RNG 0.1.13

Random Number Generator: API for the C++ Random library as a c-extension for Python3

RNG: Random Number Generator

Default Random Engine: Mersenne Twister 64, with hardware entropy. Additional engines and seeding strategies are planned to be available in the unbounded future. More info about MT64: https://en.wikipedia.org/wiki/Mersenne_Twister

The RNG module is not suitable for cryptography, and perfect for other non-deterministic needs like data science, experimental programming, A.I. and games.

Recommended Installation: $ pip install RNG

RNG is not intended to be a drop-in replacement for the Python random module, RNG is a whole different beast.

Number Types & Sizes:

• Float: Python float -> long double at the C++ layer.
  • Supports 16 digits of precision round trip. A bit higher internally.
• Int: Python int -> long long at the C++ layer.
  • Input & Output Range: [-2**63, 2**63) or approximately +/- 9.2 billion billion.

Random Binary Function

• random_bool(truth_factor: float) -> bool
  • Bernoulli distribution.
  • @param truth_factor :: the probability of True. Expected input range: [0.0, 1.0]
  • @return :: True or False

Random Integer Functions

• random_int(lo: int, hi: int) -> int
  • Flat uniform distribution, distributed according to the discrete probability function.
  • Parameter order does not matter, that is to say, it is no longer required that lo <= hi, it just works.
  • @param lo :: the lower bound.
  • @param hi :: the upper bound.
  • @return :: random integer in the inclusive range [lo..hi]
• random_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.
• random_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.
• random_geometric(probability: float) -> int
  • Same as random_negative_binomial(1, probability).
• random_poisson(mean: float) -> int
  • @param mean :: sets the average output of the function.
  • @return :: random integer, poisson distribution centered on the mean.
• random_discrete(count: int, xmin: int, xmax: int, step: int) -> int
  • @param count :: number of weighted values
  • @param xmin :: smallest weight of the set
  • @param xmin :: largest weight of the set
  • @param step :: value stepping

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(lo: float, hi: float) -> float
  • Suffers from the same biclusive feature/bug noted for generate_canonical().
  • @param lo :: lower bound Float
  • @param hi :: upper bound Float
  • @return :: random Float in range {lo, hi} biclusive. The spec defines the output range to be [lo, hi).
• random_normal(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.
• random_log_normal(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.
• random_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.
• random_gamma(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.
• random_weibull(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.
• random_extreme_value(location: float, scale: float) -> float
  • Based on Extreme Value Theory.
  • Used for statistical models of the magnitude of earthquakes and volcanoes.
• random_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.
• random_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.
• random_fisher_f(degrees_of_freedom_1: float, degrees_of_freedom_2: float) -> float
  • F distributions often arise when comparing ratios of variances.
• random_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.
• piecewise_constant_distribution coming soon
  • Produces real values distributed on constant subintervals.
• piecewise_linear_distribution coming soon
  • Produces real values distributed on defined subintervals.

Engines

• mersenne_twister_engine internal only.
  • Implements 64 bit Mersenne twister algorithm. Default engine on most systems.
• linear_congruential_engine coming soon
  • Implements linear congruential algorithm.
• subtract_with_carry_engine coming soon
  • Implements a subtract-with-carry (lagged Fibonacci) algorithm.

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 coming soon
  • Discards some output of a random number engine.
• independent_bits_engine coming soon
  • Packs the output of a random number engine into blocks of a specified number of bits.
• shuffle_order_engine maybe coming soon
  • 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 std::random_device using a pseudo-random number engine if there is no support for non-deterministic random number generation.
• seed_seq maybe coming soon
  • General-purpose bias-eliminating scrambled seed sequence generator.
• User defined seed. maybe coming soon.

Distribution & Performance Test Suite

• distribution_timer(func: staticmethod, *args, **kwargs) -> None
  • For brute force analysis of non-deterministic functions.
  • The timer is only a rough estimate and machine dependant, so only compare results within the same test run.
  • @param func :: Function to analyze. func(*args, **kwargs)
  • @optional_kwarg num_cycles :: Total number of samples for the distribution tests.
  • @optional_kwarg post_processor :: Used to scale a large set of data into a smaller set of groupings.
• quick_test(n=1000)
  • Runs a battery of preconfigured tests for each random distribution function and any associated base cases if applicable.
  • @param n :: the total number of samples to collect for each test.

