# Deming's Funnel

Deming's Funnel is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

W Edwards Deming was an American statistician and management guru who used physical demonstrations to illuminate his teachings. In one demonstration Deming repeatedly dropped marbles through a funnel at a target, marking where they landed, and observing the resulting pattern. He applied a sequence of "rules" to try to improve performance. In each case the experiment begins with the funnel positioned directly over the target.

• Rule 1: The funnel remains directly above the target.
• Rule 2: Adjust the funnel position by shifting the target to compensate after each drop. E.g. If the last drop missed 1 cm east, move the funnel 1 cm to the west of its current position.
• Rule 3: As rule 2, but first move the funnel back over the target, before making the adjustment. E.g. If the funnel is 2 cm north, and the marble lands 3 cm north, move the funnel 3 cm south of the target.
• Rule 4: The funnel is moved directly over the last place a marble landed.

Apply the four rules to the set of 50 pseudorandom displacements provided (e.g in the Racket solution) for the dxs and dys. Output: calculate the mean and standard-deviations of the resulting x and y values for each rule.

Note that rules 2, 3, and 4 give successively worse results. Trying to deterministically compensate for a random process is counter-productive, but -- according to Deming -- quite a popular pastime: see the Further Information, below for examples.

Stretch goal 1: Generate fresh pseudorandom data. The radial displacement of the drop from the funnel position is given by a Gaussian distribution (standard deviation is 1.0) and the angle of displacement is uniformly distributed.

Stretch goal 2: Show scatter plots of all four results.

Further information

## D

Translation of: Python
`import std.stdio, std.math, std.algorithm, std.range, std.typecons; auto mean(T)(in T[] xs) pure nothrow @nogc {    return xs.sum / xs.length;} auto stdDev(T)(in T[] xs) pure nothrow {    immutable m = xs.mean;    return sqrt(xs.map!(x => (x - m) ^^ 2).sum / xs.length);} alias TF = double function(in double, in double) pure nothrow @nogc; auto funnel(T)(in T[] dxs, in T[] dys, in TF rule) {    T x = 0, y = 0;    immutable(T)[] rxs, rys;     foreach (const dx, const dy; zip(dxs, dys)) {        immutable rx = x + dx;        immutable ry = y + dy;        x = rule(x, dx);        y = rule(y, dy);        rxs ~= rx;        rys ~= ry;    }     return tuple!("x", "y")(rxs, rys);} void experiment(T)(in string label,                   in T[] dxs, in T[] dys, in TF rule) {    //immutable (rxs, rys) = funnel(dxs, dys, rule);    immutable rs = funnel(dxs, dys, rule);    label.writeln;    writefln("Mean x, y:    %.4f, %.4f", rs.x.mean, rs.y.mean);    writefln("Std dev x, y: %.4f, %.4f", rs.x.stdDev, rs.y.stdDev);    writeln;} void main() {    immutable dxs = [    -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,     1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,    -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,     0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,    -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,    -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,     0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,    -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,     0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,    -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,     0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,     0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,     0.443, -0.521, -0.799,  0.087];     immutable dys = [     0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,     0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,     0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,     0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,    -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,     0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,     0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,     1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,    -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,     0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,    -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,     1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,    -0.947, -1.424, -0.542, -1.032];     static assert(dxs.length == dys.length);     experiment("Rule 1:", dxs, dys, (z, dz) => 0.0);    experiment("Rule 2:", dxs, dys, (z, dz) => -dz);    experiment("Rule 3:", dxs, dys, (z, dz) => -(z + dz));    experiment("Rule 4:", dxs, dys, (z, dz) => z + dz);}`
Output:
```Rule 1:
Mean x, y:    0.0004, 0.0702
Std dev x, y: 0.7153, 0.6462

Rule 2:
Mean x, y:    0.0008, -0.0103
Std dev x, y: 1.0371, 0.8999

Rule 3:
Mean x, y:    0.0438, -0.0063
Std dev x, y: 7.9871, 4.7784

Rule 4:
Mean x, y:    3.1341, 5.4210
Std dev x, y: 1.5874, 3.9304```

## Elixir

Translation of: Ruby
`defmodule Deming do  def funnel(dxs, rule) do    {_, rxs} = Enum.reduce(dxs, {0, []}, fn dx,{x,rxs} ->      {rule.(x, dx), [x + dx | rxs]}    end)    rxs  end   def mean(xs), do: Enum.sum(xs) / length(xs)   def stddev(xs) do    m = mean(xs)    Enum.reduce(xs, 0.0, fn x,sum -> sum + (x-m)*(x-m) / length(xs) end)    |> :math.sqrt  end   def experiment(label, dxs, dys, rule) do    {rxs, rys} = {funnel(dxs, rule), funnel(dys, rule)}    IO.puts label    :io.format "Mean x, y    : ~7.4f, ~7.4f~n",   [mean(rxs), mean(rys)]    :io.format "Std dev x, y : ~7.4f, ~7.4f~n~n", [stddev(rxs), stddev(rys)]  endend dxs = [ -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,         1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,        -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,         0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,        -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,        -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,         0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,        -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,         0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,        -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,         0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,         0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,         0.443, -0.521, -0.799,  0.087] dys = [  0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,         0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,         0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,         0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,        -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,         0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,         0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,         1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,        -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,         0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,        -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,         1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,        -0.947, -1.424, -0.542, -1.032] Deming.experiment("Rule 1:", dxs, dys, fn _z, _dz -> 0 end)Deming.experiment("Rule 2:", dxs, dys, fn _z, dz -> -dz end)Deming.experiment("Rule 3:", dxs, dys, fn z, dz -> -(z+dz) end)Deming.experiment("Rule 4:", dxs, dys, fn z, dz -> z+dz end)`
Output:
```Rule 1:
Mean x, y    :  0.0004,  0.0702
Std dev x, y :  0.7153,  0.6462

Rule 2:
Mean x, y    :  0.0009, -0.0103
Std dev x, y :  1.0371,  0.8999

Rule 3:
Mean x, y    :  0.0439, -0.0063
Std dev x, y :  7.9871,  4.7784

Rule 4:
Mean x, y    :  3.1341,  5.4210
Std dev x, y :  1.5874,  3.9304
```

## Go

Translation of: Python
`package main import (    "fmt"    "math") type rule func(float64, float64) float64 var dxs = []float64{    -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,     1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,    -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,     0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,    -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,    -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,     0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,    -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,     0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,    -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,     0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,     0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,     0.443, -0.521, -0.799,  0.087,} var dys = []float64{     0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,     0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,     0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,     0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,    -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,     0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,     0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,     1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,    -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,     0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,    -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,     1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,    -0.947, -1.424, -0.542, -1.032,} func funnel(fa []float64, r rule) []float64 {    x := 0.0    result := make([]float64, len(fa))    for i, f := range fa {        result[i] = x + f        x = r(x, f)    }    return result} func mean(fa []float64) float64 {    sum := 0.0    for _, f := range fa {        sum += f    }    return sum / float64(len(fa))} func stdDev(fa []float64) float64 {    m := mean(fa)    sum := 0.0    for _, f := range fa {        sum += (f - m) * (f - m)    }    return math.Sqrt(sum / float64(len(fa)))} func experiment(label string, r rule) {    rxs := funnel(dxs, r)    rys := funnel(dys, r)    fmt.Println(label, " :      x        y")    fmt.Printf("Mean    :  %7.4f, %7.4f\n", mean(rxs), mean(rys))    fmt.Printf("Std Dev :  %7.4f, %7.4f\n", stdDev(rxs), stdDev(rys))    fmt.Println()} func main() {    experiment("Rule 1", func(_, _ float64) float64 {        return 0.0    })    experiment("Rule 2", func(_, dz float64) float64 {        return -dz    })    experiment("Rule 3", func(z, dz float64) float64 {        return -(z + dz)    })    experiment("Rule 4", func(z, dz float64) float64 {        return z + dz    })}`
Output:
```Rule 1  :      x        y
Mean    :   0.0004,  0.0702
Std Dev :   0.7153,  0.6462

