RNG 1.4.1

Python3 API for the C++ Random library

RNG Engine for Python

Python3 interface to the c++ random library

Designed for python developers familiar with the c++ random header

Sister Projects:

• Fortuna: Collection of abstractions to make custom random value generators. https://pypi.org/project/Fortuna/
• Pyewacket: Complete drop-in replacement for the Python3 Random Module. https://pypi.org/project/Pyewacket/

Support these and other random projects: https://www.patreon.com/brokencode

Warning: RNG is not suitable for cryptography or secure hashing.

Quick Install for Mac and Linux: $ pip install RNG, import RNG as needed.

---

Random Generator Specifications

Random Boolean

• RNG.bernoulli_distribution(ratio_of_truth: float) -> bool
  • Produce a Bernoulli distribution of booleans.
  • @param ratio_of_truth :: the probability of True. Expected input range: [0.0, 1.0], clamped.
  • @return :: True or False

# bernoulli_distribution.py
from RNG import bernoulli_distribution


print(bernoulli_distribution(0.25))
# prints a random boolean, 25% probability of True

Random Integer

• RNG.uniform_int_distribution(left_limit: int, right_limit: int) -> int
  • Flat uniform distribution.
  • 20x faster than random.randint()
  • @param left_limit :: input A.
  • @param right_limit :: input B.
  • @return :: random integer in the inclusive range [A, B] or [B, A] if B < A

# uniform_int_distribution.py
from RNG import uniform_int_distribution


print(uniform_int_distribution(-6, 5))
# prints a random int in range [-6, 5]

• RNG.binomial_distribution(number_of_trials: int, probability: float) -> int
  • Based on the idea of flipping a coin and counting how many heads come up after some number of flips.
  • @param number_of_trials :: how many times to flip a coin.
  • @param probability :: how likely heads will be flipped. 0.5 is a fair coin. 1.0 is a double headed coin.
  • @return :: count of how many heads came up.
• RNG.negative_binomial_distribution(trial_successes: int, probability: float) -> int
  • Based on the idea of flipping a coin as long as it takes to succeed.
  • @param trial_successes :: the required number of heads flipped to succeed.
  • @param probability :: how likely heads will be flipped. 0.50 is a fair coin.
  • @return :: the count of how many tails came up before the required number of heads.
• RNG.geometric_distribution(probability: float) -> int
  • Same as random_negative_binomial(1, probability).
• RNG.poisson_distribution(mean: float) -> int
  • @param mean :: sets the average output of the function.
  • @return :: random integer, poisson distribution centered on the mean.

Random Floating Point

• RNG.generate_canonical() -> float
  • Evenly distributes floats of maximum precision.
  • @return :: random float in range (0.0, 1.0)

# generate_canonical.py
from RNG import generate_canonical


print(generate_canonical())
# prints a random float in range (0.0, 1.0)

