{"id":16141800,"url":"https://github.com/kermalis/krand","last_synced_at":"2026-03-11T14:34:15.648Z","repository":{"id":101968207,"uuid":"596934253","full_name":"Kermalis/KRand","owner":"Kermalis","description":"C# Xoshiro256** library THAT USES INCLUSIVE MAX VALUES geez","archived":false,"fork":false,"pushed_at":"2024-06-08T23:47:00.000Z","size":38,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T13:39:47.534Z","etag":null,"topics":["csharp","library","net8","performance","random","random-generation","xoshiro","xoshiro256"],"latest_commit_sha":null,"homepage":"","language":"C#","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Kermalis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-03T08:52:47.000Z","updated_at":"2024-06-08T23:47:03.000Z","dependencies_parsed_at":"2024-06-09T00:09:06.171Z","dependency_job_id":"c1a34c2d-0cca-47cc-a59c-6ff34a50d91f","html_url":"https://github.com/Kermalis/KRand","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kermalis%2FKRand","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kermalis%2FKRand/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kermalis%2FKRand/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kermalis%2FKRand/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Kermalis","download_url":"https://codeload.github.com/Kermalis/KRand/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248795175,"owners_count":21162725,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["csharp","library","net8","performance","random","random-generation","xoshiro","xoshiro256"],"created_at":"2024-10-09T23:56:54.479Z","updated_at":"2026-03-11T14:34:15.603Z","avatar_url":"https://github.com/Kermalis.png","language":"C#","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 💻 KRand\n\n[![NuGet](https://img.shields.io/nuget/v/KRand.svg)](https://www.nuget.org/packages/KRand)\n[![NuGet downloads](https://img.shields.io/nuget/dt/KRand)](https://www.nuget.org/packages/KRand)\n\nI wrote this .NET 8.0 library since nobody else seems to want inclusive max values in their randomizers for some reason.\n\nAnyway 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.\n\nIt's also only 1 file large.\nIt's based on the `Xoshiro256**` algorithm, which is the go-to in this time period I think.\nYou can supply your own seeds/states or not, the outputted values are evenly distributed, and it returns Boolean/8bit/16bit/32bit/64bit/Single/Double.\n(Half is not supported because the resolution is too low for even distribution - I tried it and it skewed towards one side.\nIf you need Half support, you should try casting Single to it, but I don't know if this would solve the resolution issue...)\nAND MOST IMPORTANTLY, THE MAXIMUM VALUES OF EACH RANGE ARE INCLUSIVE.\n\n```cs\nsbyte val = _rand.NextSByte(); // [sbyte.MinValue, sbyte.MaxValue]\nshort val = _rand.NextUInt16(-14, 23); // [-14, 23]\nuint val = _rand.NextUInt32(); // [uint.MinValue, uint.MaxValue]\nlong val = _rand.NextUInt64(-5_000_000_000, 5_000_000_000); // [-5,000,000,000, 5,000,000,000]\nfloat val = _rand.NextSingle(); // [0, 1]\ndouble val = _rand.NextDouble(-500.123, 500.47); // [-500.123, 500.47]\nbool val = _rand.NextBoolean(); // 1/2 (50%) True\nbool val = _rand.NextBoolean(3, 10); // 3/10 (30%) True\nbool val = _rand.NextBoolean(8, 447); // 8/447 (~1.78970917225951%) True\n```\n\nYou can inherit from the `KRand` class too and add other stuff.\nIt 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.\n\nIf you want to really see how evenly distributed it is, run the included tests.\nI spent a lot of time and research to make the tests actually accurate with their reporting.