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Verify distribution uniformity/Chi-squared test

From Rosetta Code
Task
Verify distribution uniformity/Chi-squared test
You are encouraged to solve this task according to the task description, using any language you may know.
Task

Write a function to verify that a given distribution of values is uniform by using the test to see if the distribution has a likelihood of happening of at least the significance level (conventionally 5%).

The function should return a boolean that is true if the distribution is one that a uniform distribution (with appropriate number of degrees of freedom) may be expected to produce.


Reference



Ada[edit]

First, we specify a simple package to compute the Chi-Square Distance from the uniform distribution:

package Chi_Square is
 
type Flt is digits 18;
type Bins_Type is array(Positive range <>) of Natural;
 
function Distance(Bins: Bins_Type) return Flt;
 
end Chi_Square;

Next, we implement that package:

package body Chi_Square is
 
function Distance(Bins: Bins_Type) return Flt is
Bad_Bins: Natural := 0;
Sum: Natural := 0;
Expected: Flt;
Result: Flt;
begin
for I in Bins'Range loop
if Bins(I) < 5 then
Bad_Bins := Bad_Bins + 1;
end if;
Sum := Sum + Bins(I);
end loop;
if 5*Bad_Bins > Bins'Length then
raise Program_Error with "too many (almost) empty bins";
end if;
 
Expected := Flt(Sum) / Flt(Bins'Length);
Result := 0.0;
for I in Bins'Range loop
Result := Result + ((Flt(Bins(I)) - Expected)**2) / Expected;
end loop;
return Result;
end Distance;
 
end Chi_Square;

Finally, we actually implement the Chi-square test. We do not actually compute the Chi-square probability; rather we hardcode a table of values for 5% significance level, which has been picked from Wikipedia [1]:

with Ada.Text_IO, Ada.Command_Line, Chi_Square; use Ada.Text_IO;
 
procedure Test_Chi_Square is
 
package Ch2 renames Chi_Square; use Ch2;
package FIO is new Float_IO(Flt);
 
B: Bins_Type(1 .. Ada.Command_Line.Argument_Count);
Bound_For_5_Per_Cent: constant array(Positive range <>) of Flt :=
( 1 => 3.84, 2 => 5.99, 3 => 7.82, 4 => 9.49, 5 => 11.07,
6 => 12.59, 7 => 14.07, 8 => 15.51, 9 => 16.92, 10 => 18.31);
-- picked from http://en.wikipedia.org/wiki/Chi-squared_distribution
 
Dist: Flt;
 
begin
for I in B'Range loop
B(I) := Natural'Value(Ada.Command_Line.Argument(I));
end loop;
Dist := Distance(B);
Put("Degrees of Freedom:" & Integer'Image(B'Length-1) & ", Distance: ");
FIO.Put(Dist, Fore => 6, Aft => 2, Exp => 0);
if Dist <= Bound_For_5_Per_Cent(B'Length-1) then
Put_Line("; (apparently uniform)");
else
Put_Line("; (deviates significantly from uniform)");
end if;
end;
Output:
$ ./Test_Chi_Square 199809 200665 199607 200270 199649  
Degrees of Freedom: 4, Distance:      4.15; (apparently uniform)
$ ./Test_Chi_Square 522573 244456 139979 71531 21461
Degrees of Freedom: 4, Distance: 790063.28; (deviates significantly from uniform)

C[edit]

This first sections contains the functions required to compute the Chi-Squared probability. These are not needed if a library containing the necessary function is availabile (e.g. see Numerical Integration, Gamma function).

#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
 
typedef double (* Ifctn)( double t);
/* Numerical integration method */
double Simpson3_8( Ifctn f, double a, double b, int N)
{
int j;
double l1;
double h = (b-a)/N;
double h1 = h/3.0;
double sum = f(a) + f(b);
 
for (j=3*N-1; j>0; j--) {
l1 = (j%3)? 3.0 : 2.0;
sum += l1*f(a+h1*j) ;
}
return h*sum/8.0;
}
 
#define A 12
double Gamma_Spouge( double z )
{
int k;
static double cspace[A];
static double *coefs = NULL;
double accum;
double a = A;
 
if (!coefs) {
double k1_factrl = 1.0;
coefs = cspace;
coefs[0] = sqrt(2.0*M_PI);
for(k=1; k<A; k++) {
coefs[k] = exp(a-k) * pow(a-k,k-0.5) / k1_factrl;
k1_factrl *= -k;
}
}
 
accum = coefs[0];
for (k=1; k<A; k++) {
accum += coefs[k]/(z+k);
}
accum *= exp(-(z+a)) * pow(z+a, z+0.5);
return accum/z;
}
 
double aa1;
double f0( double t)
{
return pow(t, aa1)*exp(-t);
}
 
double GammaIncomplete_Q( double a, double x)
{
double y, h = 1.5e-2; /* approximate integration step size */
 
/* this cuts off the tail of the integration to speed things up */
y = aa1 = a-1;
while((f0(y) * (x-y) > 2.0e-8) && (y < x)) y += .4;
if (y>x) y=x;
 
return 1.0 - Simpson3_8( &f0, 0, y, (int)(y/h))/Gamma_Spouge(a);
}

This section contains the functions specific to the task.

double chi2UniformDistance( double *ds, int dslen)
{
double expected = 0.0;
double sum = 0.0;
int k;
 
for (k=0; k<dslen; k++)
expected += ds[k];
expected /= k;
 
for (k=0; k<dslen; k++) {
double x = ds[k] - expected;
sum += x*x;
}
return sum/expected;
}
 
double chi2Probability( int dof, double distance)
{
return GammaIncomplete_Q( 0.5*dof, 0.5*distance);
}
 
int chiIsUniform( double *dset, int dslen, double significance)
{
int dof = dslen -1;
double dist = chi2UniformDistance( dset, dslen);
return chi2Probability( dof, dist ) > significance;
}

Testing

int main(int argc, char **argv)
{
double dset1[] = { 199809., 200665., 199607., 200270., 199649. };
double dset2[] = { 522573., 244456., 139979., 71531., 21461. };
double *dsets[] = { dset1, dset2 };
int dslens[] = { 5, 5 };
int k, l;
double dist, prob;
int dof;
 
for (k=0; k<2; k++) {
printf("Dataset: [ ");
for(l=0;l<dslens[k]; l++)
printf("%.0f, ", dsets[k][l]);
 
printf("]\n");
dist = chi2UniformDistance(dsets[k], dslens[k]);
dof = dslens[k]-1;
printf("dof: %d distance: %.4f", dof, dist);
prob = chi2Probability( dof, dist );
printf(" probability: %.6f", prob);
printf(" uniform? %s\n", chiIsUniform(dsets[k], dslens[k], 0.05)? "Yes":"No");
}
return 0;
}

D[edit]

import std.stdio, std.algorithm, std.mathspecial;
 
real x2Dist(T)(in T[] data) pure nothrow @safe @nogc {
immutable avg = data.sum / data.length;
immutable sqs = reduce!((a, b) => a + (b - avg) ^^ 2)(0.0L, data);
return sqs / avg;
}
 
real x2Prob(in real dof, in real distance) pure nothrow @safe @nogc {
return gammaIncompleteCompl(dof / 2, distance / 2);
}
 
bool x2IsUniform(T)(in T[] data, in real significance=0.05L)
pure nothrow @safe @nogc {
return x2Prob(data.length - 1.0L, x2Dist(data)) > significance;
}
 
void main() {
immutable dataSets = [[199809, 200665, 199607, 200270, 199649],
[522573, 244456, 139979, 71531, 21461]];
writefln(" %4s %12s  %12s %8s  %s",
"dof", "distance", "probability", "Uniform?", "dataset");
foreach (immutable ds; dataSets) {
immutable dof = ds.length - 1;
immutable dist = ds.x2Dist;
immutable prob = x2Prob(dof, dist);
writefln("%4d %12.3f  %12.8f  %5s  %6s",
dof, dist, prob, ds.x2IsUniform ? "YES" : "NO", ds);
}
}
Output:
  dof     distance   probability Uniform?   dataset
   4        4.146    0.38657083      YES    [199809, 200665, 199607, 200270, 199649]
   4   790063.276    0.00000000       NO    [522573, 244456, 139979,  71531,  21461]

Elixir[edit]