• RNG.uniform_real_distribution(left_limit: float, right_limit: float) -> float
  • Flat uniform distribution of floats.
  • @return :: random Float between left_limit and right_limit.
• RNG.normal_distribution(mean: float, std_dev: float) -> float
  • @param mean :: sets the average output of the function.
  • @param std_dev :: standard deviation. Specifies spread of data from the mean.
• RNG.lognormal_distribution(log_mean: float, log_deviation: float) -> float
  • @param log_mean :: sets the log of the mean of the function.
  • @param log_deviation :: log of the standard deviation. Specifies spread of data from the mean.
• RNG.exponential_distribution(lambda_rate: float) -> float
  • Produces random non-negative floating-point values, distributed according to probability density function.
  • @param lambda_rate :: λ constant rate of a random event per unit of time/distance.
  • @return :: The time/distance until the next random event. For example, this distribution describes the time between the clicks of a Geiger counter or the distance between point mutations in a DNA strand.
• RNG.gamma_distribution(shape: float, scale: float) -> float
  • Generalization of the exponential distribution.
  • Produces random positive floating-point values, distributed according to probability density function.
  • @param shape :: α the number of independent exponentially distributed random variables.
  • @param scale :: β the scale factor or the mean of each of the distributed random variables.
  • @return :: the sum of α independent exponentially distributed random variables, each of which has a mean of β.
• RNG.weibull_distribution(shape: float, scale: float) -> float
  • Generalization of the exponential distribution.
  • Similar to the gamma distribution but uses a closed form distribution function.
  • Popular in reliability and survival analysis.
• RNG.extreme_value_distribution(location: float, scale: float) -> float
  • Based on Extreme Value Theory.
  • Used for statistical models of the magnitude of earthquakes and volcanoes.
• RNG.chi_squared_distribution(degrees_of_freedom: float) -> float
  • Used with the Chi Squared Test and Null Hypotheses to test if sample data fits an expected distribution.
• RNG.cauchy_distribution(location: float, scale: float) -> float
  • @param location :: It specifies the location of the peak. The default value is 0.0.
  • @param scale :: It represents the half-width at half-maximum. The default value is 1.0.
  • @return :: Continuous Distribution.
• RNG.fisher_f_distribution(degrees_of_freedom_1: float, degrees_of_freedom_2: float) -> float
  • F distributions often arise when comparing ratios of variances.
• RNG.student_t_distribution(degrees_of_freedom: float) -> float
  • T distribution. Same as a normal distribution except it uses the sample standard deviation rather than the population standard deviation.
  • As degrees_of_freedom goes to infinity it converges with the normal distribution.

Distribution & Performance Test Suite

• RNG.quick_test() -> None
  • Runs a quick battery of tests for every function in the module.
• RNG.timer(func: staticmethod, *args, **kwargs) -> None
  • Temporal analysis of non-deterministic functions.
  • @param func :: Function, method or lambda to analyze. func(*args, **kwargs)
• RNG.distribution(func: staticmethod, *args, **kwargs) -> None
  • Statistical analysis of non-deterministic functions.
  • @param func :: Function, method or lambda to analyze. func(*args, **kwargs)
• RNG.distribution_timer(func: staticmethod, *args, **kwargs) -> None
  • Statistical and temporal analysis of non-deterministic functions.
  • @param func :: Function, method or lambda to analyze. func(*args, **kwargs)
  • @optional_kw num_cycles :: Total number of samples for distribution analysis, statistical analysis is limited to the first 1000 samples, timing estimates are handled separately.
  • @optional_kw post_processor :: Used to scale a large set of data into a smaller set of groupings, this function is invoked on the output and collated after the stats battery.

Example Terminal Usage: distribution_timer

$ python3
Python 3.7.3 ...
>>> import RNG
>>> RNG.distribution_timer(RNG.student_t_distribution, 8.0, post_processor=round)
Output Analysis: student_t_distribution(8.0)
Typical Timing: 188 ± 12 ns
Statistics of 1024 samples:
 Minimum: -5.529308742357413
 Median: (-0.03683794055898661, -0.036446794399765815)
 Maximum: 4.271791506160894
 Mean: -0.0028963868958856932
 Std Deviation: 1.1614795623947607
Post-processor distribution of 100000 samples using round method:
 -16: 0.001%
 -8: 0.002%
 -7: 0.006%
 -6: 0.02%
 -5: 0.058%
 -4: 0.289%
 -3: 1.426%
 -2: 6.705%
 -1: 23.096%
 0: 36.703%
 1: 23.048%
 2: 6.769%
 3: 1.52%
 4: 0.269%
 5: 0.06%
 6: 0.02%
 7: 0.006%
 9: 0.001%
 10: 0.001%

Development Log

RNG 1.4.1

• Test Patch for new API
• Documentation Updates

RNG 1.4.0

• API Refactoring

RNG 1.3.4

• Storm Update 3.1.1

RNG 1.3.3

• Installer script update

RNG 1.3.2

• Minor Bug Fix

RNG 1.3.1

• Test Update

RNG 1.3.1

• Fixed Typos

RNG 1.3.0

• Storm Update

RNG 1.2.5

• Low level clean up

RNG 1.2.4

• Minor Typos Fixed

RNG 1.2.3

• Documentation Update
• Test Update
• Bug Fixes

RNG 1.0.0 - 1.2.2, internal

• API Changes:
  • randint changed to random_int
  • randbelow changed to random_below
  • random changed to generate_canonical
  • uniform changed to random_float

RNG 0.2.3

• Bug Fixes

RNG 0.2.2

• discrete() removed.