Development Log

RNG 0.1.13 beta

• Fixed a few typos.

RNG 0.1.12 beta

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

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

RNG 0.1.13 BETA

Binary RNG Tests

Output Analysis: random_bool(truth_factor=0.3333333333333333)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 187ns
Raw Samples: False, False, True, True, False
Test Samples: 1000000
Sample Statistics:
 Minimum: False
 Median: 0.0
 Maximum: True
 Mean: 0.333644
 Std Deviation: 0.4715144786641271
Sample Distribution:
 False: 66.6356%
 True: 33.3644%


Integer RNG Tests

Base Case for random_int:
Output Analysis: Random.randint(a=1, b=6)
Approximate Single Execution Time: Min: 1156ns, Mid: 1187ns, Max: 2250ns
Raw Samples: 6, 3, 1, 5, 6
Test Samples: 1000000
Sample Statistics:
 Minimum: 1
 Median: 4.0
 Maximum: 6
 Mean: 3.500536
 Std Deviation: 1.708841312949401
Sample Distribution:
 1: 16.6959%
 2: 16.6451%
 3: 16.6486%
 4: 16.6242%
 5: 16.6923%
 6: 16.6939%

Output Analysis: random_int(left_limit=1, right_limit=6)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 281ns
Raw Samples: 1, 6, 4, 6, 4
Test Samples: 1000000
Sample Statistics:
 Minimum: 1
 Median: 3.0
 Maximum: 6
 Mean: 3.501395
 Std Deviation: 1.707996185951822
Sample Distribution:
 1: 16.6546%
 2: 16.6207%
 3: 16.7288%
 4: 16.6269%
 5: 16.6645%
 6: 16.7045%

Output Analysis: random_binomial(number_of_trials=4, probability=0.5)
Approximate Single Execution Time: Min: 156ns, Mid: 187ns, Max: 437ns
Raw Samples: 2, 3, 2, 2, 1
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 2.0
 Maximum: 4
 Mean: 1.999595
 Std Deviation: 1.0003278645678308
Sample Distribution:
 0: 6.275%
 1: 24.9737%
 2: 37.5224%
 3: 24.9746%
 4: 6.2543%

Output Analysis: random_negative_binomial(number_of_trials=5, probability=0.75)
Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 187ns
Raw Samples: 3, 1, 4, 1, 3
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 1.0
 Maximum: 15
 Mean: 1.668605
 Std Deviation: 1.4912674400836283
Sample Distribution:
 0: 23.685%
 1: 29.6666%
 2: 22.2228%
 3: 13.0178%
 4: 6.5069%
 5: 2.9241%
 6: 1.2143%
 7: 0.4866%
 8: 0.1811%
 9: 0.0588%
 10: 0.0251%
 11: 0.0069%
 12: 0.0026%
 13: 0.001%
 14: 0.0003%
 15: 0.0001%

Output Analysis: random_geometric(probability=0.75)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 93ns
Raw Samples: 1, 0, 2, 0, 0
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 0.0
 Maximum: 11
 Mean: 0.332909
 Std Deviation: 0.665805557964216
Sample Distribution:
 0: 75.0308%
 1: 18.7108%
 2: 4.7005%
 3: 1.179%
 4: 0.2833%
 5: 0.0735%
 6: 0.0163%
 7: 0.0037%
 8: 0.0014%
 9: 0.0006%
 11: 0.0001%

Output Analysis: random_poisson(mean=4.5)
Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 250ns
Raw Samples: 3, 2, 4, 1, 4
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 4.0
 Maximum: 22
 Mean: 4.499946
 Std Deviation: 2.122295573482285
Sample Distribution:
 0: 1.1062%
 1: 4.9862%
 2: 11.255%
 3: 16.9261%
 4: 18.9474%
 5: 17.116%
 6: 12.78%
 7: 8.2077%
 8: 4.626%
 9: 2.3225%
 10: 1.0513%
 11: 0.429%
 12: 0.165%
 13: 0.058%
 14: 0.0172%
 15: 0.0047%
 16: 0.0009%
 17: 0.0005%
 18: 0.0001%
 19: 0.0001%
 22: 0.0001%

