 = fn input =>
let
infix 2 Worb
infix 2 Wandb
fun (p Worb q ) = Word.orb (p,q);
fun (p Wandb q ) = Word.andb (p,q);
fun decode #"/" = 0wx3F
| decode #"+" = 0wx3E
| decode c = if Char.isDigit c then Word.fromInt (ord c) + 0wx04
else if Char.isLower c then Word.fromInt (ord c) - 0wx047
else if Char.isUpper c then Word.fromInt (ord c) - 0wx041
else 0wx00 ;
fun convert (a::b::c::d::_) =
let
val w = Word.<< (a,0wx12) Worb Word.<< (b,0wx0c) Worb Word.<< (c,0wx06) Worb d
in
[Word.>> (w,0wx10), Word.>> ((w Wandb 0wx00ff00),0wx08) , w Wandb 0wx0000ff ]
end
| convert _ = [] ;
fun decodeLst [] = []
| decodeLst ilist = ( convert o map decode )( List.take (ilist,4) ) @ decodeLst (List.drop (ilist,4))
in
String.implode ( List.map (chr o Word.toInt) ( decodeLst (String.explode input )) )
end handle Subscript => "Invalid code\n" ; |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Tcl | Tcl | package require tcl 8.6
set data VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g=
puts [binary decode base64 $data] |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Applesoft_BASIC | Applesoft BASIC | 100 LET YY(1) = .16
110 XX(2) = .85:XY(2) = .04
120 YX(2) = - .04:YY(2) = .85
130 LET Y(2) = 1.6
140 XX(3) = .20:XY(3) = - .26
150 YX(3) = .23:YY(3) = .22
160 LET Y(3) = 1.6
170 XX(4) = - .15:XY(4) = .28
180 YX(4) = .26:YY(4) = .24
190 LET Y(4) = .44
200 HGR :I = PEEK (49234)
210 HCOLOR= 1
220 LET X = 0:Y = 0
230 FOR I = 1 TO 100000
240 R = INT ( RND (1) * 100)
250 F = (R < 7) + (R < 14) + 2
260 F = F - (R = 99)
270 X = XX(F) * X + XY(F) * Y
280 Y = YX(F) * X + YY(F) * Y
290 Y = Y + Y(F)
300 X% = 62 + X * 27.9
320 Y% = 192 - Y * 19.1
330 HPLOT X% * 2 + 1,Y%
340 NEXT
|
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #BASIC256 | BASIC256 | # adjustable window altoght
# call the subroutine with the altoght you want
# it's possible to have a window that's large than your display
call barnsley(800)
end
subroutine barnsley(alto)
graphsize alto / 2, alto
color rgb(0, 255, 0)
f = alto / 10.6
c = alto / 4 - alto / 40
x = 0 : y = 0
for n = 1 to alto * 50
p = rand * 100
begin case
case p <= 1
nx = 0
ny = 0.16 * y
case p <= 8
nx = 0.2 * x - 0.26 * y
ny = 0.23 * x + 0.22 * y + 1.6
case p <= 15
nx = -0.15 * x + 0.28 * y
ny = 0.26 * x + 0.24 * y + 0.44
else
nx = 0.85 * x + 0.04 * y
ny = -0.04 * x + 0.85 * y + 1.6
end case
x = nx : y = ny
plot(c + x * f, alto - y * f)
next n
# remove comment (#) in next line to save window as .png file
# imgsave("Barnsley_fern.png")
end subroutine |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Erlang | Erlang | rms(Nums) ->
math:sqrt(lists:foldl(fun(E,S) -> S+E*E end, 0, Nums) / length(Nums)).
rms([1,2,3,4,5,6,7,8,9,10]). |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #ERRE | ERRE |
PROGRAM ROOT_MEAN_SQUARE
BEGIN
N=10
FOR I=1 TO N DO
S=S+I*I
END FOR
X=SQR(S/N)
PRINT("Root mean square is";X)
END PROGRAM
|
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Visual_Basic_.NET | Visual Basic .NET | Module Module1
Sub Main()
Dim data = "VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g="
Console.WriteLine(data)
Console.WriteLine()
Dim decoded = Text.Encoding.ASCII.GetString(Convert.FromBase64String(data))
Console.WriteLine(decoded)
End Sub
End Module |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Vlang | Vlang | import encoding.base64
fn main() {
msg := "Rosetta Code Base64 decode data task"
println("Original : $msg")
encoded := base64.encode_str(msg)
println("\nEncoded : $encoded")
decoded := base64.decode_str(encoded)
println("\nDecoded : $decoded")
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #BBC_BASIC | BBC BASIC | GCOL 2 : REM Green Graphics Color
X=0 : Y=0
FOR I%=1 TO 100000
R%=RND(100)
CASE TRUE OF
WHEN R% == 1 NewX= 0 : NewY= .16 * Y
WHEN R% < 9 NewX= .20 * X - .26 * Y : NewY= .23 * X + .22 * Y + 1.6
WHEN R% < 16 NewX=-.15 * X + .28 * Y : NewY= .26 * X + .24 * Y + .44
OTHERWISE NewX= .85 * X + .04 * Y : NewY=-.04 * X + .85 * Y + 1.6
ENDCASE
X=NewX : Y=NewY
PLOT 1000 + X * 130 , Y * 130
NEXT
END |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #C | C |
#include<graphics.h>
#include<stdlib.h>
#include<stdio.h>
#include<time.h>
void barnsleyFern(int windowWidth, unsigned long iter){
double x0=0,y0=0,x1,y1;
int diceThrow;
time_t t;
srand((unsigned)time(&t));
while(iter>0){
diceThrow = rand()%100;
if(diceThrow==0){
x1 = 0;
y1 = 0.16*y0;
}
else if(diceThrow>=1 && diceThrow<=7){
x1 = -0.15*x0 + 0.28*y0;
y1 = 0.26*x0 + 0.24*y0 + 0.44;
}
else if(diceThrow>=8 && diceThrow<=15){
x1 = 0.2*x0 - 0.26*y0;
y1 = 0.23*x0 + 0.22*y0 + 1.6;
}
else{
x1 = 0.85*x0 + 0.04*y0;
y1 = -0.04*x0 + 0.85*y0 + 1.6;
}
putpixel(30*x1 + windowWidth/2.0,30*y1,GREEN);
x0 = x1;
y0 = y1;
iter--;
}
}
int main()
{
unsigned long num;
printf("Enter number of iterations : ");
scanf("%ld",&num);
initwindow(500,500,"Barnsley Fern");
barnsleyFern(500,num);
getch();
closegraph();
return 0;
}
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Euphoria | Euphoria | function rms(sequence s)
atom sum
if length(s) = 0 then
return 0
end if
sum = 0
for i = 1 to length(s) do
sum += power(s[i],2)
end for
return sqrt(sum/length(s))
end function
constant s = {1,2,3,4,5,6,7,8,9,10}
? rms(s) |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #Wren | Wren | import "io" for Stdout
import "/fmt" for Conv, Fmt
import "/str" for Str
var alpha = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
var decode = Fn.new { |s|
if (s == "") return s
var d = ""
for (b in s) {
var ix = alpha.indexOf(b)
if (ix >= 0) d = d + Fmt.swrite("$06b", ix)
}
if (s.endsWith("==")) {
d = d[0..-5]
} else if (s.endsWith("=")){
d = d[0..-3]
}
var i = 0
while (i < d.count) {
var ch = String.fromByte(Conv.atoi(d[i..i+7], 2))
System.write(ch)
i = i + 8
}
Stdout.flush()
}
var s = "VG8gZXJyIGlzIGh1bWFuLCBidXQgdG8gcmVhbGx5IGZvdWwgdGhpbmdzIHVwIHlvdSBuZWVkIGEgY29tcHV0ZXIuCiAgICAtLSBQYXVsIFIuIEVocmxpY2g="
for (chunk in Str.chunks(s, 4)) decode.call(chunk)
System.print() |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #C.23 | C# | using System;
using System.Diagnostics;
using System.Drawing;
namespace RosettaBarnsleyFern
{
class Program
{
static void Main(string[] args)
{
const int w = 600;
const int h = 600;
var bm = new Bitmap(w, h);
var r = new Random();
double x = 0;
double y = 0;
for (int count = 0; count < 100000; count++)
{
bm.SetPixel((int)(300 + 58 * x), (int)(58 * y), Color.ForestGreen);
int roll = r.Next(100);
double xp = x;
if (roll < 1)
{
x = 0;
y = 0.16 * y;
} else if (roll < 86)
{
x = 0.85 * x + 0.04 * y;
y = -0.04 * xp + 0.85 * y + 1.6;
} else if (roll < 93)
{
x = 0.2 * x - 0.26 * y;
y = 0.23 * xp + 0.22 * y + 1.6;
} else
{
x = -0.15 * x + 0.28 * y;
y = 0.26 * xp + 0.24 * y + 0.44;
}
}
const string filename = "Fern.png";
bm.Save(filename);
Process.Start(filename);
}
}
} |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Excel | Excel |
=SQRT(SUMSQ($A1:$A10)/COUNT($A1:$A10))
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #F.23 | F# | let RMS (x:float list) : float = List.map (fun y -> y**2.0) x |> List.average |> System.Math.Sqrt
let res = RMS [1.0..10.0] |
http://rosettacode.org/wiki/Base64_decode_data | Base64 decode data | See Base64 encode data.