Translation of: Ruby
defmodule Verify do
defp gammaInc_Q(a, x) do
a1 = a-1
f0 = fn t -> :math.pow(t, a1) * :math.exp(-t) end
df0 = fn t -> (a1-t) * :math.pow(t, a-2) * :math.exp(-t) end
y = while_loop(f0, x, a1)
n = trunc(y / 3.0e-4)
h = y / n
hh = 0.5 * h
sum = Enum.reduce(n-1 .. 0, 0, fn j,sum ->
t = h * j
sum + f0.(t) + hh * df0.(t)
end)
h * sum / gamma_spounge(a, make_coef)
end
 
defp while_loop(f, x, y) do
if f.(y)*(x-y) > 2.0e-8 and y < x, do: while_loop(f, x, y+0.3), else: min(x, y)
end
 
@a 12
defp make_coef do
coef0 = [:math.sqrt(2.0 * :math.pi)]
{_, coef} = Enum.reduce([email protected], {1.0, coef0}, fn k,{k1_factrl,c} ->
h = :math.exp(@a-k) * :math.pow(@a-k, k-0.5) / k1_factrl
{-k1_factrl*k, [h | c]}
end)
Enum.reverse(coef) |> List.to_tuple
end
 
defp gamma_spounge(z, coef) do
accm = Enum.reduce([email protected], elem(coef,0), fn k,res -> res + elem(coef,k) / (z+k) end)
accm * :math.exp(-([email protected])) * :math.pow([email protected], z+0.5) / z
end
 
def chi2UniformDistance(dataSet) do
expected = Enum.sum(dataSet) / length(dataSet)
Enum.reduce(dataSet, 0, fn d,sum -> sum + (d-expected)*(d-expected) end) / expected
end
 
def chi2Probability(dof, distance) do
1.0 - gammaInc_Q(0.5*dof, 0.5*distance)
end
 
def chi2IsUniform(dataSet, significance\\0.05) do
dof = length(dataSet) - 1
dist = chi2UniformDistance(dataSet)
chi2Probability(dof, dist) > significance
end
end
 
dsets = [ [ 199809, 200665, 199607, 200270, 199649 ],
[ 522573, 244456, 139979, 71531, 21461 ] ]
 
Enum.each(dsets, fn ds ->
IO.puts "Data set:#{inspect ds}"
dof = length(ds) - 1
IO.puts " degrees of freedom: #{dof}"
distance = Verify.chi2UniformDistance(ds)
 :io.fwrite " distance: ~.4f~n", [distance]
 :io.fwrite " probability: ~.4f~n", [Verify.chi2Probability(dof, distance)]
 :io.fwrite " uniform? ~s~n", [(if Verify.chi2IsUniform(ds), do: "Yes", else: "No")]
end)
Output:
Data set:[199809, 200665, 199607, 200270, 199649]
  degrees of freedom: 4
  distance:           4.1463
  probability:        0.3866
  uniform?            Yes
Data set:[522573, 244456, 139979, 71531, 21461]
  degrees of freedom: 4
  distance:           790063.2759
  probability:        -0.0000
  uniform?            No

Fortran[edit]

Works with: Fortran version 95

Instead of implementing the incomplete gamma function by ourselves, we bind to GNU Scientific Library; so we need a module to interface to the function we need (gsl_sf_gamma_inc)

module GSLMiniBind
implicit none
 
interface
real(c_double) function gsl_sf_gamma_inc(a, x) bind(C)
use iso_c_binding
real(c_double), value, intent(in) :: a, x
end function gsl_sf_gamma_inc
end interface
end module GSLMiniBind

Now we're ready to complete the task.

program ChiTest
use GSLMiniBind
use iso_c_binding
implicit none
 
real, dimension(5) :: dset1 = (/ 199809., 200665., 199607., 200270., 199649. /)
real, dimension(5) :: dset2 = (/ 522573., 244456., 139979., 71531., 21461. /)
 
real :: dist, prob
integer :: dof
 
print *, "Dataset 1:"
print *, dset1
dist = chi2UniformDistance(dset1)
dof = size(dset1) - 1
write(*, '(A,I4,A,F12.4)') 'dof: ', dof, ' distance: ', dist
prob = chi2Probability(dof, dist)
write(*, '(A,F9.6)') 'probability: ', prob
write(*, '(A,L)') 'uniform? ', chiIsUniform(dset1, 0.05)
 
! Lazy copy/past :|
print *, "Dataset 2:"
print *, dset2
dist = chi2UniformDistance(dset2)
dof = size(dset2) - 1
write(*, '(A,I4,A,F12.4)') 'dof: ', dof, ' distance: ', dist
prob = chi2Probability(dof, dist)
write(*, '(A,F9.6)') 'probability: ', prob
write(*, '(A,L)') 'uniform? ', chiIsUniform(dset2, 0.05)
 
contains
 
function chi2Probability(dof, distance)
real :: chi2Probability
integer, intent(in) :: dof
real, intent(in) :: distance
 
! in order to make this work, we need linking with GSL library
chi2Probability = gsl_sf_gamma_inc(real(0.5*dof, c_double), real(0.5*distance, c_double))
end function chi2Probability
 
function chiIsUniform(dset, significance)
logical :: chiIsUniform
real, dimension(:), intent(in) :: dset
real, intent(in) :: significance
 
integer :: dof
real :: dist
 
dof = size(dset) - 1
dist = chi2UniformDistance(dset)
chiIsUniform = chi2Probability(dof, dist) > significance
end function chiIsUniform
 
function chi2UniformDistance(ds)
real :: chi2UniformDistance
real, dimension(:), intent(in) :: ds
 
real :: expected, summa = 0.0
 
expected = sum(ds) / size(ds)
summa = sum( (ds - expected) ** 2 )
 
chi2UniformDistance = summa / expected
end function chi2UniformDistance
 
end program ChiTest

Go[edit]

Translation of: C

Go has a nice gamma function in the library. Otherwise, it's mostly a port from C. Note, this implementation of the incomplete gamma function works for these two test cases, but, I believe, has serious limitations. See talk page.

package main
 
import (
"fmt"
"math"
)
 
type ifctn func(float64) float64
 
func simpson38(f ifctn, a, b float64, n int) float64 {
h := (b - a) / float64(n)
h1 := h / 3
sum := f(a) + f(b)
for j := 3*n - 1; j > 0; j-- {
if j%3 == 0 {
sum += 2 * f(a+h1*float64(j))
} else {
sum += 3 * f(a+h1*float64(j))
}
}
return h * sum / 8
}
 
func gammaIncQ(a, x float64) float64 {
aa1 := a - 1
var f ifctn = func(t float64) float64 {
return math.Pow(t, aa1) * math.Exp(-t)
}
y := aa1
h := 1.5e-2
for f(y)*(x-y) > 2e-8 && y < x {
y += .4
}
if y > x {
y = x
}
return 1 - simpson38(f, 0, y, int(y/h/math.Gamma(a)))
}
 
func chi2ud(ds []int) float64 {
var sum, expected float64
for _, d := range ds {
expected += float64(d)
}
expected /= float64(len(ds))
for _, d := range ds {
x := float64(d) - expected
sum += x * x
}
return sum / expected
}
 
func chi2p(dof int, distance float64) float64 {
return gammaIncQ(.5*float64(dof), .5*distance)
}
 
const sigLevel = .05
 
func main() {
for _, dset := range [][]int{
{199809, 200665, 199607, 200270, 199649},
{522573, 244456, 139979, 71531, 21461},
} {
utest(dset)
}
}
 
func utest(dset []int) {
fmt.Println("Uniform distribution test")
var sum int
for _, c := range dset {
sum += c
}
fmt.Println(" dataset:", dset)
fmt.Println(" samples: ", sum)
fmt.Println(" categories: ", len(dset))
 
dof := len(dset) - 1
fmt.Println(" degrees of freedom: ", dof)
 
dist := chi2ud(dset)
fmt.Println(" chi square test statistic: ", dist)
 
p := chi2p(dof, dist)
fmt.Println(" p-value of test statistic: ", p)
 
sig := p < sigLevel
fmt.Printf(" significant at %2.0f%% level?  %t\n", sigLevel*100, sig)
fmt.Println(" uniform? ", !sig, "\n")
}

Output:

Uniform distribution test
 dataset: [199809 200665 199607 200270 199649]
 samples:                       1000000
 categories:                    5
 degrees of freedom:            4
 chi square test statistic:     4.14628
 p-value of test statistic:     0.3865708330827673
 significant at  5% level?      false
 uniform?                       true 

Uniform distribution test
 dataset: [522573 244456 139979 71531 21461]
 samples:                       1000000
 categories:                    5
 degrees of freedom:            4
 chi square test statistic:     790063.27594
 p-value of test statistic:     2.3528290427066167e-11
 significant at  5% level?      true
 uniform?                       false 

Hy[edit]

(import
[scipy.stats [chisquare]]
[collections [Counter]])
 
(defn uniform? [f repeats &optional [alpha .05]]
"Call 'f' 'repeats' times and do a chi-squared test for uniformity
of the resulting discrete distribution. Return false iff the
null hypothesis of uniformity is rejected for the test with
size 'alpha'."