RNG 0.2.1

• minor typos
• discrete() depreciated.

RNG 0.2.0

• Major Rebuild.

RNG 0.1.22

• The RNG Storm Engine is now the default standard.
• Experimental Vortex Engine added for testing.

RNG 0.1.21 beta

• Small update to the testing suite.

RNG 0.1.20 beta

• Changed default inputs for random_int and random_below to sane values.
  • random_int(left_limit=1, right_limit=20) down from -2**63, 2**63 - 1
  • random_below(upper_bound=10) down from 2**63 - 1

RNG 0.1.19 beta

• Broke some fixed typos, for a change of pace.

RNG 0.1.18 beta

• Fixed some typos.

RNG 0.1.17 beta

• Major Refactoring.
• New primary engine: Hurricane.
• Experimental engine Typhoon added: random_below() only.

RNG 0.1.16 beta

• Internal Engine Performance Tuning.

RNG 0.1.15 beta

• Engine Testing.

RNG 0.1.14 beta

• Fixed a few typos.

RNG 0.1.13 beta

• Fixed a few typos.

RNG 0.1.12 beta

• Major Test Suite Upgrade.
• Major Bug Fixes.
  • Removed several 'foot-guns' in prep for fuzz testing in future releases.

RNG 0.1.11 beta

• Fixed small bug in the install script.

RNG 0.1.10 beta

• Fixed some typos.

RNG 0.1.9 beta

• Fixed some typos.

RNG 0.1.8 beta

• Fixed some typos.
• More documentation added.

RNG 0.1.7 beta

• The random_floating_point function renamed to random_float.
• The function c_rand() has been removed as well as all the cruft it required.
• Major Documentation Upgrade.
• Fixed an issue where keyword arguments would fail to propagate. Both, positional args and kwargs now work as intended.
• Added this Dev Log.

RNG 0.0.6 alpha

• Minor ABI changes.

RNG 0.0.5 alpha

• Tests redesigned slightly for Float functions.

RNG 0.0.4 alpha

• Random Float Functions Implemented.

RNG 0.0.3 alpha

• Random Integer Functions Implemented.

RNG 0.0.2 alpha

• Random Bool Function Implemented.

RNG 0.0.1 pre-alpha

• Planning & Design.

Distribution and Performance Test Suite

Quick Test: RNG Storm Engine
=========================================================================

Boolean Variate Distributions

Output Analysis: bernoulli_distribution(0.0)
Typical Timing: 63 ± 1 ns
Statistics of 1024 samples:
 Minimum: False
 Median: False
 Maximum: False
 Mean: 0
 Std Deviation: 0.0
Distribution of 10240 samples:
 False: 100.0%

Output Analysis: bernoulli_distribution(0.3333333333333333)
Typical Timing: 63 ± 22 ns
Statistics of 1024 samples:
 Minimum: False
 Median: False
 Maximum: True
 Mean: 0.330078125
 Std Deviation: 0.4704707338273809
Distribution of 10240 samples:
 False: 66.484375%
 True: 33.515625%

Output Analysis: bernoulli_distribution(0.5)
Typical Timing: 63 ± 22 ns
Statistics of 1024 samples:
 Minimum: False
 Median: False
 Maximum: True
 Mean: 0.484375
 Std Deviation: 0.5
Distribution of 10240 samples:
 False: 49.375%
 True: 50.625%

Output Analysis: bernoulli_distribution(0.6666666666666666)
Typical Timing: 63 ± 16 ns
Statistics of 1024 samples:
 Minimum: False
 Median: True
 Maximum: True
 Mean: 0.671875
 Std Deviation: 0.46976003108344483
Distribution of 10240 samples:
 False: 33.57421875%
 True: 66.42578125%