Now write a program that takes the output of the Base64 encode data task as input and regenerate the original file.
When working on the VBA implementation I found several 'solutions' on the net, including one from the software maker himself, that showed output with incorrect padding. Obviously with incorrect padding in the output you can not decode correctly to the original file again.
| #zkl | zkl | var [const] MsgHash=Import("zklMsgHash"), Curl=Import("zklCurl");
icon:=Curl().get("http://rosettacode.org/favicon.ico"); //-->(Data(4,331),693,0)
icon=icon[0][icon[1],*]; // remove header
iconEncoded:=MsgHash.base64encode(icon);
iconDecoded:=MsgHash.base64decode(iconEncoded);
File("rosettaCodeIcon.ico","wb").write(iconDecoded); # eyeball checking says good
println("Is the Rosetta Code icon the same (byte for byte) encoded then decoded: ",
icon==iconDecoded); |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #C.2B.2B | C++ |
#include <windows.h>
#include <ctime>
#include <string>
const int BMP_SIZE = 600, ITERATIONS = static_cast<int>( 15e5 );
class myBitmap {
public:
myBitmap() : pen( NULL ), brush( NULL ), clr( 0 ), wid( 1 ) {}
~myBitmap() {
DeleteObject( pen ); DeleteObject( brush );
DeleteDC( hdc ); DeleteObject( bmp );
}
bool create( int w, int h ) {
BITMAPINFO bi;
ZeroMemory( &bi, sizeof( bi ) );
bi.bmiHeader.biSize = sizeof( bi.bmiHeader );
bi.bmiHeader.biBitCount = sizeof( DWORD ) * 8;
bi.bmiHeader.biCompression = BI_RGB;
bi.bmiHeader.biPlanes = 1;
bi.bmiHeader.biWidth = w;
bi.bmiHeader.biHeight = -h;
HDC dc = GetDC( GetConsoleWindow() );
bmp = CreateDIBSection( dc, &bi, DIB_RGB_COLORS, &pBits, NULL, 0 );
if( !bmp ) return false;
hdc = CreateCompatibleDC( dc );
SelectObject( hdc, bmp );
ReleaseDC( GetConsoleWindow(), dc );
width = w; height = h;
return true;
}
void clear( BYTE clr = 0 ) {
memset( pBits, clr, width * height * sizeof( DWORD ) );
}
void setBrushColor( DWORD bClr ) {
if( brush ) DeleteObject( brush );
brush = CreateSolidBrush( bClr );
SelectObject( hdc, brush );
}
void setPenColor( DWORD c ) {
clr = c; createPen();
}
void setPenWidth( int w ) {
wid = w; createPen();
}
void saveBitmap( std::string path ) {
BITMAPFILEHEADER fileheader;
BITMAPINFO infoheader;
BITMAP bitmap;
DWORD wb;
GetObject( bmp, sizeof( bitmap ), &bitmap );
DWORD* dwpBits = new DWORD[bitmap.bmWidth * bitmap.bmHeight];
ZeroMemory( dwpBits, bitmap.bmWidth * bitmap.bmHeight * sizeof( DWORD ) );
ZeroMemory( &infoheader, sizeof( BITMAPINFO ) );
ZeroMemory( &fileheader, sizeof( BITMAPFILEHEADER ) );
infoheader.bmiHeader.biBitCount = sizeof( DWORD ) * 8;
infoheader.bmiHeader.biCompression = BI_RGB;
infoheader.bmiHeader.biPlanes = 1;
infoheader.bmiHeader.biSize = sizeof( infoheader.bmiHeader );
infoheader.bmiHeader.biHeight = bitmap.bmHeight;
infoheader.bmiHeader.biWidth = bitmap.bmWidth;
infoheader.bmiHeader.biSizeImage = bitmap.bmWidth * bitmap.bmHeight * sizeof( DWORD );
fileheader.bfType = 0x4D42;
fileheader.bfOffBits = sizeof( infoheader.bmiHeader ) + sizeof( BITMAPFILEHEADER );
fileheader.bfSize = fileheader.bfOffBits + infoheader.bmiHeader.biSizeImage;
GetDIBits( hdc, bmp, 0, height, ( LPVOID )dwpBits, &infoheader, DIB_RGB_COLORS );
HANDLE file = CreateFile( path.c_str(), GENERIC_WRITE, 0, NULL, CREATE_ALWAYS,
FILE_ATTRIBUTE_NORMAL, NULL );
WriteFile( file, &fileheader, sizeof( BITMAPFILEHEADER ), &wb, NULL );
WriteFile( file, &infoheader.bmiHeader, sizeof( infoheader.bmiHeader ), &wb, NULL );
WriteFile( file, dwpBits, bitmap.bmWidth * bitmap.bmHeight * 4, &wb, NULL );
CloseHandle( file );
delete [] dwpBits;
}
HDC getDC() const { return hdc; }
int getWidth() const { return width; }
int getHeight() const { return height; }
private:
void createPen() {
if( pen ) DeleteObject( pen );
pen = CreatePen( PS_SOLID, wid, clr );
SelectObject( hdc, pen );
}
HBITMAP bmp; HDC hdc;
HPEN pen; HBRUSH brush;
void *pBits; int width, height, wid;
DWORD clr;
};
class fern {
public:
void draw() {
bmp.create( BMP_SIZE, BMP_SIZE );
float x = 0, y = 0; HDC dc = bmp.getDC();
int hs = BMP_SIZE >> 1;
for( int f = 0; f < ITERATIONS; f++ ) {
SetPixel( dc, hs + static_cast<int>( x * 55.f ),
BMP_SIZE - 15 - static_cast<int>( y * 55.f ),
RGB( static_cast<int>( rnd() * 80.f ) + 20,
static_cast<int>( rnd() * 128.f ) + 128,
static_cast<int>( rnd() * 80.f ) + 30 ) );
getXY( x, y );
}
bmp.saveBitmap( "./bf.bmp" );
}
private:
void getXY( float& x, float& y ) {
float g, xl, yl;
g = rnd();
if( g < .01f ) { xl = 0; yl = .16f * y; }
else if( g < .07f ) {
xl = .2f * x - .26f * y;
yl = .23f * x + .22f * y + 1.6f;
} else if( g < .14f ) {
xl = -.15f * x + .28f * y;
yl = .26f * x + .24f * y + .44f;
} else {
xl = .85f * x + .04f * y;
yl = -.04f * x + .85f * y + 1.6f;
}
x = xl; y = yl;
}
float rnd() {
return static_cast<float>( rand() ) / static_cast<float>( RAND_MAX );
}
myBitmap bmp;
};
int main( int argc, char* argv[]) {
srand( static_cast<unsigned>( time( 0 ) ) );
fern f; f.draw(); return 0;
}
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Factor | Factor | : root-mean-square ( seq -- mean )
[ [ sq ] map-sum ] [ length ] bi / sqrt ; |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Fantom | Fantom | class Main
{
static Float averageRms (Float[] nums)
{
if (nums.size == 0) return 0.0f
Float sum := 0f
nums.each { sum += it * it }
return (sum / nums.size.toFloat).sqrt
}
public static Void main ()
{
a := [1f,2f,3f,4f,5f,6f,7f,8f,9f,10f]
echo ("RMS Average of $a is: " + averageRms(a))
}
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Common_Lisp | Common Lisp | (defpackage #:barnsley-fern
(:use #:cl
#:opticl))
(in-package #:barnsley-fern)
(defparameter *width* 800)
(defparameter *height* 800)
(defparameter *factor* (/ *height* 13))
(defparameter *x-offset* (/ *width* 2))
(defparameter *y-offset* (/ *height* 10))
(defun f1 (x y)
(declare (ignore x))
(values 0 (* 0.16 y)))
(defun f2 (x y)
(values (+ (* 0.85 x) (* 0.04 y))
(+ (* -0.04 x) (* 0.85 y) 1.6)))
(defun f3 (x y)
(values (+ (* 0.2 x) (* -0.26 y))
(+ (* 0.23 x) (* 0.22 y) 1.6)))
(defun f4 (x y)