Output Analysis: random_discrete(count=7, xmin=1, xmax=30, step=1)
Approximate Single Execution Time: Min: 500ns, Mid: 531ns, Max: 1468ns
Raw Samples: 2, 3, 3, 3, 6
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 4.0
 Maximum: 6
 Mean: 4.002681
 Std Deviation: 1.7298210325116887
Sample Distribution:
 0: 3.529%
 1: 7.1089%
 2: 10.7468%
 3: 14.28%
 4: 17.8673%
 5: 21.4516%
 6: 25.0164%


Floating Point RNG Tests

Base Case for generate_canonical:
Output Analysis: Random.random()
Approximate Single Execution Time: Min: 31ns, Mid: 31ns, Max: 93ns
Raw Samples: 0.665362468875466, 0.947957822835718, 0.7732052667282289, 0.24723177617196623, 0.2847948078835567
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.6955760417936006e-07
 Median: 0.5003573633308372
 Maximum: 0.9999999278038924
 Mean: 0.5001202011683943
 Std Deviation: 0.28895636368527927
Post-processor Distribution using round method:
 0: 49.9633%
 1: 50.0367%

Output Analysis: generate_canonical()
Approximate Single Execution Time: Min: 31ns, Mid: 31ns, Max: 62ns
Raw Samples: 0.7899691355259951, 0.2115580961392136, 0.11924485634180597, 0.31966524193701346, 0.9665028282293725
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 3.297363750791619e-07
 Median: 0.49967507135470324
 Maximum: 0.9999985995720332
 Mean: 0.4995624774594023
 Std Deviation: 0.2885057282833452
Post-processor Distribution using round method:
 0: 50.0292%
 1: 49.9708%

Output Analysis: random_float(left_limit=0.0, right_limit=10.0)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 93ns
Raw Samples: 5.876284270118109, 4.408971486351368, 2.7766722432107533, 6.774297221165701, 0.4452207877355885
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.9673529436082214e-06
 Median: 4.995905058053005
 Maximum: 9.999992236017288
 Mean: 4.9950797780986855
 Std Deviation: 2.8860948764742362
Post-processor Distribution using ceil method:
 1: 10.017%
 2: 9.9844%
 3: 10.0667%
 4: 10.0115%
 5: 9.9616%
 6: 10.0544%
 7: 10.0095%
 8: 9.9727%
 9: 9.9107%
 10: 10.0115%

Base Case for random_exponential:
Output Analysis: Random.expovariate(lambd=1.0)
Approximate Single Execution Time: Min: 375ns, Mid: 406ns, Max: 2687ns
Raw Samples: 1.384908710238084, 0.6329225743585651, 1.1779663293840221, 0.7360545390590587, 2.5186838032654166
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 9.515821902724921e-07
 Median: 0.6935131717334361
 Maximum: 13.193832803473704
 Mean: 1.0003732833313428
 Std Deviation: 1.0000266893765004
Post-processor Distribution using floor method:
 0: 63.218%
 1: 23.2529%
 2: 8.5594%
 3: 3.1297%
 4: 1.1637%
 5: 0.428%
 6: 0.1576%
 7: 0.0605%
 8: 0.0188%
 9: 0.0073%
 10: 0.0025%
 11: 0.0013%
 12: 0.0002%
 13: 0.0001%

Output Analysis: random_exponential(lambda_rate=1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 500ns
Raw Samples: 0.4455178109900347, 0.019798829043565124, 1.960084236314644, 0.6737792153243346, 0.3036200301324886
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 7.87392539285199e-07
 Median: 0.6925454838352073
 Maximum: 13.806404850281261
 Mean: 0.9992349999349004
 Std Deviation: 0.9998804337934343
Post-processor Distribution using floor method:
 0: 63.29%
 1: 23.1823%
 2: 8.5519%
 3: 3.1602%
 4: 1.1489%
 5: 0.4181%
 6: 0.158%
 7: 0.0549%
 8: 0.022%
 9: 0.0083%
 10: 0.0031%
 11: 0.0016%
 12: 0.0006%
 13: 0.0001%

