RNG 0.1.20

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

RNG: Random Number Generator

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 & Sizes:

• Float: Python float -> double at the C++ layer.
• Integer: 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(left_limit: int, right_limit: 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 left_limit :: input A.
  • @param right_limit :: input B.
  • @return :: random integer in the inclusive range [A, B]
• random_below(upper_bound: int) -> int
  • Featuring The Typhoon Engine.
  • Flat uniform distribution.
  • @param upper_bound :: inout A
  • @return :: random integer in exclusive range [0, A) or (A, 0] if A < 0
• 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) -> int
  • @param count :: number of weighted values
  • @param xmin :: smallest weight of the set
  • @param xmin :: largest weight of the set

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).
• 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
  • Implements 64 bit Mersenne twister algorithm. Default engine on most systems.
• linear_congruential_engine
  • Implements linear congruential algorithm.
• subtract_with_carry_engine
  • 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
  • Discards some output of a random number engine.
• independent_bits_engine
  • Packs the output of a random number engine into blocks of a specified number of bits.
• shuffle_order_engine
  • Delivers the output of a random number engine in a different order.

Seeds & Entropy Source

• random_device
  • 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
  • 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 statistical 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 the distribution tests.
  • @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 each random distribution function.
  • @param n :: the total number of samples to collect for each test. Default: 10,000

Development Log

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

RNG Quick Test: Hurricane Engine
 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 Analysis: random_bool(truth_factor=0.3333333333333333)
Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 281ns
Raw Samples: False, True, False, False, False
Test Samples: 1000000
Sample Statistics:
 Minimum: False
 Median: 0.0
 Maximum: True
 Mean: 0.333513
 Std Deviation: 0.47146823977156843
Sample Distribution:
 False: 66.6487%
 True: 33.3513%


Integer Tests

Base Case for random_int:
Output Analysis: Random.randint(a=1, b=6)
Approximate Single Execution Time: Min: 1187ns, Mid: 1250ns, Max: 3625ns
Raw Samples: 4, 4, 4, 2, 4
Test Samples: 1000000
Sample Statistics:
 Minimum: 1
 Median: 3.0
 Maximum: 6
 Mean: 3.499715
 Std Deviation: 1.7073045521124326
Sample Distribution:
 1: 16.6453%
 2: 16.6847%
 3: 16.706%
 4: 16.6221%
 5: 16.701%
 6: 16.6409%

Output Analysis: random_int(left_limit=1, right_limit=6)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 156ns
Raw Samples: 1, 5, 4, 2, 6
Test Samples: 1000000
Sample Statistics:
 Minimum: 1
 Median: 3.0
 Maximum: 6
 Mean: 3.497492
 Std Deviation: 1.7089407919570034
Sample Distribution:
 1: 16.7291%
 2: 16.698%
 3: 16.6397%
 4: 16.6276%
 5: 16.639%
 6: 16.6666%

Typhoon Engine:
Output Analysis: random_below(upper_bound=6)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 937ns
Raw Samples: 4, 0, 2, 2, 5
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 3.0
 Maximum: 5
 Mean: 2.501864
 Std Deviation: 1.7074007850301114
Sample Distribution:
 0: 16.6147%
 1: 16.6673%
 2: 16.6801%
 3: 16.697%
 4: 16.6366%
 5: 16.7043%

Output Analysis: random_binomial(number_of_trials=4, probability=0.5)
Approximate Single Execution Time: Min: 187ns, Mid: 187ns, Max: 281ns
Raw Samples: 1, 2, 2, 4, 3
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 2.0
 Maximum: 4
 Mean: 2.000732
 Std Deviation: 0.9995501304469242
Sample Distribution:
 0: 6.2083%
 1: 25.0387%
 2: 37.4793%
 3: 25.0189%
 4: 6.2548%