(values (+ (* -0.15 x) (* 0.28 y))
(+ (* 0.26 x) (* 0.24 y) 0.44)))
(defun choose-transform ()
(let ((r (random 1.0)))
(cond ((< r 0.01) #'f1)
((< r 0.86) #'f2)
((< r 0.93) #'f3)
(t #'f4))))
(defun set-pixel (image x y)
(let ((%x (round (+ (* *factor* x) *x-offset*)))
(%y (round (- *height* (* *factor* y) *y-offset*))))
(setf (pixel image %y %x) (values 0 255 0))))
(defun fern (filespec &optional (iterations 10000000))
(let ((image (make-8-bit-rgb-image *height* *width* :initial-element 0))
(x 0)
(y 0))
(dotimes (i iterations)
(set-pixel image x y)
(multiple-value-setq (x y) (funcall (choose-transform) x y)))
(write-png-file filespec image))) |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Forth | Forth | : rms ( faddr len -- frms )
dup >r 0e
floats bounds do
i f@ fdup f* f+
float +loop
r> s>f f/ fsqrt ;
create test 1e f, 2e f, 3e f, 4e f, 5e f, 6e f, 7e f, 8e f, 9e f, 10e f,
test 10 rms f. \ 6.20483682299543 |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Fortran | Fortran | print *,sqrt( sum(x**2)/size(x) ) |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #D | D | #!/usr/bin/env dub
/+ dub.sdl:
dependency "dlib" version="~>0.21.0"
+/
import std.random;
import dlib.image;
void main()
{
enum WIDTH = 640;
enum HEIGHT = 640;
enum ITERATIONS = 2E6;
float x = 0.0f;
float y = 0.0f;
auto rng = Random(unpredictableSeed);
auto color = Color4f(0.0f, 1.0f, 0.0f);
auto img = image(WIDTH, HEIGHT);
foreach (_; 0..ITERATIONS)
{
auto r = uniform(0, 101, rng);
if (r <= 1)
{
x = 0.0;
y = 0.16 * y;
}
else
{
if (r <= 8)
{
x = 0.20 * x - 0.26 * y;
y = 0.23 * x + 0.22 * y + 1.60;
}
else
{
if (r <= 15)
{
x = -0.15 * x + 0.28 * y;
y = 0.26 * x + 0.24 * y + 0.44;
}
else
{
x = 0.85 * x + 0.04 * y;
y = -0.04 * x + 0.85 * y + 1.6;
}
}
}
auto X = cast(int) (WIDTH / 2.0 + x * 60);
auto Y = HEIGHT - cast(int)(y * 60);
img[X, Y] = color;
}
img.saveImage(`barnsley_dlib.png`);
} |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #FreeBASIC | FreeBASIC |
' FB 1.05.0 Win64
Function QuadraticMean(array() As Double) As Double
Dim length As Integer = Ubound(array) - Lbound(array) + 1
Dim As Double sum = 0.0
For i As Integer = LBound(array) To UBound(array)
sum += array(i) * array(i)
Next
Return Sqr(sum/length)
End Function
Dim vector(1 To 10) As Double
For i As Integer = 1 To 10
vector(i) = i
Next
Print "Quadratic mean (or RMS) is :"; QuadraticMean(vector())
Print
Print "Press any key to quit the program"
Sleep
|
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Delphi | Delphi | unit Unit1;
interface
uses
Windows, SysUtils, Graphics, Forms, Controls, Classes, ExtCtrls;
type
TForm1 = class(TForm)
PaintBox1: TPaintBox;
procedure FormPaint(Sender: TObject);
private
{ Private declarations }
public
{ Public declarations }
end;
var
Form1: TForm1;
implementation
{$R *.dfm}
procedure CreateFern(const w, h: integer);
var r, x, y: double;
tmpx, tmpy: double;
i: integer;
begin
x := 0;
y := 0;
randomize();
for i := 0 to 200000 do begin
r := random(100000000) / 99999989;
if r <= 0.01 then begin
tmpx := 0;
tmpy := 0.16 * y;
end
else if r <= 0.08 then begin
tmpx := 0.2 * x - 0.26 * y;
tmpy := 0.23 * x + 0.22 * y + 1.6;
end
else if r <= 0.15 then begin
tmpx := -0.15 * x + 0.28 * y;
tmpy := 0.26 * x + 0.24 * y + 0.44;
end
else begin
tmpx := 0.85 * x + 0.04 * y;
tmpy := -0.04 * x + 0.85 * y + 1.6;
end;
x := tmpx;
y := tmpy;
Form1.PaintBox1.Canvas.Pixels[round(w / 2 + x * w / 11), round(h - y * h / 11)] := clGreen;
end;
end;
procedure TForm1.FormPaint(Sender: TObject);
begin
CreateFern(Form1.ClientWidth, Form1.ClientHeight);
end;
end. |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Futhark | Futhark |
import "futlib/math"
fun main(as: [n]f64): f64 =
f64.sqrt ((reduce (+) 0.0 (map (**2.0) as)) / f64(n))
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #GEORGE | GEORGE |
1, 10 rep (i)
i i | (v) ;
0
1, 10 rep (i)
i dup mult +
]
10 div
sqrt
print
|
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #EasyLang | EasyLang | color 060
for i range 200000
r = randomf
if r < 0.01
nx = 0
ny = 0.16 * y
elif r < 0.08
nx = 0.2 * x - 0.26 * y
ny = 0.23 * x + 0.22 * y + 1.6
elif r < 0.15
nx = -0.15 * x + 0.28 * y
ny = 0.26 * x + 0.24 * y + 0.44
else
nx = 0.85 * x + 0.04 * y
ny = -0.04 * x + 0.85 * y + 1.6
.
x = nx
y = ny
move 50 + x * 15 100 - y * 10
rect 0.3 0.3
. |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Emacs_Lisp | Emacs Lisp | ; Barnsley fern
(defun make-array (size)
"Create an empty array with size*size elements."
(setq m-array (make-vector size nil))
(dotimes (i size)
(setf (aref m-array i) (make-vector size 0)))
m-array)
(defun barnsley-next (p)
"Return the next Barnsley fern coordinates."
(let ((r (random 100))
(x (car p))
(y (cdr p)))
(cond ((< r 2) (setq nx 0) (setq ny (* 0.16 y)))
((< r 9) (setq nx (- (* 0.2 x) (* 0.26 y)))
(setq ny (+ 1.6 (* 0.23 x) (* 0.22 y))))
((< r 16) (setq nx (+ (* -0.15 x) (* 0.28 y)))
(setq ny (+ 0.44 (* 0.26 x) (* 0.24 y))))
(t (setq nx (+ (* 0.85 x) (* 0.04 y)))
(setq ny (+ 1.6 (* -0.04 x) (* 0.85 y)))))
(cons nx ny)))
(defun barnsley-lines (arr size)
"Turn array into a string for XPM conversion."
(setq all "")
(dotimes (y size)
(setq line "")
(dotimes (x size)
(setq line (concat line (if (= (elt (elt arr y) x) 1) "*" "."))))
(setq all (concat all "\"" line "\",\n")))
all)
(defun barnsley-show (arr size)
"Convert size*size array to XPM image and show it."
(insert-image (create-image (concat (format "/* XPM */
static char * barnsley[] = {
\"%i %i 2 1\",
\". c #000000\",
\"* c #00ff00\"," size size)
(barnsley-lines arr size) "};") 'xpm t)))
(defun barnsley (size scale max-iter)
"Plot the Barnsley fern."
(let ((arr (make-array size))
(p (cons 0 0)))
(dotimes (it max-iter)
(setq p (barnsley-next p))
(setq x (round (+ (/ size 2) (* scale (car p)))))
(setq y (round (- size (* scale (cdr p)) 1)))
(setf (elt (elt arr y) x) 1))
(barnsley-show arr size)))
(barnsley 400 35 100000) |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Go | Go | package main
import (
"fmt"
"math"
)
func main() {
const n = 10
sum := 0.
