RNG 1.2.3

Python API for the C++ Random library

Random Number Generator: RNG Storm Engine

RNG is not suitable for cryptography, but it could be perfect for other random stuff, data science, experimental programming, A.I. and games.

Recommended Installation: $ pip install RNG

Number Types, Precision & Size:

• Float: Python float -> double at the C++ layer.

  • Min Float: -1.7976931348623157e+308
  • Max Float: 1.7976931348623157e+308
  • Min Below Zero: -5e-324
  • Min Above Zero: 5e-324

• Integer: Python int -> long long at the C++ layer.

  • Input & Output Range: (-2**63, 2**63) or approximately +/- 9.2 billion billion.
  • Min Integer: -9223372036854775807
  • Max Integer: 9223372036854775807

Random Binary Function

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

Random Integer Functions

• random_int(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
• random_below(upper_bound: int) -> int
  • Flat uniform distribution.
  • @param upper_bound :: inout A
  • @return :: random integer in exclusive range [0, A) or (A, 0] if A < 0
• binomial(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.
• negative_binomial(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.
• geometric(probability: float) -> int
  • Same as random_negative_binomial(1, probability).
• poisson(mean: float) -> int
  • @param mean :: sets the average output of the function.
  • @return :: random integer, poisson distribution centered on the mean.

Random Floating Point Functions

• generate_canonical() -> float
  • Evenly distributes real values of maximum precision.
  • @return :: random Float in range {0.0, 1.0} biclusive. The spec defines the output range to be [0.0, 1.0).
    • biclusive: feature/bug rendering the exclusivity of this function a bit more mysterious than desired. This is a known compiler bug.
• random_float(left_limit: float, right_limit: float) -> float
  • Suffers from the same biclusive feature/bug noted for generate_canonical().
  • @param left_limit :: input A
  • @param right_limit :: input B
  • @return :: random Float in range {A, B} biclusive. The spec defines the output range to be [A, B).
• normalvariate(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.
• lognormvariate(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.
• exponential(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.
• gammavariate(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.
• weibullvariate(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.
• extreme_value(location: float, scale: float) -> float
  • Based on Extreme Value Theory.
  • Used for statistical models of the magnitude of earthquakes and volcanoes.
• chi_squared(degrees_of_freedom: float) -> float
  • Used with the Chi Squared Test and Null Hypotheses to test if sample data fits an expected distribution.
• cauchy(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.
• fisher_f(degrees_of_freedom_1: float, degrees_of_freedom_2: float) -> float
  • F distributions often arise when comparing ratios of variances.
• student_t(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.

Engines

• mersenne_twister_engine: internal only
  • Implements 64 bit Mersenne twister algorithm. Default engine on most systems.
• linear_congruential_engine: internal only
  • Implements linear congruential algorithm.
• subtract_with_carry_engine: internal only
  • Implements a subtract-with-carry (lagged Fibonacci) algorithm.
• storm_engine: internal only
  • RNG: Custom Engine
  • Default Standard

Engine Adaptors

Engine adaptors generate pseudo-random numbers using another random number engine as entropy source. They are generally used to alter the spectral characteristics of the underlying engine.

• discard_block_engine: internal only
  • Discards some output of a random number engine.
• independent_bits_engine: internal only
  • Packs the output of a random number engine into blocks of a specified number of bits.
• shuffle_order_engine: internal only
  • Delivers the output of a random number engine in a different order.

Seeds & Entropy Source

• random_device: internal only
  • Non-deterministic uniform random bit generator, although implementations are allowed to implement random_device using a pseudo-random number engine if there is no support for non-deterministic random number generation.
• seed_seq: internal only
  • General-purpose bias-eliminating scrambled seed sequence generator.

Distribution & Performance Test Suite

• distribution_timer(func: staticmethod, *args, **kwargs) -> None
  • For statistical analysis of non-deterministic numeric functions.
  • @param func :: Function method or lambda to analyze. func(*args, **kwargs)
  • @optional_kw num_cycles :: Total number of samples for distribution analysis.
  • @optional_kw post_processor :: Used to scale a large set of data into a smaller set of groupings.
• quick_test(n=10000)
  • Runs a battery of tests for every random distribution function in the module.
  • @param n :: the total number of samples to collect for each test. Default: 10,000

Development Log

RNG 1.2.3

• Documentation Update
• Test Update
• Bug Fix

RNG 1.0.0 - 1.2.2, internal

• Major Update
• 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

Round Trip Numeric Limits:
 Min Integer: -9223372036854775807
 Max Integer:  9223372036854775807
 Min Float: -1.7976931348623157e+308
 Max Float:  1.7976931348623157e+308
 Min Below Zero: -5e-324
 Min Above Zero:  5e-324


