Metadata-Version: 2.1
Name: RNG
Version: 0.2.2
Summary: Python API for the C++ Random library
Home-page: https://sharpdesigndigital.com
Author: Broken aka Robert Sharp
Author-email: webmaster@sharpdesigndigital.com
License: Free for non-commercial use
Description: # 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
        - `randint(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
        - `randbelow(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
        - `random() -> 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.
        - `uniform(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`
            - 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.
        - `storm_engine`
            - 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`
            - 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.
        
        
        #### 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 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
         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: 31ns, Mid: 62ns, Max: 125ns
        Raw Samples: True, False, False, True, True
        Test Samples: 10000
        Sample Statistics:
         Minimum: False
         Median: False
         Maximum: True
         Mean: 0.3298
         Std Deviation: 0.4701638708009588
        Sample Distribution:
         False: 67.02%
         True: 32.98%
        
        
        Integer Tests
        
        Output Distribution: Random.randint(1, 6)
        Approximate Single Execution Time: Min: 1406ns, Mid: 1609ns, Max: 2250ns
        Raw Samples: 6, 5, 3, 5, 1
        Test Samples: 10000
        Sample Statistics:
         Minimum: 1
         Median: 3
         Maximum: 6
         Mean: 3.482
         Std Deviation: 1.7009307206737296
        Sample Distribution:
         1: 16.9%
         2: 16.39%
         3: 17.19%
         4: 16.85%
         5: 16.47%
         6: 16.2%
        
        Output Distribution: randint(1, 6)
        Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 93ns
        Raw Samples: 1, 2, 1, 4, 5
        Test Samples: 10000
        Sample Statistics:
         Minimum: 1
         Median: 4
         Maximum: 6
         Mean: 3.5006
         Std Deviation: 1.7051364722622362
        Sample Distribution:
         1: 16.7%
         2: 16.47%
         3: 16.65%
         4: 16.97%
         5: 16.67%
         6: 16.54%
        
        Output Distribution: Random.randrange(6)
        Approximate Single Execution Time: Min: 812ns, Mid: 843ns, Max: 1062ns
        Raw Samples: 1, 3, 4, 5, 4
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 2
         Maximum: 5
         Mean: 2.4764
         Std Deviation: 1.7017439975693542
        Sample Distribution:
         0: 16.59%
         1: 17.29%
         2: 16.78%
         3: 17.05%
         4: 15.81%
         5: 16.48%
        
        Output Distribution: randbelow(6)
        Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 250ns
        Raw Samples: 3, 5, 1, 1, 3
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 3
         Maximum: 5
         Mean: 2.5202
         Std Deviation: 1.7011999788786778
        Sample Distribution:
         0: 16.23%
         1: 16.24%
         2: 17.09%
         3: 17.16%
         4: 16.28%
         5: 17.0%
        
        Output Distribution: binomial(4, 0.5)
        Approximate Single Execution Time: Min: 156ns, Mid: 156ns, Max: 375ns
        Raw Samples: 3, 3, 2, 1, 2
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 2
         Maximum: 4
         Mean: 2.013
         Std Deviation: 1.0004654382382787
        Sample Distribution:
         0: 6.06%
         1: 24.77%
         2: 37.43%
         3: 25.29%
         4: 6.45%
        
        Output Distribution: negative_binomial(5, 0.75)
        Approximate Single Execution Time: Min: 125ns, Mid: 125ns, Max: 281ns
        Raw Samples: 3, 2, 1, 1, 1
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 1
         Maximum: 13
         Mean: 1.6589
         Std Deviation: 1.5055821026256837
        Sample Distribution:
         0: 24.51%
         1: 28.64%
         2: 22.76%
         3: 12.98%
         4: 6.35%
         5: 2.65%
         6: 1.24%
         7: 0.55%
         8: 0.18%
         9: 0.06%
         10: 0.03%
         11: 0.04%
         13: 0.01%
        