for x := 1.; x <= n; x++ {
sum += x * x
}
fmt.Println(math.Sqrt(sum / n))
} |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Groovy | Groovy | def quadMean = { list ->
list == null \
? null \
: list.empty \
? 0 \
: ((list.collect { it*it }.sum()) / list.size()) ** 0.5
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #F.23 | F# |
open System.Drawing
let (|F1|F2|F3|F4|) r =
if r < 0.01 then F1
else if r < 0.08 then F3
else if r < 0.15 then F4
else F2
let barnsleyFernFunction (x, y) = function
| F1 -> (0.0, 0.16*y)
| F2 -> ((0.85*x + 0.04*y), (-0.04*x + 0.85*y + 1.6))
| F3 -> ((0.2*x - 0.26*y), (0.23*x + 0.22*y + 1.6))
| F4 -> ((-0.15*x + 0.28*y), (0.26*x + 0.24*y + 0.44))
let barnsleyFern () =
let rnd = System.Random()
(0.0, 0.0)
|> Seq.unfold (fun point -> Some (point, barnsleyFernFunction point (rnd.NextDouble())))
let run width height =
let emptyBitmap = new Bitmap(int width,int height)
let bitmap =
barnsleyFern ()
|> Seq.take 250000 // calculate points
|> Seq.map (fun (x,y) -> (int (width/2.0+(width*x/11.0)), int (height-(height*y/11.0)))) // transform to pixels
|> Seq.fold (fun (b:Bitmap) (x,y) -> b.SetPixel(x-1,y-1,Color.ForestGreen); b) emptyBitmap // add pixels to bitmap
bitmap.Save("BFFsharp.png")
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Haskell | Haskell | main = print $ mean 2 [1 .. 10] |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #HicEst | HicEst | sum = 0
DO i = 1, 10
sum = sum + i^2
ENDDO
WRITE(ClipBoard) "RMS(1..10) = ", (sum/10)^0.5 |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #11l | 11l | T SMA
[Float] data
sum = 0.0
index = 0
n_filled = 0
Int period
F (period)
.period = period
.data = [0.0] * period
F add(v)
.sum += v - .data[.index]
.data[.index] = v
.index = (.index + 1) % .period
.n_filled = min(.period, .n_filled + 1)
R .sum / .n_filled
V sma3 = SMA(3)
V sma5 = SMA(5)
L(e) [1, 2, 3, 4, 5, 5, 4, 3, 2, 1]
print(‘Added #., sma(3) = #.6, sma(5) = #.6’.format(e, sma3.add(e), sma5.add(e))) |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Forth | Forth |
s" SDL2" add-lib
\c #include <SDL2/SDL.h>
c-function sdl-init SDL_Init n -- n
c-function sdl-quit SDL_Quit -- void
c-function sdl-createwindow SDL_CreateWindow a n n n n n -- a
c-function sdl-createrenderer SDL_CreateRenderer a n n -- a
c-function sdl-setdrawcolor SDL_SetRenderDrawColor a n n n n -- n
c-function sdl-drawpoint SDL_RenderDrawPoint a n n -- n
c-function sdl-renderpresent SDL_RenderPresent a -- void
c-function sdl-delay SDL_Delay n -- void
require random.fs
0 value window
0 value renderer
variable x
variable y
: initFern ( -- )
$20 sdl-init drop
s\" Rosetta Task : Barnsley fern\x0" drop 0 0 1000 1000 $0 sdl-createwindow to window
window -1 $2 sdl-createrenderer to renderer
renderer 0 255 0 255 sdl-setdrawcolor drop
;
create coefficients
0 , 0 , 0 , 160 , 0 , \ 1% of the time - f1
200 , -260 , 230 , 220 , 1600 , \ 7% of the time - f3
-150 , 280 , 260 , 240 , 440 , \ 7% of the time - f4
850 , 40 , -40 , 850 , 1600 , \ 85% of the time - f2
: nextcoeff ( n -- n+1 coeff ) coefficients over cells + @ swap 1+ swap ;
: transformation ( n -- )
nextcoeff x @ * swap nextcoeff y @ * rot + 1000 / swap
nextcoeff x @ * swap nextcoeff y @ * rot + 1000 / swap nextcoeff rot + y ! drop
x ! \ x shall be modified after y calculation
;
: randomchoice ( -- index )
100 random
dup 0 > swap
dup 7 > swap
dup 14 > swap drop
+ + negate 5 *
;
: fern
initFern
20000 0 do
randomchoice transformation
renderer x @ 10 / 500 + y @ 10 / sdl-drawpoint drop
loop
renderer sdl-renderpresent
5000 sdl-delay
sdl-quit
;
fern |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Icon_and_Unicon | Icon and Unicon | procedure main()
every put(x := [], 1 to 10)
writes("x := [ "); every writes(!x," "); write("]")
write("Quadratic mean:",q := qmean!x)
end |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #360_Assembly | 360 Assembly | * Averages/Simple moving average 26/08/2015
AVGSMA CSECT
USING AVGSMA,R12
LR R12,R15
ST R14,SAVER14
ZAP II,=P'0' ii=0
LA R7,1
LH R3,NA
SRA R3,1 na/2
LOOPA CR R7,R3 do i=1 to na/2
BH ELOOPA
AP II,=P'1000' ii=ii+1000
LR R1,R7 i
MH R1,=H'6'
LA R4,A-6(R1)
MVC 0(6,R4),II a(i)=ii
LH R1,NA na
SR R1,R7 -i
MH R1,=H'6'
LA R4,A(R1)
MVC 0(6,R4),II a(na+1-i)=ii
LA R7,1(R7)
B LOOPA
ELOOPA XPRNT =CL30' n sma3 sma5 ',30
XPRNT =CL30' ----- ----------- -----------',30
LA R7,1 i=1
LOOP CH R7,NA do i=1 to na
BH RETURN
STH R7,N n=i
XDECO R7,C i
MVC BUF+1(5),C+7
MVC P,=H'3' p=3
BAL R14,SMA
MVC C(13),EDMASK
ED C(13),SS sma(3,i)
MVC BUF+7(11),C+2
MVC P,=H'5' p=5
BAL R14,SMA
MVC C(13),EDMASK
ED C(13),SS sma(5,i)
MVC BUF+19(11),C+2
XPRNT BUF,30 output i,sma3,sma5
LA R7,1(R7)
B LOOP
* ***** sub sma(p,n) returns(PL6)
SMA LH R5,N
SH R5,P
A R5,=F'1' ia=n-p+1
C R5,=F'1'
BH OKIA
LA R5,1 ia=1
OKIA LH R6,NA ib=na
CH R6,N
BL OKIB
LH R6,N ib=n
OKIB ZAP II,=P'0' ii=0
ZAP SS,=P'0' ss=0
LR R3,R5 k=ia
LOOPK CR R3,R6 do k=ia to ib
BH ELOOPK
AP II,=P'1' ii=ii+1
LR R1,R3
MH R1,=H'6'
LA R4,A-6(R1)
MVC C(6),0(R4) ss=ss+a(k)
AP SS,C(6)
LA R3,1(R3)
B LOOPK
ELOOPK ZAP C,SS
DP C,II
ZAP SS,C(10) ss=ss/ii
BR R14
RETURN L R14,SAVER14 restore caller address
XR R15,R15
BR R14
SAVER14 DS F
NN EQU 10
NA DC AL2(NN)
A DS (NN)PL6
II DS PL6
SS DS PL6
P DS H
N DS H
C DS CL16
BUF DC CL30' ' buffer
EDMASK DC X'4020202020202021204B202020' CL13
YREGS
END AVGSMA |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Fortran | Fortran |
!Generates an output file "plot.dat" that contains the x and y coordinates
!for a scatter plot that can be visualized with say, GNUPlot
program BarnsleyFern
implicit none
double precision :: p(4), a(4), b(4), c(4), d(4), e(4), f(4), trx, try, prob
integer :: itermax, i
!The probabilites and coefficients can be modified to generate other
!fractal ferns, e.g. http://www.home.aone.net.au/~byzantium/ferns/fractal.html
!probabilities
p(1) = 0.01; p(2) = 0.85; p(3) = 0.07; p(4) = 0.07
!coefficients
a(1) = 0.00; a(2) = 0.85; a(3) = 0.20; a(4) = -0.15
b(1) = 0.00; b(2) = 0.04; b(3) = -0.26; b(4) = 0.28
c(1) = 0.00; c(2) = -0.04; c(3) = 0.23; c(4) = 0.26
d(1) = 0.16; d(2) = 0.85; d(3) = 0.22; d(4) = 0.24
e(1) = 0.00; e(2) = 0.00; e(3) = 0.00; e(4) = 0.00
f(1) = 0.00; f(2) = 1.60; f(3) = 1.60; f(4) = 0.44
itermax = 100000
trx = 0.0D0
try = 0.0D0
open(1, file="plot.dat")
write(1,*) "#X #Y"
write(1,'(2F10.5)') trx, try
do i = 1, itermax
call random_number(prob)
if (prob < p(1)) then
trx = a(1) * trx + b(1) * try + e(1)
try = c(1) * trx + d(1) * try + f(1)
else if(prob < (p(1) + p(2))) then
trx = a(2) * trx + b(2) * try + e(2)
try = c(2) * trx + d(2) * try + f(2)
else if ( prob < (p(1) + p(2) + p(3))) then
trx = a(3) * trx + b(3) * try + e(3)
try = c(3) * trx + d(3) * try + f(3)
else
trx = a(4) * trx + b(4) * try + e(4)
try = c(4) * trx + d(4) * try + f(4)
end if
write(1,'(2F10.5)') trx, try
end do
close(1)
end program BarnsleyFern
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Io | Io | rms := method (figs, (figs map(** 2) reduce(+) / figs size) sqrt)
rms( Range 1 to(10) asList ) println |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #J | J | rms=: (+/ % #)&.:*: |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Ada | Ada | generic
Max_Elements : Positive;
type Number is digits <>;
package Moving is
procedure Add_Number (N : Number);
function Moving_Average (N : Number) return Number;
function Get_Average return Number;
end Moving; |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #FreeBASIC | FreeBASIC | ' version 10-10-2016
' compile with: fbc -s console
Sub barnsley(height As UInteger)
Dim As Double x, y, xn, yn
Dim As Double f = height / 10.6
Dim As UInteger offset_x = height \ 4 - height \ 40
Dim As UInteger n, r