Rule 2  :      x        y
Mean    :   0.0009, -0.0103
Std Dev :   1.0371,  0.8999

Rule 3  :      x        y
Mean    :   0.0439, -0.0063
Std Dev :   7.9871,  4.7784

Rule 4  :      x        y
Mean    :   3.1341,  5.4210
Std Dev :   1.5874,  3.9304
```

Translation of: Python
`import Data.List (mapAccumL, genericLength)import Text.Printf funnel :: (Num a) => (a -> a -> a) -> [a] -> [a]funnel rule = snd . mapAccumL (\x dx -> (rule x dx, x + dx)) 0 mean :: (Fractional a) => [a] -> a mean xs = sum xs / genericLength xs stddev :: (Floating a) => [a] -> astddev xs = sqrt \$ sum [(x-m)**2 | x <- xs] / genericLength xs where              m = mean xs experiment :: String -> [Double] -> [Double] -> (Double -> Double -> Double) -> IO ()experiment label dxs dys rule = do  let rxs = funnel rule dxs      rys = funnel rule dys  putStrLn label  printf "Mean x, y    : %7.4f, %7.4f\n" (mean rxs) (mean rys)  printf "Std dev x, y : %7.4f, %7.4f\n" (stddev rxs) (stddev rys)  putStrLn ""  dxs = [ -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,         1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,        -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,         0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,        -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,        -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,         0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,        -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,         0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,        -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,         0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,         0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,         0.443, -0.521, -0.799,  0.087] dys = [  0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,         0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,         0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,         0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,        -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,         0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,         0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,         1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,        -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,         0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,        -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,         1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,        -0.947, -1.424, -0.542, -1.032] main :: IO ()main = do  experiment "Rule 1:" dxs dys (\_ _  -> 0)  experiment "Rule 2:" dxs dys (\_ dz -> -dz)  experiment "Rule 3:" dxs dys (\z dz -> -(z+dz))  experiment "Rule 4:" dxs dys (\z dz -> z+dz)`
Output:
```Rule 1:
Mean x, y    :  0.0004,  0.0702
Std dev x, y :  0.7153,  0.6462

Rule 2:
Mean x, y    :  0.0009, -0.0103
Std dev x, y :  1.0371,  0.8999

Rule 3:
Mean x, y    :  0.0439, -0.0063
Std dev x, y :  7.9871,  4.7784

Rule 4:
Mean x, y    :  3.1341,  5.4210
Std dev x, y :  1.5874,  3.9304
```

## J

` dx=:".0 :0-.LF _0.533 0.270 0.859 _0.043 _0.205 _0.127 _0.071 0.275 1.251 _0.231 _0.401 0.269 0.491 0.951 1.150 0.001 _0.382 0.161 0.915 2.080 _2.337 0.034 _0.126 0.014 0.709 0.129 _1.093 _0.483 _1.193 0.020 _0.051 0.047 _0.095 0.695 0.340 _0.182 0.287 0.213 _0.423 _0.021 _0.134 1.798 0.021 _1.099 _0.361 1.636 _1.134 1.315 0.201 0.034 0.097 _0.170 0.054 _0.553 _0.024 _0.181 _0.700 _0.361 _0.789 0.279 _0.174 _0.009 _0.323 _0.658 0.348 _0.528 0.881 0.021 _0.853 0.157 0.648 1.774 _1.043 0.051 0.021 0.247 _0.310 0.171 0.000 0.106 0.024 _0.386 0.962 0.765 _0.125 _0.289 0.521 0.017 0.281 _0.749 _0.149 _2.436 _0.909 0.394 _0.113 _0.598 0.443 _0.521 _0.799 0.087) dy=:".0 :0-.LF 0.136 0.717 0.459 _0.225 1.392 0.385 0.121 _0.395 0.490 _0.682 _0.065 0.242 _0.288 0.658 0.459 0.000 0.426 0.205 _0.765 _2.188 _0.742 _0.010 0.089 0.208 0.585 0.633 _0.444 _0.351 _1.087 0.199 0.701 0.096 _0.025 _0.868 1.051 0.157 0.216 0.162 0.249 _0.007 0.009 0.508 _0.790 0.723 0.881 _0.508 0.393 _0.226 0.710 0.038 _0.217 0.831 0.480 0.407 0.447 _0.295 1.126 0.380 0.549 _0.445 _0.046 0.428 _0.074 0.217 _0.822 0.491 1.347 _0.141 1.230 _0.044 0.079 0.219 0.698 0.275 0.056 0.031 0.421 0.064 0.721 0.104 _0.729 0.650 _1.103 0.154 _1.720 0.051 _0.385 0.477 1.537 _0.901 0.939 _0.411 0.341 _0.411 0.106 0.224 _0.947 _1.424 _0.542 _1.032) Rule1=: ]Rule2=: -/\.&.|.Rule3=: ]-0,}:Rule4=: ]+0,}: smoutput '  Rule 1 (x,y):'smoutput '  Mean: ',":dx ,&mean&Rule1 dysmoutput '  Std dev: ',":dx ,&stddev&Rule1 dysmoutput '  'smoutput '  Rule 2 (x,y):'smoutput '  Mean: ',":dx ,&mean&Rule2 dysmoutput '  Std dev: ',":dx ,&stddev&Rule2 dysmoutput '  'smoutput '  Rule 3 (x,y):'smoutput '  Mean: ',":dx ,&mean&Rule3 dysmoutput '  Std dev: ',":dx ,&stddev&Rule3 dysmoutput '  'smoutput '  Rule 4 (x,y):'smoutput '  Mean: ',":dx ,&mean&Rule4 dysmoutput '  Std dev: ',":dx ,&stddev&Rule4 dy`

Displayed result:

``` Rule 1 (x,y):
Mean: 0.0004 0.07023
Std dev: 0.718875 0.649462

Rule 2 (x,y):
Mean: 0.04386 _0.0063
Std dev: 8.02735 4.80249

Rule 3 (x,y):
Mean: 0.00087 _0.01032
Std dev: 1.04236 0.904482

Rule 4 (x,y):
Mean: _7e_5 0.15078
Std dev: 0.990174 0.918942
```

Author's note: these numbers are different from those of other implementations. I claim that this represents errors in the other implementations and invite proof that I am wrong.

## Java

Translation of Python via D

Works with: Java version 8
`import static java.lang.Math.*;import java.util.Arrays;import java.util.function.BiFunction; public class DemingsFunnel {     public static void main(String[] args) {        double[] dxs = {            -0.533, 0.270, 0.859, -0.043, -0.205, -0.127, -0.071, 0.275,            1.251, -0.231, -0.401, 0.269, 0.491, 0.951, 1.150, 0.001,            -0.382, 0.161, 0.915, 2.080, -2.337, 0.034, -0.126, 0.014,            0.709, 0.129, -1.093, -0.483, -1.193, 0.020, -0.051, 0.047,            -0.095, 0.695, 0.340, -0.182, 0.287, 0.213, -0.423, -0.021,            -0.134, 1.798, 0.021, -1.099, -0.361, 1.636, -1.134, 1.315,            0.201, 0.034, 0.097, -0.170, 0.054, -0.553, -0.024, -0.181,            -0.700, -0.361, -0.789, 0.279, -0.174, -0.009, -0.323, -0.658,            0.348, -0.528, 0.881, 0.021, -0.853, 0.157, 0.648, 1.774,            -1.043, 0.051, 0.021, 0.247, -0.310, 0.171, 0.000, 0.106,            0.024, -0.386, 0.962, 0.765, -0.125, -0.289, 0.521, 0.017,            0.281, -0.749, -0.149, -2.436, -0.909, 0.394, -0.113, -0.598,            0.443, -0.521, -0.799, 0.087};         double[] dys = {            0.136, 0.717, 0.459, -0.225, 1.392, 0.385, 0.121, -0.395,            0.490, -0.682, -0.065, 0.242, -0.288, 0.658, 0.459, 0.000,            0.426, 0.205, -0.765, -2.188, -0.742, -0.010, 0.089, 0.208,            0.585, 0.633, -0.444, -0.351, -1.087, 0.199, 0.701, 0.096,            -0.025, -0.868, 1.051, 0.157, 0.216, 0.162, 0.249, -0.007,            0.009, 0.508, -0.790, 0.723, 0.881, -0.508, 0.393, -0.226,            0.710, 0.038, -0.217, 0.831, 0.480, 0.407, 0.447, -0.295,            1.126, 0.380, 0.549, -0.445, -0.046, 0.428, -0.074, 0.217,            -0.822, 0.491, 1.347, -0.141, 1.230, -0.044, 0.079, 0.219,            0.698, 0.275, 0.056, 0.031, 0.421, 0.064, 0.721, 0.104,            -0.729, 0.650, -1.103, 0.154, -1.720, 0.051, -0.385, 0.477,            1.537, -0.901, 0.939, -0.411, 0.341, -0.411, 0.106, 0.224,            -0.947, -1.424, -0.542, -1.032};         experiment("Rule 1:", dxs, dys, (z, dz) -> 0.0);        experiment("Rule 2:", dxs, dys, (z, dz) -> -dz);        experiment("Rule 3:", dxs, dys, (z, dz) -> -(z + dz));        experiment("Rule 4:", dxs, dys, (z, dz) -> z + dz);    }     static void experiment(String label, double[] dxs, double[] dys,            BiFunction<Double, Double, Double> rule) {         double[] resx = funnel(dxs, rule);        double[] resy = funnel(dys, rule);        System.out.println(label);        System.out.printf("Mean x, y:    %.4f, %.4f%n", mean(resx), mean(resy));        System.out.printf("Std dev x, y: %.4f, %.4f%n", stdDev(resx), stdDev(resy));        System.out.println();    }     static double[] funnel(double[] input, BiFunction<Double, Double, Double> rule) {        double x = 0;        double[] result = new double[input.length];         for (int i = 0; i < input.length; i++) {            double rx = x + input[i];            x = rule.apply(x, input[i]);            result[i] = rx;        }        return result;    }     static double mean(double[] xs) {        return Arrays.stream(xs).sum() / xs.length;    }     static double stdDev(double[] xs) {        double m = mean(xs);        return sqrt(Arrays.stream(xs).map(x -> pow((x - m), 2)).sum() / xs.length);    }}`
```Rule 1:
Mean x, y:    0,0004, 0,0702
Std dev x, y: 0,7153, 0,6462

Rule 2:
Mean x, y:    0,0009, -0,0103
Std dev x, y: 1,0371, 0,8999

Rule 3:
Mean x, y:    0,0439, -0,0063
Std dev x, y: 7,9871, 4,7784

Rule 4:
Mean x, y:    3,1341, 5,4210
Std dev x, y: 1,5874, 3,9304```

## Kotlin

Translation of: Python
`// version 1.1.3 typealias Rule = (Double, Double) -> Double val dxs = doubleArrayOf(    -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,     1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,    -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,     0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,    -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,    -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,     0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,    -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,     0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,    -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,     0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,     0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,     0.443, -0.521, -0.799,  0.087) val dys = doubleArrayOf(     0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,     0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,     0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,     0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,    -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,     0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,     0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,     1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,    -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,     0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,    -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,     1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,    -0.947, -1.424, -0.542, -1.032) fun funnel(da: DoubleArray, rule: Rule): DoubleArray {    var x = 0.0    val result = DoubleArray(da.size)    for ((i, d) in da.withIndex()) {        result[i] = x + d        x = rule(x, d)    }    return result} fun mean(da: DoubleArray) = da.average() fun stdDev(da: DoubleArray): Double {    val m = mean(da)    return Math.sqrt(da.map { (it - m) * (it - m) }.average())} fun experiment(label: String, rule: Rule) {    val rxs = funnel(dxs, rule)    val rys = funnel(dys, rule)    println("\$label  :      x        y")    println("Mean    :  \${"%7.4f, %7.4f".format(mean(rxs), mean(rys))}")    println("Std Dev :  \${"%7.4f, %7.4f".format(stdDev(rxs), stdDev(rys))}")    println()} fun main(args: Array<String>) {    experiment("Rule 1") { _, _  -> 0.0 }    experiment("Rule 2") { _, dz -> -dz }    experiment("Rule 3") { z, dz -> -(z + dz) }    experiment("Rule 4") { z, dz -> z + dz }}`
Output:
```Rule 1  :      x        y
Mean    :   0.0004,  0.0702
Std Dev :   0.7153,  0.6462

Rule 2  :      x        y
Mean    :   0.0009, -0.0103
Std Dev :   1.0371,  0.8999

Rule 3  :      x        y
Mean    :   0.0439, -0.0063
Std Dev :   7.9871,  4.7784

Rule 4  :      x        y
Mean    :   3.1341,  5.4210
Std Dev :   1.5874,  3.9304
```

## PARI/GP

This is a work-in-progress.
`drop(drops, rule, rnd)={  my(v=vector(drops),target=0);  v[1]=rule(target, 0);  for(i=2,drops,    target=rule(target, v[i-1]);    v[i]=rnd(n)+target  );  v};R=[-.533-.136*I,.27-.717*I,.859-.459*I,-.043+.225*I,-.205-1.39*I,-.127-.385*I,-.071-.121*I,.275+.395*I,1.25-.490*I,-.231+.682*I,-.401+.0650*I,.269-.242*I,.491+.288*I,.951-.658*I,1.15-.459*I,.001,-.382-.426*I,.161-.205*I,.915+.765*I,2.08+2.19*I,-2.34+.742*I,.034+.0100*I,-.126-.0890*I,.014-.208*I,.709-.585*I,.129-.633*I,-1.09+.444*I,-.483+.351*I,-1.19+1.09*I,.02-.199*I,-.051-.701*I,.047-.0960*I,-.095+.0250*I,.695+.868*I,.34-1.05*I,-.182-.157*I,.287-.216*I,.213-.162*I,-.423-.249*I,-.021+.00700*I,-0.134-.00900*I,1.8-.508*I,.021+.790*I,-1.1-.723*I,-.361-.881*I,1.64+.508*I,-1.13-.393*I,1.32+.226*I,.201-.710*I,.034-.0380*I,.097+.217*I,-.17-.831*I,.054-.480*I,-.553-.407*I,-.024-.447*I,-.181+.295*I,-.7-1.13*I,-.361-.380*I,-.789-.549*I,.279+.445*I,-.174+.0460*I,-.009-.428*I,-.323+.0740*I,-.658-.217*I,.348+.822*I,-.528-.491*I,.881-1.35*I,.021+.141*I,-.853-1.23*I,.157+.0440*I,.648-.0790*I,1.77-.219*I,-1.04-.698*I,.051-.275*I,.021-.0560*I,.247-.0310*I,-.31-.421*I,.171-.0640*I,-.721*I,.106-.104*I,.024+.729*I,-.386-.650*I,.962+1.10*I,.765-.154*I,-.125+1.72*I,-.289-.0510*I,.521+.385*I,.017-.477*I,.281-1.54*I,-.749+.901*I,-.149-.939*I,-2.44+.411*I,-.909-.341*I,.394+.411*I,-.113-.106*I,-.598-.224*I,.443+.947*I,-.521+1.42*I,-.799+.542*I,.087+1.03*I];rule1(target, result)=0;rule2(target, result)=target-result;rule3(target, result)=-result;rule4(target, result)=result;mean(v)=sum(i=1,#v,v[i])/#v;stdev(v,mu=mean(v))=sqrt(sum(i=1,#v,(v[i]-mu)^2)/#v);main()={  my(V);  V=apply(f->drop(100,f,n->R[n]), [rule1, rule2, rule3, rule4]);  for(i=1,4,    print("Method #"i);    print("Means: ", mean(real(V[i])), "\t", mean(imag(V[i])));    print("StDev: ", stdev(real(V[i])), "\t", stdev(imag(V[i])));    print()  )}`

