Fortuna 4.1.8

High Performance Random Value Toolkit

Fortuna: Generative Modeling Toolkit for Python3

© 2021 Robert Sharp, all rights reserved.

Fortuna's main goal is to provide a quick and easy way to build custom random functions for your data that are byte-code fast and thread aware. Fortuna also offers a variety of high-performance random number functions like random_range(start, stop, step) and dice(rolls, sides).

The core functionality of Fortuna is based on the Storm Engine - created by the same developer (Robert Sharp). While Storm is a high quality, hardware seeded random engine - it is not appropriate for cryptography of any kind. Fortuna is meant for games, data science, A.I. and experimental programming... not security!

Quick Install $ pip install Fortuna

Installation may require the following:

• Modern version of macOS or Linux.
• WSL: Windows requires Fortuna to be installed via Python3 running inside the Windows Subsystem for Linux.
• Modern C++17 Compiler and Standard Library: Clang or GCC. Typically, not required on the latest version of macOS.
  • If you're running an older version of macOS you can install Xcode to get the Clang compiler and all dependencies.

Sister Projects:

• Pyewacket: Drop-in replacement for Python3 random module. https://pypi.org/project/Pyewacket/
• MonkeyScope: Framework for testing non-deterministic functions and methods. https://pypi.org/project/MonkeyScope/
• Storm: Random Number Engine. https://github.com/BrokenShell/Storm

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Table of Contents:

• Numeric Limits
• Random Value Classes
  • RandomValue(Iterable) -> Callable -> Value
  • TruffleShuffle(Iterable) -> Callable -> Value
  • QuantumMonty(Iterable) -> Callable -> Value
  • CumulativeWeightedChoice(Iterable[Tuple[int, Any]]) -> Callable -> Value
  • RelativeWeightedChoice(Iterable[Tuple[int, Any]]) -> Callable -> Value
  • FlexCat(Dict[str, Iterable[Any]]) -> Callable -> Value
• Random Value Functions
  • random_value(data: Sequence[Any]) -> Any
  • cumulative_weighted_choice(Sequence[Tuple[int, Any]]) -> Any
  • truffle_shuffle(data: Iterable[Any]) -> Callable -> Any
• Random Integer Functions
  • random_below(Integer) -> Integer
  • random_int(Integer, Integer) -> Integer
  • random_range(Integer, Integer, Integer) -> Integer
  • d(Integer) -> Integer
  • dice(Integer, Integer) -> Integer
  • plus_or_minus(Integer) -> Integer
  • plus_or_minus_linear(Integer) -> Integer
  • plus_or_minus_gauss(Integer) -> Integer
  • binomial_variate(Integer, Float) -> Integer
  • negative_binomial_variate(Integer, Float) -> Integer
  • geometric_variate(Float) -> Integer
  • poisson_variate(Float) -> Integer
• Random Index Functions
  • ZeroCool Specification: f(N) -> [0, N) or f(-N) -> [-N, 0)
  • random_index(Integer) -> Integer
  • front_gauss(Integer) -> Integer
  • middle_gauss(Integer) -> Integer
  • back_gauss(Integer) -> Integer
  • quantum_gauss(Integer) -> Integer
  • front_poisson(Integer) -> Integer
  • middle_poisson(Integer) -> Integer
  • back_poisson(Integer) -> Integer
  • quantum_poisson(Integer) -> Integer
  • front_linear(Integer) -> Integer
  • middle_linear(Integer) -> Integer
  • back_linear(Integer) -> Integer
  • quantum_linear(Integer) -> Integer
  • quantum_monty(Integer) -> Integer
• Random Float Functions
  • canonical() -> Float
  • random_float(Float, Float) -> Float
  • triangular(Float, Float, Float) -> Float
  • normal_variate(Float, Float) -> Float
  • lognormal_variate(Float, Float) -> Float
  • exponential_variate(Float) -> Float
  • gamma_variate(Float, Float) -> Float
  • weibull_variate(Float, Float) -> Float
  • extreme_value_variate(Float, Float) -> Float
  • chi_squared_variate(Float) -> Float
  • cauchy_variate(Float, Float) -> Float
  • fisher_f_variate(Float, Float) -> Float
  • student_t_variate(Float) -> Float
  • beta_variate(Float, Float) -> Float
  • pareto_variate(Float) -> Float
  • vonmises_variate(Float, Float) -> Float
• Random Boolean Functions
  • percent_true(Float) -> Boolean
  • bernoulli_variate(Float) -> Boolean
• Inplace Shuffle Algorithms
  • shuffle(List[Any]) -> None
  • knuth_a(List[Any]) -> None
  • fisher_yates(List[Any]) -> None
• Utilities
  • flatten(Object, *args, Boolean, **kwargs) -> Object
  • smart_clamp(Integer, Integer, Integer) -> Integer
  • distribution_range(Callable, Integer, Integer) -> Callable
  • min_float() -> Float
  • max_float() -> Float
  • min_below() -> Float
  • min_above() -> Float
• Development Log
• Test Suite Output
• Legal Information

Numeric Limits:

• Integer: 64 bit signed integer.
  • Range: ±9223372036854775807, approximately ±9.2 billion billion
• Float: 64 bit floating point.
  • Range: ±1.7976931348623157e+308
  • Epsilon Delta: 5e-305 to 5e-324, platform dependent

---

Random Value Engines

Fortuna.RandomValue

Fortuna.RandomValue(collection: Iterable[Any], zero_cool=random_index, flat=True) -> Callable -> Value Random Value Engine Class that supports dependency injection.

Takes an iterable (tuple) and a distribution function and returns a callable to produce random values from the iterable with the same distribution.

• @param collection :: Iterable of Values. Tuple recommended.
• @param zero_cool :: Optional ZeroCool Callable, kwarg only. Default = random_index(). This function must follow the ZeroCool Spec.
• @param flat :: Bool. Default: True. Option to automatically flatten callable values with lazy evaluation.
• @return :: Callable Object. Callable(*args, **kwargs) -> Value
  • @param *args, **kwargs :: Optional arguments used to flatten the return Value (below) if Callable.
  • @return Value or Value(*args, **kwargs) if the value itself is callable. This is recursive.

RandomValue Dependency Injection: Rare Apples Example

RandomValue supports dependency injection, it is the only Fortuna class to do so. The injected functor must follow the ZeroCool Specification: f(x: int) -> int in [0, x) with any distribution. Many ZeroCool functions are provided, in the following example we'll see front_linear and back_linear used together.

If one of the builtin ZeroCool distributions is required, it is highly recommended to employ QuantumMonty rather than RandomValue. ZeroCool distributions are used here for the demonstration of RandomValue only. QuantumMonty offers the same ZeroCool distributions, but with less overhead. RandomValue is specifically designed for custom dependency injection, where you might design your own distribution and use it with RandomValue.

from Fortuna import RandomValue, front_linear, back_linear


# Data Setup
random_apple = RandomValue((
    "Delicious", 
    "Empire", 
    "Granny Smith", 
    "Honey Crisp", 
    "Macintosh",
), zero_cool=front_linear)

random_fruit = RandomValue((
    lambda: f"Apple, {random_apple()}",
    "Banana",
    "Cherry",
    "Grapes",
    "Orange",
), zero_cool=back_linear)

# Usage
print(random_fruit())
# prints a random 'Fruit' with a compound linear distribution
# where 'Delicious' is the most common 'Apple'
# and 'Apple' is the most rare 'Fruit'

QuantumMonty: Rare Apples Example

Same as above but with QuantumMonty - for syntax comparison.

from Fortuna import QuantumMonty


# Data Setup
random_apple = QuantumMonty((
    "Delicious", 
    "Empire", 
    "Granny Smith", 
    "Honey Crisp", 
    "Macintosh",
)).front_linear

random_fruit = QuantumMonty((
    lambda: f"Apple, {random_apple()}",
    "Banana",
    "Cherry",
    "Grapes",
    "Orange",
)).back_linear

# Usage
print(random_fruit())
# prints a random fruit with the correct compound distribution

Auto Flattening

Auto Flattening works with all random generator classes in Fortuna, and it's on by default. Flattening is lazy: it happens at call time as the last step. Flattening is recursive: this allows a nested lambda stack to be collapsed automatically. Flattening is resilient: if for any reason a callable can not be flatted - it will be returned in an un-flattened state without error.

