KRand 1.1.1

💻 KRand

I wrote this .NET 8.0 library since nobody else seems to want inclusive max values in their randomizers for some reason.

Anyway I'm only making it a library since I've been using a version of this in pretty much everything I do, it has no dependencies, and it's lightning fast, so I might as well make it public.

It's also only 1 file large. It's based on the Xoshiro256** algorithm, which is the go-to in this time period I think. You can supply your own seeds/states or not, the outputted values are evenly distributed, and it returns Boolean/8bit/16bit/32bit/64bit/Single/Double. (Half is not supported because the resolution is too low for even distribution - I tried it and it skewed towards one side. If you need Half support, you should try casting Single to it, but I don't know if this would solve the resolution issue...) AND MOST IMPORTANTLY, THE MAXIMUM VALUES OF EACH RANGE ARE INCLUSIVE.

sbyte val = _rand.NextSByte(); // [sbyte.MinValue, sbyte.MaxValue]
short val = _rand.NextUInt16(-14, 23); // [-14, 23]
uint val = _rand.NextUInt32(); // [uint.MinValue, uint.MaxValue]
long val = _rand.NextUInt64(-5_000_000_000, 5_000_000_000); // [-5,000,000,000, 5,000,000,000]
float val = _rand.NextSingle(); // [0, 1]
double val = _rand.NextDouble(-500.123, 500.47); // [-500.123, 500.47]
bool val = _rand.NextBoolean(); // 1/2 (50%) True
bool val = _rand.NextBoolean(3, 10); // 3/10 (30%) True
bool val = _rand.NextBoolean(8, 447); // 8/447 (~1.78970917225951%) True

You can inherit from the KRand class too and add other stuff. It has Vector3 which just grabs 3 random floats, since it's very common to need 3 (color, position, etc.), so you can just add so much randomization like that but I kept Vector3 for everyone.

If you want to really see how evenly distributed it is, run the included tests. I spent a lot of time and research to make the tests actually accurate with their reporting.

🚀 Example Tests:

Boolean: 50/50 chance | 1,000,000,000 iterations

False | 499,988,064 (50%)
 True | 500,011,936 (50%)

Boolean: 30/70 chance | 1,000,000,000 iterations

False | 150,004,312 (30%)
 True | 349,995,688 (70%)

Double: 50,000,000 iterations

Bucket 0 | [0.0, 0.1) | R AVG = 0.05000 ||| 5,002,518 (10.01%) | AVG = 0.05000
Bucket 1 | [0.1, 0.2) | R AVG = 0.15000 ||| 4,999,991 (10.00%) | AVG = 0.15000
Bucket 2 | [0.2, 0.3) | R AVG = 0.25000 ||| 5,000,154 (10.00%) | AVG = 0.25001
Bucket 3 | [0.3, 0.4) | R AVG = 0.35000 ||| 4,998,428 (10.00%) | AVG = 0.34999
Bucket 4 | [0.4, 0.5) | R AVG = 0.45000 ||| 4,999,807 (10.00%) | AVG = 0.45001
Bucket 5 | [0.5, 0.6) | R AVG = 0.55000 ||| 4,998,929 (10.00%) | AVG = 0.54999
Bucket 6 | [0.6, 0.7) | R AVG = 0.65000 ||| 5,000,104 (10.00%) | AVG = 0.64998
Bucket 7 | [0.7, 0.8) | R AVG = 0.75000 ||| 4,997,370 ( 9.99%) | AVG = 0.74999
Bucket 8 | [0.8, 0.9) | R AVG = 0.85000 ||| 5,000,855 (10.00%) | AVG = 0.85002
Bucket 9 | [0.9, 1.0] | R AVG = 0.95000 ||| 5,001,844 (10.00%) | AVG = 0.95001