Output Analysis: random_negative_binomial(number_of_trials=5, probability=0.75)
Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 218ns
Raw Samples: 1, 0, 0, 5, 1
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 1.0
 Maximum: 17
 Mean: 1.667842
 Std Deviation: 1.491791301917694
Sample Distribution:
 0: 23.6981%
 1: 29.6548%
 2: 22.303%
 3: 12.9467%
 4: 6.4832%
 5: 2.9323%
 6: 1.2263%
 7: 0.4717%
 8: 0.1807%
 9: 0.0673%
 10: 0.0239%
 11: 0.0078%
 12: 0.0022%
 13: 0.0016%
 14: 0.0002%
 15: 0.0001%
 17: 0.0001%

Output Analysis: random_geometric(probability=0.75)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 156ns
Raw Samples: 0, 0, 0, 0, 0
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 0.0
 Maximum: 9
 Mean: 0.333196
 Std Deviation: 0.6670298872710806
Sample Distribution:
 0: 75.0359%
 1: 18.6944%
 2: 4.7088%
 3: 1.1648%
 4: 0.3007%
 5: 0.0698%
 6: 0.0191%
 7: 0.0053%
 8: 0.0011%
 9: 0.0001%

Output Analysis: random_poisson(mean=4.5)
Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 406ns
Raw Samples: 7, 4, 7, 6, 4
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 4.0
 Maximum: 19
 Mean: 4.50258
 Std Deviation: 2.1232093282617828
Sample Distribution:
 0: 1.1166%
 1: 4.9826%
 2: 11.2378%
 3: 16.9127%
 4: 18.9217%
 5: 17.0505%
 6: 12.8048%
 7: 8.2521%
 8: 4.6868%
 9: 2.3388%
 10: 1.0259%
 11: 0.4297%
 12: 0.1581%
 13: 0.0556%
 14: 0.0186%
 15: 0.0051%
 16: 0.0019%
 17: 0.0004%
 18: 0.0002%
 19: 0.0001%

Output Analysis: random_discrete(count=7, xmin=1, xmax=30)
Approximate Single Execution Time: Min: 562ns, Mid: 593ns, Max: 937ns
Raw Samples: 6, 4, 5, 6, 3
Test Samples: 1000000
Sample Statistics:
 Minimum: 0
 Median: 4.0
 Maximum: 6
 Mean: 4.004473
 Std Deviation: 1.7299554864264528
Sample Distribution:
 0: 3.5324%
 1: 7.1023%
 2: 10.7147%
 3: 14.2666%
 4: 17.8702%
 5: 21.4478%
 6: 25.066%


Floating Point Tests

Base Case for generate_canonical:
Output Analysis: Random.random()
Approximate Single Execution Time: Min: 31ns, Mid: 46ns, Max: 62ns
Raw Samples: 0.6570047697196703, 0.15224885571692315, 0.3016126670295707, 0.27907194383835776, 0.7574862846673812
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 2.9815953883627344e-07
 Median: 0.49949096543909177
 Maximum: 0.9999995187658669
 Mean: 0.4997294571076714
 Std Deviation: 0.2887497663042492
Post-processor Distribution using round method:
 0: 50.0497%
 1: 49.9503%

Output Analysis: generate_canonical()
Approximate Single Execution Time: Min: 31ns, Mid: 62ns, Max: 125ns
Raw Samples: 0.6970074952603725, 0.9119397436113943, 0.9346890033668273, 0.9217520201011971, 0.9566401834440911
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 3.0367002228985336e-06
 Median: 0.4993868783055178
 Maximum: 0.999999989509601
 Mean: 0.49962081680742315
 Std Deviation: 0.2886834892383035
Post-processor Distribution using round method:
 0: 50.064%
 1: 49.936%

Output Analysis: random_float(left_limit=0.0, right_limit=10.0)
Approximate Single Execution Time: Min: 62ns, Mid: 77ns, Max: 687ns
Raw Samples: 7.250072331699807, 2.19419532526034, 9.565847452510264, 2.0359165984956977, 4.82969972900843
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 4.615397130295251e-06
 Median: 4.997286628857793
 Maximum: 9.999993060485252
 Mean: 4.998945688758831
 Std Deviation: 2.888279348290616
Post-processor Distribution using ceil method:
 1: 10.0294%
 2: 9.9813%
 3: 10.0128%
 4: 10.01%
 5: 9.9948%
 6: 10.0037%
 7: 9.9569%
 8: 9.9782%
 9: 10.0292%
 10: 10.0037%