(<= alpha (second (chisquare
(.values (Counter (take repeats (repeatedly f))))))))

Examples of use:

(import [random [randint]])
 
(for [f [
(fn [] (randint 1 10))
(fn [] (if (randint 0 1) (randint 1 9) (randint 1 10)))]]
(print (uniform? f 5000)))

J[edit]

Solution (Tacit):

require 'stats/base'
 
countCats=: #@~. NB. counts the number of unique items
getExpected=: #@] % [ NB. divides no of items by category count
getObserved=: #/[email protected]] NB. counts frequency for each category
calcX2=: [: +/ *:@(getObserved - getExpected) % getExpected NB. calculates test statistic
calcDf=: <:@[ NB. calculates degrees of freedom for uniform distribution
 
NB.*isUniform v Tests (5%) whether y is uniformly distributed
NB. result is: boolean describing if distribution y is uniform
NB. y is: distribution to test
NB. x is: optionally specify number of categories possible
isUniform=: (countCats $: ]) : (0.95 > calcDf chisqcdf :: 1: calcX2)

Solution (Explicit):

require 'stats/base'
 
NB.*isUniformX v Tests (5%) whether y is uniformly distributed
NB. result is: boolean describing if distribution y is uniform
NB. y is: distribution to test
NB. x is: optionally specify number of categories possible
isUniformX=: verb define
(#~. y) isUniformX y
:
signif=. 0.95 NB. set significance level
expected=. (#y) % x NB. number of items divided by the category count
observed=. #/.~ y NB. frequency count for each category
X2=. +/ (*: observed - expected) % expected NB. the test statistic
degfreedom=. <: x NB. degrees of freedom
signif > degfreedom chisqcdf :: 1: X2
)

Example Usage:

   FairDistrib=:      1e6 [email protected]$ 5
UnfairDistrib=: (9.5e5 [email protected]$ 5) , (5e4 [email protected]$ 4)
isUniformX FairDistrib
1
isUniformX UnfairDistrib
0
isUniform 4 4 4 5 5 5 5 5 5 5 NB. uniform if only 2 categories possible
1
4 isUniform 4 4 4 5 5 5 5 5 5 5 NB. not uniform if 4 categories possible
0

Java[edit]

Translation of: D
Works with: Java version 8
import static java.lang.Math.pow;
import java.util.Arrays;
import static java.util.Arrays.stream;
import org.apache.commons.math3.special.Gamma;
 
public class Test {
 
static double x2Dist(double[] data) {
double avg = stream(data).sum() / data.length;
double sqs = stream(data).reduce(0, (a, b) -> a + pow((b - avg), 2));
return sqs / avg;
}
 
static double x2Prob(double dof, double distance) {
return Gamma.regularizedGammaQ(dof / 2, distance / 2);
}
 
static boolean x2IsUniform(double[] data, double significance) {
return x2Prob(data.length - 1.0, x2Dist(data)) > significance;
}
 
public static void main(String[] a) {
double[][] dataSets = {{199809, 200665, 199607, 200270, 199649},
{522573, 244456, 139979, 71531, 21461}};
 
System.out.printf(" %4s %12s  %12s %8s  %s%n",
"dof", "distance", "probability", "Uniform?", "dataset");
 
for (double[] ds : dataSets) {
int dof = ds.length - 1;
double dist = x2Dist(ds);
double prob = x2Prob(dof, dist);
System.out.printf("%4d %12.3f  %12.8f  %5s  %6s%n",
dof, dist, prob, x2IsUniform(ds, 0.05) ? "YES" : "NO",
Arrays.toString(ds));
}
}
}
  dof     distance   probability Uniform?   dataset
   4        4,146    0,38657083      YES    [199809.0, 200665.0, 199607.0, 200270.0, 199649.0]
   4   790063,276    0,00000000       NO    [522573.0, 244456.0, 139979.0, 71531.0, 21461.0]

Julia[edit]

# v0.6
 
using Distributions
 
function eqdist(data::Vector{T}, α::Float64=0.05)::Bool where T <: Real
if ! (0 ≤ α ≤ 1); error("α must be in [0, 1]") end
exp = mean(data)
chisqval = sum((x - exp) ^ 2 for x in data) / exp
pval = ccdf(Chisq(2), chisqval)
return pval > α
end
 
data1 = [199809, 200665, 199607, 200270, 199649]
data2 = [522573, 244456, 139979, 71531, 21461]
 
for data in (data1, data2)
println("Data:\n$data")
println("Hypothesis test: the original population is ", (eqdist(data) ? "" : "not "), "uniform.\n")
end
Output:
Data:
[199809, 200665, 199607, 200270, 199649]
Hypothesis test: the original population is uniform.

Data:
[522573, 244456, 139979, 71531, 21461]
Hypothesis test: the original population is not uniform.

Kotlin[edit]

This program reuses Kotlin code from the Gamma function and Numerical Integration tasks but otherwise is a translation of the C entry for this task.

// version 1.1.51
 
typealias Func = (Double) -> Double
 
fun gammaLanczos(x: Double): Double {
var xx = x
val p = doubleArrayOf(
0.99999999999980993,
676.5203681218851,
-1259.1392167224028,
771.32342877765313,
-176.61502916214059,
12.507343278686905,
-0.13857109526572012,
9.9843695780195716e-6,
1.5056327351493116e-7
)
val g = 7
if (xx < 0.5) return Math.PI / (Math.sin(Math.PI * xx) * gammaLanczos(1.0 - xx))
xx--
var a = p[0]
val t = xx + g + 0.5
for (i in 1 until p.size) a += p[i] / (xx + i)
return Math.sqrt(2.0 * Math.PI) * Math.pow(t, xx + 0.5) * Math.exp(-t) * a
}
 
fun integrate(a: Double, b: Double, n: Int, f: Func): Double {
val h = (b - a) / n
var sum = 0.0
for (i in 0 until n) {
val x = a + i * h
sum += (f(x) + 4.0 * f(x + h / 2.0) + f(x + h)) / 6.0
}
return sum * h
}
 
fun gammaIncompleteQ(a: Double, x: Double): Double {
val aa1 = a - 1.0
fun f0(t: Double) = Math.pow(t, aa1) * Math.exp(-t)
val h = 1.5e-2
var y = aa1
while ((f0(y) * (x - y) > 2.0e-8) && y < x) y += 0.4
if (y > x) y = x
return 1.0 - integrate(0.0, y, (y / h).toInt(), ::f0) / gammaLanczos(a)
}
 
fun chi2UniformDistance(ds: DoubleArray): Double {
val expected = ds.average()
val sum = ds.map { val x = it - expected; x * x }.sum()
return sum / expected
}
 
fun chi2Probability(dof: Int, distance: Double) =
gammaIncompleteQ(0.5 * dof, 0.5 * distance)
 
fun chiIsUniform(ds: DoubleArray, significance: Double):Boolean {
val dof = ds.size - 1
val dist = chi2UniformDistance(ds)
return chi2Probability(dof, dist) > significance
}
 
fun main(args: Array<String>) {
val dsets = listOf(
doubleArrayOf(199809.0, 200665.0, 199607.0, 200270.0, 199649.0),
doubleArrayOf(522573.0, 244456.0, 139979.0, 71531.0, 21461.0)
)
for (ds in dsets) {
println("Dataset: ${ds.asList()}")
val dist = chi2UniformDistance(ds)
val dof = ds.size - 1
print("DOF: $dof Distance: ${"%.4f".format(dist)}")
val prob = chi2Probability(dof, dist)
print(" Probability: ${"%.6f".format(prob)}")
val uniform = if (chiIsUniform(ds, 0.05)) "Yes" else "No"
println(" Uniform? $uniform\n")
}
}
Output:
Dataset: [199809.0, 200665.0, 199607.0, 200270.0, 199649.0]
DOF: 4  Distance: 4.1463  Probability: 0.386571  Uniform? Yes

