Skip to main content

Random Number Generators

Project description

RNG: Random Number Generator

Provides low-level access to the C++ Random library.

Random Engine: Mersenne Twister 64.

More info: https://en.wikipedia.org/wiki/Mersenne_Twister.

Random Bool

  • random_bool(ratio: float) -> bool

Random Integer

  • random_int(lo_limit: int, hi_limit: int) -> int
  • random_binomial(number_of_trials: int, probability: float) -> int
  • random_negative_binomial(number_of_trials: int, probability: float) -> int
  • random_geometric(probability: float) -> int
  • random_poisson(average: float) -> int
  • random_discrete(count: int, xmin: int, xmax: int) -> int

Random Floating Point

  • random_floating_point(lo_limit: float, hi_limit: float) -> float
  • random_exponential(lambda_rate: float) -> float
  • random_gamma(shape: float, scale: float) -> float
  • random_weibull(shape: float, scale: float) -> float
  • random_extreme_value(shape: float, scale: float) -> float
  • random_normal(average: float, std_dev: float) -> float
  • random_log_normal(log_mean: float, log_deviation: float) -> float
  • random_chi_squared(degrees_of_freedom: float) -> float
  • random_cauchy(location: float, scale: float) -> float
  • random_fisher_f(degrees_of_freedom_1: float, degrees_of_freedom_2: float) -> float
  • random_student_t(degrees_of_freedom: float) -> float

C-style Random

  • c_rand() -> int

Distribution and Performance Test Suite

Binary RNG Tests

Function: random_bool(0.3333)
Time: min: 31ns, mode: 62ns, mean: 63ns, max: 218ns
Distribution:
False: 66.6507%
True: 33.3493%


Integer RNG Tests

Function: random_int(1, 6)
Time: min: 62ns, mode: 62ns, mean: 85ns, max: 343ns
Distribution:
1: 16.698%
2: 16.6572%
3: 16.6128%
4: 16.7057%
5: 16.711%
6: 16.6153%

Function: random_binomial(4, 0.5)
Time: min: 156ns, mode: 156ns, mean: 188ns, max: 718ns
Distribution:
0: 6.2078%
1: 24.9412%
2: 37.539%
3: 25.0835%
4: 6.2285%

Function: random_negative_binomial(5, 0.75)
Time: min: 125ns, mode: 125ns, mean: 134ns, max: 187ns
Distribution:
0: 23.7542%
1: 29.7228%
2: 22.2209%
3: 12.9664%
4: 6.486%
5: 2.8932%
6: 1.2104%
7: 0.473%
8: 0.1769%
9: 0.0645%
10: 0.022%
11: 0.0066%
12: 0.002%
13: 0.0009%
15: 0.0001%
17: 0.0001%

Function: random_geometric(0.75)
Time: min: 31ns, mode: 62ns, mean: 56ns, max: 93ns
Distribution:
0: 74.9651%
1: 18.7735%
2: 4.716%
3: 1.1644%
4: 0.2874%
5: 0.068%
6: 0.0199%
7: 0.0042%
8: 0.0008%
9: 0.0006%
10: 0.0001%

Function: random_poisson(4.5)
Time: min: 93ns, mode: 125ns, mean: 121ns, max: 437ns
Distribution:
0: 1.1284%
1: 4.9802%
2: 11.2596%
3: 16.8643%
4: 18.9139%
5: 17.1252%
6: 12.8555%
7: 8.2521%
8: 4.6164%
9: 2.2961%
10: 1.0518%
11: 0.4202%
12: 0.1578%
13: 0.0539%
14: 0.0185%
15: 0.0039%
16: 0.0018%
17: 0.0003%
19: 0.0001%

Function: random_discrete(5, 1, 5)
Time: min: 406ns, mode: 406ns, mean: 417ns, max: 562ns
Distribution:
0: 12.0356%
1: 16.005%
2: 19.9592%
3: 23.9771%
4: 28.0231%


Floating Point RNG Tests

Function: random_floating_point(0, 10)
Time: min: 62ns, mode: 62ns, mean: 73ns, max: 125ns
Floored Distribution:
0: 9.9679%
1: 10.0064%
2: 9.9984%
3: 9.9833%
4: 10.0267%
5: 10.0031%
6: 10.0157%
7: 10.0111%
8: 9.9659%
9: 10.0215%