ScreenRes height \ 2, height, 32
For n = 1 To height * 50
r = Int(Rnd * 100) ' f from 0 to 99
Select Case As Const r
Case 0 To 84
xn = 0.85 * x + 0.04 * y
yn = -0.04 * x + 0.85 * y + 1.6
Case 85 To 91
xn = 0.2 * x - 0.26 * y
yn = 0.23 * x + 0.22 * y + 1.6
Case 92 To 98
xn = -0.15 * x + 0.28 * y
yn = 0.26 * x + 0.24 * y + 0.44
Case Else
xn = 0
yn = 0.16 * y
End Select
x = xn : y = yn
PSet( offset_x + x * f, height - y * f), RGB(0, 255, 0)
Next
' remove comment (') in next line to save window as .bmp file
' BSave "barnsley_fern_" + Str(height) + ".bmp", 0
End Sub
' ------=< MAIN >=------
' adjustable window height
' call the subroutine with the height you want
' it's possible to have a window that's large than your display
barnsley(800)
' empty keyboard buffer
While Inkey <> "" : Wend
Windowtitle "hit any key to end program"
Sleep
End |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Java | Java | public class RootMeanSquare {
public static double rootMeanSquare(double... nums) {
double sum = 0.0;
for (double num : nums)
sum += num * num;
return Math.sqrt(sum / nums.length);
}
public static void main(String[] args) {
double[] nums = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0};
System.out.println("The RMS of the numbers from 1 to 10 is " + rootMeanSquare(nums));
}
} |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #JavaScript | JavaScript | function root_mean_square(ary) {
var sum_of_squares = ary.reduce(function(s,x) {return (s + x*x)}, 0);
return Math.sqrt(sum_of_squares / ary.length);
}
print( root_mean_square([1,2,3,4,5,6,7,8,9,10]) ); // ==> 6.2048368229954285 |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #ALGOL_68 | ALGOL 68 | MODE SMAOBJ = STRUCT(
LONG REAL sma,
LONG REAL sum,
INT period,
REF[]LONG REAL values,
INT lv
);
MODE SMARESULT = UNION (
REF SMAOBJ # handle #,
LONG REAL # sma #,
REF[]LONG REAL # values #
);
MODE SMANEW = INT,
SMAFREE = STRUCT(REF SMAOBJ free obj),
SMAVALUES = STRUCT(REF SMAOBJ values obj),
SMAADD = STRUCT(REF SMAOBJ add obj, LONG REAL v),
SMAMEAN = STRUCT(REF SMAOBJ mean obj, REF[]LONG REAL v);
MODE ACTION = UNION ( SMANEW, SMAFREE, SMAVALUES, SMAADD, SMAMEAN );
PROC sma = ([]ACTION action)SMARESULT:
(
SMARESULT result;
REF SMAOBJ obj;
LONG REAL v;
FOR i FROM LWB action TO UPB action DO
CASE action[i] IN
(SMANEW period):( # args: INT period #
HEAP SMAOBJ handle;
sma OF handle := 0.0;
period OF handle := period;
values OF handle := HEAP [period OF handle]LONG REAL;
lv OF handle := 0;
sum OF handle := 0.0;
result := handle
),
(SMAFREE args):( # args: REF SMAOBJ free obj #
free obj OF args := REF SMAOBJ(NIL) # let the garbage collector do it's job #
),
(SMAVALUES args):( # args: REF SMAOBJ values obj #
result := values OF values obj OF args
),
(SMAMEAN args):( # args: REF SMAOBJ mean obj #
result := sma OF mean obj OF args
),
(SMAADD args):( # args: REF SMAOBJ add obj, LONG REAL v #
obj := add obj OF args;
v := v OF args;
IF lv OF obj < period OF obj THEN
(values OF obj)[lv OF obj+:=1] := v;
sum OF obj +:= v;
sma OF obj := sum OF obj / lv OF obj
ELSE
sum OF obj -:= (values OF obj)[ 1+ lv OF obj MOD period OF obj];
sum OF obj +:= v;
sma OF obj := sum OF obj / period OF obj;
(values OF obj)[ 1+ lv OF obj MOD period OF obj ] := v; lv OF obj+:=1
FI;
result := sma OF obj
)
OUT
SKIP
ESAC
OD;
result
);
[]LONG REAL v = ( 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 );
main: (
INT i;
REF SMAOBJ h3 := ( sma(SMANEW(3)) | (REF SMAOBJ obj):obj );
REF SMAOBJ h5 := ( sma(SMANEW(5)) | (REF SMAOBJ obj):obj );
FOR i FROM LWB v TO UPB v DO
printf(($"next number "g(0,6)", SMA_3 = "g(0,6)", SMA_5 = "g(0,6)l$,
v[i], (sma(SMAADD(h3, v[i]))|(LONG REAL r):r), ( sma(SMAADD(h5, v[i])) | (LONG REAL r):r )
))
OD#;
sma(SMAFREE(h3));
sma(SMAFREE(h5))
#
) |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Frink | Frink |
g = new graphics
g.backgroundColor[0,0,0] // black
g.color[0,0.5,0] // green
x = 0
y = 0
for i = 1 to 100000
{
g.fillEllipseCenter[x*10,y*-10,0.25,0.25]
z = random[1, 100]
if z == 1
{
xn = 0
yn = 0.16 * y
}
if z >= 2 and z <= 86
{
xn = 0.85 * x + 0.04 * y
yn = -0.04 * x + 0.85 * y + 1.6
}
if z >= 87 and z <= 93
{
xn = 0.2 * x - 0.26 * y
yn = 0.23 * x + 0.22 * y + 1.6
}
if z >= 94 and z <= 100
{
xn = -0.15 * x + 0.28 * y
yn = 0.26 * x + 0.24 * y + 0.44
}
x = xn
y = yn
}
g.show[]
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #jq | jq | def rms: length as $length
| if $length == 0 then null
else map(. * .) | add | sqrt / $length
end ; |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Julia | Julia | sqrt(sum(A.^2.) / length(A)) |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #AutoHotkey | AutoHotkey | MsgBox % MovingAverage(5,3) ; 5, averaging length <- 3
MsgBox % MovingAverage(1) ; 3
MsgBox % MovingAverage(-3) ; 1
MsgBox % MovingAverage(8) ; 2
MsgBox % MovingAverage(7) ; 4
MovingAverage(x,len="") { ; for integers (faster)
Static
Static sum:=0, n:=0, m:=10 ; default averaging length = 10
If (len>"") ; non-blank 2nd parameter: set length, reset
sum := n := i := 0, m := len
If (n < m) ; until the buffer is not full
sum += x, n++ ; keep summing data
Else ; when buffer is full
sum += x-v%i% ; add new, subtract oldest
v%i% := x, i := mod(i+1,m) ; remember last m inputs, cycle insertion point
Return sum/n
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #F.C5.8Drmul.C3.A6 | Fōrmulæ |
# Put this into a new file 'fern.gmic' and invoke it from the command line, like this:
# $ gmic fern.gmic -barnsley_fern
barnsley_fern :
1024,2048
-skip {"
f1 = [ 0,0,0,0.16 ]; g1 = [ 0,0 ];
f2 = [ 0.2,-0.26,0.23,0.22 ]; g2 = [ 0,1.6 ];
f3 = [ -0.15,0.28,0.26,0.24 ]; g3 = [ 0,0.44 ];
f4 = [ 0.85,0.04,-0.04,0.85 ]; g4 = [ 0,1.6 ];
xy = [ 0,0 ];
for (n = 0, n<2e6, ++n,
r = u(100);
xy = r<=1?((f1**xy)+=g1):
r<=8?((f2**xy)+=g2):
r<=15?((f3**xy)+=g3):
((f4**xy)+=g4);
uv = xy*200 + [ 480,0 ];
uv[1] = h - uv[1];
I(uv) = 0.7*I(uv) + 0.3*255;
)"}
-r 40%,40%,1,1,2
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #K | K |
rms:{_sqrt (+/x^2)%#x}
rms 1+!10
6.204837
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Kotlin | Kotlin | // version 1.0.5-2
fun quadraticMean(vector: Array<Double>) : Double {
val sum = vector.sumByDouble { it * it }
return Math.sqrt(sum / vector.size)
}
fun main(args: Array<String>) {
val vector = Array(10, { (it + 1).toDouble() })
print("Quadratic mean of numbers 1 to 10 is ${quadraticMean(vector)}")
} |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #AWK | AWK | #!/usr/bin/awk -f
# Moving average over the first column of a data file
BEGIN {
P = 5;
}
{
x = $1;
i = NR % P;
MA += (x - Z[i]) / P;
Z[i] = x;
print MA;
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #G.27MIC | G'MIC |
# Put this into a new file 'fern.gmic' and invoke it from the command line, like this:
# $ gmic fern.gmic -barnsley_fern
barnsley_fern :
1024,2048
-skip {"
f1 = [ 0,0,0,0.16 ]; g1 = [ 0,0 ];
f2 = [ 0.2,-0.26,0.23,0.22 ]; g2 = [ 0,1.6 ];
f3 = [ -0.15,0.28,0.26,0.24 ]; g3 = [ 0,0.44 ];
f4 = [ 0.85,0.04,-0.04,0.85 ]; g4 = [ 0,1.6 ];
xy = [ 0,0 ];
for (n = 0, n<2e6, ++n,
r = u(100);
xy = r<=1?((f1**xy)+=g1):
r<=8?((f2**xy)+=g2):
r<=15?((f3**xy)+=g3):
((f4**xy)+=g4);
uv = xy*200 + [ 480,0 ];
uv[1] = h - uv[1];
I(uv) = 0.7*I(uv) + 0.3*255;
)"}
-r 40%,40%,1,1,2
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Lasso | Lasso | define rms(a::staticarray)::decimal => {
return math_sqrt((with n in #a sum #n*#n) / decimal(#a->size))
}
rms(generateSeries(1,10)->asStaticArray) |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Liberty_BASIC | Liberty BASIC | ' [RC] Averages/Root mean square
SourceList$ ="1 2 3 4 5 6 7 8 9 10"
' If saved as an array we'd have to have a flag for last data.