Binary Tests

Output Distribution: bernoulli(0.3333333333333333)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 156ns
Raw Samples: False, False, False, False, True
Test Samples: 10000
Sample Statistics:
 Minimum: False
 Median: False
 Maximum: True
 Mean: 0.3406
 Std Deviation: 0.4739347016310802
Sample Distribution:
 False: 65.94%
 True: 34.06%


Integer Tests

Output Distribution: Random.randint(1, 6)
Approximate Single Execution Time: Min: 1156ns, Mid: 1187ns, Max: 2093ns
Raw Samples: 2, 6, 4, 3, 5
Test Samples: 10000
Sample Statistics:
 Minimum: 1
 Median: 3
 Maximum: 6
 Mean: 3.4912
 Std Deviation: 1.699473854208447
Sample Distribution:
 1: 16.58%
 2: 16.64%
 3: 17.06%
 4: 16.84%
 5: 16.56%
 6: 16.32%

Output Distribution: random_int(1, 6)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 281ns
Raw Samples: 1, 5, 4, 5, 3
Test Samples: 10000
Sample Statistics:
 Minimum: 1
 Median: 4
 Maximum: 6
 Mean: 3.5312
 Std Deviation: 1.7139779263552968
Sample Distribution:
 1: 16.46%
 2: 16.24%
 3: 16.56%
 4: 16.46%
 5: 17.02%
 6: 17.26%

Output Distribution: Random.randrange(6)
Approximate Single Execution Time: Min: 812ns, Mid: 843ns, Max: 1031ns
Raw Samples: 3, 0, 3, 2, 1
Test Samples: 10000
Sample Statistics:
 Minimum: 0
 Median: 3
 Maximum: 5
 Mean: 2.5291
 Std Deviation: 1.701717595336379
Sample Distribution:
 0: 15.81%
 1: 16.98%
 2: 16.54%
 3: 16.9%
 4: 16.7%
 5: 17.07%

Output Distribution: random_below(6)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 281ns
Raw Samples: 4, 4, 4, 3, 4
Test Samples: 10000
Sample Statistics:
 Minimum: 0
 Median: 2
 Maximum: 5
 Mean: 2.494
 Std Deviation: 1.7014856759169426
Sample Distribution:
 0: 16.46%
 1: 17.01%
 2: 16.75%
 3: 16.53%
 4: 16.95%
 5: 16.3%

Output Distribution: binomial(4, 0.5)
Approximate Single Execution Time: Min: 156ns, Mid: 156ns, Max: 250ns
Raw Samples: 2, 2, 1, 1, 3
Test Samples: 10000
Sample Statistics:
 Minimum: 0
 Median: 2
 Maximum: 4
 Mean: 2.0052
 Std Deviation: 1.0011359484699027
Sample Distribution:
 0: 6.32%
 1: 24.49%
 2: 37.94%
 3: 24.85%
 4: 6.4%

Output Distribution: negative_binomial(5, 0.75)
Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 187ns
Raw Samples: 2, 5, 1, 0, 0
Test Samples: 10000
Sample Statistics:
 Minimum: 0
 Median: 1
 Maximum: 10
 Mean: 1.6575
 Std Deviation: 1.4775019631814776
Sample Distribution:
 0: 23.56%
 1: 30.15%
 2: 22.28%
 3: 12.76%
 4: 6.42%
 5: 2.99%
 6: 1.13%
 7: 0.43%
 8: 0.21%
 9: 0.04%
 10: 0.03%

Output Distribution: geometric(0.75)
Approximate Single Execution Time: Min: 31ns, Mid: 62ns, Max: 125ns
Raw Samples: 1, 0, 0, 0, 1
Test Samples: 10000
Sample Statistics:
 Minimum: 0
 Median: 0
 Maximum: 5
 Mean: 0.3407
 Std Deviation: 0.6790210725461295
Sample Distribution:
 0: 74.74%
 1: 18.86%
 2: 4.51%
 3: 1.45%
 4: 0.36%
 5: 0.08%

Output Distribution: poisson(4.5)
Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 187ns
Raw Samples: 8, 8, 7, 4, 9
Test Samples: 10000
Sample Statistics:
 Minimum: 0
 Median: 4
 Maximum: 14
 Mean: 4.4543
 Std Deviation: 2.1062656873040853
Sample Distribution:
 0: 1.13%
 1: 5.18%
 2: 11.54%
 3: 16.67%
 4: 19.96%
 5: 16.82%
 6: 12.38%
 7: 8.03%
 8: 4.41%
 9: 2.27%
 10: 1.01%
 11: 0.37%
 12: 0.16%
 13: 0.05%
 14: 0.02%