\n\n----\n## 🚀 Example Tests:\n\n### Boolean: 50/50 chance | 1,000,000,000 iterations\n```\nFalse | 499,988,064 (50%)\n True | 500,011,936 (50%)\n```\n### Boolean: 30/70 chance | 1,000,000,000 iterations\n```\nFalse | 150,004,312 (30%)\n True | 349,995,688 (70%)\n```\n### Double: 50,000,000 iterations\n```\nBucket 0 | [0.0, 0.1) | R AVG = 0.05000 ||| 5,002,518 (10.01%) | AVG = 0.05000\nBucket 1 | [0.1, 0.2) | R AVG = 0.15000 ||| 4,999,991 (10.00%) | AVG = 0.15000\nBucket 2 | [0.2, 0.3) | R AVG = 0.25000 ||| 5,000,154 (10.00%) | AVG = 0.25001\nBucket 3 | [0.3, 0.4) | R AVG = 0.35000 ||| 4,998,428 (10.00%) | AVG = 0.34999\nBucket 4 | [0.4, 0.5) | R AVG = 0.45000 ||| 4,999,807 (10.00%) | AVG = 0.45001\nBucket 5 | [0.5, 0.6) | R AVG = 0.55000 ||| 4,998,929 (10.00%) | AVG = 0.54999\nBucket 6 | [0.6, 0.7) | R AVG = 0.65000 ||| 5,000,104 (10.00%) | AVG = 0.64998\nBucket 7 | [0.7, 0.8) | R AVG = 0.75000 ||| 4,997,370 ( 9.99%) | AVG = 0.74999\nBucket 8 | [0.8, 0.9) | R AVG = 0.85000 ||| 5,000,855 (10.00%) | AVG = 0.85002\nBucket 9 | [0.9, 1.0] | R AVG = 0.95000 ||| 5,001,844 (10.00%) | AVG = 0.95001\n```\n### SByte: [-128, 127] range | 100,000,000 iterations\n```\nBucket  0 | [-128.0000, -112.0625) | R AVG = -120.03125 ||| 6,247,921 (6.25%) | SUM = -752,877,326 | AVG = -120.50046\nBucket  1 | [-112.0625, - 96.1250) | R AVG = -104.09375 ||| 6,249,791 (6.25%) | SUM = -653,101,180 | AVG = -104.49968\nBucket  2 | [- 96.1250, - 80.1875) | R AVG = - 88.15625 ||| 6,249,963 (6.25%) | SUM = -553,146,671 | AVG = - 88.50399\nBucket  3 | [- 80.1875, - 64.2500) | R AVG = - 72.21875 ||| 6,251,926 (6.25%) | SUM = -453,265,928 | AVG = - 72.50021\nBucket  4 | [- 64.2500, - 48.3125) | R AVG = - 56.28125 ||| 6,251,060 (6.25%) | SUM = -353,194,202 | AVG = - 56.50149\nBucket  5 | [- 48.3125, - 32.3750) | R AVG = - 40.34375 ||| 6,249,829 (6.25%) | SUM = -253,123,610 | AVG = - 40.50089\nBucket  6 | [- 32.3750, - 16.4375) | R AVG = - 24.40625 ||| 6,251,392 (6.25%) | SUM = -153,166,308 | AVG = - 24.50115\nBucket  7 | [- 16.4375, -  0.5000) | R AVG = -  8.46875 ||| 6,248,948 (6.25%) | SUM = - 53,109,748 | AVG = -  8.49899\nBucket  8 | [-  0.5000,   15.4375) | R AVG =    7.46875 ||| 6,252,128 (6.25%) | SUM =   46,901,862 | AVG =    7.50174\nBucket  9 | [  15.4375,   31.3750) | R AVG =   23.40625 ||| 6,249,748 (6.25%) | SUM =  146,873,429 | AVG =   23.50070\nBucket 10 | [  31.3750,   47.3125) | R AVG =   39.34375 ||| 6,249,075 (6.25%) | SUM =  246,840,056 | AVG =   39.50025\nBucket 11 | [  47.3125,   63.2500) | R AVG =   55.28125 ||| 6,249,723 (6.25%) | SUM =  346,845,398 | AVG =   55.49772\nBucket 12 | [  63.2500,   79.1875) | R AVG =   71.21875 ||| 6,248,698 (6.25%) | SUM =  446,780,166 | AVG =   71.49972\nBucket 13 | [  79.1875,   95.1250) | R AVG =   87.15625 ||| 6,249,838 (6.25%) | SUM =  546,851,142 | AVG =   87.49845\nBucket 14 | [  95.1250,  111.0625) | R AVG =  103.09375 ||| 6,250,293 (6.25%) | SUM =  646,908,657 | AVG =  103.50053\nBucket 15 | [ 111.0625,  127.0000] | R AVG =  119.03125 ||| 6,249,667 (6.25%) | SUM =  746,843,599 | AVG =  119.50134\n```\n### UInt64: [1,000,000,000, 18,446,244,013,709,451,615] range | 50,000,000 iterations\n```\nBucket 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\nBucket 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\nBucket 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\nBucket 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\nBucket 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\nBucket 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\nBucket 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\nBucket 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\nBucket 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\nBucket 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\n```\n\n----\n## KRandTesting Uses:\n* [xUnit.net](https://github.com/xunit/xunit)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkermalis%2Fkrand","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkermalis%2Fkrand","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkermalis%2Fkrand/lists"}