Function: random_exponential(1.0)
Time: min: 62ns, mode: 62ns, mean: 78ns, max: 125ns
Floored Distribution:
0: 63.2954%
1: 23.1848%
2: 8.5824%
3: 3.1309%
4: 1.1421%
5: 0.4158%
6: 0.1541%
7: 0.0619%
8: 0.0211%
9: 0.0076%
10: 0.0032%
11: 0.0004%
12: 0.0001%
13: 0.0002%

Function: random_gamma(1.0, 1.0)
Time: min: 62ns, mode: 93ns, mean: 80ns, max: 125ns
Floored Distribution:
0: 63.1506%
1: 23.2629%
2: 8.5489%
3: 3.1741%
4: 1.1709%
5: 0.4392%
6: 0.1572%
7: 0.0626%
8: 0.023%
9: 0.0062%
10: 0.0035%
11: 0.0003%
12: 0.0005%
13: 0.0001%

Function: random_weibull(1.0, 1.0)
Time: min: 125ns, mode: 125ns, mean: 132ns, max: 375ns
Floored Distribution:
0: 63.2818%
1: 23.1514%
2: 8.5913%
3: 3.153%
4: 1.1537%
5: 0.423%
6: 0.1571%
7: 0.0562%
8: 0.0205%
9: 0.0081%
10: 0.0031%
11: 0.0007%
13: 0.0001%

Function: random_extreme_value(0.0, 1.0)
Time: min: 93ns, mode: 93ns, mean: 100ns, max: 125ns
Rounded Distribution:
-3: 0.0003%
-2: 1.1218%
-1: 18.1563%
0: 35.2462%
1: 25.4654%
2: 12.1703%
3: 4.8777%
4: 1.8614%
5: 0.7011%
6: 0.2571%
7: 0.089%
8: 0.0334%
9: 0.0138%
10: 0.0038%
11: 0.0014%
12: 0.0006%
13: 0.0004%

Function: random_normal(5, 2)
Time: min: 125ns, mode: 125ns, mean: 140ns, max: 187ns
Rounded Distribution:
-6: 0.0001%
-4: 0.0007%
-3: 0.0083%
-2: 0.0478%
-1: 0.2389%
0: 0.9091%
1: 2.8127%
2: 6.5272%
3: 12.108%
4: 17.4766%
5: 19.6871%
6: 17.4814%
7: 12.0896%
8: 6.5874%
9: 2.8163%
10: 0.9097%
11: 0.243%
12: 0.0475%
13: 0.0074%
14: 0.0012%

Function: random_log_normal(1.6, 0.25)
Time: min: 156ns, mode: 156ns, mean: 157ns, max: 218ns
Floored Distribution:
1: 0.0146%
2: 2.2307%
3: 17.3887%
4: 31.8718%
5: 26.3727%
6: 13.8204%
7: 5.5586%
8: 1.9049%
9: 0.5938%
10: 0.1756%
11: 0.0492%
12: 0.0135%
13: 0.0044%
14: 0.0009%
15: 0.0001%
16: 0.0001%

Function: random_chi_squared(1.0)
Time: min: 156ns, mode: 187ns, mean: 175ns, max: 250ns
Floored Distribution:
0: 68.2917%
1: 15.991%
2: 7.42%
3: 3.7794%
4: 1.9956%
5: 1.0965%
6: 0.6021%
7: 0.3572%
8: 0.1958%
9: 0.1119%
10: 0.0661%
11: 0.0373%
12: 0.0249%
13: 0.0114%
14: 0.007%
15: 0.0049%
16: 0.0028%
17: 0.0015%
18: 0.001%
19: 0.0008%
20: 0.0006%
21: 0.0004%
22: 0.0001%

Function: random_cauchy(0.0, 0.0005)
Time: min: 93ns, mode: 93ns, mean: 101ns, max: 156ns
Rounded Distribution:
-2908: 0.0001%
-458: 0.0001%
-139: 0.0001%
-31: 0.0001%
-29: 0.0001%
-25: 0.0002%
-22: 0.0001%
-17: 0.0001%
-15: 0.0001%
-13: 0.0001%
-12: 0.0003%
-11: 0.0004%
-10: 0.0001%
-8: 0.0001%
-7: 0.0007%
-6: 0.0002%
-5: 0.0005%
-4: 0.0009%
-3: 0.0021%
-2: 0.0037%
-1: 0.0212%
0: 99.9331%
1: 0.0231%
2: 0.0051%
3: 0.0026%
4: 0.0007%
5: 0.0012%
6: 0.0003%
7: 0.0001%
8: 0.0005%
9: 0.0002%
10: 0.0003%
11: 0.0001%
13: 0.0002%
14: 0.0003%
15: 0.0002%
31: 0.0001%
34: 0.0001%
35: 0.0001%
43: 0.0001%
48: 0.0001%
64: 0.0001%
71: 0.0001%