Output Analysis: bernoulli_distribution(1.0)
Typical Timing: 63 ± 1 ns
Statistics of 1024 samples:
 Minimum: True
 Median: True
 Maximum: True
 Mean: 1
 Std Deviation: 0.0
Distribution of 10240 samples:
 True: 100.0%


Integer Variate Distributions

Base Case
Output Analysis: Random.randint(1, 6)
Typical Timing: 1250 ± 36 ns
Statistics of 1024 samples:
 Minimum: 1
 Median: 4
 Maximum: 6
 Mean: 3.5400390625
 Std Deviation: 1.7125716511292923
Distribution of 10240 samples:
 1: 16.5234375%
 2: 16.58203125%
 3: 17.255859375%
 4: 16.9140625%
 5: 16.650390625%
 6: 16.07421875%

Output Analysis: uniform_int_distribution(1, 6)
Typical Timing: 63 ± 22 ns
Statistics of 1024 samples:
 Minimum: 1
 Median: 3
 Maximum: 6
 Mean: 3.4990234375
 Std Deviation: 1.705032234378946
Distribution of 10240 samples:
 1: 16.81640625%
 2: 16.005859375%
 3: 17.158203125%
 4: 16.396484375%
 5: 17.001953125%
 6: 16.62109375%

Output Analysis: binomial_distribution(4, 0.5)
Typical Timing: 125 ± 26 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 2
 Maximum: 4
 Mean: 2.037109375
 Std Deviation: 0.9939159219826769
Distribution of 10240 samples:
 0: 6.103515625%
 1: 25.68359375%
 2: 36.8359375%
 3: 25.322265625%
 4: 6.0546875%

Output Analysis: negative_binomial_distribution(5, 0.75)
Typical Timing: 125 ± 26 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 1
 Maximum: 10
 Mean: 1.65625
 Std Deviation: 1.5120887911161052
Distribution of 10240 samples:
 0: 23.84765625%
 1: 29.345703125%
 2: 21.73828125%
 3: 13.212890625%
 4: 7.119140625%
 5: 2.705078125%
 6: 1.181640625%
 7: 0.576171875%
 8: 0.17578125%
 9: 0.048828125%
 10: 0.029296875%
 11: 0.01953125%

Output Analysis: geometric_distribution(0.75)
Typical Timing: 63 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 0
 Maximum: 4
 Mean: 0.3505859375
 Std Deviation: 0.6999089791664276
Distribution of 10240 samples:
 0: 74.62890625%
 1: 19.140625%
 2: 4.58984375%
 3: 1.3671875%
 4: 0.205078125%
 5: 0.05859375%
 8: 0.009765625%

Output Analysis: poisson_distribution(4.5)
Typical Timing: 125 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 4
 Maximum: 14
 Mean: 4.4013671875
 Std Deviation: 2.161160328330198
Distribution of 10240 samples:
 0: 1.025390625%
 1: 4.892578125%
 2: 11.93359375%
 3: 16.85546875%
 4: 19.23828125%
 5: 16.962890625%
 6: 12.63671875%
 7: 7.98828125%
 8: 4.3359375%
 9: 2.3828125%
 10: 0.99609375%
 11: 0.478515625%
 12: 0.185546875%
 13: 0.048828125%
 14: 0.029296875%
 16: 0.009765625%


Floating Point Variate Distributions

Base Case
Output Analysis: Random.random()
Typical Timing: 63 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0.0015374923967464982
 Median: (0.5196775499786613, 0.5201452992848603)
 Maximum: 0.9987149343350875
 Mean: 0.5135985165451736
 Std Deviation: 0.2872077742443504
Post-processor distribution of 10240 samples using round method:
 0: 49.990234375%
 1: 50.009765625%

Output Analysis: generate_canonical()
Typical Timing: 63 ± 1 ns
Statistics of 1024 samples:
 Minimum: 0.0024532443909225936
 Median: (0.4704302613312534, 0.47070265570766634)
 Maximum: 0.9998085591861998
 Mean: 0.4816443312228179
 Std Deviation: 0.28505829105163644
Post-processor distribution of 10240 samples using round method:
 0: 50.76171875%
 1: 49.23828125%