SByte: [-128, 127] range | 100,000,000 iterations

Bucket  0 | [-128.0000, -112.0625) | R AVG = -120.03125 ||| 6,247,921 (6.25%) | SUM = -752,877,326 | AVG = -120.50046
Bucket  1 | [-112.0625, - 96.1250) | R AVG = -104.09375 ||| 6,249,791 (6.25%) | SUM = -653,101,180 | AVG = -104.49968
Bucket  2 | [- 96.1250, - 80.1875) | R AVG = - 88.15625 ||| 6,249,963 (6.25%) | SUM = -553,146,671 | AVG = - 88.50399
Bucket  3 | [- 80.1875, - 64.2500) | R AVG = - 72.21875 ||| 6,251,926 (6.25%) | SUM = -453,265,928 | AVG = - 72.50021
Bucket  4 | [- 64.2500, - 48.3125) | R AVG = - 56.28125 ||| 6,251,060 (6.25%) | SUM = -353,194,202 | AVG = - 56.50149
Bucket  5 | [- 48.3125, - 32.3750) | R AVG = - 40.34375 ||| 6,249,829 (6.25%) | SUM = -253,123,610 | AVG = - 40.50089
Bucket  6 | [- 32.3750, - 16.4375) | R AVG = - 24.40625 ||| 6,251,392 (6.25%) | SUM = -153,166,308 | AVG = - 24.50115
Bucket  7 | [- 16.4375, -  0.5000) | R AVG = -  8.46875 ||| 6,248,948 (6.25%) | SUM = - 53,109,748 | AVG = -  8.49899
Bucket  8 | [-  0.5000,   15.4375) | R AVG =    7.46875 ||| 6,252,128 (6.25%) | SUM =   46,901,862 | AVG =    7.50174
Bucket  9 | [  15.4375,   31.3750) | R AVG =   23.40625 ||| 6,249,748 (6.25%) | SUM =  146,873,429 | AVG =   23.50070
Bucket 10 | [  31.3750,   47.3125) | R AVG =   39.34375 ||| 6,249,075 (6.25%) | SUM =  246,840,056 | AVG =   39.50025
Bucket 11 | [  47.3125,   63.2500) | R AVG =   55.28125 ||| 6,249,723 (6.25%) | SUM =  346,845,398 | AVG =   55.49772
Bucket 12 | [  63.2500,   79.1875) | R AVG =   71.21875 ||| 6,248,698 (6.25%) | SUM =  446,780,166 | AVG =   71.49972
Bucket 13 | [  79.1875,   95.1250) | R AVG =   87.15625 ||| 6,249,838 (6.25%) | SUM =  546,851,142 | AVG =   87.49845
Bucket 14 | [  95.1250,  111.0625) | R AVG =  103.09375 ||| 6,250,293 (6.25%) | SUM =  646,908,657 | AVG =  103.50053
Bucket 15 | [ 111.0625,  127.0000] | R AVG =  119.03125 ||| 6,249,667 (6.25%) | SUM =  746,843,599 | AVG =  119.50134

UInt64: [1,000,000,000, 18,446,244,013,709,451,615] range | 50,000,000 iterations

Bucket 0 | [             1,000,000,000.0,  1,844,624,402,270,945,161.5) | R AVG =    922,312,201,635,472,580.75 ||| 4,994,986 ( 9.99%) | AVG =    922,474,043,596,388,696.65119
Bucket 1 | [ 1,844,624,402,270,945,161.5,  3,689,248,803,541,890,323.0) | R AVG =  2,766,936,602,906,417,742.25 ||| 4,998,958 (10.00%) | AVG =  2,766,910,383,201,381,190.82857
Bucket 2 | [ 3,689,248,803,541,890,323.0,  5,533,873,204,812,835,484.5) | R AVG =  4,611,561,004,177,362,903.75 ||| 4,999,225 (10.00%) | AVG =  4,611,519,291,355,730,455.11597
Bucket 3 | [ 5,533,873,204,812,835,484.5,  7,378,497,606,083,780,646.0) | R AVG =  6,456,185,405,448,308,065.25 ||| 5,001,415 (10.00%) | AVG =  6,456,205,489,846,172,361.83540
Bucket 4 | [ 7,378,497,606,083,780,646.0,  9,223,122,007,354,725,807.5) | R AVG =  8,300,809,806,719,253,226.75 ||| 4,999,282 (10.00%) | AVG =  8,300,866,809,324,480,645.48271
Bucket 5 | [ 9,223,122,007,354,725,807.5, 11,067,746,408,625,670,969.0) | R AVG = 10,145,434,207,990,198,388.25 ||| 5,002,822 (10.01%) | AVG = 10,145,444,838,329,452,382.21921
Bucket 6 | [11,067,746,408,625,670,969.0, 12,912,370,809,896,616,130.5) | R AVG = 11,990,058,609,261,143,549.75 ||| 5,002,139 (10.00%) | AVG = 11,990,031,099,442,315,059.18939
Bucket 7 | [12,912,370,809,896,616,130.5, 14,756,995,211,167,561,292.0) | R AVG = 13,834,683,010,532,088,711.25 ||| 5,001,811 (10.00%) | AVG = 13,834,757,620,438,795,154.50919
Bucket 8 | [14,756,995,211,167,561,292.0, 16,601,619,612,438,506,453.5) | R AVG = 15,679,307,411,803,033,872.75 ||| 4,999,848 (10.00%) | AVG = 15,679,071,349,122,356,922.71914
Bucket 9 | [16,601,619,612,438,506,453.5, 18,446,244,013,709,451,615.0] | R AVG = 17,523,931,813,073,979,034.25 ||| 4,999,514 (10.00%) | AVG = 17,524,134,996,440,728,893.58987

KRandTesting Uses:

• xUnit.net