Example: RandomValue with Auto Flattening

from Fortuna import RandomValue


auto_flat = RandomValue([lambda: 1, lambda: 2, lambda: 3])
print(auto_flat())  # will print the value 1, 2 or 3.
# Note: the lambda will not be called until call time and stays dynamic for the life of the object.

auto_flat_with = RandomValue([lambda x: x, lambda x: x + 1, lambda x:  x + 2])
print(auto_flat_with(2))  # will print the value 2, 3 or 4
# Note: if this is called with no args it will simply return the lambda in an uncalled state.

un_flat = RandomValue([lambda: 1, lambda: 2, lambda: 3], flat=False)
print(un_flat()())  # will print the value 1, 2 or 3, 
# mind the double-double parenthesis, they are required to manually unpack the lambdas

auto_un_flat = RandomValue([lambda x: x, lambda x: x + 1, lambda x:  x + 2], flat=False)
# Note: flat=False is not required here because the lambdas can not be called without input x satisfied.
# It is recommended to specify flat=False if non-flat output is intended.
print(auto_un_flat()(1))  # will print the value 1, 2 or 3, mind the double-double parenthesis.

Mixing Static Objects with Callable Objects

Auto Flattening works with all random generator classes in Fortuna, and it's on by default.

from Fortuna import RandomValue


""" With automatic flattening active, `lambda() -> int` can be treated as an `int`. """
mixed_flat = RandomValue([1, 2, lambda: 3])
print(mixed_flat())  # will print 1, 2 or 3

""" Mixed Anti-pattern """
mixed_un_flat = RandomValue([1, 2, lambda: 3], flat=False) # this is not recommended.
print(mixed_flat())  # will print 1, 2 or "Function <lambda at some_address>"
# This pattern is not recommended because you wont know the nature of what you get back.
# This is almost always not what you want, and it can give rise to messy logic in other areas of your code.

Dynamic Strings

To successfully express a dynamic string, and keep it dynamic for the duration of the program, at least one level of indirection is required. Without a lambda - the f-string would collapse into a static string too soon. This works with all random generator classes in Fortuna.

from Fortuna import RandomValue, d


# d() is a simple dice function, d(n) -> [1, n] flat uniform distribution.
dynamic_string = RandomValue((
    # while the probability of all A == all B == all C, individual probabilities of each possible string will differ based on the number of possible outputs of each category.
    lambda: f"A{d(2)}",  # -> A1 - A2, each are twice as likely as any particular B, and three times as likely as any C.
    lambda: f"B{d(4)}",  # -> B1 - B4, each are half as likely as any particular A, and 3/2 as likely as any C.
    lambda: f"C{d(6)}",  # -> C1 - C6, each are 1/3 as likely as any particular A and 2/3 as likely of any B.
))

print(dynamic_string())  # prints a random dynamic string, generated at call time.

TruffleShuffle

Fortuna.TruffleShuffle(collection: Iterable[Any], flat=True) -> Callable -> Value

• @param collection :: Iterable of Values. Set recommended but not required.
• @param flat :: Bool. Default: True. Option to automatically flatten callable values with lazy evaluation.
• @return :: Callable Object. Callable(*args, **kwargs) -> Value
  • @param *args, **kwargs :: Optional arguments used to flatten the return Value (below) if Callable.
  • @return :: Random value from the collection with a Wide Uniform Distribution.

Wide Uniform Distribution: "Wide" refers to the average distance between consecutive occurrences of the same value. The average width of the output distribution will naturally scale up with the size of the collection. The goal of this type of distribution is to keep the output sequence free of clumps or streaks of the same value, while maintaining randomness and uniform probability. This is not the same as a flat uniform distribution. The two distributions over time will be statistically similar for any given set, but the repetitiveness of the output sequence will be very different.

TruffleShuffle, Basic Use

from Fortuna import TruffleShuffle

# Data Setup
list_of_values = { 1, 2, 3, 4, 5, 6 }
truffle_shuffle = TruffleShuffle(list_of_values)

# Usage
print(truffle_shuffle())  # this will print one of the numbers 1-6, 
# repeated calls will produce a wide distribution.

Nesting Dolls

This works with all random generator classes in Fortuna, interchangeably.

from Fortuna import RandomValue, TruffleShuffle

# Data Setup
nesting_dolls = TruffleShuffle((
    RandomValue(("A", "B", "C", "D", "E")),
    RandomValue(("F", "G", "H", "I", "J")),
    RandomValue(("K", "L", "M", "N", "O")),
    RandomValue(("P", "Q", "R", "S", "T")),
))

# Usage
print(nesting_dolls())  
# Prints one of the letters A-T.
# Produces a wide distribution of each category -
# and a flat uniform distribution within each category.

QuantumMonty

Fortuna.QuantumMonty(collection: Iterable[Any], flat=True) -> Callable -> Value

• @param collection :: Iterable of Values. Tuple recommended.
• @param flat :: Bool. Default: True. Option to automatically flatten callable values with lazy evaluation.
• @return :: Callable Object with Monty Methods for producing various distributions of the data.
  • @param *args, **kwargs :: Optional arguments used to flatten the return Value (below) if Callable.
  • @return :: Random value from the data. The instance will produce random values from the list using the selected distribution model or "monty". The default monty is the Quantum Monty Algorithm.

from Fortuna import QuantumMonty

# Data Setup
list_of_values = [1, 2, 3, 4, 5, 6]
monty = QuantumMonty(list_of_values)

# Usage
print(monty())               # prints a random value from the list_of_values
                             # uses the default Quantum Monty Algorithm

print(monty.flat_uniform())  # prints a random value from the list_of_values
                             # uses the "flat_uniform" monty
                             # equivalent to random.choice(list_of_values)

The QuantumMonty class represents a diverse collection of strategies for producing random values from a sequence where the output distribution is based on the method you choose. Generally speaking, each value in the sequence will have a probability that is based on its position in the sequence. For example: the "front" monty produces random values where the beginning of the sequence is geometrically more common than the back. Given enough samples the "front" monty will always converge to a 45 degree slope down for any list of unique values.

There are three primary method families: linear, gaussian, and poisson. Each family has three base methods; 'front', 'middle', 'back', plus a 'quantum' method that incorporates all three base methods. The quantum algorithms for each family produce distributions by overlapping the probability waves of the other methods in their family. The Quantum Monty Algorithm incorporates all nine base methods.

import Fortuna

# Data Setup
monty = Fortuna.QuantumMonty(
    ["Alpha", "Beta", "Delta", "Eta", "Gamma", "Kappa", "Zeta"]
)

# Usage
# Each of the following methods will return a random value from the sequence.
# Each method has its own unique distribution model.

""" Flat Base Case """
monty.flat_uniform()        # Flat Uniform Distribution

""" Geometric Positional """
monty.front_linear()        # Linear Descending, Triangle
monty.middle_linear()       # Linear Median Peak, Equilateral Triangle
monty.back_linear()         # Linear Ascending, Triangle
monty.quantum_linear()      # Linear Overlay, 3-way monty.

