Seven-sided dice from five-sided dice: Difference between revisions
Seven-sided dice from five-sided dice (view source)
Revision as of 22:56, 13 February 2024
, 3 months ago→{{header|EasyLang}}
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Line 566:
}
println(bins.snapshot())</syntaxhighlight>
=={{header|EasyLang}}==
<syntaxhighlight>
func dice5 .
return randint 5
.
func dice25 .
return (dice5 - 1) * 5 + dice5
.
func dice7a .
return dice25 mod1 7
.
func dice7b .
repeat
h = dice25
until h <= 21
.
return h mod1 7
.
numfmt 3 0
n = 1000000
len dist[] 7
#
proc checkdist . .
for i to len dist[]
h = dist[i] / n * 7
if abs (h - 1) > 0.01
bad = 1
.
dist[i] = 0
print h
.
if bad = 1
print "-> not uniform"
else
print "-> uniform"
.
.
#
for i to n
dist[dice7a] += 1
.
checkdist
#
print ""
for i to n
dist[dice7b] += 1
.
checkdist
</syntaxhighlight>
{{out}}
<pre>
1.122
1.118
1.121
1.117
0.840
0.842
0.840
-> not uniform
0.996
1.003
1.001
0.997
1.004
0.998
1.001
-> uniform
</pre>
=={{header|Elixir}}==
Line 1,186 ⟶ 1,257:
according to which we are entitled to reject the null hypothesis of
uniform randomness if the χ2 statistic is less than 1.69 or greater
than 16.013, assuming the number of trials is large enough.
See https://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm
Line 1,255 ⟶ 1,326:
["DEBUG:",[19,14,6,18,7,5,1]]
The χ2 statistic for a trial of 70 virtual tosses is 29.2.
Using a two-sided χ2-test with
this is higher than would be expected for a fair die.
["DEBUG:",[9,11,9,10,15,11,5]]
The χ2 statistic for a trial of 70 virtual tosses is 5.4.
Using a two-sided χ2-test with
this is consistent with the die being fair.
</pre>
'''Results for 1,000,000 trials:'''
<pre>
Low
["DEBUG:",[41440,57949,15946,44821,117168,339337,383339]]
The χ2 statistic for a trial of 1000000 virtual tosses is 982157.0011039999.
Using a two-sided χ2-test with
this is higher than would be expected for a fair die.
High
["DEBUG:",[142860,143087,142213,142065,143359,143494,142922]]
The χ2 statistic for a trial of 1000000 virtual tosses is 12.298347999999999.
Using a two-sided χ2-test with
this is consistent with the die being fair.
</pre>
=={{header|Julia}}==
<syntaxhighlight lang="julia">
using Random: seed!
seed!(1234) # for reproducibility
dice5() = rand(1:5)
function dice7()
while true
a = dice5()
b = dice5()
c = a + 5(b - 1)
if c <= 21
return mod1(c, 7)
end
end
end
rolls = (dice7() for i in 1:100000)
roll_counts = Dict{Int,Int}()
for roll in rolls
roll_counts[roll] = get(roll_counts, roll, 0) + 1
end
foreach(println, sort(roll_counts))
</syntaxhighlight>
Output:
<pre>
1 => 14530
2 => 13872
3 => 14422
4 => 14425
5 => 14323
6 => 14315
7 => 14113
</pre>
Line 2,064 ⟶ 2,174:
7 6 3 5 2 2 7 1 2 7 3 7 4 4 4 2 3 2 6 1
</pre>
=={{header|RPL}}==
<code>UNIF?</code> is defined at [[Verify distribution uniformity/Naive#RPL|Verify distribution uniformity/Naive]]
{{works with|Halcyon Calc|4.2.7}}
≪ ≪ RAND 5 * CEIL ≫ → dice5
≪ '''WHILE'''
dice5 EVAL 5 *
dice5 EVAL 6 - +
DUP 21 ≥
'''REPEAT''' DROP '''END'''
7 MOD 1 +
≫ ≫ '<span style="color:blue">DICE7</span>' STO
≪ <span style="color:blue">DICE7</span> ≫ 100000 .1 <span style="color:blue">UNIF?</span>
{{out}}
<pre>
1: [ 14557 14245 14255 14400 14224 14151 14168 ]
</pre>
Watchdog timer limits the loop to 100,000 items.
=={{header|Ruby}}==
Line 2,381 ⟶ 2,510:
{{libheader|Wren-sort}}
{{libheader|Wren-fmt}}
<syntaxhighlight lang="
import "./sort" for Sort
import "./fmt" for Fmt
var r = Random.new()
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