Output Analysis: uniform_real_distribution(0.0, 10.0)
Typical Timing: 63 ± 1 ns
Statistics of 1024 samples:
 Minimum: 0.0304742668372541
 Median: (5.0679328370819, 5.0745963145984)
 Maximum: 9.995076243319797
 Mean: 5.03638577315081
 Std Deviation: 2.927418688090237
Post-processor distribution of 10240 samples using floor method:
 0: 9.716796875%
 1: 10.205078125%
 2: 9.53125%
 3: 10.0390625%
 4: 9.84375%
 5: 9.873046875%
 6: 10.244140625%
 7: 10.107421875%
 8: 10.244140625%
 9: 10.1953125%

Base Case
Output Analysis: Random.expovariate(1.0)
Typical Timing: 313 ± 22 ns
Statistics of 1024 samples:
 Minimum: 0.0011568737678774406
 Median: (0.7158428546719676, 0.7163397895389789)
 Maximum: 7.058762791117456
 Mean: 1.0003670451068172
 Std Deviation: 0.9929125036109278
Post-processor distribution of 10240 samples using floor method:
 0: 63.232421875%
 1: 23.45703125%
 2: 8.330078125%
 3: 3.115234375%
 4: 1.181640625%
 5: 0.458984375%
 6: 0.13671875%
 7: 0.05859375%
 8: 0.029296875%

Output Analysis: exponential_distribution(1.0)
Typical Timing: 63 ± 26 ns
Statistics of 1024 samples:
 Minimum: 0.0025122822776642434
 Median: (0.6926712837170756, 0.6930328371051944)
 Maximum: 9.39530049353006
 Mean: 1.0056226590260526
 Std Deviation: 1.0484645113797768
Post-processor distribution of 10240 samples using floor method:
 0: 62.83203125%
 1: 23.69140625%
 2: 8.798828125%
 3: 2.9296875%
 4: 1.15234375%
 5: 0.439453125%
 6: 0.078125%
 7: 0.048828125%
 8: 0.01953125%
 9: 0.009765625%

Base Case
Output Analysis: Random.gammavariate(1.0, 1.0)
Typical Timing: 500 ± 22 ns
Statistics of 1024 samples:
 Minimum: 0.00093728283210992
 Median: (0.7020867139788857, 0.7071066908471326)
 Maximum: 5.852497568696576
 Mean: 1.016675397909145
 Std Deviation: 0.9686118387182048
Post-processor distribution of 10240 samples using floor method:
 0: 63.330078125%
 1: 23.02734375%
 2: 8.515625%
 3: 3.1640625%
 4: 1.25%
 5: 0.458984375%
 6: 0.17578125%
 7: 0.048828125%
 8: 0.01953125%
 11: 0.009765625%

Output Analysis: gamma_distribution(1.0, 1.0)
Typical Timing: 63 ± 22 ns
Statistics of 1024 samples:
 Minimum: 0.001372659872622037
 Median: (0.6646145271070529, 0.6674215920332173)
 Maximum: 7.826229254409556
 Mean: 1.0171504870770647
 Std Deviation: 1.0534461608954686
Post-processor distribution of 10240 samples using floor method:
 0: 63.69140625%
 1: 23.466796875%
 2: 8.33984375%
 3: 2.87109375%
 4: 0.9765625%
 5: 0.3515625%
 6: 0.185546875%
 7: 0.078125%
 8: 0.01953125%
 9: 0.009765625%
 10: 0.009765625%

Base Case
Output Analysis: Random.weibullvariate(1.0, 1.0)
Typical Timing: 438 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0.0002213042054813901
 Median: (0.6637421285607475, 0.6647256311160707)
 Maximum: 7.577325950724918
 Mean: 0.9388616508262337
 Std Deviation: 0.9262630791900074
Post-processor distribution of 10240 samples using floor method:
 0: 63.095703125%
 1: 23.525390625%
 2: 8.369140625%
 3: 3.154296875%
 4: 1.11328125%
 5: 0.419921875%
 6: 0.185546875%
 7: 0.09765625%
 8: 0.01953125%
 9: 0.009765625%
 10: 0.009765625%