## Perl

`@dx = qw<    -0.533  0.270  0.859 -0.043 -0.205 -0.127 -0.071  0.275     1.251 -0.231 -0.401  0.269  0.491  0.951  1.150  0.001    -0.382  0.161  0.915  2.080 -2.337  0.034 -0.126  0.014     0.709  0.129 -1.093 -0.483 -1.193  0.020 -0.051  0.047    -0.095  0.695  0.340 -0.182  0.287  0.213 -0.423 -0.021    -0.134  1.798  0.021 -1.099 -0.361  1.636 -1.134  1.315     0.201  0.034  0.097 -0.170  0.054 -0.553 -0.024 -0.181    -0.700 -0.361 -0.789  0.279 -0.174 -0.009 -0.323 -0.658     0.348 -0.528  0.881  0.021 -0.853  0.157  0.648  1.774    -1.043  0.051  0.021  0.247 -0.310  0.171  0.000  0.106     0.024 -0.386  0.962  0.765 -0.125 -0.289  0.521  0.017     0.281 -0.749 -0.149 -2.436 -0.909  0.394 -0.113 -0.598     0.443 -0.521 -0.799  0.087>; @dy = qw<     0.136  0.717  0.459 -0.225  1.392  0.385  0.121 -0.395     0.490 -0.682 -0.065  0.242 -0.288  0.658  0.459  0.000     0.426  0.205 -0.765 -2.188 -0.742 -0.010  0.089  0.208     0.585  0.633 -0.444 -0.351 -1.087  0.199  0.701  0.096    -0.025 -0.868  1.051  0.157  0.216  0.162  0.249 -0.007     0.009  0.508 -0.790  0.723  0.881 -0.508  0.393 -0.226     0.710  0.038 -0.217  0.831  0.480  0.407  0.447 -0.295     1.126  0.380  0.549 -0.445 -0.046  0.428 -0.074  0.217    -0.822  0.491  1.347 -0.141  1.230 -0.044  0.079  0.219     0.698  0.275  0.056  0.031  0.421  0.064  0.721  0.104    -0.729  0.650 -1.103  0.154 -1.720  0.051 -0.385  0.477     1.537 -0.901  0.939 -0.411  0.341 -0.411  0.106  0.224    -0.947 -1.424 -0.542 -1.032>; sub mean   { my \$s; \$s += \$_ for @_; \$s / @_ }sub stddev { sqrt( mean(map { \$_**2 } @_) - mean(@_)**2) } @rules = (sub { 0 },sub { -\$_[1] },sub { -\$_[0] - \$_[1] },sub {  \$_[0] + \$_[1] }); for (@rules) {    print "Rule " . ++\$cnt . "\n";     my @ddx; my \$tx = 0;    for my \$x (@dx) { push @ddx, \$tx + \$x; \$tx = &\$_(\$tx, \$x) }    my @ddy; my \$ty = 0;    for my \$y (@dy) { push @ddy, \$ty + \$y; \$ty = &\$_(\$ty, \$y) }     printf "Mean    x, y   : %7.4f %7.4f\n",   mean(@ddx),   mean(@ddy);    printf "Std dev x, y   : %7.4f %7.4f\n", stddev(@ddx), stddev(@ddy);}`
Output:
```
Rule 1
Mean    x, y   :  0.0004  0.0702
Std dev x, y   :  0.7153  0.6462
Rule 2
Mean    x, y   :  0.0009 -0.0103
Std dev x, y   :  1.0371  0.8999
Rule 3
Mean    x, y   :  0.0439 -0.0063
Std dev x, y   :  7.9871  4.7784
Rule 4
Mean    x, y   :  3.1341  5.4210

Std dev x, y   :  1.5874  3.9304```

## Perl 6

Works with: Rakudo version 2018.10
`sub mean { @_ R/ [+] @_ }sub stddev {    # <(x - <x>)²> = <x²> - <x>²    sqrt( mean(@_ »**» 2) - mean(@_)**2 )} constant @dz = <    -0.533  0.270  0.859 -0.043 -0.205 -0.127 -0.071  0.275     1.251 -0.231 -0.401  0.269  0.491  0.951  1.150  0.001    -0.382  0.161  0.915  2.080 -2.337  0.034 -0.126  0.014     0.709  0.129 -1.093 -0.483 -1.193  0.020 -0.051  0.047    -0.095  0.695  0.340 -0.182  0.287  0.213 -0.423 -0.021    -0.134  1.798  0.021 -1.099 -0.361  1.636 -1.134  1.315     0.201  0.034  0.097 -0.170  0.054 -0.553 -0.024 -0.181    -0.700 -0.361 -0.789  0.279 -0.174 -0.009 -0.323 -0.658     0.348 -0.528  0.881  0.021 -0.853  0.157  0.648  1.774    -1.043  0.051  0.021  0.247 -0.310  0.171  0.000  0.106     0.024 -0.386  0.962  0.765 -0.125 -0.289  0.521  0.017     0.281 -0.749 -0.149 -2.436 -0.909  0.394 -0.113 -0.598     0.443 -0.521 -0.799  0.087> Z+ (1i X* <     0.136  0.717  0.459 -0.225  1.392  0.385  0.121 -0.395     0.490 -0.682 -0.065  0.242 -0.288  0.658  0.459  0.000     0.426  0.205 -0.765 -2.188 -0.742 -0.010  0.089  0.208     0.585  0.633 -0.444 -0.351 -1.087  0.199  0.701  0.096    -0.025 -0.868  1.051  0.157  0.216  0.162  0.249 -0.007     0.009  0.508 -0.790  0.723  0.881 -0.508  0.393 -0.226     0.710  0.038 -0.217  0.831  0.480  0.407  0.447 -0.295     1.126  0.380  0.549 -0.445 -0.046  0.428 -0.074  0.217    -0.822  0.491  1.347 -0.141  1.230 -0.044  0.079  0.219     0.698  0.275  0.056  0.031  0.421  0.064  0.721  0.104    -0.729  0.650 -1.103  0.154 -1.720  0.051 -0.385  0.477     1.537 -0.901  0.939 -0.411  0.341 -0.411  0.106  0.224    -0.947 -1.424 -0.542 -1.032>); constant @rule = -> \z, \dz { 0 },-> \z, \dz { -dz },-> \z, \dz { -z - dz },-> \z, \dz {  z + dz },; for @rule {    say "Rule \$(++\$):";    my \$target = 0i;    my @z = gather for @dz -> \$dz {	take \$target + \$dz;	\$target = .(\$target, \$dz)    }    printf "Mean    x, y   : %7.4f %7.4f\n",   mean(@z».re),   mean(@z».im);    printf "Std dev x, y   : %7.4f %7.4f\n", stddev(@z».re), stddev(@z».im);}`
Output:
```Rule 1:
Mean    x, y   :  0.0004  0.0702
Std dev x, y   :  0.7153  0.6462
Rule 2:
Mean    x, y   :  0.0009 -0.0103
Std dev x, y   :  1.0371  0.8999
Rule 3:
Mean    x, y   :  0.0439 -0.0063
Std dev x, y   :  7.9871  4.7784
Rule 4:
Mean    x, y   :  3.1341  5.4210
Std dev x, y   :  1.5874  3.9304```