Base Case for random_gamma:
Output Analysis: Random.gammavariate(alpha=1.0, beta=1.0)
Approximate Single Execution Time: Min: 531ns, Mid: 562ns, Max: 1562ns
Raw Samples: 0.1120036730981629, 0.288596514467854, 1.4292200076261976, 0.6082620352247636, 3.025292360943196
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.3458546646558586e-06
 Median: 0.6950869621497608
 Maximum: 13.937324897267649
 Mean: 1.0005255880421755
 Std Deviation: 0.9995891000928212
Post-processor Distribution using floor method:
 0: 63.1368%
 1: 23.3652%
 2: 8.5279%
 3: 3.1291%
 4: 1.1627%
 5: 0.4295%
 6: 0.1604%
 7: 0.0546%
 8: 0.0226%
 9: 0.0064%
 10: 0.0025%
 11: 0.0014%
 12: 0.0006%
 13: 0.0003%

Output Analysis: random_gamma(shape=1.0, scale=1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 156ns
Raw Samples: 2.2986161513068795, 0.856268261312037, 0.6986203795251699, 2.0667376569243614, 0.48882949879448945
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 2.3229543874873112e-07
 Median: 0.6933814925300313
 Maximum: 16.380238876018392
 Mean: 0.9995706626366362
 Std Deviation: 0.9978855495132456
Post-processor Distribution using floor method:
 0: 63.2079%
 1: 23.2697%
 2: 8.5675%
 3: 3.1459%
 4: 1.1419%
 5: 0.4236%
 6: 0.1548%
 7: 0.0559%
 8: 0.0209%
 9: 0.0076%
 10: 0.0029%
 11: 0.0007%
 12: 0.0005%
 16: 0.0002%

Output Analysis: random_weibull(shape=1.0, scale=1.0)
Approximate Single Execution Time: Min: 156ns, Mid: 156ns, Max: 250ns
Raw Samples: 0.5356547797305844, 0.068398157865677, 0.9269669403718449, 0.07398596623630445, 0.5347870598609802
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.3927117192774708e-07
 Median: 0.6934664345763201
 Maximum: 14.112539985494557
 Mean: 1.0001282069851674
 Std Deviation: 1.0000455077035848
Post-processor Distribution using floor method:
 0: 63.2026%
 1: 23.2492%
 2: 8.569%
 3: 3.1557%
 4: 1.1492%
 5: 0.4253%
 6: 0.1565%
 7: 0.0559%
 8: 0.0216%
 9: 0.0099%
 10: 0.0036%
 11: 0.0011%
 12: 0.0001%
 13: 0.0002%
 14: 0.0001%

Output Analysis: random_extreme_value(location=0.0, scale=1.0)
Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 187ns
Raw Samples: -1.1943053022404513, 1.4198532377155055, 0.2538023338768577, -0.6332535085988988, 0.9622022748389344
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -2.698691350764175
 Median: 0.36694626460926594
 Maximum: 14.124217944089759
 Mean: 0.5768250476833159
 Std Deviation: 1.2823422487043867
Post-processor Distribution using round method:
 -3: 0.0003%
 -2: 1.1352%
 -1: 18.099%
 0: 35.2663%
 1: 25.4851%
 2: 12.148%
 3: 4.9101%
 4: 1.86%
 5: 0.6841%
 6: 0.2619%
 7: 0.093%
 8: 0.0351%
 9: 0.0143%
 10: 0.0048%
 11: 0.0018%
 12: 0.0006%
 13: 0.0003%
 14: 0.0001%