Dataset: [522573.0, 244456.0, 139979.0, 71531.0, 21461.0]
DOF: 4  Distance: 790063.2759  Probability: 0.000000  Uniform? No

Mathematica[edit]

This code explicity assumes a discrete uniform distribution since the chi square test is a poor test choice for continuous distributions and requires Mathematica version 2 or later

discreteUniformDistributionQ[data_, {min_Integer, max_Integer}, confLevel_: .05] :=
If[$VersionNumber >= 8,
confLevel <= PearsonChiSquareTest[data, DiscreteUniformDistribution[{min, max}]],
Block[{v, k = max - min, n = [email protected]},
v = (k + 1) (Plus @@ (((Length /@ Split[[email protected]]))^2))/n - n;
GammaRegularized[k/2, 0, v/2] <= 1 - confLevel]]
 
discreteUniformDistributionQ[data_] :=discreteUniformDistributionQ[data, data[[Ordering[data][[{1, -1}]]]]]

code used to create test data requires Mathematica version 6 or later

uniformData = RandomInteger[10, 100];
nonUniformData = [email protected][10, {5, 100}];
{discreteUniformDistributionQ[uniformData],discreteUniformDistributionQ[nonUniformData]}
Output:
{True,False}


Nim[edit]

Translation of: Go

We use the gamma function from the “math” module. To simplify the code, we use also the “lenientops” module which provides mixed operations between floats ane integers.

import lenientops, math, stats, strformat, sugar
 
func simpson38(f: (float) -> float; a, b: float; n: int): float =
let h = (b - a) / n
let h1 = h / 3
var sum = f(a) + f(b)
for i in countdown(3 * n - 1, 1):
if i mod 3 == 0:
sum += 2 * f(a + h1 * i)
else:
sum += 3 * f(a + h1 * i)
result = h * sum / 8
 
func gammaIncQ(a, x: float): float =
let aa1 = a - 1
func f(t: float): float = pow(t, aa1) * exp(-t)
var y = aa1
let h = 1.5e-2
while f(y) * (x - y) > 2e-8 and y < x:
y += 0.4
if y > x: y = x
result = 1 - simpson38(f, 0, y, (y / h / gamma(a)).toInt)
 
func chi2ud(ds: openArray[int]): float =
let expected = mean(ds)
var s = 0.0
for d in ds:
let x = d.toFloat - expected
s += x * x
result = s / expected
 
func chi2p(dof: int; distance: float): float =
gammaIncQ(0.5 * dof, 0.5 * distance)
 
const SigLevel = 0.05
 
proc utest(dset: openArray[int]) =
 
echo "Uniform distribution test"
let s = sum(dset)
echo " dataset:", dset
echo " samples: ", s
echo " categories: ", dset.len
 
let dof = dset.len - 1
echo " degrees of freedom: ", dof
 
let dist = chi2ud(dset)
echo " chi square test statistic: ", dist
 
let p = chi2p(dof, dist)
echo " p-value of test statistic: ", p
 
let sig = p < SigLevel
echo &" significant at {int(SigLevel * 100)}% level? {sig}"
echo &" uniform? {not sig}\n"
 
 
for dset in [[199809, 200665, 199607, 200270, 199649],
[522573, 244456, 139979, 71531, 21461]]:
utest(dset)
Output:
Uniform distribution test
 dataset:[199809, 200665, 199607, 200270, 199649]
 samples:                      1000000
 categories:                   5
 degrees of freedom:           4
 chi square test statistic:    4.14628
 p-value of test statistic:    0.3865708330827673
 significant at 5% level?      false
 uniform?                      true

Uniform distribution test
 dataset:[522573, 244456, 139979, 71531, 21461]
 samples:                      1000000
 categories:                   5
 degrees of freedom:           4
 chi square test statistic:    790063.27594
 p-value of test statistic:    2.34864350190378e-11
 significant at 5% level?      true
 uniform?                      false

OCaml[edit]

This code needs to be compiled with library gsl.cma.

let sqr x = x *. x
 
let chi2UniformDistance distrib =
let count, len = Array.fold_left (fun (s, c) e -> s + e, succ c)
(0, 0) distrib in
let expected = float count /. float len in
let distance = Array.fold_left (fun s e ->
s +. sqr (float e -. expected) /. expected
) 0. distrib in
let dof = float (pred len) in
dof, distance
 
let chi2Proba dof distance =
Gsl_sf.gamma_inc_Q (0.5 *. dof) (0.5 *. distance)
 
let chi2IsUniform distrib significance =
let dof, distance = chi2UniformDistance distrib in
let likelihoodOfRandom = chi2Proba dof distance in
likelihoodOfRandom > significance
 
let _ =
List.iter (fun distrib ->
let dof, distance = chi2UniformDistance distrib in
Printf.printf "distribution ";
Array.iter (Printf.printf "\t%d") distrib;
Printf.printf "\tdistance %g" distance;
Printf.printf "\t[%g > 0.05]" (chi2Proba dof distance);
if chi2IsUniform distrib 0.05 then Printf.printf " fair\n"
else Printf.printf " unfair\n"
)
[
[| 199809; 200665; 199607; 200270; 199649 |];
[| 522573; 244456; 139979; 71531; 21461 |]
]

Output

distribution    199809  200665  199607  200270  199649  distance 4.14628        [0.386571 > 0.05] fair
distribution    522573  244456  139979  71531   21461   distance 790063 [0 > 0.05] unfair

PARI/GP[edit]

The solution is very easy in GP since PARI includes a good incomplete gamma implementation; the sum function is also useful for clarity. Most of the code is just for displaying results.

The sample data for the test was taken from Go.

cumChi2(chi2,dof)={
my(g=gamma(dof/2));
incgam(dof/2,chi2/2,g)/g
};
test(v,alpha=.05)={
my(chi2,p,s=sum(i=1,#v,v[i]),ave=s/#v);
print("chi^2 statistic: ",chi2=sum(i=1,#v,(v[i]-ave)^2)/ave);
print("p-value: ",p=cumChi2(chi2,#v-1));
if(p<alpha,
print("Significant at the alpha = "alpha" level: not uniform");
,
print("Not significant at the alpha = "alpha" level: uniform");
)
};
 
test([199809, 200665, 199607, 200270, 199649])
test([522573, 244456, 139979, 71531, 21461])

Perl[edit]

Translation of: Raku
use List::Util qw(sum reduce);
use constant pi => 3.14159265;
 
sub incomplete_G_series {
my($s, $z) = @_;
my $n = 10;
push @numers, $z**$_ for 1..$n;
my @denoms = $s+1;
push @denoms, $denoms[-1]*($s+$_) for 2..$n;
my $M = 1;
$M += $numers[$_-1]/$denoms[$_-1] for 1..$n;
$z**$s / $s * exp(-$z) * $M;
}
 
sub G_of_half {
my($n) = @_;
if ($n % 2) { f(2*$_) / (4**$_ * f($_)) * sqrt(pi) for int ($n-1) / 2 }
else { f(($n/2)-1) }
}
 
sub f { reduce { $a * $b } 1, 1 .. $_[0] } # factorial
 
sub chi_squared_cdf {
my($k, $x) = @_;
my $f = $k < 20 ? 20 : 10;
if ($x == 0) { 0.0 }
elsif ($x < $k + $f*sqrt($k)) { incomplete_G_series($k/2, $x/2) / G_of_half($k) }
else { 1.0 }
}
sub chi_squared_test {
my(@bins) = @_;
$significance = 0.05;
my $n = @bins;
my $N = sum @bins;
my $expected = $N / $n;
my $chi_squared = sum map { ($_ - $expected)**2 / $expected } @bins;
my $p_value = 1 - chi_squared_cdf($n-1, $chi_squared);
return $chi_squared, $p_value, $p_value > $significance ? 'True' : 'False';
}
 
for $dataset ([199809, 200665, 199607, 200270, 199649], [522573, 244456, 139979, 71531, 21461]) {
printf "C2 = %10.3f, p-value = %.3f, uniform = %s\n", chi_squared_test(@$dataset);
}
Output:
C2 =      4.146, p-value = 0.387, uniform = True
C2 = 790063.276, p-value = 0.000, uniform = False