        Output Distribution: geometric(0.75)
        Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 156ns
        Raw Samples: 0, 0, 0, 0, 2
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 0
         Maximum: 7
         Mean: 0.3328
         Std Deviation: 0.6725246381649478
        Sample Distribution:
         0: 75.32%
         1: 18.35%
         2: 4.54%
         3: 1.43%
         4: 0.27%
         5: 0.07%
         6: 0.01%
         7: 0.01%
        
        Output Distribution: poisson(4.5)
        Approximate Single Execution Time: Min: 125ns, Mid: 156ns, Max: 1843ns
        Raw Samples: 5, 2, 1, 6, 1
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 4
         Maximum: 15
         Mean: 4.4792
         Std Deviation: 2.0852822831022237
        Sample Distribution:
         0: 1.17%
         1: 4.9%
         2: 11.45%
         3: 16.48%
         4: 18.88%
         5: 17.92%
         6: 12.88%
         7: 8.08%
         8: 4.68%
         9: 1.97%
         10: 1.15%
         11: 0.32%
         12: 0.06%
         13: 0.04%
         14: 0.01%
         15: 0.01%
        
        Output Distribution: discrete(7, 1, 30, 1)
        Approximate Single Execution Time: Min: 656ns, Mid: 687ns, Max: 1125ns
        Raw Samples: 2, 2, 4, 6, 6
        Test Samples: 10000
        Sample Statistics:
         Minimum: 0
         Median: 4
         Maximum: 6
         Mean: 4.0281
         Std Deviation: 1.719362094092184
        Sample Distribution:
         0: 3.58%
         1: 6.67%
         2: 10.06%
         3: 15.03%
         4: 17.77%
         5: 21.49%
         6: 25.4%
        
        
        Floating Point Tests
        
        Output Distribution: Random.random()
        Approximate Single Execution Time: Min: 31ns, Mid: 46ns, Max: 93ns
        Raw Samples: 0.28191876901126556, 0.34571049244439367, 0.5761883274557041, 0.5805467771548505, 0.9142637634051997
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 6.997243834505618e-05
         Median: (0.4980955259203752, 0.4981058794530985)
         Maximum: 0.9998432526044941
         Mean: 0.49774923104713104
         Std Deviation: 0.28818355339000473
        Post-processor Distribution using round method:
         0: 50.2%
         1: 49.8%
        
        Output Distribution: random()
        Approximate Single Execution Time: Min: 31ns, Mid: 62ns, Max: 62ns
        Raw Samples: 0.9277214931837745, 0.6509596650539976, 0.3884877983068201, 0.3001884108775585, 0.1926429270120671
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 0.00012157670983074331
         Median: (0.5015995486843443, 0.5016697332763964)
         Maximum: 0.9998875813469635
         Mean: 0.5009411253451888
         Std Deviation: 0.28731500760837936
        Post-processor Distribution using round method:
         0: 49.86%
         1: 50.14%
        
        Output Distribution: uniform(0.0, 10.0)
        Approximate Single Execution Time: Min: 31ns, Mid: 62ns, Max: 375ns
        Raw Samples: 9.52326631756773, 5.788502600367225, 7.415690772199376, 7.569192571902404, 6.5217292266843785
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 4.3133609512452766e-05
         Median: (4.958204426955742, 4.958965795675051)
         Maximum: 9.999683844464784
         Mean: 5.005599483579845
         Std Deviation: 2.914603141441438
        Post-processor Distribution using ceil method:
         1: 10.22%
         2: 10.04%
         3: 9.91%
         4: 10.12%
         5: 10.14%
         6: 9.53%
         7: 9.45%
         8: 10.0%
         9: 9.98%
         10: 10.61%
        