""" Gaussian Positional """
monty.front_gauss()         # Front Gamma
monty.middle_gauss()        # Scaled Gaussian
monty.back_gauss()          # Reversed Gamma
monty.quantum_gauss()       # Gaussian Overlay, 3-way monty.

""" Poisson Positional """
monty.front_poisson()       # 1/4 Mean Poisson
monty.middle_poisson()      # 1/2 Mean Poisson
monty.back_poisson()        # 3/4 Mean Poisson
monty.quantum_poisson()     # Poisson Overlay, 3-way monty.

""" Quantum Monty Algorithm: Default Monty """
monty()                     # Quantum Monty Algorithm, 9-way monty.
monty.quantum_monty()       #  same as above

Weighted Choice: Base Class

Weighted Choice offers two strategies for selecting random values from a sequence where programmable rarity is desired. Both produce a custom distribution of values based on the weights of the values.

The choice to use one strategy over the other is purely about which one suits you or your data best. Relative weights are easier to understand at a glance. However, many RPG Treasure Tables map rather nicely to a cumulative weighted strategy.

Cumulative Weighted Choice

Fortuna.CumulativeWeightedChoice(weighted_table: Iterable[Tuple[int, Any]], flat=True) -> Callable -> Value

• @param weighted_table :: Table of weighted pairs. Tuple of Tuples recommended.
• @param flat :: Bool. Default: True. Option to automatically flatten callable values with lazy evaluation.
• @return :: Callable Instance
  • @param *args, **kwargs :: Optional arguments used to flatten the return Value (below) if Callable.
  • @return :: Random value from the weighted_table, distribution based on the weights of the values.

Note: Logic dictates Cumulative Weights must be unique!

from Fortuna import CumulativeWeightedChoice

# Data Setup
cum_weighted_choice = CumulativeWeightedChoice((
    (7, "Apple"),
    (11, "Banana"),
    (13, "Cherry"),
    (23, "Grape"),
    (26, "Lime"),
    (30, "Orange"),  # same as relative weight 4 because 30 - 26 = 4
))
# Usage
print(cum_weighted_choice())  # prints a weighted random value

Relative Weighted Choice

Fortuna.RelativeWeightedChoice(weighted_table: Iterable[Tuple[int, Any]]) -> Callable -> Value

• @param weighted_table :: Table of weighted pairs. Tuple of Tuples recommended.
• @param flat :: Bool. Default: True. Option to automatically flatten callable values with lazy evaluation.
• @return :: Callable Instance
  • @param *args, **kwargs :: Optional arguments used to flatten the return Value (below) if Callable.
  • @return :: Random value from the weighted_table, distribution based on the weights of the values.

from Fortuna import RelativeWeightedChoice

# Data
population = ["Apple", "Banana", "Cherry", "Grape", "Lime", "Orange"]
rel_weights = [7, 4, 2, 10, 3, 4]

# Setup
rel_weighted_choice = RelativeWeightedChoice(zip(rel_weights, population))

# Usage
print(rel_weighted_choice())  # prints a weighted random value

FlexCat

Fortuna.FlexCat(matrix_data: Matrix, key_bias="front_linear", val_bias="truffle_shuffle", flat=True) -> Callable -> Value

• @param matrix_data :: Dictionary of Sequences. Dict[str, Iterable[Any]]
• @parm key_bias :: Default is "front_linear". String indicating the name of the algorithm to use for random key selection.
• @parm val_bias :: Default is "truffle_shuffle". String indicating the name of the algorithm to use for random value selection.
• @param flat :: Bool. Default is True. Option to automatically flatten callable values with lazy evaluation.
• @return :: Callable Instance
  • @param cat_key :: Optional String. Default is None. Key selection by name. If specified, this will override the key_bias for a single call.
  • @param *args, **kwargs :: Optional arguments used to flatten the return Value (below) if Callable.
  • @return :: Value. Returns a random value generated with val_bias from a random sequence generated with key_bias.

FlexCat is like a multi dimensional QuantumMonty.

The constructor takes two optional keyword arguments to specify the algorithms to be used to make random selections. The algorithm specified for selecting a key need not be the same as the one for selecting values. An optional key may be provided at call time to bypass the random key selection. Keys passed in this way must exactly match a key in the Matrix.

By default, FlexCat will use key_bias="front_linear" and val_bias="truffle_shuffle", this will make the top of the data structure geometrically more common than the bottom and it will truffle shuffle the sequence values. This config is known as TopCat, it produces a descending-step, micro-shuffled distribution sequence. Many other combinations are available.

Algorithmic Options: See QuantumMonty & TruffleShuffle for more details.

• "front_linear", Linear Descending
• "middle_linear", Linear Median Peak
• "back_linear", Linear Ascending
• "quantum_linear", Linear 3-way monty
• "front_gauss", Gamma Descending
• "middle_gauss", Scaled Gaussian
• "back_gauss", Gamma Ascending
• "quantum_gauss", Gaussian 3-way monty
• "front_poisson", Front 1/3 Mean Poisson
• "middle_poisson", Middle Mean Poisson
• "back_poisson", Back 1/3 Mean Poisson
• "quantum_poisson", Poisson 3-way monty
• "quantum_monty", Quantum Monty Algorithm, 9-way monty
• "flat_uniform", uniform flat distribution
• "truffle_shuffle", TruffleShuffle wide uniform distribution

from Fortuna import FlexCat, d


#                           |- Collection Generator, does not require lambda.
# Data                      |
matrix_data = {#            $                         |- Dynamic Value Expression
    "Cat_A": (f"A{i}" for i in range(1, 6)),  #       |  Lazy, 1 of 4 possibilities
    "Cat_B": ("B1", "B2", "B3", "B4", "B5"),  #       $  lambda required for dynamic eval
    "Cat_C": ("C1", "C2", "C3", f"C4.{d(2)}", lambda: f"C5.{d(4)}"),
}#   $       $       $              $                        $
#    |       |       |- Value       |                        |- Fair die method: d4
#    |       |                      |
#    |       |- Collection          |- Static Value Expression
#    |                              |  Eager, 1 or 2 permanently
#    |- Collection Key, "cat_key"

#                               |- Collection Algorithm     |- Value Algorithm
# Setup                         $  y-axis                   $  x-axis
flex_cat = FlexCat(matrix_data, key_bias="front_linear", val_bias="flat_uniform")
#    $       $       $
#    |       |       |- Dictionary of Collections
#    |       |
#    |       |- FlexCat Constructor
#    |       
#    |- Callable Random Value Generator

# Usage
flex_cat()  # returns a Value from the Matrix.
flex_cat(cat_key="Cat_B")  # returns a Value specifically from the "Cat_B" Collection.

Random Value Functions

Fortuna.random_value(Sequence[Any]) -> Any

Essentially the same as Random.choice()

• @param data :: Sequence of Values
• @return :: Random value from the sequence. Flat uniform distribution.

Fortuna.cumulative_weighted_choice(weighted_table: Sequence[Tuple[int, Any]]) -> Any

Similar to Random.choices()

• @param weighted_table :: Sequence of weighted value pairs. [(w1, v1), (w2, v2)...]
• @return :: Returns a random value. Distribution depends on weights.

Random Integer Functions

Fortuna.random_below(limit: int) -> int

• @param limit :: Any 64bit Integer
• @return :: Returns a random integer in the range...
  • random_below(N) -> [0, N) for positive limit.
  • random_below(N) -> (N, 0] for negative limit.
  • random_below(0) -> 0 Always returns zero when limit is zero
• Flat uniform distribution.