Base Case for random_exponential:
Output Analysis: Random.expovariate(lambd=1.0)
Approximate Single Execution Time: Min: 468ns, Mid: 468ns, Max: 812ns
Raw Samples: 1.1018688812063477, 0.6024554673121082, 1.6686416144822886, 0.9164516490715121, 0.15661897291107288
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 3.3318097247187586e-07
 Median: 0.6933721837616083
 Maximum: 14.549105438967956
 Mean: 1.0001227529087031
 Std Deviation: 1.0005268303436308
Post-processor Distribution using floor_mod_10 method:
 0: 63.2201%
 1: 23.2263%
 2: 8.5896%
 3: 3.126%
 4: 1.1697%
 5: 0.42%
 6: 0.16%
 7: 0.0581%
 8: 0.0212%
 9: 0.009%

Output Analysis: random_exponential(lambda_rate=1.0)
Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 625ns
Raw Samples: 0.07386087703511517, 0.5796138698566454, 0.4366800971494448, 2.5902206566536905, 0.5612743127963082
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.5956204111521102e-06
 Median: 0.6928729297653928
 Maximum: 13.085196366919934
 Mean: 1.0000879939397769
 Std Deviation: 0.9994077363036815
Post-processor Distribution using floor_mod_10 method:
 0: 63.2424%
 1: 23.2246%
 2: 8.5672%
 3: 3.1512%
 4: 1.1513%
 5: 0.4252%
 6: 0.1501%
 7: 0.0589%
 8: 0.0217%
 9: 0.0074%

Base Case for random_gamma:
Output Analysis: Random.gammavariate(alpha=1.0, beta=1.0)
Approximate Single Execution Time: Min: 656ns, Mid: 687ns, Max: 1125ns
Raw Samples: 0.31952797125019106, 2.9110574593309626, 1.507787243375767, 0.5079294444335829, 0.37442602347488985
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.5189774997716145e-08
 Median: 0.6935264267270171
 Maximum: 14.730821845424648
 Mean: 0.9997367170432164
 Std Deviation: 0.9987025439965271
Post-processor Distribution using floor_mod_10 method:
 0: 63.1864%
 1: 23.3231%
 2: 8.5553%
 3: 3.1248%
 4: 1.1499%
 5: 0.4176%
 6: 0.1522%
 7: 0.0586%
 8: 0.0237%
 9: 0.0084%

Output Analysis: random_gamma(shape=1.0, scale=1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 218ns
Raw Samples: 1.659711871275316, 0.9281337345048667, 0.9640420158146191, 0.926760092156792, 1.6379990730513747
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 4.194584151559329e-07
 Median: 0.6910225528579438
 Maximum: 13.544102671589854
 Mean: 0.9981751157717323
 Std Deviation: 0.9979388234147085
Post-processor Distribution using floor_mod_10 method:
 0: 63.2934%
 1: 23.1918%
 2: 8.5666%
 3: 3.1235%
 4: 1.1716%
 5: 0.4164%
 6: 0.1501%
 7: 0.0602%
 8: 0.0198%
 9: 0.0066%

Output Analysis: random_weibull(shape=1.0, scale=1.0)
Approximate Single Execution Time: Min: 125ns, Mid: 156ns, Max: 718ns
Raw Samples: 0.4816125736713127, 1.37504844647496, 0.17476034816547142, 0.6737209810700767, 0.40694034561013837
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 7.293946316920661e-07
 Median: 0.693660798796579
 Maximum: 12.928130970544212
 Mean: 1.0014375097409594
 Std Deviation: 1.0034697986553105
Post-processor Distribution using floor_mod_10 method:
 0: 63.1496%
 1: 23.2699%
 2: 8.5896%
 3: 3.1477%
 4: 1.1571%
 5: 0.4314%
 6: 0.1614%
 7: 0.0612%
 8: 0.0229%
 9: 0.0092%