Phix[edit]

Translation of: Go

using gamma() from Gamma_function#Phix

function f(atom aa1, t)
return power(t, aa1) * exp(-t)
end function
 
function simpson38(atom aa1, a, b, integer n)
atom h := (b - a) / n,
h1 := h / 3,
tot := f(aa1,a) + f(aa1,b)
for j=3*n-1 to 1 by -1 do
tot += (3-(mod(j,3)=0)) * f(aa1,a+h1*j)
end for
return h * tot / 8
end function
 
function gammaIncQ(atom a, x)
atom aa1 := a - 1,
y := aa1,
h := 1.5e-2
while f(aa1,y)*(x-y) > 2e-8 and y < x do
y += 0.4
end while
if y > x then y = x end if
return 1 - simpson38(aa1, 0, y, floor(y/h/gamma(a)))
end function
 
function chi2ud(sequence ds)
atom expected = sum(ds)/length(ds),
tot = sum(sq_power(sq_sub(ds,expected),2))
return tot / expected
end function
 
function chi2p(integer dof, atom distance)
return gammaIncQ(0.5*dof, 0.5*distance)
end function
 
constant sigLevel = 0.05
constant tf = {"true","false"}
 
procedure utest(sequence dset)
printf(1,"Uniform distribution test\n")
integer tot = sum(dset)
printf(1," dataset:%s\n",{sprint(dset)})
printf(1," samples:  %d\n", tot)
printf(1," categories:  %d\n", length(dset))
 
integer dof := length(dset) - 1
printf(1," degrees of freedom:  %d\n", dof)
 
atom dist := chi2ud(dset)
printf(1," chi square test statistic:  %g\n", dist)
 
atom p := chi2p(dof, dist)
printf(1," p-value of test statistic:  %g\n", p)
 
bool sig := p < sigLevel
printf(1," significant at %2.0f%% level?  %s\n", {sigLevel*100, tf[2-sig]})
printf(1," uniform?  %s\n",{tf[sig+1]})
end procedure
 
utest({199809, 200665, 199607, 200270, 199649})
utest({522573, 244456, 139979, 71531, 21461})
Output:
Uniform distribution test
 dataset:{199809,200665,199607,200270,199649}
 samples:                      1000000
 categories:                   5
 degrees of freedom:           4
 chi square test statistic:    4.14628
 p-value of test statistic:    0.386571
 significant at  5% level?     false
 uniform?                      true
Uniform distribution test
 dataset:{522573,244456,139979,71531,21461}
 samples:                      1000000
 categories:                   5
 degrees of freedom:           4
 chi square test statistic:    790063
 p-value of test statistic:    2.35282e-11
 significant at  5% level?     true
 uniform?                      false

Python[edit]

Python: Using only standard libraries[edit]

Implements the Chi Square Probability function with an integration. I'm sure there are better ways to do this. Compare to OCaml implementation.

import math
import random
 
def GammaInc_Q( a, x):
a1 = a-1
a2 = a-2
def f0( t ):
return t**a1*math.exp(-t)
 
def df0(t):
return (a1-t)*t**a2*math.exp(-t)
 
y = a1
while f0(y)*(x-y) >2.0e-8 and y < x: y += .3
if y > x: y = x
 
h = 3.0e-4
n = int(y/h)
h = y/n
hh = 0.5*h
gamax = h * sum( f0(t)+hh*df0(t) for t in ( h*j for j in xrange(n-1, -1, -1)))
 
return gamax/gamma_spounge(a)
 
c = None
def gamma_spounge( z):
global c
a = 12
 
if c is None:
k1_factrl = 1.0
c = []
c.append(math.sqrt(2.0*math.pi))
for k in range(1,a):
c.append( math.exp(a-k) * (a-k)**(k-0.5) / k1_factrl )
k1_factrl *= -k
 
accm = c[0]
for k in range(1,a):
accm += c[k] / (z+k)
accm *= math.exp( -(z+a)) * (z+a)**(z+0.5)
return accm/z;
 
def chi2UniformDistance( dataSet ):
expected = sum(dataSet)*1.0/len(dataSet)
cntrd = (d-expected for d in dataSet)
return sum(x*x for x in cntrd)/expected
 
def chi2Probability(dof, distance):
return 1.0 - GammaInc_Q( 0.5*dof, 0.5*distance)
 
def chi2IsUniform(dataSet, significance):
dof = len(dataSet)-1
dist = chi2UniformDistance(dataSet)
return chi2Probability( dof, dist ) > significance
 
dset1 = [ 199809, 200665, 199607, 200270, 199649 ]
dset2 = [ 522573, 244456, 139979, 71531, 21461 ]
 
for ds in (dset1, dset2):
print "Data set:", ds
dof = len(ds)-1
distance =chi2UniformDistance(ds)
print "dof: %d distance: %.4f" % (dof, distance),
prob = chi2Probability( dof, distance)
print "probability: %.4f"%prob,
print "uniform? ", "Yes"if chi2IsUniform(ds,0.05) else "No"

Output:

Data set: [199809, 200665, 199607, 200270, 199649]
dof: 4 distance: 4.146280 probability: 0.3866 uniform?  Yes
Data set: [522573, 244456, 139979, 71531, 21461]
dof: 4 distance: 790063.275940 probability: 0.0000 uniform?  No

Python: Using scipy[edit]

This uses the library routine scipy.stats.chisquare.

from scipy.stats import chisquare
 
 
if __name__ == '__main__':
dataSets = [[199809, 200665, 199607, 200270, 199649],
[522573, 244456, 139979, 71531, 21461]]
print(f"{'Distance':^12} {'pvalue':^12} {'Uniform?':^8} {'Dataset'}")
for ds in dataSets:
dist, pvalue = chisquare(ds)
uni = 'YES' if pvalue > 0.05 else 'NO'
print(f"{dist:12.3f} {pvalue:12.8f} {uni:^8} {ds}")
Output:
  Distance      pvalue    Uniform? Dataset
       4.146   0.38657083   YES    [199809, 200665, 199607, 200270, 199649]
  790063.276   0.00000000    NO    [522573, 244456, 139979, 71531, 21461]

R[edit]

R being a statistical computating language, the chi-squared test is built in with the function "chisq.test"

 
dset1=c(199809,200665,199607,200270,199649)
dset2=c(522573,244456,139979,71531,21461)
 
chi2IsUniform<-function(dataset,significance=0.05){
chi2IsUniform=(chisq.test(dataset)$p.value>significance)
}
 
for (ds in list(dset1,dset2)){
print(c("Data set:",ds))
print(chisq.test(ds))
print(paste("uniform?",chi2IsUniform(ds)))
}
 

Output:

[1] "Data set:" "199809"    "200665"    "199607"    "200270"    "199649"   

        Chi-squared test for given probabilities

data:  ds 
X-squared = 4.1463, df = 4, p-value = 0.3866

[1] "uniform? TRUE"
[1] "Data set:" "522573"    "244456"    "139979"    "71531"     "21461"    

        Chi-squared test for given probabilities

data:  ds 
X-squared = 790063.3, df = 4, p-value < 2.2e-16

[1] "uniform? FALSE"

Racket[edit]

 
#lang racket
(require
racket/flonum (planet williams/science:4:5/science)
(only-in (planet williams/science/unsafe-ops-utils) real->float))
 
; (chi^2-goodness-of-fit-test observed expected df)
; Given: observed, a sequence of observed frequencies
; expected, a sequence of expected frequencies
; df, the degrees of freedom
; Result: P-value = 1-chi^2cdf(X^2,df) , the p-value
(define (chi^2-goodness-of-fit-test observed expected df)
(define X^2 (for/sum ([o observed] [e expected])
(/ (sqr (- o e)) e)))
(- 1.0 (chi-squared-cdf X^2 df)))
 
(define (is-uniform? rand n α)
 ; Use significance level α to test whether
 ; n small random numbers generated by rand
 ; have a uniform distribution.
 