## Phix

`function funnel(sequence dxs, integer rule)    atom x:=0.0    sequence rxs = {}    for i=1 to length(dxs) do        atom dx = dxs[i]        rxs = append(rxs,x + dx)        switch rule            case 2: x = -dx            case 3: x = -(x+dx)            case 4: x = x+dx        end switch    end for    return rxsend function function mean(sequence xs)    return sum(xs)/length(xs)end function function stddev(sequence xs)    atom m = mean(xs)    return sqrt(sum(sq_power(sq_sub(xs,m),2))/length(xs))end function procedure experiment(integer n, sequence dxs, dys)    sequence rxs = funnel(dxs,n),             rys = funnel(dys,n)   printf(1,"Mean x, y    : %7.4f, %7.4f\n",{mean(rxs), mean(rys)})   printf(1,"Std dev x, y : %7.4f, %7.4f\n",{stddev(rxs), stddev(rys)})end procedure constant dxs = {-0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,                 1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,                -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,                 0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,                -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,                -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,                 0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,                -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,                 0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,                -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,                 0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,                 0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,                 0.443, -0.521, -0.799,  0.087} constant dys = { 0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,                 0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,                 0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,                 0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,                -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,                 0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,                 0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,                 1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,                -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,                 0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,                -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,                 1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,                -0.947, -1.424, -0.542, -1.032} for i=1 to 4 do    experiment(i, dxs, dys)end for`
Output:
```Mean x, y    :  0.0004,  0.0702
Std dev x, y :  0.7153,  0.6462
Mean x, y    :  0.0009, -0.0103
Std dev x, y :  1.0371,  0.8999
Mean x, y    :  0.0439, -0.0063
Std dev x, y :  7.9871,  4.7784
Mean x, y    :  3.1341,  5.4210
Std dev x, y :  1.5874,  3.9304
```

## Python

Translation of: Racket
`import math  dxs = [-0.533, 0.27, 0.859, -0.043, -0.205, -0.127, -0.071, 0.275, 1.251,       -0.231, -0.401, 0.269, 0.491, 0.951, 1.15, 0.001, -0.382, 0.161, 0.915,       2.08, -2.337, 0.034, -0.126, 0.014, 0.709, 0.129, -1.093, -0.483, -1.193,        0.02, -0.051, 0.047, -0.095, 0.695, 0.34, -0.182, 0.287, 0.213, -0.423,       -0.021, -0.134, 1.798, 0.021, -1.099, -0.361, 1.636, -1.134, 1.315, 0.201,        0.034, 0.097, -0.17, 0.054, -0.553, -0.024, -0.181, -0.7, -0.361, -0.789,       0.279, -0.174, -0.009, -0.323, -0.658, 0.348, -0.528, 0.881, 0.021, -0.853,       0.157, 0.648, 1.774, -1.043, 0.051, 0.021, 0.247, -0.31, 0.171, 0.0, 0.106,       0.024, -0.386, 0.962, 0.765, -0.125, -0.289, 0.521, 0.017, 0.281, -0.749,       -0.149, -2.436, -0.909, 0.394, -0.113, -0.598, 0.443, -0.521, -0.799,        0.087] dys = [0.136, 0.717, 0.459, -0.225, 1.392, 0.385, 0.121, -0.395, 0.49, -0.682,       -0.065, 0.242, -0.288, 0.658, 0.459, 0.0, 0.426, 0.205, -0.765, -2.188,        -0.742, -0.01, 0.089, 0.208, 0.585, 0.633, -0.444, -0.351, -1.087, 0.199,       0.701, 0.096, -0.025, -0.868, 1.051, 0.157, 0.216, 0.162, 0.249, -0.007,        0.009, 0.508, -0.79, 0.723, 0.881, -0.508, 0.393, -0.226, 0.71, 0.038,        -0.217, 0.831, 0.48, 0.407, 0.447, -0.295, 1.126, 0.38, 0.549, -0.445,        -0.046, 0.428, -0.074, 0.217, -0.822, 0.491, 1.347, -0.141, 1.23, -0.044,        0.079, 0.219, 0.698, 0.275, 0.056, 0.031, 0.421, 0.064, 0.721, 0.104,        -0.729, 0.65, -1.103, 0.154, -1.72, 0.051, -0.385, 0.477, 1.537, -0.901,        0.939, -0.411, 0.341, -0.411, 0.106, 0.224, -0.947, -1.424, -0.542, -1.032] def funnel(dxs, rule):    x, rxs = 0, []    for dx in dxs:        rxs.append(x + dx)        x = rule(x, dx)    return rxs def mean(xs): return sum(xs) / len(xs) def stddev(xs):    m = mean(xs)    return math.sqrt(sum((x-m)**2 for x in xs) / len(xs)) def experiment(label, rule):    rxs, rys = funnel(dxs, rule), funnel(dys, rule)    print label    print 'Mean x, y    : %.4f, %.4f' % (mean(rxs), mean(rys))    print 'Std dev x, y : %.4f, %.4f' % (stddev(rxs), stddev(rys))    print  experiment('Rule 1:', lambda z, dz: 0)experiment('Rule 2:', lambda z, dz: -dz)experiment('Rule 3:', lambda z, dz: -(z+dz))experiment('Rule 4:', lambda z, dz: z+dz)`
Output:
```Rule 1:
Mean x, y    : 0.0004, 0.0702
Std dev x, y : 0.7153, 0.6462

Rule 2:
Mean x, y    : 0.0009, -0.0103
Std dev x, y : 1.0371, 0.8999

Rule 3:
Mean x, y    : 0.0439, -0.0063
Std dev x, y : 7.9871, 4.7784

Rule 4:
Mean x, y    : 3.1341, 5.4210
Std dev x, y : 1.5874, 3.9304
```

Alternative: [Generates pseudo-random data and gives some interpretation.] The funnel experiment is performed in one dimension. The other dimension would act similarly.