Floating Point Tests

Output Distribution: Random.random()
Approximate Single Execution Time: Min: 31ns, Mid: 46ns, Max: 93ns
Raw Samples: 0.3780410932327104, 0.8084437054192711, 0.6934019255623044, 0.1082335251836073, 0.1590501662288043
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 0.00011645247548208726
 Median: (0.5014348621977325, 0.5014459481905253)
 Maximum: 0.999634601876778
 Mean: 0.5004093154048272
 Std Deviation: 0.2899887209371455
Post-processor Distribution using round method:
 0: 49.84%
 1: 50.16%

Output Distribution: generate_canonical()
Approximate Single Execution Time: Min: 31ns, Mid: 46ns, Max: 218ns
Raw Samples: 0.40558551414201616, 0.49243275066488074, 0.8821848215906136, 0.7851182320242607, 0.019546083864710767
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 4.2972410566289524e-05
 Median: (0.5052684745683926, 0.5052726674268214)
 Maximum: 0.9999351902772498
 Mean: 0.5033252127303914
 Std Deviation: 0.28759094115484624
Post-processor Distribution using round method:
 0: 49.52%
 1: 50.48%

Output Distribution: random_float(0.0, 10.0)
Approximate Single Execution Time: Min: 31ns, Mid: 62ns, Max: 156ns
Raw Samples: 6.219916797619756, 8.292149335448954, 2.302584999594653, 7.320639650852665, 9.314441886529952
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 0.001421453294764285
 Median: (4.98351740220654, 4.9838010480955095)
 Maximum: 9.998933319433425
 Mean: 4.993001031206097
 Std Deviation: 2.8869609812840116
Post-processor Distribution using floor method:
 0: 10.04%
 1: 10.14%
 2: 10.17%
 3: 9.76%
 4: 10.09%
 5: 9.96%
 6: 10.26%
 7: 9.51%
 8: 9.91%
 9: 10.16%

Output Distribution: Random.expovariate(1.0)
Approximate Single Execution Time: Min: 312ns, Mid: 343ns, Max: 593ns
Raw Samples: 0.509881431722116, 0.6475838177460885, 0.21862665996597738, 0.4920366925127701, 0.8254652426559526
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 3.136626530062925e-05
 Median: (0.6839197272461246, 0.6840091209577261)
 Maximum: 9.392282858339623
 Mean: 0.986135765490063
 Std Deviation: 0.9857014919837869
Post-processor Distribution using floor_mod_10 method:
 0: 63.59%
 1: 22.97%
 2: 8.82%
 3: 2.87%
 4: 1.1%
 5: 0.43%
 6: 0.14%
 7: 0.04%
 8: 0.03%
 9: 0.01%

Output Distribution: expovariate(1.0)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 156ns
Raw Samples: 0.11013706266927288, 0.23826892100589178, 0.07185630951456551, 2.0812059874165114, 0.6261969619931065
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 2.4208073051235006e-05
 Median: (0.6921525257137233, 0.6924103550144298)
 Maximum: 13.73203553919845
 Mean: 1.0055373095837483
 Std Deviation: 1.0122319801110002
Post-processor Distribution using floor_mod_10 method:
 0: 62.8%
 1: 23.53%
 2: 8.66%
 3: 3.17%
 4: 1.14%
 5: 0.47%
 6: 0.13%
 7: 0.08%
 8: 0.02%

Output Distribution: Random.gammavariate(1.0, 1.0)
Approximate Single Execution Time: Min: 468ns, Mid: 500ns, Max: 1562ns
Raw Samples: 0.17083796272250396, 0.44923958600222236, 0.12517820556728634, 0.8796603237734281, 1.0783511332333202
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 3.192382722971875e-05
 Median: (0.6952140137902232, 0.6953934798982648)
 Maximum: 9.113194161313617
 Mean: 1.000999845783478
 Std Deviation: 0.9976854307088173
Post-processor Distribution using floor_mod_10 method:
 0: 62.98%
 1: 23.5%
 2: 8.43%
 3: 3.19%
 4: 1.25%
 5: 0.5%
 6: 0.09%
 7: 0.02%
 8: 0.03%
 9: 0.01%

Output Distribution: gammavariate(1.0, 1.0)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 156ns
Raw Samples: 3.782842941655382, 0.7933125774384413, 1.3070109224116055, 0.1584295391745783, 1.2357822531388665
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 0.0001662305761542579
 Median: (0.6887692670856015, 0.6887956366480802)
 Maximum: 9.399938329270688
 Mean: 1.0029837010125928
 Std Deviation: 0.998178884336878
Post-processor Distribution using floor_mod_10 method:
 0: 63.0%
 1: 23.06%
 2: 8.96%
 3: 3.16%
 4: 1.2%
 5: 0.45%
 6: 0.11%
 7: 0.01%
 8: 0.04%
 9: 0.01%