        Output Distribution: Random.expovariate(1.0)
        Approximate Single Execution Time: Min: 406ns, Mid: 437ns, Max: 718ns
        Raw Samples: 1.32394193144772, 0.8401838247284389, 0.3453007881938971, 0.5768026156187437, 1.389079549244938
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 0.00021917169276148592
         Median: (0.6867670428429513, 0.6868587576520037)
         Maximum: 8.993301902164722
         Mean: 0.9987437574625477
         Std Deviation: 0.995794178653755
        Post-processor Distribution using floor_mod_10 method:
         0: 63.38%
         1: 22.64%
         2: 8.84%
         3: 3.2%
         4: 1.37%
         5: 0.34%
         6: 0.19%
         7: 0.02%
         8: 0.02%
        
        Output Distribution: expovariate(1.0)
        Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 187ns
        Raw Samples: 0.7449181736161731, 2.1499135794632815, 1.9209513819104198, 0.11374160460575472, 0.032772819469421156
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 7.509111316908552e-05
         Median: (0.7012021063239969, 0.7013754068583695)
         Maximum: 10.051181350277634
         Mean: 1.0123974449763105
         Std Deviation: 0.9948400311997716
        Post-processor Distribution using floor_mod_10 method:
         0: 62.44%
         1: 23.97%
         2: 8.94%
         3: 2.87%
         4: 1.1%
         5: 0.45%
         6: 0.14%
         7: 0.06%
         8: 0.02%
         9: 0.01%
        
        Output Distribution: Random.gammavariate(1.0, 1.0)
        Approximate Single Execution Time: Min: 468ns, Mid: 500ns, Max: 656ns
        Raw Samples: 0.4248242549456541, 0.33886371041571733, 2.6616762762556223, 1.64457361834173, 0.46794770217911014
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 0.00031988918156716393
         Median: (0.6949624128501429, 0.6950700498245157)
         Maximum: 9.853792287175022
         Mean: 0.9960981136961425
         Std Deviation: 0.9785418021506311
        Post-processor Distribution using floor_mod_10 method:
         0: 63.05%
         1: 23.38%
         2: 8.87%
         3: 3.02%
         4: 1.13%
         5: 0.34%
         6: 0.15%
         7: 0.04%
         9: 0.02%
        
        Output Distribution: gammavariate(1.0, 1.0)
        Approximate Single Execution Time: Min: 62ns, Mid: 62ns, Max: 125ns
        Raw Samples: 0.1848643722791521, 0.08642349625604238, 1.7451548721325887, 0.4092167346467269, 2.7151799826636407
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 0.00011452760428491575
         Median: (0.6963156440769845, 0.6964168743660308)
         Maximum: 9.268602662993539
         Mean: 0.9986252847849186
         Std Deviation: 1.0059055124912586
        Post-processor Distribution using floor_mod_10 method:
         0: 63.48%
         1: 22.88%
         2: 8.69%
         3: 3.14%
         4: 1.08%
         5: 0.47%
         6: 0.13%
         7: 0.08%
         8: 0.02%
         9: 0.03%
        
        Output Distribution: weibullvariate(1.0, 1.0)
        Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 250ns
        Raw Samples: 0.0024417756831744118, 0.45388273365845716, 0.3746197787223339, 0.18553781359441182, 0.3433004659304176
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 0.00016580833555425683
         Median: (0.698439109055173, 0.6992482552444275)
         Maximum: 8.679623833163621
         Mean: 1.0037492539881026
         Std Deviation: 1.002106144986341
        Post-processor Distribution using floor_mod_10 method:
         0: 63.57%
         1: 23.05%
         2: 8.29%
         3: 3.22%
         4: 1.18%
         5: 0.39%
         6: 0.2%
         7: 0.08%
         8: 0.02%
        