 ; Observed values:
(define o (make-vector 10 0))
 ; generate n random integers from 0 to 9.
(for ([_ (+ n 1)])
(define r (rand 10))
(vector-set! o r (+ (vector-ref o r) 1)))
 ; Expected values:
(define ex (make-vector 10 (/ n 10)))
 
 ; Calculate the P-value:
(define P (chi^2-goodness-of-fit-test o ex (- n 1)))
 
 ; If the P-value is larger than α we accept the
 ; hypothesis that the numbers are distributed uniformly.
(> P α))
 
; Test whether the builtin generator is uniform:
(is-uniform? random 1000 0.05)
; Test whether the constant generator fails:
(is-uniform? (λ(_) 5) 1000 0.05)
 

Output:

 
#t
#f
 

Raku[edit]

(formerly Perl 6)

For the incomplete gamma function we use a series expansion related to Kummer's confluent hypergeometric function (see http://en.wikipedia.org/wiki/Incomplete_gamma_function#Evaluation_formulae). The gamma function is calculated in closed form, as we only need its value at integers and half integers.

sub incomplete-γ-series($s, $z) {
my \numers = $z X** 1..*;
my \denoms = [\*] $s X+ 1..*;
my $M = 1 + [+] (numers Z/ denoms) ... * < 1e-6;
$z**$s / $s * exp(-$z) * $M;
}
 
sub postfix:<!>(Int $n) { [*] 2..$n }
 
sub Γ-of-half(Int $n where * > 0) {
($n %% 2) ?? (($_-1)! given $n div 2)
!! ((2*$_)! / (4**$_ * $_!) * sqrt(pi) given ($n-1) div 2);
}
 
# degrees of freedom constrained due to numerical limitations
sub chi-squared-cdf(Int $k where 1..200, $x where * >= 0) {
my $f = $k < 20 ?? 20 !! 10;
given $x {
when 0 { 0.0 }
when * < $k + $f*sqrt($k) { incomplete-γ-series($k/2, $x/2) / Γ-of-half($k) }
default { 1.0 }
}
}
 
sub chi-squared-test(@bins, :$significance = 0.05) {
my $n = +@bins;
my $N = [+] @bins;
my $expected = $N / $n;
my $chi-squared = [+] @bins.map: { ($^bin - $expected)**2 / $expected }
my $p-value = 1 - chi-squared-cdf($n-1, $chi-squared);
return (:$chi-squared, :$p-value, :uniform($p-value > $significance));
}
 
for [< 199809 200665 199607 200270 199649 >],
[< 522573 244456 139979 71531 21461 >]
-> $dataset
{
my %t = chi-squared-test($dataset);
say 'data: ', $dataset;
say "χ² = {%t<chi-squared>}, p-value = {%t<p-value>.fmt('%.4f')}, uniform = {%t<uniform>}";
}
Output:
data: 199809 200665 199607 200270 199649
χ² = 4.14628, p-value = 0.3866, uniform = True
data: 522573 244456 139979 71531 21461
χ² = 790063.27594, p-value = 0.0000, uniform = False

REXX[edit]

Translation of: Go

Programming notes:

The use of the   pow   was elided as it can just be replaced with   t**(a-1).

The   gamma   was replaced with a simple version.   The argument for   gamma   is (in the cases used herein)   always
positive,   and is either an integer,   or a number which is a multiple of   1/2,   both of these cases can be calculated with
a straight─forward calculation.

/*REXX program performs a chi─squared test to verify a given distribution is uniform.   */
numeric digits length( pi() ) - length(.) /*enough decimal digs for calculations.*/
@.=; @.1= 199809 200665 199607 200270 199649
@.2= 522573 244456 139979 71531 21461
do s=1 while @.s\==''; call uTest @.s /*invoke uTest with a data set of #'s.*/
end /*s*/
exit /*stick a fork in it, we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
!: procedure; parse arg x; p=1; do j=2 to x; p= p*j; end /*j*/; return p
chi2p: procedure; parse arg dof, distance; return gammaI( dof/2, distance/2 )
f: parse arg t; if t=0 then return 0; return t ** (a-1) * exp(-t)
e: e =2.718281828459045235360287471352662497757247093699959574966967627724; return e
pi: pi=3.141592653589793238462643383279502884197169399375105820974944592308; return pi
/*──────────────────────────────────────────────────────────────────────────────────────*/
!!: procedure; parse arg x; if x<2 then return 1; p= x
do k=2+x//2 to x-1 by 2; p= p*k; end /*k*/; return p
/*──────────────────────────────────────────────────────────────────────────────────────*/
chi2ud: procedure: parse arg ds; sum=0; expect= 0
do j=1 for words(ds); expect= expect + word(ds, j)
end /*j*/
expect = expect / words(ds)
do k=1 for words(ds)
sum= sum + (word(ds, k) - expect) **2
end /*k*/
return sum / expect
/*──────────────────────────────────────────────────────────────────────────────────────*/
exp: procedure; parse arg x; ix= x%1; if abs(x-ix)>.5 then ix= ix + sign(x); x= x-ix
z=1; _=1; w=z; do j=1; _= _*x/j; z= (z + _)/1; if z==w then leave; w=z
end /*j*/; if z\==0 then z= z * e()**ix; return z
/*──────────────────────────────────────────────────────────────────────────────────────*/
gamma: procedure; parse arg x; if datatype(x, 'W') then return !(x-1) /*Int? Use fact*/
n= trunc(x) /*at this point, X is pos and a multiple of 1/2.*/
d= !!(n+n - 1) /*compute the double factorial of: 2*n - 1. */
if n//2 then p= -1 /*if N is odd, then use a negative unity. */
else p= 1 /*if N is even, then use a positive unity. */
if x>0 then return p * d * sqrt(pi()) / (2**n)
return p * (2**n) * sqrt(pi()) / d
/*──────────────────────────────────────────────────────────────────────────────────────*/
gammaI: procedure; parse arg a,x; y= a-1; do while f(y)*(x-y) > 2e-8 & y<x; y= y + .4
end /*while*/
y= min(x, y)
return 1 - simp38(0, y, y / 0.015 / gamma(a-1) % 1)
/*──────────────────────────────────────────────────────────────────────────────────────*/
simp38: procedure; parse arg a, b, n; h= (b-a) / n; h1= h / 3
sum= f(a) + f(b)
do j=3*n-1 by -1 while j>0
if j//3 == 0 then sum= sum + 2 * f(a + h1*j)
else sum= sum + 3 * f(a + h1*j)
end /*j*/
return h * sum / 8
/*──────────────────────────────────────────────────────────────────────────────────────*/
sqrt: procedure; parse arg x; if x=0 then return 0; d=digits(); numeric digits; h= d+6
numeric form; m.=9; parse value format(x,2,1,,0) 'E0' with g "E" _ .;g=g *.5'e'_%2
do j=0 while h>9; m.j=h; h=h%2+1; end /*j*/
do k=j+5 to 0 by -1; numeric digits m.k; g=(g+x/g)*.5; end /*k*/; return g
/*──────────────────────────────────────────────────────────────────────────────────────*/
uTest: procedure; parse arg dset; sum= 0; pad= left('', 11); sigLev= 1/20 /*5%*/
say; say ' ' center(" Uniform distribution test ", 75, '═')
#= words(dset); sigPC= sigLev*100/1
do j=1 for #; sum= sum + word(dset, j)
end /*j*/
say pad " dataset: " dset
say pad " samples: " sum
say pad " categories: " #
say pad " degrees of freedom: " # - 1
dist= chi2ud(dset)
P= chi2p(# - 1, dist)
sig = (abs(P) < dist * sigLev)
say pad "significant at " sigPC'% level? ' word('no yes', sig + 1)
say pad " is the dataset uniform? " word('no yes', (\(sig))+ 1)
return
output   when using the default inputs:
      ════════════════════════ Uniform distribution test ════════════════════════
                              dataset:  199809 200665 199607 200270 199649
                              samples:  1000000
                           categories:  5
                   degrees of freedom:  4
            significant at  5%  level?  no
               is the dataset uniform?  yes

      ════════════════════════ Uniform distribution test ════════════════════════
                              dataset:  522573 244456 139979 71531 21461
                              samples:  1000000
                           categories:  5
                   degrees of freedom:  4
            significant at  5%  level?  yes
               is the dataset uniform?  no