Fortuna.random_int(left_limit: int, right_limit: int) -> int

Essentially the same as Random.randint()

• @param left_limit :: Any Integer
• @param right_limit :: Any Integer
• @return :: Returns a random integer in the range [left_limit, right_limit]
  • random_int(1, 10) -> [1, 10]
  • random_int(10, 1) -> [1, 10] same as above.
  • random_int(A, B) Always returns A when A == B
• Flat uniform distribution.

Fortuna.random_range(start: int, stop: int = 0, step: int = 1) -> int

Essentially the same as Random.randrange()

• @param start :: Required starting point.
  • random_range(0) -> [0]
  • random_range(10) -> [0, 10) from 0 to 9. Same as Fortuna.random_index(N)
  • random_range(-10) -> [-10, 0) from -10 to -1. Same as Fortuna.random_index(-N)
• @param stop :: Zero by default. Optional range bound. With at least two arguments, the order of the first two does not matter.
  • random_range(0, 0) -> [0]
  • random_range(0, 10) -> [0, 10) from 0 to 9.
  • random_range(10, 0) -> [0, 10) same as above.
• @param step :: One by default. Optional step size.
  • random_range(0, 0, 0) -> [0]
  • random_range(0, 10, 2) -> [0, 10) by 2 even numbers from 0 to 8.
  • The sign of the step parameter controls the phase of the output. Negative stepping will flip the inclusively.
  • random_range(0, 10, -1) -> (0, 10] starts at 10 and ranges down to 1.
  • random_range(10, 0, -1) -> (0, 10] same as above.
  • random_range(10, 10, 0) -> [10] step size or range size of zero always returns the first parameter.
• @return :: Returns a random integer in the range [A, B) by increments of C.
• Flat uniform distribution.

Fortuna.d(sides: int) -> int

Represents a single roll of a given size die.

• @param sides :: Represents the size or number of sides, most commonly six.
• @return :: Returns a random integer in the range [1, sides].
• Flat uniform distribution.

Fortuna.dice(rolls: int, sides: int) -> int

Represents the sum total of multiple rolls of the same size die.

• @param rolls :: Represents the number of times to roll the die.
• @param sides :: Represents the die size or number of sides, most commonly six.
• @return :: Returns a random integer in range [X, Y] where X = rolls and Y = rolls * sides.
• Geometric distribution based on the number and size of the dice rolled.
• Complexity scales primarily with the number of rolls, not the size of the dice.

Fortuna.plus_or_minus(number: int) -> int

• @param number :: input to determine the output distribution range.
• @return :: Returns a random integer in range [-number, number].
• Flat uniform distribution.

Fortuna.plus_or_minus_linear(number: int) -> int

• @param number :: input to determine the output distribution range.
• @return :: Returns a random integer in range [-number, number].
• Linear geometric, 45 degree triangle distribution centered on zero.

Fortuna.plus_or_minus_gauss(number: int) -> int

• @param number :: input to determine the output distribution range.
• @return :: Returns a random integer in range [-number, number].
• Stretched gaussian distribution centered on zero.

Fortuna.binomial_variate(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.

Fortuna.negative_binomial_variate(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.

Fortuna.geometric_variate(probability: float) -> int

• Same as random_negative_binomial(1, probability).

Fortuna.poisson_variate(mean: float) -> int

• @param mean :: sets the average output of the function.
• @return :: random integer, poisson distribution centered on the mean.

Random Index, ZeroCool Specification

ZeroCool Methods are used to generate random Sequence indices.

ZeroCool methods must have the following properties:

• Any distribution model is acceptable such that...
• The method or function must take exactly one Integer parameter N.
• The method returns a random int in range [0, N) for positive values of N.
• The method returns a random int in range [N, 0) for negative values of N.
• This symmetry matches how python can index a list from the back for negative values or the front for positive values of N.

from Fortuna import random_index


some_list = [i for i in range(100)] # [0..99]

print(some_list[random_index(10)])  # prints one of the first 10 items of some_list, [0, 9]
print(some_list[random_index(-10)])  # prints one of the last 10 items of some_list, [90, 99]

ZeroCool Methods

• Fortuna.random_index(size: int) -> int Flat uniform distribution
• Fortuna.front_gauss(size: int) -> int Gamma Distribution: Front Peak
• Fortuna.middle_gauss(size: int) -> int Stretched Gaussian Distribution: Median Peak
• Fortuna.back_gauss(size: int) -> int Gamma Distribution: Back Peak
• Fortuna.quantum_gauss(size: int) -> int Quantum Gaussian: Three-way Monty
• Fortuna.front_poisson(size: int) -> int Poisson Distribution: Front 1/3 Peak
• Fortuna.middle_poisson(size: int) -> int Poisson Distribution: Middle Peak
• Fortuna.back_poisson(size: int) -> int Poisson Distribution: Back 1/3 Peak
• Fortuna.quantum_poisson(size: int) -> int Quantum Poisson: Three-way Monty
• Fortuna.front_geometric(size: int) -> int Linear Geometric: 45 Degree Front Peak
• Fortuna.middle_geometric(size: int) -> int Linear Geometric: 45 Degree Middle Peak
• Fortuna.back_geometric(size: int) -> int Linear Geometric: 45 Degree Back Peak
• Fortuna.quantum_geometric(size: int) -> int Quantum Geometric: Three-way Monty
• Fortuna.quantum_monty(size: int) -> int Quantum Monty: Nine-way Monty

from Fortuna import front_gauss, middle_gauss, back_gauss, quantum_gauss


some_list = [i for i in range(100)]

# Each of the following prints one of the first 10 items of some_list with the appropriate distribution
print(some_list[front_gauss(10)])
print(some_list[middle_gauss(10)])
print(some_list[back_gauss(10)])
print(some_list[quantum_gauss(10)])

# Each of the following prints one of the last 10 items of some_list with the appropriate distribution
print(some_list[front_gauss(-10)])  
print(some_list[middle_gauss(-10)])  
print(some_list[back_gauss(-10)])  
print(some_list[quantum_gauss(-10)])

Random Float Functions

Fortuna.canonical() -> float

• @return :: random float in range [0.0, 1.0), flat uniform.

Fortuna.random_float(a: Float, b: Float) -> Float

• @param a :: Float input
• @param b :: Float input
• @return :: random Float in range [a, b), flat uniform distribution.

Fortuna.triangular(low: Float, high: Float, mode: Float) -> Float

• @param low :: Float, minimum output
• @param high :: Float, maximum output
• @param mode :: Float, most common output, mode must be in range [low, high]
• @return :: random Float in range [low, high] with a linear distribution about the mode.

Fortuna.normal_variate(mean: Float, std_dev: Float) -> Float

• @param mean :: Float, sets the average output of the function.
• @param std_dev :: Float, standard deviation. Specifies spread of data from the mean.

Fortuna.lognormal_variate(log_mean: Float, log_deviation: Float) -> Float

• @param log_mean :: Float, sets the log of the mean of the function.
• @param log_deviation :: Float, log of the standard deviation. Specifies spread of data from the mean.

Fortuna.exponential_variate(lambda_rate: Float) -> Float

• Produces random non-negative floating-point values, distributed according to probability density function.
• @param lambda_rate :: Float, λ constant rate of a random event per unit of time/distance.
• @return :: Float, 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.

Fortuna.gamma_variate(shape: Float, scale: Float) -> Float

• Generalization of the exponential distribution.
• Produces random positive floating-point values, distributed according to probability density function.
• @param shape :: Float, α the number of independent exponentially distributed random variables.
• @param scale :: Float, β the scale factor or the mean of each of the distributed random variables.
• @return :: Float, the sum of α independent exponentially distributed random variables, each of which has a mean of β.

Fortuna.weibull_variate(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.

Fortuna.extreme_value_variate(location: Float, scale: Float) -> Float

• Based on Extreme Value Theory.
• Used for statistical models of the magnitude of earthquakes and volcanoes.