Output Distribution: Random.weibullvariate(1.0, 1.0)
Approximate Single Execution Time: Min: 406ns, Mid: 437ns, Max: 687ns
Raw Samples: 0.3493048862939782, 2.0727100184978706, 0.24248354033336805, 0.6932993180118238, 0.17790416640852508
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 2.98822607334499e-06
 Median: (0.6927478390688929, 0.6929884844488089)
 Maximum: 9.57870172356498
 Mean: 1.0097207125973697
 Std Deviation: 1.008675554264964
Post-processor Distribution using floor_mod_10 method:
 0: 63.03%
 1: 22.99%
 2: 8.9%
 3: 3.12%
 4: 1.2%
 5: 0.54%
 6: 0.16%
 7: 0.05%
 9: 0.01%

Output Distribution: weibullvariate(1.0, 1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 250ns
Raw Samples: 3.5802883750578047, 0.24152847996642599, 0.2545475320249344, 0.8566963636429774, 0.10600713139402025
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 3.6207139794139434e-05
 Median: (0.6978598011385925, 0.6979483893509106)
 Maximum: 8.976335076202105
 Mean: 0.9966878353875437
 Std Deviation: 0.9922816551220055
Post-processor Distribution using floor_mod_10 method:
 0: 63.62%
 1: 23.11%
 2: 8.16%
 3: 3.32%
 4: 1.18%
 5: 0.38%
 6: 0.17%
 7: 0.03%
 8: 0.03%

Output Distribution: extreme_value(0.0, 1.0)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 375ns
Raw Samples: 1.0357606407045439, 0.9667278454131979, 0.2975538148784999, 0.4814079474577559, 0.2270819653970907
Test Samples: 10000
Pre-processor Statistics:
 Minimum: -2.170729489542774
 Median: (0.3642400083163362, 0.3642698599784993)
 Maximum: 11.291449747048059
 Mean: 0.5707861328330677
 Std Deviation: 1.2834936272374178
Post-processor Distribution using round method:
 -2: 1.22%
 -1: 18.23%
 0: 34.78%
 1: 26.15%
 2: 11.87%
 3: 4.74%
 4: 1.9%
 5: 0.71%
 6: 0.26%
 7: 0.09%
 8: 0.03%
 9: 0.01%
 11: 0.01%

Output Distribution: Random.gauss(5.0, 2.0)
Approximate Single Execution Time: Min: 593ns, Mid: 593ns, Max: 875ns
Raw Samples: 5.227550460029358, 3.711087864289989, 5.853013879032601, 1.4266872587504462, 3.130401847894779
Test Samples: 10000
Pre-processor Statistics:
 Minimum: -3.3529980109613273
 Median: (4.995507851465447, 4.996284810380084)
 Maximum: 12.335513768782908
 Mean: 4.984776049238402
 Std Deviation: 2.0055085723932957
Post-processor Distribution using round method:
 -3: 0.04%
 -2: 0.07%
 -1: 0.16%
 0: 1.02%
 1: 2.93%
 2: 6.54%
 3: 12.66%
 4: 16.96%
 5: 19.18%
 6: 17.64%
 7: 12.45%
 8: 6.44%
 9: 2.87%
 10: 0.87%
 11: 0.15%
 12: 0.02%

Output Distribution: normalvariate(5.0, 2.0)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 156ns
Raw Samples: 5.522275408891425, 8.197789813844915, 4.029252653905018, 3.166539832973694, 4.231734217492221
Test Samples: 10000
Pre-processor Statistics:
 Minimum: -2.9356305265692013
 Median: (4.972526114721259, 4.972937385856109)
 Maximum: 12.922855531716774
 Mean: 4.970021502820491
 Std Deviation: 1.9701336828777862
Post-processor Distribution using round method:
 -3: 0.01%
 -2: 0.04%
 -1: 0.29%
 0: 0.92%
 1: 2.62%
 2: 6.52%
 3: 12.47%
 4: 17.59%
 5: 20.2%
 6: 17.78%
 7: 11.29%
 8: 6.65%
 9: 2.63%
 10: 0.73%
 11: 0.22%
 12: 0.03%
 13: 0.01%