' LB has the very useful word$() to read from delimited strings.
' The default delimiter is a space character, " ".
SumOfSquares =0
n =0 ' This holds index to number, and counts number of data.
data$ ="666" ' temporary dummy to enter the loop.
while data$ <>"" ' we loop until no data left.
data$ =word$( SourceList$, n +1) ' first data, as a string
NewVal =val( data$) ' convert string to number
SumOfSquares =SumOfSquares +NewVal^2 ' add to existing sum of squares
n =n +1 ' increment number of data items found
wend
n =n -1
print "Supplied data was "; SourceList$
print "This contained "; n; " numbers."
print "R.M.S. value is "; ( SumOfSquares /n)^0.5
end |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #BBC_BASIC | BBC BASIC | MAXPERIOD = 10
FOR n = 1 TO 5
PRINT "Number = ";n TAB(12) " SMA3 = ";FNsma(n,3) TAB(30) " SMA5 = ";FNsma(n,5)
NEXT
FOR n = 5 TO 1 STEP -1
PRINT "Number = ";n TAB(12) " SMA3 = ";FNsma(n,3) TAB(30) " SMA5 = ";FNsma(n,5)
NEXT
END
DEF FNsma(number, period%)
PRIVATE nums(), accum(), index%(), window%()
DIM nums(MAXPERIOD,MAXPERIOD), accum(MAXPERIOD)
DIM index%(MAXPERIOD), window%(MAXPERIOD)
accum(period%) += number - nums(period%,index%(period%))
nums(period%,index%(period%)) = number
index%(period%) = (index%(period%) + 1) MOD period%
IF window%(period%)<period% window%(period%) += 1
= accum(period%) / window%(period%) |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #gnuplot | gnuplot |
## Barnsley fern fractal 2/17/17 aev
reset
fn="BarnsleyFernGnu"; clr='"green"';
ttl="Barnsley fern fractal"
dfn=fn.".dat"; ofn=fn.".png";
set terminal png font arial 12 size 640,640
set print dfn append
set output ofn
unset border; unset xtics; unset ytics; unset key;
set size square
set title ttl font "Arial:Bold,12"
n=100000; max=100; x=y=xw=yw=p=0;
randgp(top) = floor(rand(0)*top)
do for [i=1:n] {
p=randgp(max);
if (p==1) {xw=0;yw=0.16*y;}
if (1<p&&p<=8) {xw=0.2*x-0.26*y;yw=0.23*x+0.22*y+1.6;}
if (8<p&&p<=15) {xw=-0.15*x+0.28*y;yw=0.26*x+0.24*y+0.44;}
if (p>15) {xw=0.85*x+0.04*y;yw=-0.04*x+0.85*y+1.6;}
x=xw;y=yw; print x," ",y;
}
plot dfn using 1:2 with points pt 7 ps 0.5 lc @clr
set output
unset print
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Logo | Logo | to rms :v
output sqrt quotient (apply "sum map [? * ?] :v) count :v
end
show rms iseq 1 10 |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Lua | Lua | function sumsq(a, ...) return a and a^2 + sumsq(...) or 0 end
function rms(t) return (sumsq(unpack(t)) / #t)^.5 end
print(rms{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}) |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #BQN | BQN | SMA ← {(+´÷≠)¨(1↓𝕨↑↑𝕩)∾<˘𝕨↕𝕩}
v ← (⊢∾⌽)1+↕5
•Show 5 SMA v
SMA2 ← {
𝕊 size:
nums ← ⟨⟩
sum ← 0
{
nums ∾↩ 𝕩
gb ← {(≠nums)≤size ? 0 ; a←⊑nums, nums↩1↓nums, a}
sum +↩ 𝕩 - gb
sum ÷ ≠nums
}
}
fun ← SMA2 5
Fun¨ v |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Go | Go | package main
import (
"image"
"image/color"
"image/draw"
"image/png"
"log"
"math/rand"
"os"
)
// values from WP
const (
xMin = -2.1820
xMax = 2.6558
yMin = 0.
yMax = 9.9983
)
// parameters
var (
width = 200
n = int(1e6)
c = color.RGBA{34, 139, 34, 255} // forest green
)
func main() {
dx := xMax - xMin
dy := yMax - yMin
fw := float64(width)
fh := fw * dy / dx
height := int(fh)
r := image.Rect(0, 0, width, height)
img := image.NewRGBA(r)
draw.Draw(img, r, &image.Uniform{color.White}, image.ZP, draw.Src)
var x, y float64
plot := func() {
// transform computed float x, y to integer image coordinates
ix := int(fw * (x - xMin) / dx)
iy := int(fh * (yMax - y) / dy)
img.SetRGBA(ix, iy, c)
}
plot()
for i := 0; i < n; i++ {
switch s := rand.Intn(100); {
case s < 85:
x, y =
.85*x+.04*y,
-.04*x+.85*y+1.6
case s < 85+7:
x, y =
.2*x-.26*y,
.23*x+.22*y+1.6
case s < 85+7+7:
x, y =
-.15*x+.28*y,
.26*x+.24*y+.44
default:
x, y = 0, .16*y
}
plot()
}
// write img to png file
f, err := os.Create("bf.png")
if err != nil {
log.Fatal(err)
}
if err := png.Encode(f, img); err != nil {
log.Fatal(err)
}
} |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Maple | Maple | y := [ seq(1..10) ]:
RMS := proc( x )
return sqrt( Statistics:-Mean( x ^~ 2 ) );
end proc:
RMS( y );
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Mathematica_.2F_Wolfram_Language | Mathematica / Wolfram Language | RootMeanSquare@Range[10] |
http://rosettacode.org/wiki/Babbage_problem | Babbage problem |
Charles Babbage, looking ahead to the sorts of problems his Analytical Engine would be able to solve, gave this example:
What is the smallest positive integer whose square ends in the digits 269,696?
— Babbage, letter to Lord Bowden, 1837; see Hollingdale and Tootill, Electronic Computers, second edition, 1970, p. 125.
He thought the answer might be 99,736, whose square is 9,947,269,696; but he couldn't be certain.
Task[edit]
The task is to find out if Babbage had the right answer — and to do so, as far as your language allows it, in code that Babbage himself would have been able to read and understand.
As Babbage evidently solved the task with pencil and paper, a similar efficient solution is preferred.
For these purposes, Charles Babbage may be taken to be an intelligent person, familiar with mathematics and with the idea of a computer; he has written the first drafts of simple computer programmes in tabular form. [Babbage Archive Series L].