Base Case for random_normal:
Output Analysis: Random.gauss(mu=5.0, sigma=2.0)
Approximate Single Execution Time: Min: 625ns, Mid: 656ns, Max: 1125ns
Raw Samples: 4.940537091982698, 3.2533272917806793, 7.331904123480079, 7.895007996710751, 1.8715004238405721
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -4.7812286656023435
 Median: 5.004411041481515
 Maximum: 16.035283651223565
 Mean: 5.003388232099493
 Std Deviation: 2.000093411436824
Post-processor Distribution using round method:
 -5: 0.0001%
 -4: 0.0011%
 -3: 0.0059%
 -2: 0.0504%
 -1: 0.2453%
 0: 0.9189%
 1: 2.7618%
 2: 6.5736%
 3: 12.0449%
 4: 17.4782%
 5: 19.6532%
 6: 17.5339%
 7: 12.1513%
 8: 6.5675%
 9: 2.7925%
 10: 0.926%
 11: 0.2368%
 12: 0.0478%
 13: 0.0096%
 14: 0.0011%
 16: 0.0001%

Output Analysis: random_normal(mean=5.0, std_dev=2.0)
Approximate Single Execution Time: Min: 125ns, Mid: 156ns, Max: 406ns
Raw Samples: 6.896821054387403, 2.2259821390482526, 6.1966925155621135, 5.191711938385315, 4.521973218802774
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -4.323151343105488
 Median: 4.997033426486281
 Maximum: 15.118371252008215
 Mean: 4.996139376561458
 Std Deviation: 2.0017935418046746
Post-processor Distribution using round method:
 -4: 0.0014%
 -3: 0.008%
 -2: 0.0488%
 -1: 0.2443%
 0: 0.9253%
 1: 2.8223%
 2: 6.5753%
 3: 12.1066%
 4: 17.5109%
 5: 19.6934%
 6: 17.4529%
 7: 12.0317%
 8: 6.5537%
 9: 2.8179%
 10: 0.9103%
 11: 0.2404%
 12: 0.0471%
 13: 0.0085%
 14: 0.001%
 15: 0.0002%

Base Case for random_log_normal:
Output Analysis: Random.lognormvariate(mu=1.6, sigma=0.25)
Approximate Single Execution Time: Min: 875ns, Mid: 937ns, Max: 1375ns
Raw Samples: 6.732135560517046, 5.445007708381849, 3.7969512997120463, 4.324175072743914, 4.197102505392857
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.3092099246434619
 Median: 4.9491157618393125
 Maximum: 16.54370752032964
 Mean: 5.108152109942813
 Std Deviation: 1.2964479787653773
Post-processor Distribution using round method:
 1: 0.0002%
 2: 0.3172%
 3: 7.909%
 4: 26.888%
 5: 31.2023%
 6: 19.8656%
 7: 8.9993%
 8: 3.2968%
 9: 1.0589%
 10: 0.3294%
 11: 0.0948%
 12: 0.0291%
 13: 0.0072%
 14: 0.0018%
 15: 0.0001%
 16: 0.0002%
 17: 0.0001%

Output Analysis: random_log_normal(log_mean=1.6, log_deviation=0.25)
Approximate Single Execution Time: Min: 187ns, Mid: 187ns, Max: 250ns
Raw Samples: 5.966528261843649, 4.156498510324634, 4.304660149026884, 3.8446319586392836, 3.78386357361859
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.5588557243013828
 Median: 4.953146707233678
 Maximum: 16.067562230649695
 Mean: 5.111201015211827
 Std Deviation: 1.298877434913344
Post-processor Distribution using round method:
 2: 0.316%
 3: 7.9102%
 4: 26.838%
 5: 31.1919%
 6: 19.8384%
 7: 9.0154%
 8: 3.3429%
 9: 1.0875%
 10: 0.3282%
 11: 0.0968%
 12: 0.0257%
 13: 0.0066%
 14: 0.0019%
 15: 0.0003%
 16: 0.0002%

Output Analysis: random_chi_squared(degrees_of_freedom=1.0)
Approximate Single Execution Time: Min: 156ns, Mid: 187ns, Max: 593ns
Raw Samples: 0.03371388065252083, 0.5191144013965825, 0.3924971202295526, 0.8498757186681486, 1.921449571037342
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 2.726463610641794e-13
 Median: 0.45398990231214187
 Maximum: 23.1562359909827
 Mean: 0.9983703892126362
 Std Deviation: 1.412874195552223
Post-processor Distribution using floor method:
 0: 68.3361%
 1: 15.9883%
 2: 7.3672%
 3: 3.7738%
 4: 2.0088%
 5: 1.1008%
 6: 0.617%
 7: 0.3357%
 8: 0.1944%
 9: 0.1149%
 10: 0.0692%
 11: 0.0405%
 12: 0.0236%
 13: 0.0124%
 14: 0.008%
 15: 0.0037%
 16: 0.0031%
 17: 0.0008%
 18: 0.0006%
 19: 0.0008%
 20: 0.0001%
 21: 0.0001%
 23: 0.0001%