        Output Distribution: extreme_value(0.0, 1.0)
        Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 187ns
        Raw Samples: -0.11241366431993004, -0.6419972165998858, -0.5915740518117466, 1.8516325182456397, 1.4911620758558133
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: -2.3190180335156594
         Median: (0.38956671577541396, 0.38972187184305407)
         Maximum: 7.924377123063762
         Mean: 0.5908199593570642
         Std Deviation: 1.2786545980930044
        Post-processor Distribution using round method:
         -2: 1.02%
         -1: 17.94%
         0: 34.61%
         1: 26.02%
         2: 12.6%
         3: 5.03%
         4: 1.7%
         5: 0.68%
         6: 0.25%
         7: 0.09%
         8: 0.06%
        
        Output Distribution: Random.gauss(5.0, 2.0)
        Approximate Single Execution Time: Min: 687ns, Mid: 718ns, Max: 1281ns
        Raw Samples: 4.596778666373776, 5.574956391552119, 4.927272977874008, 3.3576119610202855, 2.4100020707684218
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: -3.6083886202460214
         Median: (5.033226242209256, 5.0335060153604045)
         Maximum: 12.632576601043258
         Mean: 5.046530547342325
         Std Deviation: 2.004364914617757
        Post-processor Distribution using round method:
         -4: 0.01%
         -3: 0.01%
         -2: 0.03%
         -1: 0.21%
         0: 0.73%
         1: 2.69%
         2: 6.59%
         3: 11.78%
         4: 17.96%
         5: 18.44%
         6: 17.64%
         7: 13.08%
         8: 6.58%
         9: 2.9%
         10: 0.95%
         11: 0.31%
         12: 0.08%
         13: 0.01%
        
        Output Distribution: normalvariate(5.0, 2.0)
        Approximate Single Execution Time: Min: 62ns, Mid: 93ns, Max: 312ns
        Raw Samples: 5.038030209503104, 3.359561942445354, 4.572812939693202, 8.126416493515116, 4.216197473154431
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: -2.8244138455476255
         Median: (5.010628778981261, 5.011392832574677)
         Maximum: 13.269369959352195
         Mean: 5.0215758697664
         Std Deviation: 1.9947500854542106
        Post-processor Distribution using round method:
         -3: 0.01%
         -2: 0.05%
         -1: 0.21%
         0: 0.96%
         1: 2.39%
         2: 6.48%
         3: 12.26%
         4: 17.68%
         5: 19.43%
         6: 17.81%
         7: 11.93%
         8: 6.7%
         9: 2.62%
         10: 1.12%
         11: 0.28%
         12: 0.06%
         13: 0.01%
        
        Output Distribution: Random.lognormvariate(1.6, 0.25)
        Approximate Single Execution Time: Min: 812ns, Mid: 875ns, Max: 1187ns
        Raw Samples: 4.474956815444135, 5.406628012593374, 4.3147748443859975, 5.229698007026149, 5.805648508074925
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 2.1592473372696683
         Median: (4.981247654229032, 4.981431587007508)
         Maximum: 12.671163322520604
         Mean: 5.132779483395387
         Std Deviation: 1.3094393573417653
        Post-processor Distribution using round method:
         2: 0.23%
         3: 7.61%
         4: 26.52%
         5: 31.34%
         6: 20.31%
         7: 8.77%
         8: 3.51%
         9: 1.18%
         10: 0.35%
         11: 0.15%
         12: 0.02%
         13: 0.01%
        
        Output Distribution: lognormvariate(1.6, 0.25)
        Approximate Single Execution Time: Min: 93ns, Mid: 125ns, Max: 406ns
        Raw Samples: 4.938919260086636, 5.434117630265477, 6.292684741793481, 5.591227647047072, 6.383314060542857
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 1.9012210055454757
         Median: (4.930393438457221, 4.930698683927044)
         Maximum: 12.24543656869312
         Mean: 5.0809499926488515
         Std Deviation: 1.2940795405602143
        Post-processor Distribution using round method:
         2: 0.37%
         3: 8.09%
         4: 27.58%
         5: 31.01%
         6: 19.6%
         7: 8.6%
         8: 3.14%
         9: 1.23%
         10: 0.28%
         11: 0.07%
         12: 0.03%
        