Output Analysis: random_extreme_value(location=0.0, scale=1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 218ns
Raw Samples: 8.755189280896964, 1.194033732536873, 1.2684866265139922, 1.3447492505523024, -0.4218304682806628
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -2.727102044096202
 Median: 0.36527548204890337
 Maximum: 15.845177261761595
 Mean: 0.5759963198306385
 Std Deviation: 1.2819436444760983
Post-processor Distribution using round method:
 -3: 0.0008%
 -2: 1.1423%
 -1: 18.1057%
 0: 35.3232%
 1: 25.4256%
 2: 12.1522%
 3: 4.8859%
 4: 1.8689%
 5: 0.6831%
 6: 0.2586%
 7: 0.0984%
 8: 0.0356%
 9: 0.0119%
 10: 0.005%
 11: 0.0018%
 12: 0.0007%
 14: 0.0001%
 16: 0.0002%

Base Case for random_normal:
Output Analysis: Random.gauss(mu=5.0, sigma=2.0)
Approximate Single Execution Time: Min: 718ns, Mid: 718ns, Max: 1187ns
Raw Samples: 6.300763276240546, 1.794491443283512, 5.198255642441661, 4.726653498300128, 5.518522425054493
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -5.298297695835354
 Median: 5.000649683218311
 Maximum: 14.056745918354773
 Mean: 5.001638655916973
 Std Deviation: 1.9991343569491313
Post-processor Distribution using round method:
 -5: 0.0001%
 -4: 0.0013%
 -3: 0.0081%
 -2: 0.0494%
 -1: 0.2312%
 0: 0.9296%
 1: 2.7835%
 2: 6.559%
 3: 12.0424%
 4: 17.4838%
 5: 19.7402%
 6: 17.4687%
 7: 12.1565%
 8: 6.5254%
 9: 2.8079%
 10: 0.9218%
 11: 0.2346%
 12: 0.0486%
 13: 0.0069%
 14: 0.001%

Output Analysis: random_normal(mean=5.0, std_dev=2.0)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 250ns
Raw Samples: 1.0323867658347494, 6.630004237421952, 2.8829346314043462, 7.518046479914521, 5.012168864358874
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -4.970879933162836
 Median: 4.999800412668707
 Maximum: 14.927920090816256
 Mean: 4.999574230845622
 Std Deviation: 1.9995376817999204
Post-processor Distribution using round method:
 -5: 0.0001%
 -4: 0.0009%
 -3: 0.0078%
 -2: 0.0486%
 -1: 0.2497%
 0: 0.931%
 1: 2.7631%
 2: 6.5322%
 3: 12.154%
 4: 17.4644%
 5: 19.7271%
 6: 17.4248%
 7: 12.1475%
 8: 6.5528%
 9: 2.7816%
 10: 0.9142%
 11: 0.2457%
 12: 0.0461%
 13: 0.0074%
 14: 0.0008%
 15: 0.0002%

Base Case for random_log_normal:
Output Analysis: Random.lognormvariate(mu=1.6, sigma=0.25)
Approximate Single Execution Time: Min: 1000ns, Mid: 1093ns, Max: 1468ns
Raw Samples: 4.134952691559435, 4.411991171895773, 4.315384669101187, 5.025571391824962, 4.2588612031488315
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.2537749127366524
 Median: 4.95223696948567
 Maximum: 20.278351569884403
 Mean: 5.109536606294799
 Std Deviation: 1.299854786332313
Post-processor Distribution using round method:
 1: 0.0001%
 2: 0.3052%
 3: 7.9529%
 4: 26.8665%
 5: 31.1629%
 6: 19.8398%
 7: 9.0017%
 8: 3.3003%
 9: 1.1084%
 10: 0.3306%
 11: 0.0939%
 12: 0.0277%
 13: 0.0075%
 14: 0.0017%
 15: 0.0005%
 16: 0.0001%
 18: 0.0001%
 20: 0.0001%