Ruby[edit]

Translation of: Python
def gammaInc_Q(a, x)
a1, a2 = a-1, a-2
f0 = lambda {|t| t**a1 * Math.exp(-t)}
df0 = lambda {|t| (a1-t) * t**a2 * Math.exp(-t)}
 
y = a1
y += 0.3 while f0[y]*(x-y) > 2.0e-8 and y < x
y = x if y > x
 
h = 3.0e-4
n = (y/h).to_i
h = y/n
hh = 0.5 * h
sum = 0
(n-1).step(0, -1) do |j|
t = h * j
sum += f0[t] + hh * df0[t]
end
h * sum / gamma_spounge(a)
end
 
A = 12
k1_factrl = 1.0
coef = [Math.sqrt(2.0*Math::PI)]
COEF = (1...A).each_with_object(coef) do |k,c|
c << Math.exp(A-k) * (A-k)**(k-0.5) / k1_factrl
k1_factrl *= -k
end
 
def gamma_spounge(z)
accm = (1...A).inject(COEF[0]){|res,k| res += COEF[k] / (z+k)}
accm * Math.exp(-(z+A)) * (z+A)**(z+0.5) / z
end
 
def chi2UniformDistance(dataSet)
expected = dataSet.inject(:+).to_f / dataSet.size
dataSet.map{|d|(d-expected)**2}.inject(:+) / expected
end
 
def chi2Probability(dof, distance)
1.0 - gammaInc_Q(0.5*dof, 0.5*distance)
end
 
def chi2IsUniform(dataSet, significance=0.05)
dof = dataSet.size - 1
dist = chi2UniformDistance(dataSet)
chi2Probability(dof, dist) > significance
end
 
dsets = [ [ 199809, 200665, 199607, 200270, 199649 ],
[ 522573, 244456, 139979, 71531, 21461 ] ]
 
for ds in dsets
puts "Data set:#{ds}"
dof = ds.size - 1
puts " degrees of freedom: %d" % dof
distance = chi2UniformDistance(ds)
puts " distance:  %.4f" % distance
puts " probability:  %.4f" % chi2Probability(dof, distance)
puts " uniform?  %s" % (chi2IsUniform(ds) ? "Yes" : "No")
end
Output:
Data set:[199809, 200665, 199607, 200270, 199649]
  degrees of freedom: 4
  distance:           4.1463
  probability:        0.3866
  uniform?            Yes
Data set:[522573, 244456, 139979, 71531, 21461]
  degrees of freedom: 4
  distance:           790063.2759
  probability:        -0.0000
  uniform?            No

Rust[edit]

 
use statrs::function::gamma::gamma_li;
 
fn chi_distance(dataset: &[u32]) -> f64 {
let expected = f64::from(dataset.iter().sum::<u32>()) / dataset.len() as f64;
dataset
.iter()
.fold(0., |acc, &elt| acc + (elt as f64 - expected).powf(2.))
/ expected
}
 
fn chi2_probability(dof: f64, distance: f64) -> f64 {
1. - gamma_li(dof * 0.5, distance * 0.5)
}
 
fn chi2_uniform(dataset: &[u32], significance: f64) -> bool {
let d = chi_distance(&dataset);
chi2_probability(dataset.len() as f64 - 1., d) > significance
}
 
fn main() {
let dsets = vec![
vec![199809, 200665, 199607, 200270, 199649],
vec![522573, 244456, 139979, 71531, 21461],
];
 
for ds in dsets {
println!("Data set: {:?}", ds);
let d = chi_distance(&ds);
print!("Distance: {:.6} ", d);
print!(
"Chi2 probability: {:.6} ",
chi2_probability(ds.len() as f64 - 1., d)
);
print!("Uniform? {}\n", chi2_uniform(&ds, 0.05));
}
}
 
 
Output:
Data set: [199809, 200665, 199607, 200270, 199649]
Distance: 4.146280 Chi2 probability: 0.386571 Uniform? true
Data set: [522573, 244456, 139979, 71531, 21461]
Distance: 790063.275940 Chi2 probability: 0.000000 Uniform? false

Scala[edit]

Output:
See it yourself by running in your browser Scastie (remote JVM).
Works with: Scala version 2.13
import org.apache.commons.math3.special.Gamma.regularizedGammaQ
 
object ChiSquare extends App {
private val dataSets: Seq[Seq[Double]] =
Seq(
Seq(199809, 200665, 199607, 200270, 199649),
Seq(522573, 244456, 139979, 71531, 21461)
)
 
private def χ2IsUniform(data: Seq[Double], significance: Double) =
χ2Prob(data.size - 1.0, χ2Dist(data)) > significance
 
private def χ2Dist(data: Seq[Double]) = {
val avg = data.sum / data.size
 
data.reduce((a, b) => a + math.pow(b - avg, 2)) / avg
}
 
private def χ2Prob(dof: Double, distance: Double) =
regularizedGammaQ(dof / 2, distance / 2)
 
printf(" %4s %10s  %12s %8s  %s%n",
"d.f.", "χ²distance", "χ²probability", "Uniform?", "dataset")
dataSets.foreach { ds =>
val (dist, dof) = (χ2Dist(ds), ds.size - 1)
 
printf("%4d %11.3f  %13.8f  %5s  %6s%n",
dof, dist, χ2Prob(dof.toDouble, dist), if (χ2IsUniform(ds, 0.05)) "YES" else "NO", ds.mkString(", "))
}
}

Sidef[edit]

# Confluent hypergeometric function of the first kind F_1(a;b;z)
func F1(a, b, z, limit=100) {
sum(0..limit, {|k|
rising_factorial(a, k) / rising_factorial(b, k) * z**k / k!
})
}
 
func γ(a,x) { # lower incomplete gamma function γ(a,x)
#a**(-1) * x**a * F1(a, a+1, -x) # simpler formula
a**(-1) * x**a * exp(-x) * F1(1, a+1, x) # slightly better convergence
}
 
func P(a,z) { # regularized gamma function P(a,z)
γ(a,z) / Γ(a)
}
 
func chi_squared_cdf (k, x) {
var f = (k<20 ? 20 : 10)
given(x) {
when (0) { 0 }
case (. < (k + f*sqrt(k))) { P(k/2, x/2) }
else { 1 }
}
}
 
func chi_squared_test(arr, significance = 0.05) {
var n = arr.len
var N = arr.sum
var expected = N/n
var χ_squared = arr.sum_by {|v| (v-expected)**2 / expected }
var p_value = (1 - chi_squared_cdf(n-1, χ_squared))
[χ_squared, p_value, p_value > significance]
}
 
[
%n< 199809 200665 199607 200270 199649 >,
%n< 522573 244456 139979 71531 21461 >,
].each {|dataset|
var r = chi_squared_test(dataset)
say "data: #{dataset}"
say "χ² = #{r[0]}, p-value = #{r[1].round(-4)}, uniform = #{r[2]}\n"
}
Output:
data: [199809, 200665, 199607, 200270, 199649]
χ² = 4.14628, p-value = 0.3866, uniform = true

data: [522573, 244456, 139979, 71531, 21461]
χ² = 790063.27594, p-value = 0, uniform = false

Tcl[edit]

Works with: Tcl version 8.5
Library: Tcllib (Package: math::statistics)
package require Tcl 8.5
package require math::statistics
 
proc isUniform {distribution {significance 0.05}} {
set count [tcl::mathop::+ {*}[dict values $distribution]]
set expected [expr {double($count) / [dict size $distribution]}]
set X2 0.0
foreach value [dict values $distribution] {
set X2 [expr {$X2 + ($value - $expected)**2 / $expected}]
}
set degreesOfFreedom [expr {[dict size $distribution] - 1}]
set likelihoodOfRandom [::math::statistics::incompleteGamma \
[expr {$degreesOfFreedom / 2.0}] [expr {$X2 / 2.0}]]
expr {$likelihoodOfRandom > $significance}
}

Testing:

proc makeDistribution {operation {count 1000000}} {
for {set i 0} {$i<$count} {incr i} {incr distribution([uplevel 1 $operation])}
return [array get distribution]
}
 
set distFair [makeDistribution {expr int(rand()*5)}]
puts "distribution \"$distFair\" assessed as [expr [isUniform $distFair]?{fair}:{unfair}]"
set distUnfair [makeDistribution {expr int(rand()*rand()*5)}]
puts "distribution \"$distUnfair\" assessed as [expr [isUniform $distUnfair]?{fair}:{unfair}]"

Output:

distribution "0 199809 4 199649 1 200665 2 199607 3 200270" assessed as fair
distribution "4 21461 0 522573 1 244456 2 139979 3 71531" assessed as unfair

VBA[edit]

The built in worksheetfunction ChiSq_Dist of Excel VBA is used. Output formatted like R.