        Output Distribution: chi_squared(1.0)
        Approximate Single Execution Time: Min: 125ns, Mid: 156ns, Max: 281ns
        Raw Samples: 0.6805083519116017, 1.9550487232090856, 3.1923066239705897, 0.7482375804617535, 0.05893635106952869
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 1.2950679446718288e-08
         Median: (0.4447742902987775, 0.4447876206214249)
         Maximum: 17.282815421421066
         Mean: 0.9852303311570941
         Std Deviation: 1.4162939999610573
        Post-processor Distribution using floor_mod_10 method:
         0: 69.18%
         1: 15.69%
         2: 7.22%
         3: 3.71%
         4: 1.92%
         5: 1.02%
         6: 0.49%
         7: 0.46%
         8: 0.2%
         9: 0.11%
        
        Output Distribution: cauchy(0.0, 1.0)
        Approximate Single Execution Time: Min: 93ns, Mid: 93ns, Max: 343ns
        Raw Samples: 0.3685024684657171, 2.136219163967241, -7.187956700268334, 0.5046978970967958, 0.25077902780286254
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: -4053.635265519228
         Median: (-0.016399201162256117, -0.01628035430859785)
         Maximum: 2460.8880793857484
         Mean: -0.2140735774162542
         Std Deviation: 55.57660735278535
        Post-processor Distribution using floor_mod_10 method:
         0: 25.92%
         1: 11.41%
         2: 5.87%
         3: 3.64%
         4: 3.01%
         5: 2.89%
         6: 3.83%
         7: 5.64%
         8: 11.32%
         9: 26.47%
        
        Output Distribution: fisher_f(8.0, 8.0)
        Approximate Single Execution Time: Min: 187ns, Mid: 218ns, Max: 250ns
        Raw Samples: 0.9903817011827296, 1.676148670773987, 0.5863562883570347, 0.4007789603150157, 0.8292160403875725
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: 0.03385437523363254
         Median: (0.9992376778614822, 0.9994361477620272)
         Maximum: 21.51882952241599
         Mean: 1.3380931208465716
         Std Deviation: 1.1949223974559842
        Post-processor Distribution using floor_mod_10 method:
         0: 50.06%
         1: 32.12%
         2: 10.59%
         3: 4.03%
         4: 1.63%
         5: 0.67%
         6: 0.46%
         7: 0.18%
         8: 0.16%
         9: 0.1%
        
        Output Distribution: student_t(8.0)
        Approximate Single Execution Time: Min: 156ns, Mid: 156ns, Max: 187ns
        Raw Samples: -2.3130996405163233, -0.2087723821531655, -3.5190475767973295, 0.7309835695070587, 0.28003380239529446
        Test Samples: 10000
        Pre-processor Statistics:
         Minimum: -6.946803668089897
         Median: (-0.014189823253801227, -0.01365902594078134)
         Maximum: 6.934129090915844
         Mean: -0.012459466691732571
         Std Deviation: 1.1542721013079078
        Post-processor Distribution using round method:
         -7: 0.01%
         -6: 0.01%
         -5: 0.09%
         -4: 0.29%
         -3: 1.49%
         -2: 6.53%
         -1: 23.85%
         0: 36.72%
         1: 22.35%
         2: 6.65%
         3: 1.65%
         4: 0.29%
         5: 0.05%
         6: 0.01%
         7: 0.01%
        
        
        =========================================================================
        Total Test Time: 1.0982 seconds
        
        ```
        
Keywords: rng,Mersenne Twister,random number generator,cpp random library,random integer,Bernoulli,binomial,negative_binomial,geometric,poisson,discrete,normal,distribution,log normal,gamma,exponential,weibull,extreme value,chi squared,cauchy,fisher f,student t
Platform: Darwin
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Cython
Classifier: Programming Language :: C++
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires: Cython
Requires-Python: >=3.7
Description-Content-Type: text/markdown