Fortuna.chi_squared_variate(degrees_of_freedom: Float) -> Float

• Used with the Chi Squared Test and Null Hypotheses to test if sample data fits an expected distribution.

Fortuna.cauchy_variate(location: Float, scale: Float) -> Float

• @param location :: Float, specifies the location of the peak. The default value is 0.0.
• @param scale :: Float, represents the half-width at half-maximum. The default value is 1.0.
• @return :: Float, Continuous Distribution.

Fortuna.fisher_f_variate(degrees_of_freedom_1: Float, degrees_of_freedom_2: Float) -> Float

• F distributions often arise when comparing ratios of variances.

Fortuna.student_t_variate(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.

Fortuna.beta_variate(alpha: Float, beta: Float) -> Float

Fortuna.pareto_variate(alpha: Float) -> Float

Fortuna.vonmises_variate(mu: Float, kappa: Float) -> Float

Random Truth Functions

Fortuna.percent_true(truth_factor: Float = 50.0) -> Boolean

• Produces a distribution of boolean values.
• @param truth_factor :: Float, probability of True as a percentage. Default is 50 percent. Expected input range: [0.0, 100.0], clamped.
• @return :: Produces True or False based on the truth_factor as a percent of true.

Fortuna.bernoulli_variate(ratio_of_truth: Float) -> Boolean

• Produces a Bernoulli distribution of boolean values.
• @param ratio_of_truth :: Float, the probability of True as a ratio. Expected input range: [0.0, 1.0], clamped.
• @return :: True or False

Shuffle Algorithm

Fortuna.shuffle(array: List[Any]) -> None

• Knuth B Shuffle Algorithm. Destructive, in-place shuffle.
• @param array :: List to be shuffled.
• @return :: None

Fortuna.knuth_a(array: List[Any]) -> None

• Knuth A Shuffle Algorithm. Destructive, in-place shuffle.
• @param array :: List to be shuffled.
• @return :: None

Fortuna.fisher_yates(array: List[Any]) -> None

• Fisher Yates Shuffle Algorithm. Destructive, in-place shuffle.
• @param array :: List to be shuffled.
• @return :: None

Utilities

Fortuna.flatten(maybe_callable, *args, flat=True, **kwargs) -> flatten(maybe_callable(*args, **kwargs))

• Recursively calls the input object and returns the result. The arguments are only passed in on the first evaluation.
• If the maybe_callable is not callable it is simply returned without error.
• Conceptually this is somewhat like collapsing the wave function. Often used as the last step in lazy evaluation.
• @param maybe_callable :: Any Object that might be callable.
• @param flat :: Boolean, default is True. Optional, keyword only. Disables flattening if flat is set to False, conceptually turns flatten into the identity function.
• @param *args, **kwargs :: Optional arguments used to flatten the maybe_callable object.
• @return :: Recursively Flattened Object.

Fortuna.smart_clamp(target: int, lo: int, hi: int) -> int

• Used to clamp the target in range [lo, hi] by saturating the bounds.
• Essentially the same as median for exactly three integers.
• @return :: Returns the middle value, input order does not matter.

Fortuna.distribution_range(func: Callable, lo: int, hi: int) -> Callable Higher-order function for producing integer distribution ranges based on a ZeroCool function. If given a function like random_below, this function will produce random values with the same distribution but in the range lo to hi, rather than from zero to N-1. Essentially, this turns a function like random_below(B+1) into random_int(A, B).

• @param func: ZeroCool random distribution, F(N) -> [0, N-1]
• @param lo: minimum limit
• @param hi: maximum limit
• @return: Callable() -> int
  • @return: random value in range [lo, hi]

Fortuna Development Log

Fortuna 4.1.7

• Adds GitHub repo link to the project for PyPi
• Adds minor documentation & example file updates

Fortuna 4.1.6

• Fixes 'zero rolls' bug in 'dice()'

Fortuna 4.1.5

• Fixes minor bug

Fortuna 4.1.4

• Updates requirements.txt

Fortuna 4.1.3

• Small refactoring

Fortuna 4.1.2

• Updates tests

Fortuna 4.1.1

• Fixes some minor typos

Fortuna 4.1.0

• Adds distribution_range utility

Fortuna 4.0.0

• RNG merge, adds all features from the RNG library that were not already here

Fortuna 3.20.3

• Minor low level Storm update
• Minor test tweaks

Fortuna 3.20.2

• adds example: fortuna_extras/multi_threading.py

Fortuna 3.20.1

• fixes typos

Fortuna 3.20.0

• Updates Storm: 3.4.0: Storm is now Thread Safe
• Adds platform limit meters
• Deprecates MultiChoice

Fortuna 3.19.1

• fixes typos

Fortuna 3.19.0

• Storm 3.3.6 update

Fortuna 3.18.2

• Fixes typos

Fortuna 3.18.1

• Installer update

Fortuna 3.18.0

• Performance Enhancement
• Storm 3.3.4 update
• updated test output

Fortuna 3.17.13

• Fixes some typos

Fortuna 3.17.12

• Adds pyproject.toml file to ease the installation process

Fortuna 3.17.11

• Documentation Update

Fortuna 3.17.10

• Documentation Update

Fortuna 3.17.9

• Typo

Fortuna 3.17.8

• Typo

Fortuna 3.17.7

• Typo

Fortuna 3.17.6

• Documentation update

Fortuna 3.17.5

• Typos

Fortuna 3.17.4

• Reorganized

Fortuna 3.17.3

• Streamlining: dropped auxiliary packages.

Fortuna 3.17.2

• Documentation update

Fortuna 3.17.1

• Update for backwards compatibility for platforms that do not support std::clamp.

Fortuna 3.17.0, internal

• Installation issue detected on some platforms that lack std::clamp.

Fortuna 3.16.9

• Documentation Update

Fortuna 3.16.8

• TruffleShuffle update
• Adds truffle_shuffle()

Fortuna 3.16.7

• Documentation Update

Fortuna 3.16.6

• Adds distribution_range()

Fortuna 3.16.5

• Documentation Update

Fortuna 3.16.4

• Documentation Update

Fortuna 3.16.3

• Major TruffleShuffle performance upgrade

Fortuna 3.16.2 - Internal

• Testing

Fortuna 3.16.1

• Documentation Update

Fortuna 3.16.0

• Storm 3.3.2 Update

Fortuna 3.15.1

• Docs updated

Fortuna 3.15.0

• Type Hints Clarified via Typing Module

Fortuna 3.14.1

• Fixed another installer bug affecting gcc.

Fortuna 3.14.0

• Minor TruffleShuffle Update
• Fisher Yates, and Knuth A Shuffle Algorithms added for comparison with Fortuna.shuffle()
  • Some platforms may prefer one over another. Intel favors Knuth B (Fortuna.shuffle) by more than double.

Fortuna 3.13.0 - Internal

• Development & Testing Environment Updated to Python 3.8
  • Python3.8 brings a 10-20% performance boost over all.
• RandomValue API redesign. Dependency Injection is now handled at instantiation rather than call time.

Fortuna 3.12.2

• Installer update.
• Clarified the docs for MultiChoice.

Fortuna 3.12.1

• MultiChoice now accepts a default.

Fortuna 3.12.0

• MultiChoice added

Fortuna 3.10.2

• Doc string update for clarity.
• Test update
• MonkeyScope Update

Fortuna 3.10.1

• Documentation fix, RandomValue examples are now together.

Fortuna 3.10.0

• Fortuna now includes both RNG and Pyewacket.
• Documentation update.

Fortuna 3.9.11

• Installer Update, properly installs MonkeyScope as intended.