Output Analysis: weibull_distribution(1.0, 1.0)
Typical Timing: 125 ± 1 ns
Statistics of 1024 samples:
 Minimum: 0.0021553647893082424
 Median: (0.6423084268263919, 0.6449853983396836)
 Maximum: 8.25735324498559
 Mean: 0.9488955731431038
 Std Deviation: 0.9685188492760336
Post-processor distribution of 10240 samples using floor method:
 0: 62.646484375%
 1: 24.189453125%
 2: 8.251953125%
 3: 3.06640625%
 4: 1.064453125%
 5: 0.498046875%
 6: 0.15625%
 7: 0.087890625%
 8: 0.0390625%

Output Analysis: extreme_value_distribution(0.0, 1.0)
Typical Timing: 63 ± 30 ns
Statistics of 1024 samples:
 Minimum: -2.2790218979883816
 Median: (0.4189807983736489, 0.4190244749042273)
 Maximum: 8.473866194466257
 Mean: 0.6289731042527902
 Std Deviation: 1.2785070103129572
Post-processor distribution of 10240 samples using round method:
 -2: 1.03515625%
 -1: 18.1640625%
 0: 36.3671875%
 1: 24.951171875%
 2: 11.93359375%
 3: 4.677734375%
 4: 1.708984375%
 5: 0.712890625%
 6: 0.341796875%
 7: 0.05859375%
 8: 0.029296875%
 9: 0.009765625%
 15: 0.009765625%

Base Case
Output Analysis: Random.gauss(5.0, 2.0)
Typical Timing: 625 ± 16 ns
Statistics of 1024 samples:
 Minimum: -2.3907657725045857
 Median: (4.985395439098846, 4.996011596071374)
 Maximum: 10.555392950303524
 Mean: 4.9661175609442205
 Std Deviation: 1.9645358055174982
Post-processor distribution of 10240 samples using round method:
 -3: 0.009765625%
 -2: 0.05859375%
 -1: 0.13671875%
 0: 0.810546875%
 1: 2.65625%
 2: 6.58203125%
 3: 11.943359375%
 4: 17.412109375%
 5: 19.951171875%
 6: 17.724609375%
 7: 12.177734375%
 8: 6.85546875%
 9: 2.55859375%
 10: 0.859375%
 11: 0.224609375%
 12: 0.0390625%

Output Analysis: normal_distribution(5.0, 2.0)
Typical Timing: 63 ± 26 ns
Statistics of 1024 samples:
 Minimum: -1.62657420753664
 Median: (5.013721347171976, 5.020716600368835)
 Maximum: 11.229805396983904
 Mean: 5.013390643443666
 Std Deviation: 1.9906280203704514
Post-processor distribution of 10240 samples using round method:
 -2: 0.048828125%
 -1: 0.263671875%
 0: 0.771484375%
 1: 2.87109375%
 2: 6.650390625%
 3: 12.255859375%
 4: 17.6171875%
 5: 19.86328125%
 6: 17.34375%
 7: 12.021484375%
 8: 6.34765625%
 9: 2.83203125%
 10: 0.810546875%
 11: 0.2734375%
 12: 0.029296875%

Base Case
Output Analysis: Random.lognormvariate(1.6, 0.25)
Typical Timing: 813 ± 61 ns
Statistics of 1024 samples:
 Minimum: 2.444946183342167
 Median: (4.858676895757656, 4.861475879554381)
 Maximum: 9.95451975540616
 Mean: 5.02207782789318
 Std Deviation: 1.2616130112120112
Post-processor distribution of 10240 samples using round method:
 2: 0.41015625%
 3: 8.37890625%
 4: 26.50390625%
 5: 31.806640625%
 6: 19.31640625%
 7: 8.84765625%
 8: 3.232421875%
 9: 1.005859375%
 10: 0.361328125%
 11: 0.09765625%
 12: 0.009765625%
 13: 0.01953125%
 17: 0.009765625%