Output Analysis: random_cauchy(location=0.0, scale=1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 156ns
Raw Samples: 0.08320374629685201, 1.331991267593043, 1.475657928077641, 0.37409929787086277, -0.6546748901491992
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -344475.4939877482
 Median: 0.003185727693504345
 Maximum: 108745.02117269565
 Mean: -0.748560721002727
 Std Deviation: 493.4933617789858
Post-processor Distribution using floor_mod_10 method:
 0: 26.1011%
 1: 11.3465%
 2: 5.7057%
 3: 3.8047%
 4: 3.1228%
 5: 3.1332%
 6: 3.794%
 7: 5.6726%
 8: 11.2839%
 9: 26.0355%

Output Analysis: random_fisher_f(degrees_of_freedom_1=8.0, degrees_of_freedom_2=8.0)
Approximate Single Execution Time: Min: 343ns, Mid: 375ns, Max: 406ns
Raw Samples: 2.33300344246744, 0.3386774713420087, 0.42999794923145873, 0.8382789352604338, 0.4977586326690908
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 0.011508397337115296
 Median: 1.000360378192011
 Maximum: 63.631747659089505
 Mean: 1.3350473834495895
 Std Deviation: 1.243134064837772
Post-processor Distribution using floor method:
 0: 49.9806%
 1: 32.6681%
 2: 10.2498%
 3: 3.7388%
 4: 1.5771%
 5: 0.7667%
 6: 0.3947%
 7: 0.2212%
 8: 0.1297%
 9: 0.0857%
 10: 0.0534%
 11: 0.0356%
 12: 0.024%
 13: 0.0183%
 14: 0.0116%
 15: 0.0096%
 16: 0.0073%
 17: 0.0045%
 18: 0.0047%
 19: 0.003%
 20: 0.0022%
 21: 0.0021%
 22: 0.0014%
 23: 0.0015%
 24: 0.0008%
 25: 0.001%
 26: 0.0008%
 27: 0.0011%
 28: 0.0007%
 29: 0.0004%
 30: 0.0003%
 31: 0.0004%
 32: 0.0002%
 33: 0.0005%
 34: 0.0004%
 35: 0.0001%
 36: 0.0001%
 37: 0.0003%
 38: 0.0004%
 40: 0.0001%
 42: 0.0001%
 43: 0.0001%
 45: 0.0001%
 49: 0.0002%
 53: 0.0001%
 55: 0.0001%
 63: 0.0001%

Output Analysis: random_student_t(degrees_of_freedom=8.0)
Approximate Single Execution Time: Min: 218ns, Mid: 250ns, Max: 812ns
Raw Samples: 0.6559188880695308, -0.03653765521785164, -1.488394093370608, -0.42678547647829446, 1.7662915657173328
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -12.38228193001556
 Median: -0.00025264886625567533
 Maximum: 11.816282198317431
 Mean: -0.0007172606813375191
 Std Deviation: 1.155660971912555
Post-processor Distribution using round method:
 -12: 0.0003%
 -10: 0.0002%
 -9: 0.0004%
 -8: 0.0019%
 -7: 0.0067%
 -6: 0.0162%
 -5: 0.0697%
 -4: 0.3133%
 -3: 1.4424%
 -2: 6.8191%
 -1: 22.9018%
 0: 36.8806%
 1: 22.954%
 2: 6.7255%
 3: 1.4577%
 4: 0.3082%
 5: 0.0754%
 6: 0.0183%
 7: 0.0052%
 8: 0.0021%
 9: 0.0004%
 10: 0.0001%
 11: 0.0003%
 12: 0.0002%


All tests passed!