Motivation
The aim of the task is to write a program that is sufficiently clear and well-documented for such a person to be able to read it and be confident that it does indeed solve the specified problem.
| #11l | 11l | V n = 1
L n ^ 2 % 1000000 != 269696
n++
print(n) |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Bracmat | Bracmat | ( ( I
= buffer
. (new$=):?freshEmptyBuffer
&
' ( buffer avg
. ( avg
= L S n
. 0:?L:?S
& whl
' ( !arg:%?n ?arg
& !n+!S:?S
& 1+!L:?L
)
& (!L:0&0|!S*!L^-1)
)
& (buffer=$freshEmptyBuffer)
& !arg !(buffer.):?(buffer.)
& ( !(buffer.):?(buffer.) [($arg) ?
|
)
& avg$!(buffer.)
)
)
& ( pad
= len w
. @(!arg:? [?len)
& @(" ":? [!len ?w)
& !w !arg
)
& I$3:(=?sma3)
& I$5:(=?sma5)
& 1 2 3 4 5 5 4 3 2 1:?K
& whl
' ( !K:%?k ?K
& out
$ (str$(!k " - sma3:" pad$(sma3$!k) " sma5:" pad$(sma5$!k)))
)
); |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Groovy | Groovy | import javafx.animation.AnimationTimer
import javafx.application.Application
import javafx.scene.Group
import javafx.scene.Scene
import javafx.scene.image.ImageView
import javafx.scene.image.WritableImage
import javafx.scene.paint.Color
import javafx.stage.Stage
class BarnsleyFern extends Application {
@Override
void start(Stage primaryStage) {
primaryStage.title = 'Barnsley Fern'
primaryStage.scene = getScene()
primaryStage.show()
}
def getScene() {
def root = new Group()
def scene = new Scene(root, 640, 640)
def imageWriter = new WritableImage(640, 640)
def imageView = new ImageView(imageWriter)
root.children.add imageView
def pixelWriter = imageWriter.pixelWriter
def x = 0, y = 0
({
50.times {
def r = Math.random()
if (r <= 0.01) {
x = 0
y = 0.16 * y
} else if (r <= 0.08) {
x = 0.2 * x - 0.26 * y
y = 0.23 * x + 0.22 * y + 1.6
} else if (r <= 0.15) {
x = -0.15 * x + 0.28 * y
y = 0.26 * x + 0.24 * y + 0.44
} else {
x = 0.85 * x + 0.04 * y
y = -0.04 * x + 0.85 * y + 1.6
}
pixelWriter.setColor(Math.round(640 / 2 + x * 640 / 11) as Integer, Math.round(640 - y * 640 / 11) as Integer, Color.GREEN)
}
} as AnimationTimer).start()
scene
}
static void main(args) {
launch(BarnsleyFern)
}
}
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #MATLAB | MATLAB | function rms = quadraticMean(list)
rms = sqrt(mean(list.^2));
end |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Maxima | Maxima | L: makelist(i, i, 10)$
rms(L) := sqrt(lsum(x^2, x, L)/length(L))$
rms(L), numer; /* 6.204836822995429 */ |
http://rosettacode.org/wiki/Babbage_problem | Babbage problem |
Charles Babbage, looking ahead to the sorts of problems his Analytical Engine would be able to solve, gave this example:
What is the smallest positive integer whose square ends in the digits 269,696?
— Babbage, letter to Lord Bowden, 1837; see Hollingdale and Tootill, Electronic Computers, second edition, 1970, p. 125.
He thought the answer might be 99,736, whose square is 9,947,269,696; but he couldn't be certain.
Task[edit]
The task is to find out if Babbage had the right answer — and to do so, as far as your language allows it, in code that Babbage himself would have been able to read and understand.
As Babbage evidently solved the task with pencil and paper, a similar efficient solution is preferred.
For these purposes, Charles Babbage may be taken to be an intelligent person, familiar with mathematics and with the idea of a computer; he has written the first drafts of simple computer programmes in tabular form. [Babbage Archive Series L].
Motivation
The aim of the task is to write a program that is sufficiently clear and well-documented for such a person to be able to read it and be confident that it does indeed solve the specified problem.
| #360_Assembly | 360 Assembly |
* Find the lowest positive integer whose square ends in 269696
* The logic of the assembler program is simple :
* loop for i=524 step 2
* if (i*i modulo 1000000)=269696 then leave loop
* next i
* output 'Solution is: i=' i ' (i*i=' i*i ')'
BABBAGE CSECT beginning of the control section
USING BABBAGE,13 define the base register
B 72(15) skip savearea (72=18*4)
DC 17F'0' savearea (18 full words (17+1))
STM 14,12,12(13) prolog: save the caller registers
ST 13,4(15) prolog: link backwards
ST 15,8(13) prolog: link forwards
LR 13,15 prolog: establish addressability
LA 6,524 let register6 be i and load 524
LOOP LR 5,6 load register5 with i
MR 4,6 multiply register5 with i
LR 7,5 load register7 with the result i*i
D 4,=F'1000000' divide register5 with 1000000
C 4,=F'269696' compare the reminder with 269696
BE ENDLOOP if equal branch to ENDLOOP
LA 6,2(6) load register6 (i) with value i+2
B LOOP branch to LOOP
ENDLOOP XDECO 6,BUFFER+15 edit registrer6 (i)
XDECO 7,BUFFER+34 edit registrer7 (i squared)
XPRNT BUFFER,L'BUFFER print buffer
L 13,4(0,13) epilog: restore the caller savearea
LM 14,12,12(13) epilog: restore the caller registers
XR 15,15 epilog: set return code to 0
BR 14 epilog: branch to caller
BUFFER DC CL80'Solution is: i=............ (i*i=............)'
END BABBAGE end of the control section
|
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #Brat | Brat |
SMA = object.new
SMA.init = { period |
my.period = period
my.list = []
my.average = 0
}
SMA.prototype.add = { num |
true? my.list.length >= my.period
{ my.list.deq }
my.list << num
my.average = my.list.reduce(:+) / my.list.length
}
sma3 = SMA.new 3
sma5 = SMA.new 5
[1, 2, 3, 4, 5, 5, 4, 3, 2, 1].each { n |
p n, " - SMA3: ", sma3.add(n), " SMA5: ", sma5.add(n)
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #Haskell | Haskell | import Data.List (scanl')
import Diagrams.Backend.Rasterific.CmdLine
import Diagrams.Prelude
import System.Random
type Pt = (Double, Double)
-- Four affine transformations used to produce a Barnsley fern.
f1, f2, f3, f4 :: Pt -> Pt
f1 (x, y) = ( 0, 0.16 * y)
f2 (x, y) = ( 0.85 * x + 0.04 * y , -0.04 * x + 0.85 * y + 1.60)
f3 (x, y) = ( 0.20 * x - 0.26 * y , 0.23 * x + 0.22 * y + 1.60)
f4 (x, y) = (-0.15 * x + 0.28 * y , 0.26 * x + 0.24 * y + 0.44)
-- Given a random number in [0, 1) transform an initial point by a randomly
-- chosen function.
func :: Pt -> Double -> Pt
func p r | r < 0.01 = f1 p
| r < 0.86 = f2 p
| r < 0.93 = f3 p
| otherwise = f4 p
-- Using a sequence of uniformly distributed random numbers in [0, 1) return
-- the same number of points in the fern.
fern :: [Double] -> [Pt]
fern = scanl' func (0, 0)
-- Given a supply of random values and a count, generate a diagram of a fern
-- composed of that number of points.
drawFern :: [Double] -> Int -> Diagram B
drawFern rs n = frame 0.5 . diagramFrom . take n $ fern rs
where diagramFrom = flip atPoints (repeat dot) . map p2
dot = circle 0.005 # lc green
-- To generate a PNG image of a fern, call this program like:
--
-- fern -o fern.png -w 640 -h 640 50000
--
-- where the arguments specify the width, height and number of points in the
-- image.
main :: IO ()
main = do
rand <- getStdGen
mainWith $ drawFern (randomRs (0, 1) rand) |
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #MAXScript | MAXScript |
fn RMS arr =
(
local sumSquared = 0
for i in arr do sumSquared += i^2
return (sqrt (sumSquared/arr.count as float))
)
|
http://rosettacode.org/wiki/Averages/Root_mean_square | Averages/Root mean square | Task[edit]
Compute the Root mean square of the numbers 1..10.
The root mean square is also known by its initials RMS (or rms), and as the quadratic mean.
The RMS is calculated as the mean of the squares of the numbers, square-rooted:
x
r
m
s
=
x
1
2
+
x
2
2
+
⋯
+
x
n
2
n
.
{\displaystyle x_{\mathrm {rms} }={\sqrt {{{x_{1}}^{2}+{x_{2}}^{2}+\cdots +{x_{n}}^{2}} \over n}}.}
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #min | min | (dup *) :sq
('sq map sum) :sum-sq
(('sum-sq 'size) => cleave / sqrt) :rms
(1 2 3 4 5 6 7 8 9 10) rms puts! |
http://rosettacode.org/wiki/Babbage_problem | Babbage problem |
Charles Babbage, looking ahead to the sorts of problems his Analytical Engine would be able to solve, gave this example:
What is the smallest positive integer whose square ends in the digits 269,696?