Output Analysis: random_log_normal(log_mean=1.6, log_deviation=0.25)
Approximate Single Execution Time: Min: 125ns, Mid: 156ns, Max: 281ns
Raw Samples: 4.638566203068009, 6.488569436892637, 4.746135513863415, 9.043467938363431, 5.495642596245647
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 1.293629829698226
 Median: 4.956065050948405
 Maximum: 15.52340281958146
 Mean: 5.112381442906878
 Std Deviation: 1.2984785016330378
Post-processor Distribution using round method:
 1: 0.0001%
 2: 0.3069%
 3: 7.8998%
 4: 26.7776%
 5: 31.2469%
 6: 19.9085%
 7: 9.0007%
 8: 3.3119%
 9: 1.0726%
 10: 0.3384%
 11: 0.0952%
 12: 0.0305%
 13: 0.0075%
 14: 0.0023%
 15: 0.001%
 16: 0.0001%

Output Analysis: random_chi_squared(degrees_of_freedom=1.0)
Approximate Single Execution Time: Min: 125ns, Mid: 156ns, Max: 281ns
Raw Samples: 1.8682538839390197, 0.1417458922993199, 0.35609409265427794, 0.10488140810614487, 0.0537827928584459
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 2.1198627145029897e-12
 Median: 0.45560153411558757
 Maximum: 21.441173685147888
 Mean: 0.9999549710727771
 Std Deviation: 1.4121520350785501
Post-processor Distribution using floor_mod_10 method:
 0: 68.3143%
 1: 16.0487%
 2: 7.4482%
 3: 3.8009%
 4: 2.0053%
 5: 1.1056%
 6: 0.6216%
 7: 0.3505%
 8: 0.1957%
 9: 0.1092%

Output Analysis: random_cauchy(location=0.0, scale=1.0)
Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 250ns
Raw Samples: -0.5314701578336324, 0.09423819174982691, 0.0064472189131502475, -26.05628572449915, 0.83766832973519
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -792640.7171117909
 Median: 0.0017499052419489148
 Maximum: 467179.39816305996
 Mean: -0.4710975417831596
 Std Deviation: 1159.7291680368085
Post-processor Distribution using floor_mod_10 method:
 0: 26.0603%
 1: 11.3498%
 2: 5.697%
 3: 3.798%
 4: 3.1417%
 5: 3.1362%
 6: 3.7826%
 7: 5.7139%
 8: 11.3031%
 9: 26.0174%

Output Analysis: random_fisher_f(degrees_of_freedom_1=8.0, degrees_of_freedom_2=8.0)
Approximate Single Execution Time: Min: 218ns, Mid: 250ns, Max: 656ns
Raw Samples: 0.8213120498960351, 0.880247304396062, 0.9794153235132154, 0.6848216175255734, 0.6540665752635523
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: 0.01697910105829946
 Median: 1.0007647509684978
 Maximum: 161.6347427592095
 Mean: 1.335511566842662
 Std Deviation: 1.26567995129718
Post-processor Distribution using floor_mod_10 method:
 0: 50.0205%
 1: 32.6974%
 2: 10.3344%
 3: 3.717%
 4: 1.6027%
 5: 0.7697%
 6: 0.4078%
 7: 0.2212%
 8: 0.1402%
 9: 0.0891%

Output Analysis: random_student_t(degrees_of_freedom=8.0)
Approximate Single Execution Time: Min: 156ns, Mid: 187ns, Max: 312ns
Raw Samples: 1.0636774816078634, -0.12042681077334667, -0.3233596860824123, 1.8924021728239548, 0.03240701144977185
Test Samples: 1000000
Pre-processor Statistics:
 Minimum: -11.347507412744367
 Median: 0.0001797843119758288
 Maximum: 15.741851962630925
 Mean: 0.0006966434111483332
 Std Deviation: 1.1528091788818284
Post-processor Distribution using round method:
 -11: 0.0001%
 -10: 0.0002%
 -9: 0.0006%
 -8: 0.0022%
 -7: 0.0046%
 -6: 0.0179%
 -5: 0.0726%
 -4: 0.3052%
 -3: 1.44%
 -2: 6.7055%
 -1: 22.9215%
 0: 37.0059%
 1: 22.9333%
 2: 6.7552%
 3: 1.436%
 4: 0.3012%
 5: 0.0708%
 6: 0.0187%
 7: 0.0046%
 8: 0.0022%
 9: 0.0011%
 10: 0.0003%
 11: 0.0002%
 16: 0.0001%


=========================================================================
Total Test Time: 92.9885 seconds


All tests passed!