Fortuna 3.9.10

• Fixed Typos

Fortuna 3.9.9

• Docs Update

Fortuna 3.9.8

• Test Update

Fortuna 3.9.7

• Tests for RNG and Pyewacket are now included in fortuna_extras package.

Fortuna 3.9.6

• Documentation update.

Fortuna 3.9.5

• Storm 3.2.2 Update.

Fortuna 3.9.4

• Documentation update.

Fortuna 3.9.3

• MonkeyScope update, 10% test suite performance improvement.

Fortuna 3.9.2

• Documentation update.

Fortuna 3.9.1

• flatten_with has been renamed to flatten. This should be non-breaking, please report any bugs.

Fortuna 3.9.0 - Internal

• Added many doc strings.
• Corrected many typos in Docs.
• The flatten function has been fully replaced by flatten_with.
  • All classes that support automatic flattening can now accept arbitrary arguments at call time.
  • flatten_with will be renamed to flatten in a future release.

Fortuna 3.8.9

• Fixed some typos.

Fortuna 3.8.8

• Fortuna now supports Python notebooks, python3.6 or higher required.

Fortuna 3.8.7

• Storm Update

Fortuna 3.8.6

• Attempting to make Fortuna compatible with Python Notebooks.

Fortuna 3.8.5

• Installer Config Update

Fortuna 3.8.4

• Installer Config Update

Fortuna 3.8.3

• Storm Update 3.2.0

Fortuna 3.8.2

• More Typo Fix

Fortuna 3.8.1

• Typo Fix

Fortuna 3.8.0

• Major API Update, several utilities have been deprecated. See MonkeyScope for replacements.
  • distribution
  • distribution_timer
  • timer

Fortuna 3.7.7

• Documentation Update

Fortuna 3.7.6

• Install script update.

Fortuna 3.7.5 - internal

• Storm 3.1.1 Update
• Added triangular function.

Fortuna 3.7.4

• Fixed: missing header in the project manifest, this may have caused building from source to fail.

Fortuna 3.7.3

• Storm Update

Fortuna 3.7.2

• Storm Update

Fortuna 3.7.1

• Bug fixes

Fortuna 3.7.0 - internal

• flatten_with() is now the default flattening algorithm for all Fortuna classes.

Fortuna 3.6.5

• Documentation Update
• RandomValue: New flatten-with-arguments functionality.

Fortuna 3.6.4

• RandomValue added for testing

Fortuna 3.6.3

• Developer Update

Fortuna 3.6.2

• Installer Script Update

Fortuna 3.6.1

• Documentation Update

Fortuna 3.6.0

• Storm Update
• Test Update
• Bug fix for random_range(), negative stepping is now working as intended. This bug was introduced in 3.5.0.
• Removed Features
  • lazy_cat(): use QuantumMonty class instead.
  • flex_cat(): use FlexCat class instead.
  • truffle_shuffle(): use TruffleShuffle class instead.

Fortuna 3.5.3 - internal

• Features added for testing & development
  • ActiveChoice class
  • random_rotate() function

Fortuna 3.5.2

• Documentation Updates

Fortuna 3.5.1

• Test Update

Fortuna 3.5.0

• Storm Update
• Minor Bug Fix: Truffle Shuffle
• Deprecated Features
  • lazy_cat(): use QuantumMonty class instead.
  • flex_cat(): use FlexCat class instead.
  • truffle_shuffle(): use TruffleShuffle class instead.

Fortuna 3.4.9

• Test Update

Fortuna 3.4.8

• Storm Update

Fortuna 3.4.7

• Bug fix for analytic_continuation.

Fortuna 3.4.6

• Docs Update

Fortuna 3.4.5

• Docs Update
• Range Tests Added, see extras folder.

Fortuna 3.4.4

• ZeroCool Algorithm Bug Fixes
• Typos Fixed

Fortuna 3.4.3

• Docs Update

Fortuna 3.4.2

• Typos Fixed

Fortuna 3.4.1

• Major Bug Fix: random_index()

Fortuna 3.4.0 - internal

• ZeroCool Poisson Algorithm Family Updated

Fortuna 3.3.8 - internal

• Docs Update

Fortuna 3.3.7

• Fixed Performance Bug: ZeroCool Linear Algorithm Family

Fortuna 3.3.6

• Docs Update

Fortuna 3.3.5

• ABI Updates
• Bug Fixes

Fortuna 3.3.4

• Examples Update

Fortuna 3.3.3

• Test Suite Update

Fortuna 3.3.2 - internal

• Documentation Update

Fortuna 3.3.1 - internal

• Minor Bug Fix

Fortuna 3.3.0 - internal

• Added plus_or_minus_gauss(N: int) -> int random int in range [-N, N] Stretched Gaussian Distribution

Fortuna 3.2.3

• Small Typos Fixed

Fortuna 3.2.2

• Documentation update.

Fortuna 3.2.1

• Small Typo Fixed

Fortuna 3.2.0

• API updates:
  • QunatumMonty.uniform -> QunatumMonty.flat_uniform
  • QunatumMonty.front -> QunatumMonty.front_linear
  • QunatumMonty.middle -> QunatumMonty.middle_linear
  • QunatumMonty.back -> QunatumMonty.back_linear
  • QunatumMonty.quantum -> QunatumMonty.quantum_linear
  • randindex -> random_index
  • randbelow -> random_below
  • randrange -> random_range
  • randint -> random_int

Fortuna 3.1.0

• discrete() has been removed, see Weighted Choice.
• lazy_cat() added.
• All ZeroCool methods have been raised to top level API, for use with lazy_cat()

Fortuna 3.0.1

• minor typos.

Fortuna 3.0.0

• Storm 2 Rebuild.

Fortuna 2.1.1

• Small bug fixes.
• Test updates.

Fortuna 2.1.0, Major Feature Update

• Fortuna now includes the best of RNG and Pyewacket.

Fortuna 2.0.3

• Bug fix.

Fortuna 2.0.2

• Clarified some documentation.

Fortuna 2.0.1

• Fixed some typos.

Fortuna 2.0.0b1-10

• Total rebuild. New RNG Storm Engine.

Fortuna 1.26.7.1

• README updated.

Fortuna 1.26.7

• Small bug fix.

Fortuna 1.26.6

• Updated README to reflect recent changes to the test script.

Fortuna 1.26.5

• Fixed small bug in test script.

Fortuna 1.26.4

• Updated documentation for clarity.
• Fixed a minor typo in the test script.

Fortuna 1.26.3

• Clean build.

Fortuna 1.26.2

• Fixed some minor typos.

Fortuna 1.26.1

• Release.

Fortuna 1.26.0 beta 2

• Moved README and LICENSE files into fortuna_extras folder.

Fortuna 1.26.0 beta 1

• Dynamic version scheme implemented.
• The Fortuna Extension now requires the fortuna_extras package, previously it was optional.

Fortuna 1.25.4

• Fixed some minor typos in the test script.

Fortuna 1.25.3

• Since version 1.24 Fortuna requires Python 3.7 or higher. This patch corrects an issue where the setup script incorrectly reported requiring Python 3.6 or higher.

Fortuna 1.25.2

• Updated test suite.
• Major performance update for TruffleShuffle.
• Minor performance update for QuantumMonty & FlexCat: cycle monty.

Fortuna 1.25.1

• Important bug fix for TruffleShuffle, QuantumMonty and FlexCat.

Fortuna 1.25

• Full 64bit support.
• The Distribution & Performance Tests have been redesigned.
• Bloat Control: Two experimental features have been removed.
  • RandomWalk
  • CatWalk
• Bloat Control: Several utility functions have been removed from the top level API. These function remain in the Fortuna namespace for now, but may change in the future without warning.
  • stretch_bell, internal only.
  • min_max, not used anymore.
  • analytic_continuation, internal only.
  • flatten, internal only.