— Babbage, letter to Lord Bowden, 1837; see Hollingdale and Tootill, Electronic Computers, second edition, 1970, p. 125.
He thought the answer might be 99,736, whose square is 9,947,269,696; but he couldn't be certain.
Task[edit]
The task is to find out if Babbage had the right answer — and to do so, as far as your language allows it, in code that Babbage himself would have been able to read and understand.
As Babbage evidently solved the task with pencil and paper, a similar efficient solution is preferred.
For these purposes, Charles Babbage may be taken to be an intelligent person, familiar with mathematics and with the idea of a computer; he has written the first drafts of simple computer programmes in tabular form. [Babbage Archive Series L].
Motivation
The aim of the task is to write a program that is sufficiently clear and well-documented for such a person to be able to read it and be confident that it does indeed solve the specified problem.
| #Ada | Ada | -- The program is written in the programming language Ada. The name "Ada"
-- has been chosen in honour of your friend,
-- Augusta Ada King-Noel, Countess of Lovelace (née Byron).
--
-- This is an program to search for the smallest integer X, such that
-- (X*X) mod 1_000_000 = 269_696.
--
-- In the Ada language, "*" represents the multiplication symbol, "mod" the
-- modulo reduction, and the underscore "_" after every third digit in
-- literals is supposed to simplify reading numbers for humans.
-- Everything written after "--" in a line is a comment for the human,
-- and will be ignored by the computer.
with Ada.Text_IO;
-- We need this to tell the computer how it will later output its result.
procedure Babbage_Problem is
-- We know that 99_736*99_736 is 9_947_269_696. This implies:
-- 1. The smallest X with X*X mod 1_000_000 = 269_696 is at most 99_736.
-- 2. The largest square X*X, which the program may have to deal with,
-- will be at most 9_947_269_69.
type Number is range 1 .. 99_736*99_736;
X: Number := 1;
-- X can store numbers between 1 and 99_736*99_736. Computations
-- involving X can handle intermediate results in that range.
-- Initially the value stored at X is 1.
-- When running the program, the value will become 2, 3, 4, etc.
begin
-- The program starts running.
-- The computer first squares X, then it truncates the square, such
-- that the result is a six-digit number.
-- Finally, the computer checks if this number is 269_696.
while not (((X*X) mod 1_000_000) = 269_696) loop
-- When the computer goes here, the number was not 269_696.
X := X+1;
-- So we replace X by X+1, and then go back and try again.
end loop;
-- When the computer eventually goes here, the number is 269_696.
-- E.e., the value stored at X is the value we are searching for.
-- We still have to print out this value.
Ada.Text_IO.Put_Line(Number'Image(X));
-- Number'Image(X) converts the value stored at X into a string of
-- printable characters (more specifically, of digits).
-- Ada.Text_IO.Put_Line(...) prints this string, for humans to read.
-- I did already run the program, and it did print out 25264.
end Babbage_Problem; |
http://rosettacode.org/wiki/Averages/Simple_moving_average | Averages/Simple moving average | Computing the simple moving average of a series of numbers.
Task[edit]
Create a stateful function/class/instance that takes a period and returns a routine that takes a number as argument and returns a simple moving average of its arguments so far.
Description
A simple moving average is a method for computing an average of a stream of numbers by only averaging the last P numbers from the stream, where P is known as the period.
It can be implemented by calling an initialing routine with P as its argument, I(P), which should then return a routine that when called with individual, successive members of a stream of numbers, computes the mean of (up to), the last P of them, lets call this SMA().
The word stateful in the task description refers to the need for SMA() to remember certain information between calls to it:
The period, P
An ordered container of at least the last P numbers from each of its individual calls.
Stateful also means that successive calls to I(), the initializer, should return separate routines that do not share saved state so they could be used on two independent streams of data.
Pseudo-code for an implementation of SMA is:
function SMA(number: N):
stateful integer: P
stateful list: stream
number: average
stream.append_last(N)
if stream.length() > P:
# Only average the last P elements of the stream
stream.delete_first()
if stream.length() == 0:
average = 0
else:
average = sum( stream.values() ) / stream.length()
return average
See also
Tasks for calculating statistical measures
in one go
moving (sliding window)
moving (cumulative)
Mean
Arithmetic
Statistics/Basic
Averages/Arithmetic mean
Averages/Pythagorean means
Averages/Simple moving average
Geometric
Averages/Pythagorean means
Harmonic
Averages/Pythagorean means
Quadratic
Averages/Root mean square
Circular
Averages/Mean angle
Averages/Mean time of day
Median
Averages/Median
Mode
Averages/Mode
Standard deviation
Statistics/Basic
Cumulative standard deviation
| #C | C | #include <stdio.h>
#include <stdlib.h>
#include <stdarg.h>
typedef struct sma_obj {
double sma;
double sum;
int period;
double *values;
int lv;
} sma_obj_t;
typedef union sma_result {
sma_obj_t *handle;
double sma;
double *values;
} sma_result_t;
enum Action { SMA_NEW, SMA_FREE, SMA_VALUES, SMA_ADD, SMA_MEAN };
sma_result_t sma(enum Action action, ...)
{
va_list vl;
sma_result_t r;
sma_obj_t *o;
double v;
va_start(vl, action);
switch(action) {
case SMA_NEW: // args: int period
r.handle = malloc(sizeof(sma_obj_t));
r.handle->sma = 0.0;
r.handle->period = va_arg(vl, int);
r.handle->values = malloc(r.handle->period * sizeof(double));
r.handle->lv = 0;
r.handle->sum = 0.0;
break;
case SMA_FREE: // args: sma_obj_t *handle
r.handle = va_arg(vl, sma_obj_t *);
free(r.handle->values);
free(r.handle);
r.handle = NULL;
break;
case SMA_VALUES: // args: sma_obj_t *handle
o = va_arg(vl, sma_obj_t *);
r.values = o->values;
break;
case SMA_MEAN: // args: sma_obj_t *handle
o = va_arg(vl, sma_obj_t *);
r.sma = o->sma;
break;
case SMA_ADD: // args: sma_obj_t *handle, double value
o = va_arg(vl, sma_obj_t *);
v = va_arg(vl, double);
if ( o->lv < o->period ) {
o->values[o->lv++] = v;
o->sum += v;
o->sma = o->sum / o->lv;
} else {
o->sum -= o->values[ o->lv % o->period];
o->sum += v;
o->sma = o->sum / o->period;
o->values[ o->lv % o->period ] = v; o->lv++;
}
r.sma = o->sma;
break;
}
va_end(vl);
return r;
} |
http://rosettacode.org/wiki/Barnsley_fern | Barnsley fern |
A Barnsley fern is a fractal named after British mathematician Michael Barnsley and can be created using an iterated function system (IFS).
Task
Create this fractal fern, using the following transformations:
ƒ1 (chosen 1% of the time)
xn + 1 = 0
yn + 1 = 0.16 yn
ƒ2 (chosen 85% of the time)
xn + 1 = 0.85 xn + 0.04 yn
yn + 1 = −0.04 xn + 0.85 yn + 1.6
ƒ3 (chosen 7% of the time)
xn + 1 = 0.2 xn − 0.26 yn
yn + 1 = 0.23 xn + 0.22 yn + 1.6
ƒ4 (chosen 7% of the time)
xn + 1 = −0.15 xn + 0.28 yn
yn + 1 = 0.26 xn + 0.24 yn + 0.44.
Starting position: x = 0, y = 0
| #IS-BASIC | IS-BASIC | 100 PROGRAM "Fern.bas"
110 RANDOMIZE
120 SET VIDEO MODE 1:SET VIDEO COLOR 0:SET VIDEO X 40:SET VIDEO Y 27
130 OPEN #101:"video:"
140 DISPLAY #101:AT 1 FROM 1 TO 27
150 SET PALETTE BLACK,GREEN
160 LET MX=16000:LET X,Y=0
170 FOR N=1 TO MX
180 LET P=RND(100)
190 SELECT CASE P
200 CASE IS<=1
210 LET NX=0:LET NY=.16*Y
220 CASE IS<=8
230 LET NX=.2*X-.26*Y:LET NY=.23*X+.22*Y+1.6
240 CASE IS<=15
250 LET NX=-.15*X+.28*Y:LET NY=.26*X+.24*Y+.44
260 CASE ELSE
270 LET NX=.85*X+.04*Y:LET NY=-.04*X+.85*Y+1.6
280 END SELECT
290 LET X=NX:LET Y=NY
300 PLOT X*96+600,Y*96
310 NEXT |
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