Output Analysis: lognormal_distribution(1.6, 0.25)
Typical Timing: 125 ± 1 ns
Statistics of 1024 samples:
 Minimum: 2.3351913272374305
 Median: (4.976527485880496, 4.976764063343573)
 Maximum: 11.857833898396995
 Mean: 5.101654163992373
 Std Deviation: 1.2844079163290805
Post-processor distribution of 10240 samples using round method:
 2: 0.302734375%
 3: 8.14453125%
 4: 26.884765625%
 5: 30.732421875%
 6: 20.576171875%
 7: 8.564453125%
 8: 3.26171875%
 9: 1.11328125%
 10: 0.29296875%
 11: 0.09765625%
 12: 0.029296875%

Output Analysis: chi_squared_distribution(1.0)
Typical Timing: 125 ± 16 ns
Statistics of 1024 samples:
 Minimum: 1.994422287393394e-08
 Median: (0.478090162293184, 0.48077084515478813)
 Maximum: 11.542849837937665
 Mean: 1.0195884796387298
 Std Deviation: 1.5077490645120968
Post-processor distribution of 10240 samples using floor method:
 0: 68.408203125%
 1: 15.576171875%
 2: 7.83203125%
 3: 3.33984375%
 4: 2.158203125%
 5: 1.044921875%
 6: 0.732421875%
 7: 0.380859375%
 8: 0.17578125%
 9: 0.15625%
 10: 0.078125%
 11: 0.0390625%
 12: 0.0390625%
 13: 0.009765625%
 14: 0.009765625%
 16: 0.01953125%

Output Analysis: cauchy_distribution(0.0, 1.0)
Typical Timing: 63 ± 30 ns
Statistics of 1024 samples:
 Minimum: -1873.8445794786624
 Median: (0.03842207304543185, 0.03956748654031884)
 Maximum: 708.8387160386458
 Mean: -1.1950569789186223
 Std Deviation: 65.30270851255942
Post-processor distribution of 10240 samples using floor_mod_10 method:
 0: 26.42578125%
 1: 11.2109375%
 2: 6.005859375%
 3: 3.681640625%
 4: 3.134765625%
 5: 2.919921875%
 6: 3.916015625%
 7: 5.986328125%
 8: 11.328125%
 9: 25.390625%

Output Analysis: fisher_f_distribution(8.0, 8.0)
Typical Timing: 188 ± 28 ns
Statistics of 1024 samples:
 Minimum: 0.08316936366235625
 Median: (0.9394689435023765, 0.9395990307459007)
 Maximum: 12.249274210433379
 Mean: 1.2974719840804878
 Std Deviation: 1.2755170906032696
Post-processor distribution of 10240 samples using floor method:
 0: 50.244140625%
 1: 32.412109375%
 2: 10.185546875%
 3: 3.8671875%
 4: 1.650390625%
 5: 0.68359375%
 6: 0.380859375%
 7: 0.13671875%
 8: 0.1171875%
 9: 0.068359375%
 10: 0.146484375%
 11: 0.029296875%
 12: 0.0390625%
 13: 0.01953125%
 15: 0.009765625%
 16: 0.009765625%

Output Analysis: student_t_distribution(8.0)
Typical Timing: 188 ± 16 ns
Statistics of 1024 samples:
 Minimum: -5.073069466854821
 Median: (-0.01535503285345772, -0.015291640440016156)
 Maximum: 5.711128871513843
 Mean: 0.0018949731517060805
 Std Deviation: 1.1610777090451412
Post-processor distribution of 10240 samples using round method:
 -6: 0.009765625%
 -5: 0.107421875%
 -4: 0.21484375%
 -3: 1.494140625%
 -2: 6.767578125%
 -1: 22.24609375%
 0: 36.640625%
 1: 23.3984375%
 2: 7.197265625%
 3: 1.474609375%
 4: 0.3125%
 5: 0.078125%
 6: 0.029296875%
 7: 0.009765625%
 8: 0.009765625%
 12: 0.009765625%


=========================================================================
Total Test Time: 0.3041 seconds