Function: random_fisher_f(8.0, 8.0)
Time: min: 250ns, mode: 281ns, mean: 285ns, max: 375ns
Floored Distribution:
0: 50.0217%
1: 32.6889%
2: 10.2521%
3: 3.6864%
4: 1.569%
5: 0.7592%
6: 0.3928%
7: 0.2326%
8: 0.126%
9: 0.0825%
10: 0.052%
11: 0.0336%
12: 0.0227%
13: 0.0174%
14: 0.0126%
15: 0.0104%
16: 0.0077%
17: 0.0061%
18: 0.0054%
19: 0.0033%
20: 0.0025%
21: 0.0025%
22: 0.0009%
23: 0.0017%
24: 0.0014%
25: 0.001%
26: 0.001%
27: 0.0007%
28: 0.0002%
29: 0.0009%
30: 0.0004%
31: 0.0004%
32: 0.0002%
33: 0.0005%
34: 0.0001%
35: 0.0002%
36: 0.0006%
37: 0.0004%
38: 0.0004%
40: 0.0003%
41: 0.0001%
42: 0.0001%
43: 0.0001%
46: 0.0002%
47: 0.0001%
48: 0.0001%
50: 0.0001%
55: 0.0001%
61: 0.0001%
68: 0.0001%
70: 0.0001%
73: 0.0001%

Function: random_student_t(8.0)
Time: min: 218ns, mode: 250ns, mean: 243ns, max: 312ns
Rounded Distribution:
-13: 0.0001%
-12: 0.0002%
-10: 0.0002%
-9: 0.0007%
-8: 0.0029%
-7: 0.006%
-6: 0.018%
-5: 0.0747%
-4: 0.3087%
-3: 1.4298%
-2: 6.748%
-1: 22.8909%
0: 36.8936%
1: 22.9941%
2: 6.7838%
3: 1.4525%
4: 0.2975%
5: 0.0713%
6: 0.0182%
7: 0.005%
8: 0.0028%
9: 0.0005%
10: 0.0002%
11: 0.0003%


C-style RNG Tests

Function: c_rand()
Time: min: 31ns, mode: 62ns, mean: 57ns, max: 93ns
Modulo 10 Distribution:
0: 10.0003%
1: 10.0206%
2: 9.9862%
3: 9.9965%
4: 9.9771%
5: 9.9823%
6: 10.0342%
7: 10.0239%
8: 9.9888%
9: 9.9901%


All tests passed!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

RNG-0.0.5.tar.gz (64.1 kB view details)

Uploaded Source

Built Distribution

RNG-0.0.5-cp37-cp37m-macosx_10_9_x86_64.whl (64.3 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file RNG-0.0.5.tar.gz.

File metadata

  • Download URL: RNG-0.0.5.tar.gz
  • Upload date:
  • Size: 64.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.0 setuptools/40.6.2 requests-toolbelt/0.8.0 tqdm/4.27.0 CPython/3.7.2

File hashes

Hashes for RNG-0.0.5.tar.gz
Algorithm Hash digest
SHA256 2fac611c11c1cbf3cc6aa3cd3ad1b6d59ee39c95642932528b352aea240923fa
MD5 ed1aafa8e0252d35a715da98d5b9c1e1
BLAKE2b-256 2a5c6d05510f71f689c2a7a8105475b35210ea1ae50c2d785fe6d628ce20be5b

See more details on using hashes here.

File details

Details for the file RNG-0.0.5-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: RNG-0.0.5-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 64.3 kB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.0 setuptools/40.6.2 requests-toolbelt/0.8.0 tqdm/4.27.0 CPython/3.7.2

File hashes

Hashes for RNG-0.0.5-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7ef1215b5a83ed621df4fe30a1b766101ee076ac622549cf3950d2407a6b4a7e
MD5 ded5dab2786b24050e0879a774195709
BLAKE2b-256 ea04932a16b158002081e729d2c84e01488cd542447bec4c536ef79532d2d297

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page