`from random import gaussfrom math import sqrtfrom pprint import pprint as pp NMAX=50 def statscreator():    sum_ = sum2 = n = 0    def stats(x):        nonlocal sum_, sum2, n         sum_ += x        sum2 += x*x        n    += 1.0        return sum_/n, sqrt(sum2/n - sum_*sum_/n/n)    return stats def drop(target, sigma=1.0):    'Drop ball at target'    return gauss(target, sigma) def deming(rule, nmax=NMAX):    ''' Simulate Demings funnel in 1D. '''     stats = statscreator()    target = 0    for i in range(nmax):        value = drop(target)        mean, sdev = stats(value)        target = rule(target, value)        if i == nmax - 1:            return mean, sdev def d1(target, value):    ''' Keep Funnel over target. '''     return target  def d2(target, value):    ''' The new target starts at the center, 0,0 then is adjusted to    be the previous target _minus_ the offset of the new drop from the    previous target. '''     return -value   # - (target - (target - value)) = - value def d3(target, value):    ''' The new target starts at the center, 0,0 then is adjusted to    be the previous target _minus_ the offset of the new drop from the    center, 0.0. '''     return target - value def d4(target, value):    ''' (Dumb). The new target is where it last dropped. '''     return value  def printit(rule, trials=5):    print('\nDeming simulation. %i trials using rule %s:\n %s'          % (trials, rule.__name__.upper(), rule.__doc__))    for i in range(trials):        print('    Mean: %7.3f, Sdev: %7.3f' % deming(rule))  if __name__ == '__main__':    rcomments = [ (d1, 'Should have smallest deviations ~1.0, and be centered on 0.0'),                  (d2, 'Should be centred on 0.0 with larger deviations than D1'),                  (d3, 'Should be centred on 0.0 with larger deviations than D1'),                  (d4, 'Center wanders all over the place, with deviations to match!'),                ]    for rule, comment in rcomments:        printit(rule)        print('  %s\n' % comment)`
Output:
```Deming simulation. 5 trials using rule D1:
Keep Funnel over target.
Mean:  -0.161, Sdev:   0.942
Mean:  -0.092, Sdev:   0.924
Mean:  -0.199, Sdev:   1.079
Mean:  -0.256, Sdev:   0.820
Mean:  -0.211, Sdev:   0.971
Should have smallest deviations ~1.0, and be centered on 0.0

Deming simulation. 5 trials using rule D2:
The new target starts at the center, 0,0 then is adjusted to
be the previous target _minus_ the offset of the new drop from the
previous target.
Mean:  -0.067, Sdev:   4.930
Mean:   0.035, Sdev:   4.859
Mean:  -0.080, Sdev:   2.575
Mean:   0.147, Sdev:   4.948
Mean:   0.050, Sdev:   4.149
Should be centred on 0.0 with larger deviations than D1

Deming simulation. 5 trials using rule D3:
The new target starts at the center, 0,0 then is adjusted to
be the previous target _minus_ the offset of the new drop from the
center, 0.0.
Mean:   0.006, Sdev:   1.425
Mean:  -0.039, Sdev:   1.436
Mean:   0.030, Sdev:   1.305
Mean:   0.009, Sdev:   1.419
Mean:   0.001, Sdev:   1.479
Should be centred on 0.0 with larger deviations than D1

Deming simulation. 5 trials using rule D4:
(Dumb). The new target is where it last dropped.
Mean:   5.252, Sdev:   2.839
Mean:   1.403, Sdev:   3.073
Mean:  -1.525, Sdev:   3.650
Mean:   3.844, Sdev:   2.715
Mean:  -7.697, Sdev:   3.715
Center wanders all over the place, with deviations to match!```

## Racket

The stretch solutions can be obtained by uncommenting radii etc. (delete the 4 semi-colons) to generate fresh data, and scatter-plots can be obtained by deleting the #; .

`#lang racket(require math/distributions math/statistics plot) (define dxs '(-0.533 0.270 0.859 -0.043 -0.205 -0.127 -0.071 0.275 1.251 -0.231               -0.401 0.269 0.491 0.951 1.150 0.001 -0.382 0.161 0.915 2.080 -2.337               0.034 -0.126 0.014 0.709 0.129 -1.093 -0.483 -1.193 0.020 -0.051              0.047 -0.095 0.695 0.340 -0.182 0.287 0.213 -0.423 -0.021 -0.134 1.798              0.021 -1.099 -0.361 1.636 -1.134 1.315 0.201 0.034 0.097 -0.170 0.054               -0.553 -0.024 -0.181 -0.700 -0.361 -0.789 0.279 -0.174 -0.009 -0.323              -0.658 0.348 -0.528 0.881 0.021 -0.853 0.157 0.648 1.774 -1.043 0.051               0.021 0.247 -0.310 0.171 0.000 0.106 0.024 -0.386 0.962 0.765 -0.125               -0.289 0.521 0.017 0.281 -0.749 -0.149 -2.436 -0.909 0.394 -0.113 -0.598              0.443 -0.521 -0.799 0.087)) (define dys '(0.136 0.717 0.459 -0.225 1.392 0.385 0.121 -0.395 0.490 -0.682 -0.065               0.242 -0.288 0.658 0.459 0.000 0.426 0.205 -0.765 -2.188 -0.742 -0.010               0.089 0.208 0.585 0.633 -0.444 -0.351 -1.087 0.199 0.701 0.096 -0.025               -0.868 1.051 0.157 0.216 0.162 0.249 -0.007 0.009 0.508 -0.790 0.723              0.881 -0.508 0.393 -0.226 0.710 0.038 -0.217 0.831 0.480 0.407 0.447              -0.295 1.126 0.380 0.549 -0.445 -0.046 0.428 -0.074 0.217 -0.822 0.491               1.347 -0.141 1.230 -0.044 0.079 0.219 0.698 0.275 0.056 0.031 0.421 0.064              0.721 0.104 -0.729 0.650 -1.103 0.154 -1.720 0.051 -0.385 0.477 1.537               -0.901 0.939 -0.411 0.341 -0.411 0.106 0.224 -0.947 -1.424 -0.542 -1.032)) ;(define radii (map abs (sample (normal-dist 0 1) 100)));(define angles (sample (uniform-dist (- pi) pi) 100));(define dxs (map (λ (r theta) (* r (cos theta))) radii angles));(define dys (map (λ (r theta) (* r (sin theta))) radii angles)) (define (funnel dxs rule)  (let ([x 0])    (for/fold ([rxs null])      ([dx dxs])      (let ([rx (+ x dx)])        (set! x (rule x dx))        (cons rx rxs))))) (define (experiment label rule)  (define (p s) (real->decimal-string s 4))  (let ([rxs (funnel dxs rule)]        [rys (funnel dys rule)])    (displayln label)    (printf "Mean x, y   : ~a, ~a\n" (p (mean rxs)) (p (mean rys)))    (printf "Std dev x, y: ~a, ~a\n\n" (p (stddev rxs)) (p (stddev rys)))    #;(plot (points (map vector rxs rys)          #:x-min -15 #:x-max 15 #:y-min -15 #:y-max 15)))) (experiment "Rule 1:" (λ (z dz) 0))(experiment "Rule 2:" (λ (z dz) (- dz)))(experiment "Rule 3:" (λ (z dz) (- (+ z dz))))(experiment "Rule 4:" (λ (z dz) (+ z dz))) `
Output:
```Rule 1:
Mean x, y   : 0.0004, 0.0702
Std dev x, y: 0.7153, 0.6462