Private Function Test4DiscreteUniformDistribution(ObservationFrequencies() As Variant, Significance As Single) As Boolean
'Returns true if the observed frequencies pass the Pearson Chi-squared test at the required significance level.
Dim Total As Long, Ei As Long, i As Integer
Dim ChiSquared As Double, DegreesOfFreedom As Integer, p_value As Double
Debug.Print "[1] ""Data set:"" ";
For i = LBound(ObservationFrequencies) To UBound(ObservationFrequencies)
Total = Total + ObservationFrequencies(i)
Debug.Print ObservationFrequencies(i); " ";
Next i
DegreesOfFreedom = UBound(ObservationFrequencies) - LBound(ObservationFrequencies)
'This is exactly the number of different categories minus 1
Ei = Total / (DegreesOfFreedom + 1)
For i = LBound(ObservationFrequencies) To UBound(ObservationFrequencies)
ChiSquared = ChiSquared + (ObservationFrequencies(i) - Ei) ^ 2 / Ei
Next i
p_value = 1 - WorksheetFunction.ChiSq_Dist(ChiSquared, DegreesOfFreedom, True)
Debug.Print
Debug.Print " Chi-squared test for given frequencies"
Debug.Print "X-squared ="; ChiSquared; ", ";
Debug.Print "df ="; DegreesOfFreedom; ", ";
Debug.Print "p-value = "; Format(p_value, "0.0000")
Test4DiscreteUniformDistribution = p_value > Significance
End Function
Public Sub test()
Dim O() As Variant
O = [{199809,200665,199607,200270,199649}]
Debug.Print "[1] ""Uniform? "; Test4DiscreteUniformDistribution(O, 0.05); """"
O = [{522573,244456,139979,71531,21461}]
Debug.Print "[1] ""Uniform? "; Test4DiscreteUniformDistribution(O, 0.05); """"
End Sub
{{out}
[1] "Data set:"  199809   200665   199607   200270   199649  
   Chi-squared test for given frequencies
X-squared = 4.14628 , df = 4 , p-value = 0.3866
[1] "Uniform? True"
[1] "Data set:"  522573   244456   139979   71531   21461  
   Chi-squared test for given frequencies
X-squared = 790063.27594 , df = 4 , p-value = 0.0000
[1] "Uniform? False"

Wren[edit]

Translation of: Kotlin
Library: Wren-math
Library: Wren-fmt
import "/math" for Math, Nums 
import "/fmt" for Fmt
 
var integrate = Fn.new { |a, b, n, f|
var h = (b - a) / n
var sum = 0
for (i in 0...n) {
var x = a + i*h
sum = sum + (f.call(x) + 4 * f.call(x + h/2) + f.call(x + h)) / 6
}
return sum * h
}
 
var gammaIncomplete = Fn.new { |a, x|
var am1 = a - 1
var f0 = Fn.new { |t| t.pow(am1) * Math.exp(-t) }
var h = 1.5e-2
var y = am1
while ((f0.call(y) * (x - y) > 2e-8) && y < x) y = y + 0.4
if (y > x) y = x
return 1 - integrate.call(0, y, (y/h).truncate, f0) / Math.gamma(a)
}
 
var chi2UniformDistance = Fn.new { |ds|
var expected = Nums.mean(ds)
var sum = Nums.sum(ds.map { |d| (d - expected).pow(2) }.toList)
return sum / expected
}
 
var chi2Probability = Fn.new { |dof, dist| gammaIncomplete.call(0.5*dof, 0.5*dist) }
 
var chiIsUniform = Fn.new { |ds, significance|
var dof = ds.count - 1
var dist = chi2UniformDistance.call(ds)
return chi2Probability.call(dof, dist) > significance
}
 
var dsets = [
[199809, 200665, 199607, 200270, 199649],
[522573, 244456, 139979, 71531, 21461]
]
for (ds in dsets) {
System.print("Dataset: %(ds)")
var dist = chi2UniformDistance.call(ds)
var dof = ds.count - 1
Fmt.write("DOF: $d Distance: $.4f", dof, dist)
var prob = chi2Probability.call(dof, dist)
Fmt.write(" Probability: $.6f", prob)
var uniform = chiIsUniform.call(ds, 0.05) ? "Yes" : "No"
System.print(" Uniform? %(uniform)\n")
}
Output:
Dataset: [199809, 200665, 199607, 200270, 199649]
DOF: 4  Distance: 4.1463  Probability: 0.386571  Uniform? Yes

Dataset: [522573, 244456, 139979, 71531, 21461]
DOF: 4  Distance: 790063.2759  Probability: 0.000000  Uniform? No

zkl[edit]

Translation of: C
Translation of: D
/* Numerical integration method */
fcn Simpson3_8(f,a,b,N){ // fcn,double,double,Int --> double
h,h1:=(b - a)/N, h/3.0;
h*[1..3*N - 1].reduce('wrap(sum,j){
l1:=(if(j%3) 3.0 else 2.0);
sum + l1*f(a + h1*j);
},f(a) + f(b))/8.0;
}
 
const A=12;
fcn Gamma_Spouge(z){ // double --> double
var coefs=fcn{ // this runs only once, at construction time
a,coefs:=A.toFloat(),(A).pump(List(),0.0);
k1_factrl:=1.0;
coefs[0]=(2.0*(0.0).pi).sqrt();
foreach k in ([1.0..A-1]){
coefs[k]=(a - k).exp() * (a - k).pow(k - 0.5) / k1_factrl;
k1_factrl*=-k;
}
coefs
}();
 
( [1..A-1].reduce('wrap(accum,k){ accum + coefs[k]/(z + k) },coefs[0])
* (-(z + A)).exp()*(z + A).pow(z + 0.5) )
/ z;
}
 
fcn f0(t,aa1){ t.pow(aa1)*(-t).exp() }
 
fcn GammaIncomplete_Q(a,x){ // double,double --> double
h:=1.5e-2; /* approximate integration step size */
/* this cuts off the tail of the integration to speed things up */
y:=a - 1; f:=f0.fp1(y);
while((f(y)*(x - y)>2.0e-8) and (y<x)){ y+=0.4; }
if(y>x) y=x;
1.0 - Simpson3_8(f,0.0,y,(y/h).toInt())/Gamma_Spouge(a);
}
fcn chi2UniformDistance(ds){ // --> double
dslen  :=ds.len();
expected:=dslen.reduce('wrap(sum,k){ sum + ds[k] },0.0)/dslen;
sum  := dslen.reduce('wrap(sum,k){ x:=ds[k] - expected; sum + x*x },0.0);
sum/expected
}
 
fcn chi2Probability(dof,distance){ GammaIncomplete_Q(0.5*dof, 0.5*distance) }
 
fcn chiIsUniform(dset,significance=0.05){
significance < chi2Probability(-1.0 + dset.len(),chi2UniformDistance(dset))
}
datasets:=T( T(199809.0, 200665.0, 199607.0, 200270.0, 199649.0),
T(522573.0, 244456.0, 139979.0, 71531.0, 21461.0) );
println(" %4s %12s  %12s %8s  %s".fmt(
"dof", "distance", "probability", "Uniform?", "dataset"));
foreach ds in (datasets){
dof :=ds.len() - 1;
dist:=chi2UniformDistance(ds);
prob:=chi2Probability(dof,dist);
println("%4d %12.3f  %12.8f  %5s  %6s".fmt(
dof, dist, prob, chiIsUniform(ds) and "YES" or "NO",
ds.concat(",")));
}
Output:
  dof     distance   probability Uniform?   dataset
   4        4.146    0.38657083      YES    199809,200665,199607,200270,199649
   4   790063.276    0.00000000       NO    522573,244456,139979,71531,21461