Output Distribution: Random.lognormvariate(1.6, 0.25)
Approximate Single Execution Time: Min: 843ns, Mid: 906ns, Max: 1343ns
Raw Samples: 3.141786300821496, 7.758160091596144, 5.400236477441252, 5.615494308145512, 8.456746412005062
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 1.6411301087945098
 Median: (4.948088399336401, 4.948435963627761)
 Maximum: 14.060477541570126
 Mean: 5.116380501391594
 Std Deviation: 1.2822680784227287
Post-processor Distribution using round method:
 2: 0.34%
 3: 7.28%
 4: 27.43%
 5: 30.89%
 6: 20.46%
 7: 9.03%
 8: 3.1%
 9: 0.99%
 10: 0.35%
 11: 0.1%
 12: 0.02%
 14: 0.01%

Output Distribution: lognormvariate(1.6, 0.25)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 250ns
Raw Samples: 4.252469390444378, 3.5276816065797156, 3.965153577571289, 3.9354951081697016, 5.020778574250972
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 2.1114789580940148
 Median: (4.970957467630716, 4.9711895774106445)
 Maximum: 11.7877615242533
 Mean: 5.126119492145769
 Std Deviation: 1.300001442162225
Post-processor Distribution using round method:
 2: 0.18%
 3: 7.95%
 4: 26.64%
 5: 31.23%
 6: 19.77%
 7: 9.24%
 8: 3.45%
 9: 1.04%
 10: 0.35%
 11: 0.13%
 12: 0.02%

Output Distribution: chi_squared(1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 281ns
Raw Samples: 2.664253630550353, 2.673267315997714, 6.898440657455836, 1.3987643676423347, 0.05878450303726585
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 1.2342827397482836e-08
 Median: (0.4611549672016147, 0.46121763652767395)
 Maximum: 14.648575568878092
 Mean: 1.0124497069315084
 Std Deviation: 1.433072358125687
Post-processor Distribution using floor_mod_10 method:
 0: 68.11%
 1: 16.19%
 2: 7.48%
 3: 3.41%
 4: 2.21%
 5: 1.13%
 6: 0.69%
 7: 0.44%
 8: 0.21%
 9: 0.13%

Output Distribution: cauchy(0.0, 1.0)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 156ns
Raw Samples: 0.6850754814890947, 1.8325050630418316, -1.2364663412668127, -0.3371628891630851, -0.6699066011788222
Test Samples: 10000
Pre-processor Statistics:
 Minimum: -8338.455378563782
 Median: (0.003632999486681942, 0.004096843631275747)
 Maximum: 12556.85393962235
 Mean: 2.3284035900879063
 Std Deviation: 179.8398953626324
Post-processor Distribution using floor_mod_10 method:
 0: 25.98%
 1: 11.14%
 2: 6.09%
 3: 3.4%
 4: 3.4%
 5: 3.09%
 6: 3.6%
 7: 6.03%
 8: 11.36%
 9: 25.91%

Output Distribution: fisher_f(8.0, 8.0)
Approximate Single Execution Time: Min: 156ns, Mid: 187ns, Max: 718ns
Raw Samples: 2.027747684984066, 2.41852315181602, 1.0801454414134686, 0.9063738737500582, 2.760613115508149
Test Samples: 10000
Pre-processor Statistics:
 Minimum: 0.04682307080277313
 Median: (0.9911864325252264, 0.991192681552194)
 Maximum: 32.22376808343096
 Mean: 1.3311840548972265
 Std Deviation: 1.279870203350544
Post-processor Distribution using floor_mod_10 method:
 0: 50.63%
 1: 32.0%
 2: 10.1%
 3: 3.88%
 4: 1.76%
 5: 0.77%
 6: 0.41%
 7: 0.24%
 8: 0.14%
 9: 0.07%

Output Distribution: student_t(8.0)
Approximate Single Execution Time: Min: 156ns, Mid: 187ns, Max: 250ns
Raw Samples: -0.12596746631920103, 0.2385119761120527, -0.3045203420822356, -1.566966289834177, -1.3864060518180832
Test Samples: 10000
Pre-processor Statistics:
 Minimum: -7.040747676455537
 Median: (0.011368567087962737, 0.011501589041989753)
 Maximum: 5.870958923561967
 Mean: 0.0066486846207616
 Std Deviation: 1.1693325933137946
Post-processor Distribution using round method:
 -7: 0.01%
 -6: 0.03%
 -5: 0.1%
 -4: 0.32%
 -3: 1.61%
 -2: 6.77%
 -1: 22.32%
 0: 36.43%
 1: 23.5%
 2: 6.97%
 3: 1.53%
 4: 0.34%
 5: 0.06%
 6: 0.01%


=========================================================================
Total Test Time: 1.018 seconds