Rule 2:
Mean x, y   : 0.0009, -0.0103
Std dev x, y: 1.0371, 0.8999

Rule 3:
Mean x, y   : 0.0439, -0.0063
Std dev x, y: 7.9871, 4.7784

Rule 4:
Mean x, y   : 3.1341, 5.4210
Std dev x, y: 1.5874, 3.9304
```

## Ruby

Translation of: Python
`def funnel(dxs, &rule)  x, rxs = 0, []  for dx in dxs    rxs << (x + dx)    x = rule[x, dx]  end  rxsend def mean(xs) xs.inject(:+) / xs.size end def stddev(xs)  m = mean(xs)  Math.sqrt(xs.inject(0.0){|sum,x| sum + (x-m)**2} / xs.size)end def experiment(label, dxs, dys, &rule)  rxs, rys = funnel(dxs, &rule), funnel(dys, &rule)  puts label  puts 'Mean x, y    : %7.4f, %7.4f' % [mean(rxs), mean(rys)]  puts 'Std dev x, y : %7.4f, %7.4f' % [stddev(rxs), stddev(rys)]  putsend dxs = [ -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,         1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,        -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,         0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,        -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,        -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,         0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,        -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,         0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,        -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,         0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,         0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,         0.443, -0.521, -0.799,  0.087] dys = [  0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,         0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,         0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,         0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,        -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,         0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,         0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,         1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,        -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,         0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,        -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,         1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,        -0.947, -1.424, -0.542, -1.032] experiment('Rule 1:', dxs, dys) {|z, dz| 0}experiment('Rule 2:', dxs, dys) {|z, dz| -dz}experiment('Rule 3:', dxs, dys) {|z, dz| -(z+dz)}experiment('Rule 4:', dxs, dys) {|z, dz| z+dz}`
Output:
```Rule 1:
Mean x, y    :  0.0004,  0.0702
Std dev x, y :  0.7153,  0.6462

Rule 2:
Mean x, y    :  0.0009, -0.0103
Std dev x, y :  1.0371,  0.8999

Rule 3:
Mean x, y    :  0.0439, -0.0063
Std dev x, y :  7.9871,  4.7784

Rule 4:
Mean x, y    :  3.1341,  5.4210
Std dev x, y :  1.5874,  3.9304
```

## Sidef

Translation of: Perl 6
`func x̄(a) {    a.sum / a.len} func σ(a) {    sqrt(x̄(a.map{.**2}) - x̄(a)**2)} const Δ = (%n<    -0.533  0.270  0.859 -0.043 -0.205 -0.127 -0.071  0.275     1.251 -0.231 -0.401  0.269  0.491  0.951  1.150  0.001    -0.382  0.161  0.915  2.080 -2.337  0.034 -0.126  0.014     0.709  0.129 -1.093 -0.483 -1.193  0.020 -0.051  0.047    -0.095  0.695  0.340 -0.182  0.287  0.213 -0.423 -0.021    -0.134  1.798  0.021 -1.099 -0.361  1.636 -1.134  1.315     0.201  0.034  0.097 -0.170  0.054 -0.553 -0.024 -0.181    -0.700 -0.361 -0.789  0.279 -0.174 -0.009 -0.323 -0.658     0.348 -0.528  0.881  0.021 -0.853  0.157  0.648  1.774    -1.043  0.051  0.021  0.247 -0.310  0.171  0.000  0.106     0.024 -0.386  0.962  0.765 -0.125 -0.289  0.521  0.017     0.281 -0.749 -0.149 -2.436 -0.909  0.394 -0.113 -0.598     0.443 -0.521 -0.799  0.087> ~Z+ %n<     0.136  0.717  0.459 -0.225  1.392  0.385  0.121 -0.395     0.490 -0.682 -0.065  0.242 -0.288  0.658  0.459  0.000     0.426  0.205 -0.765 -2.188 -0.742 -0.010  0.089  0.208     0.585  0.633 -0.444 -0.351 -1.087  0.199  0.701  0.096    -0.025 -0.868  1.051  0.157  0.216  0.162  0.249 -0.007     0.009  0.508 -0.790  0.723  0.881 -0.508  0.393 -0.226     0.710  0.038 -0.217  0.831  0.480  0.407  0.447 -0.295     1.126  0.380  0.549 -0.445 -0.046  0.428 -0.074  0.217    -0.822  0.491  1.347 -0.141  1.230 -0.044  0.079  0.219     0.698  0.275  0.056  0.031  0.421  0.064  0.721  0.104    -0.729  0.650 -1.103  0.154 -1.720  0.051 -0.385  0.477     1.537 -0.901  0.939 -0.411  0.341 -0.411  0.106  0.224    -0.947 -1.424 -0.542 -1.032>.map{ .i }) const rules = [    { 0 },    {|_,dz| -dz },    {|z,dz| -z - dz },    {|z,dz| z + dz },] for i,v in (rules.kv) {    say "Rule #{i+1}:"    var target = 0    var z = gather {        Δ.each { |d|            take(target + d)            target = v.run(target, d)        }    }    printf("Mean    x, y   : %.4f %.4f\n", x̄(z.map{.re}), x̄(z.map{.im}))    printf("Std dev x, y   : %.4f %.4f\n", σ(z.map{.re}), σ(z.map{.im}))}`
Output:
```Rule 1:
Mean    x, y   : 0.0004 0.0702
Std dev x, y   : 0.7153 0.6462
Rule 2:
Mean    x, y   : 0.0009 -0.0103
Std dev x, y   : 1.0371 0.8999
Rule 3:
Mean    x, y   : 0.0439 -0.0063
Std dev x, y   : 7.9871 4.7784
Rule 4:
Mean    x, y   : 3.1341 5.4210
Std dev x, y   : 1.5874 3.9304
```

## Tcl

Works with: Tcl version 8.6
Translation of: Ruby
`package require Tcl 8.6namespace path {tcl::mathop tcl::mathfunc} proc funnel {items rule} {    set x 0.0    set result {}    foreach item \$items {	lappend result [+ \$x \$item]	set x [apply \$rule \$x \$item]    }    return \$result} proc mean {items} {    / [+ {*}\$items] [double [llength \$items]]}proc stddev {items} {    set m [mean \$items]    sqrt [mean [lmap x \$items {** [- \$x \$m] 2}]]} proc experiment {label dxs dys rule} {    set rxs [funnel \$dxs \$rule]    set rys [funnel \$dys \$rule]    puts \$label    puts [format "Mean x, y    : %7.4f, %7.4f" [mean \$rxs] [mean \$rys]]    puts [format "Std dev x, y : %7.4f, %7.4f" [stddev \$rxs] [stddev \$rys]]    puts ""} set dxs {    -0.533 0.270 0.859 -0.043 -0.205 -0.127 -0.071 0.275 1.251 -0.231 -0.401    0.269 0.491 0.951 1.150 0.001 -0.382 0.161 0.915 2.080 -2.337 0.034    -0.126 0.014 0.709 0.129 -1.093 -0.483 -1.193 0.020 -0.051 0.047 -0.095    0.695 0.340 -0.182 0.287 0.213 -0.423 -0.021 -0.134 1.798 0.021 -1.099    -0.361 1.636 -1.134 1.315 0.201 0.034 0.097 -0.170 0.054 -0.553 -0.024    -0.181 -0.700 -0.361 -0.789 0.279 -0.174 -0.009 -0.323 -0.658 0.348    -0.528 0.881 0.021 -0.853 0.157 0.648 1.774 -1.043 0.051 0.021 0.247    -0.310 0.171 0.000 0.106 0.024 -0.386 0.962 0.765 -0.125 -0.289 0.521    0.017 0.281 -0.749 -0.149 -2.436 -0.909 0.394 -0.113 -0.598 0.443 -0.521    -0.799 0.087}set dys {    0.136 0.717 0.459 -0.225 1.392 0.385 0.121 -0.395 0.490 -0.682 -0.065    0.242 -0.288 0.658 0.459 0.000 0.426 0.205 -0.765 -2.188 -0.742 -0.010    0.089 0.208 0.585 0.633 -0.444 -0.351 -1.087 0.199 0.701 0.096 -0.025    -0.868 1.051 0.157 0.216 0.162 0.249 -0.007 0.009 0.508 -0.790 0.723    0.881 -0.508 0.393 -0.226 0.710 0.038 -0.217 0.831 0.480 0.407 0.447    -0.295 1.126 0.380 0.549 -0.445 -0.046 0.428 -0.074 0.217 -0.822 0.491    1.347 -0.141 1.230 -0.044 0.079 0.219 0.698 0.275 0.056 0.031 0.421 0.064    0.721 0.104 -0.729 0.650 -1.103 0.154 -1.720 0.051 -0.385 0.477 1.537    -0.901 0.939 -0.411 0.341 -0.411 0.106 0.224 -0.947 -1.424 -0.542 -1.032} puts "USING STANDARD DATA"experiment "Rule 1:" \$dxs \$dys {{z dz} {expr {0}}}experiment "Rule 2:" \$dxs \$dys {{z dz} {expr {-\$dz}}}experiment "Rule 3:" \$dxs \$dys {{z dz} {expr {-(\$z+\$dz)}}}experiment "Rule 4:" \$dxs \$dys {{z dz} {expr {\$z+\$dz}}}`

The first stretch goal:

Library: Tcllib (Package: math::constants)
Library: Tcllib (Package: simulation::random)
`package require math::constantspackage require simulation::random math::constants::constants degtorad set rng(radius) [simulation::random::prng_Normal 0.0 1.0]set rng(angle) [simulation::random::prng_Uniform 0.0 360.0]set dxs [set dys {}]for {set i 0} {\$i < 500} {incr i} {    set r [\$rng(radius)]    set theta [expr {[\$rng(angle)] * \$degtorad}]    lappend dxs [expr {\$r * cos(\$theta)}]    lappend dys [expr {\$r * sin(\$theta)}]} puts "USING RANDOM DATA"experiment "Rule 1:" \$dxs \$dys {{z dz} {expr {0}}}experiment "Rule 2:" \$dxs \$dys {{z dz} {expr {-\$dz}}}experiment "Rule 3:" \$dxs \$dys {{z dz} {expr {-(\$z+\$dz)}}}experiment "Rule 4:" \$dxs \$dys {{z dz} {expr {\$z+\$dz}}}`
Output:
```USING STANDARD DATA
Rule 1:
Mean x, y    :  0.0004,  0.0702
Std dev x, y :  0.7153,  0.6462