Fortuna 1.24.3

• Low level refactoring, non-breaking patch.

Fortuna 1.24.2

• Setup config updated to improve installation.

Fortuna 1.24.1

• Low level patch to avoid potential ADL issue. All low level function calls are now qualified.

Fortuna 1.24

• Documentation updated for even more clarity.
• Bloat Control: Two naïve utility functions that are no longer used in the module have been removed.
  • n_samples -> use a list comprehension instead. [f(x) for _ in range(n)]
  • bind -> use a lambda instead. lambda: f(x)

Fortuna 1.23.7

• Documentation updated for clarity.
• Minor bug fixes.
• TruffleShuffle has been redesigned slightly, it now uses a random rotate instead of swap.
• Custom __repr__ methods have been added to each class.

Fortuna 1.23.6

• New method for QuantumMonty: quantum_not_monty - produces the upside down quantum_monty.
• New bias option for FlexCat: not_monty.

Fortuna 1.23.5.1

• Fixed some small typos.

Fortuna 1.23.5

• Documentation updated for clarity.
• All sequence wrappers can now accept generators as input.
• Six new functions added:
  • random_float() -> float in range [0.0..1.0) exclusive, uniform flat distribution.
  • percent_true_float(num: float) -> bool, Like percent_true but with floating point precision.
  • plus_or_minus_linear_down(num: int) -> int in range [-num..num], upside down pyramid.
  • plus_or_minus_curve_down(num: int) -> int in range [-num..num], upside down bell curve.
  • mostly_not_middle(num: int) -> int in range [0..num], upside down pyramid.
  • mostly_not_center(num: int) -> int in range [0..num], upside down bell curve.
• Two new methods for QuantumMonty:
  • mostly_not_middle
  • mostly_not_center
• Two new bias options for FlexCat, either can be used to define x and/or y axis bias:
  • not_middle
  • not_center

Fortuna 1.23.4.2

• Fixed some minor typos in the README.md file.

Fortuna 1.23.4.1

• Fixed some minor typos in the test suite.

Fortuna 1.23.4

• Fortuna is now Production/Stable!
• Fortuna and Fortuna Pure now use the same test suite.

Fortuna 0.23.4, first release candidate.

• RandomCycle, BlockCycle and TruffleShuffle have been refactored and combined into one class: TruffleShuffle.
• QuantumMonty and FlexCat will now use the new TruffleShuffle for cycling.
• Minor refactoring across the module.

Fortuna 0.23.3, internal

• Function shuffle(arr: list) added.

Fortuna 0.23.2, internal

• Simplified the plus_or_minus_curve(num: int) function, output will now always be bounded to the range [-num..num].
• Function stretched_bell(num: int) added, this matches the previous behavior of an unbounded plus_or_minus_curve.

Fortuna 0.23.1, internal

• Small bug fixes and general clean up.

Fortuna 0.23.0

• The number of test cycles in the test suite has been reduced to 10,000 (down from 100,000). The performance of the pure python implementation and the c-extension are now directly comparable.
• Minor tweaks made to the examples in .../fortuna_extras/fortuna_examples.py

Fortuna 0.22.2, experimental features

• BlockCycle class added.
• RandomWalk class added.
• CatWalk class added.

Fortuna 0.22.1

• Fortuna classes no longer return lists of values, this behavior has been extracted to a free function called n_samples.

Fortuna 0.22.0, experimental features

• Function bind added.
• Function n_samples added.

Fortuna 0.21.3

• Flatten will no longer raise an error if passed a callable item that it can't call. It correctly returns such items in an uncalled state without error.
• Simplified .../fortuna_extras/fortuna_examples.py - removed unnecessary class structure.

Fortuna 0.21.2

• Fix some minor bugs.

Fortuna 0.21.1

• Fixed a bug in .../fortuna_extras/fortuna_examples.py

Fortuna 0.21.0

• Function flatten added.
• Flatten: The Fortuna classes will recursively unpack callable objects in the data set.

Fortuna 0.20.10

• Documentation updated.

Fortuna 0.20.9

• Minor bug fixes.

Fortuna 0.20.8, internal

• Testing cycle for potential new features.

Fortuna 0.20.7

• Documentation updated for clarity.

Fortuna 0.20.6

• Tests updated based on recent changes.

Fortuna 0.20.5, internal

• Documentation updated based on recent changes.

Fortuna 0.20.4, internal

• WeightedChoice (both types) can optionally return a list of samples rather than just one value, control the length of the list via the n_samples argument.

Fortuna 0.20.3, internal

• RandomCycle can optionally return a list of samples rather than just one value, control the length of the list via the n_samples argument.

Fortuna 0.20.2, internal

• QuantumMonty can optionally return a list of samples rather than just one value, control the length of the list via the n_samples argument.

Fortuna 0.20.1, internal

• FlexCat can optionally return a list of samples rather than just one value, control the length of the list via the n_samples argument.

Fortuna 0.20.0, internal

• FlexCat now accepts a standard dict as input. The ordered(ness) of dict is now part of the standard in Python 3.7.1. Previously FlexCat required an OrderedDict, now it accepts either and treats them the same.

Fortuna 0.19.7

• Fixed bug in .../fortuna_extras/fortuna_examples.py.

Fortuna 0.19.6

• Updated documentation formatting.
• Small performance tweak for QuantumMonty and FlexCat.

Fortuna 0.19.5

• Minor documentation update.

Fortuna 0.19.4

• Minor update to all classes for better debugging.

Fortuna 0.19.3

• Updated plus_or_minus_curve to allow unbounded output.

Fortuna 0.19.2

• Internal development cycle.
• Minor update to FlexCat for better debugging.

Fortuna 0.19.1

• Internal development cycle.

Fortuna 0.19.0

• Updated documentation for clarity.
• MultiCat has been removed, it is replaced by FlexCat.
• Mostly has been removed, it is replaced by QuantumMonty.

Fortuna 0.18.7

• Fixed some more README typos.

Fortuna 0.18.6

• Fixed some README typos.

Fortuna 0.18.5

• Updated documentation.
• Fixed another minor test bug.

Fortuna 0.18.4

• Updated documentation to reflect recent changes.
• Fixed some small test bugs.
• Reduced default number of test cycles to 10,000 - down from 100,000.

Fortuna 0.18.3

• Fixed some minor README typos.

Fortuna 0.18.2

• Fixed a bug with Fortuna Pure.

Fortuna 0.18.1

• Fixed some minor typos.
• Added tests for .../fortuna_extras/fortuna_pure.py

Fortuna 0.18.0

• Introduced new test format, now includes average call time in nanoseconds.
• Reduced default number of test cycles to 100,000 - down from 1,000,000.
• Added pure Python implementation of Fortuna: .../fortuna_extras/fortuna_pure.py
• Promoted several low level functions to top level.
  • zero_flat(num: int) -> int
  • zero_cool(num: int) -> int
  • zero_extreme(num: int) -> int
  • max_cool(num: int) -> int
  • max_extreme(num: int) -> int
  • analytic_continuation(func: staticmethod, num: int) -> int
  • min_max(num: int, lo: int, hi: int) -> int

Fortuna 0.17.3

• Internal development cycle.

Fortuna 0.17.2

• User Requested: dice() and d() functions now support negative numbers as input.

Fortuna 0.17.1

• Fixed some minor typos.

Fortuna 0.17.0

• Added QuantumMonty to replace Mostly, same default behavior with more options.
• Mostly is depreciated and may be removed in a future release.
• Added FlexCat to replace MultiCat, same default behavior with more options.
• MultiCat is depreciated and may be removed in a future release.
• Expanded the Treasure Table example in .../fortuna_extras/fortuna_examples.py

Fortuna 0.16.2

• Minor refactoring for WeightedChoice.