Rule 2:
Mean x, y    :  0.0009, -0.0103
Std dev x, y :  1.0371,  0.8999

Rule 3:
Mean x, y    :  0.0439, -0.0063
Std dev x, y :  7.9871,  4.7784

Rule 4:
Mean x, y    :  3.1341,  5.4210
Std dev x, y :  1.5874,  3.9304

USING RANDOM DATA
Rule 1:
Mean x, y    :  0.0053,  0.0112
Std dev x, y :  0.4954,  0.5082

Rule 2:
Mean x, y    : -0.0012, -0.0002
Std dev x, y :  0.6914,  0.7331

Rule 3:
Mean x, y    : -0.0132,  0.0098
Std dev x, y :  9.3480,  5.0290

Rule 4:
Mean x, y    : -6.3314, -4.0168
Std dev x, y :  3.2387,  4.4825

```

## zkl

Translation of: Ruby
`fcn funnel(dxs, rule){   x:=0.0; rxs:=L();   foreach dx in (dxs){      rxs.append(x + dx);      x = rule(x,dx);   }   rxs} fcn mean(xs){ xs.sum(0.0)/xs.len() } fcn stddev(xs){   m:=mean(xs);   (xs.reduce('wrap(sum,x){ sum + (x-m)*(x-m) },0.0)/xs.len()).sqrt();} fcn experiment(label,dxs,dys,rule){   rxs:=funnel(dxs,rule); rys:=funnel(dys,rule);   label.println();   "Mean x, y    : %7.4f, %7.4f".fmt(mean(rxs),  mean(rys))  .println();   "Std dev x, y : %7.4f, %7.4f".fmt(stddev(rxs),stddev(rys)).println();   println();}`
`dxs:=T( -0.533,  0.270,  0.859, -0.043, -0.205, -0.127, -0.071,  0.275,         1.251, -0.231, -0.401,  0.269,  0.491,  0.951,  1.150,  0.001,        -0.382,  0.161,  0.915,  2.080, -2.337,  0.034, -0.126,  0.014,         0.709,  0.129, -1.093, -0.483, -1.193,  0.020, -0.051,  0.047,        -0.095,  0.695,  0.340, -0.182,  0.287,  0.213, -0.423, -0.021,        -0.134,  1.798,  0.021, -1.099, -0.361,  1.636, -1.134,  1.315,         0.201,  0.034,  0.097, -0.170,  0.054, -0.553, -0.024, -0.181,        -0.700, -0.361, -0.789,  0.279, -0.174, -0.009, -0.323, -0.658,         0.348, -0.528,  0.881,  0.021, -0.853,  0.157,  0.648,  1.774,        -1.043,  0.051,  0.021,  0.247, -0.310,  0.171,  0.000,  0.106,         0.024, -0.386,  0.962,  0.765, -0.125, -0.289,  0.521,  0.017,         0.281, -0.749, -0.149, -2.436, -0.909,  0.394, -0.113, -0.598,         0.443, -0.521, -0.799,  0.087); dys:=T(  0.136,  0.717,  0.459, -0.225,  1.392,  0.385,  0.121, -0.395,         0.490, -0.682, -0.065,  0.242, -0.288,  0.658,  0.459,  0.000,         0.426,  0.205, -0.765, -2.188, -0.742, -0.010,  0.089,  0.208,         0.585,  0.633, -0.444, -0.351, -1.087,  0.199,  0.701,  0.096,        -0.025, -0.868,  1.051,  0.157,  0.216,  0.162,  0.249, -0.007,         0.009,  0.508, -0.790,  0.723,  0.881, -0.508,  0.393, -0.226,         0.710,  0.038, -0.217,  0.831,  0.480,  0.407,  0.447, -0.295,         1.126,  0.380,  0.549, -0.445, -0.046,  0.428, -0.074,  0.217,        -0.822,  0.491,  1.347, -0.141,  1.230, -0.044,  0.079,  0.219,         0.698,  0.275,  0.056,  0.031,  0.421,  0.064,  0.721,  0.104,        -0.729,  0.650, -1.103,  0.154, -1.720,  0.051, -0.385,  0.477,         1.537, -0.901,  0.939, -0.411,  0.341, -0.411,  0.106,  0.224,        -0.947, -1.424, -0.542, -1.032); experiment("Rule 1:", dxs, dys, fcn(z,dz){ 0.0     });experiment("Rule 2:", dxs, dys, fcn(z,dz){ -dz     });experiment("Rule 3:", dxs, dys, fcn(z,dz){ -(z+dz) });experiment("Rule 4:", dxs, dys, fcn(z,dz){ z+dz    });`
Output:
```Rule 1:
Mean x, y    :  0.0004,  0.0702
Std dev x, y :  0.7153,  0.6462

Rule 2:
Mean x, y    :  0.0009, -0.0103
Std dev x, y :  1.0371,  0.8999

Rule 3:
Mean x, y    :  0.0439, -0.0063
Std dev x, y :  7.9871,  4.7784

Rule 4:
Mean x, y    :  3.1341,  5.4210
Std dev x, y :  1.5874,  3.9304
```