Fortuna 0.16.1

• Redesigned fortuna_examples.py to feature a dynamic random magic item generator.
• Raised cumulative_weighted_choice function to top level.
• Added test for cumulative_weighted_choice as free function.
• Updated MultiCat documentation for clarity.

Fortuna 0.16.0

• Pushed distribution_timer to the .pyx layer.
• Changed default number of iterations of tests to 1 million, up form 1 hundred thousand.
• Reordered tests to better match documentation.
• Added Base Case Fortuna.fast_rand_below.
• Added Base Case Fortuna.fast_d.
• Added Base Case Fortuna.fast_dice.

Fortuna 0.15.10

• Internal Development Cycle

Fortuna 0.15.9

• Added Base Cases for random_value()
• Added Base Case for randint()

Fortuna 0.15.8

• Clarified MultiCat Test

Fortuna 0.15.7

• Fixed minor typos.

Fortuna 0.15.6

• Fixed minor typos.
• Simplified MultiCat example.

Fortuna 0.15.5

• Added MultiCat test.
• Fixed some minor typos in docs.

Fortuna 0.15.4

• Performance optimization for both WeightedChoice() variants.
• Cython update provides small performance enhancement across the board.
• Compilation now leverages Python3 all the way down.
• MultiCat pushed to the .pyx layer for better performance.

Fortuna 0.15.3

• Reworked the MultiCat example to include several randomizing strategies working in concert.
• Added Multi Dice 10d10 performance tests.
• Updated sudo code in documentation to be more pythonic.

Fortuna 0.15.2

• Fixed: Linux installation failure.
• Added: complete source files to the distribution (.cpp .hpp .pyx).

Fortuna 0.15.1

• Updated & simplified distribution_timer in fortuna_tests.py
• Readme updated, fixed some typos.
• Known issue preventing successful installation on some linux platforms.

Fortuna 0.15.0

• Performance tweaks.
• Readme updated, added some details.

Fortuna 0.14.1

• Readme updated, fixed some typos.

Fortuna 0.14.0

• Fixed a bug where the analytic continuation algorithm caused a rare issue during compilation on some platforms.

Fortuna 0.13.3

• Fixed Test Bug: percent sign was missing in output distributions.
• Readme updated: added update history, fixed some typos.

Fortuna 0.13.2

• Readme updated for even more clarity.

Fortuna 0.13.1

• Readme updated for clarity.

Fortuna 0.13.0

• Minor Bug Fixes.
• Readme updated for aesthetics.
• Added Tests: .../fortuna_extras/fortuna_tests.py

Fortuna 0.12.0

• Internal test for future update.

Fortuna 0.11.0

• Initial Release: Public Beta

Fortuna 0.10.0

• Module name changed from Dice to Fortuna

Dice 0.1.x - 0.9.x

• Experimental Phase

Distribution and Performance Tests

Testbed:

• Hardware: 2.3Ghz 8-core i9
• Software: MacOS 11.4, Python 3.9.2, MonkeyScope: Fortuna 4.1.8, Storm 3.4.4

MonkeyScope: Fortuna Quick Test

Random Sequence Values:

some_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Base Case
Output Analysis: Random.choice(some_list)
Typical Timing: 398 ± 27 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.493
 Std Deviation: 2.8681313040335477
Distribution of 100000 samples:
 0: 9.975%
 1: 9.831%
 2: 9.967%
 3: 10.026%
 4: 10.039%
 5: 10.038%
 6: 10.053%
 7: 9.984%
 8: 10.117%
 9: 9.97%

Output Analysis: random_value(some_list)
Typical Timing: 60 ± 1 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.338
 Std Deviation: 2.868795215066071
Distribution of 100000 samples:
 0: 10.264%
 1: 10.028%
 2: 9.948%
 3: 9.952%
 4: 10.01%
 5: 10.037%
 6: 9.879%
 7: 9.727%
 8: 10.099%
 9: 10.056%


Wide Distribution

Truffle = TruffleShuffle(some_list)
Output Analysis: Truffle()
Typical Timing: 419 ± 20 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.469
 Std Deviation: 2.880857222831138
Distribution of 100000 samples:
 0: 9.98%
 1: 9.991%
 2: 9.946%
 3: 9.921%
 4: 10.123%
 5: 9.935%
 6: 10.062%
 7: 9.989%
 8: 10.005%
 9: 10.048%

truffle = truffle_shuffle(some_list)
Output Analysis: truffle()
Typical Timing: 171 ± 15 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.406
 Std Deviation: 2.912670192048466
Distribution of 100000 samples:
 0: 10.007%
 1: 9.944%
 2: 10.048%
 3: 10.11%
 4: 10.017%
 5: 9.9%
 6: 9.968%
 7: 10.014%
 8: 10.004%
 9: 9.988%


Single objects with many distribution possibilities

some_tuple = tuple(i for i in range(10))

monty = QuantumMonty(some_tuple)
Output Analysis: monty()
Typical Timing: 422 ± 20 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.506
 Std Deviation: 2.8702268555294026
Distribution of 100000 samples:
 0: 10.542%
 1: 8.912%
 2: 9.018%
 3: 9.692%
 4: 11.5%
 5: 11.691%
 6: 9.828%
 7: 8.967%
 8: 9.013%
 9: 10.837%

rand_value = RandomValue(collection, zero_cool, flat)
Output Analysis: rand_value()
Typical Timing: 331 ± 19 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.591
 Std Deviation: 2.8604022271178753
Distribution of 100000 samples:
 0: 10.063%
 1: 9.784%
 2: 10.051%
 3: 9.951%
 4: 10.072%
 5: 10.064%
 6: 9.767%
 7: 10.002%
 8: 10.075%
 9: 10.171%


Weighted Tables:

population = ('A', 'B', 'C', 'D')
cum_weights = (1, 3, 6, 10)
rel_weights = (1, 2, 3, 4)
cum_weighted_table = zip(cum_weights, population)
rel_weighted_table = zip(rel_weights, population)

Cumulative Base Case
Output Analysis: Random.choices(population, cum_weights=cum_weights)
Typical Timing: 1156 ± 34 ns
Distribution of 100000 samples:
 A: 9.961%
 B: 20.2%
 C: 29.825%
 D: 40.014%

cum_weighted_choice = CumulativeWeightedChoice(cum_weighted_table)
Output Analysis: cum_weighted_choice()
Typical Timing: 331 ± 7 ns
Distribution of 100000 samples:
 A: 9.998%
 B: 19.937%
 C: 30.047%
 D: 40.018%

Output Analysis: cumulative_weighted_choice(tuple(zip(cum_weights, population)))
Typical Timing: 129 ± 16 ns
Distribution of 100000 samples:
 A: 9.825%
 B: 20.054%
 C: 29.956%
 D: 40.165%

Relative Base Case
Output Analysis: Random.choices(population, weights=rel_weights)
Typical Timing: 1393 ± 46 ns
Distribution of 100000 samples:
 A: 10.005%
 B: 20.018%
 C: 29.8%
 D: 40.177%

rel_weighted_choice = RelativeWeightedChoice(rel_weighted_table)
Output Analysis: rel_weighted_choice()
Typical Timing: 342 ± 14 ns
Distribution of 100000 samples:
 A: 10.084%
 B: 20.022%
 C: 29.818%
 D: 40.076%


Random Matrix Values:

some_matrix = {'A': (1, 2, 3, 4), 'B': (10, 20, 30, 40), 'C': (100, 200, 300, 400)}

flex_cat = FlexCat(some_matrix)
Output Analysis: flex_cat()
Typical Timing: 793 ± 51 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 4
 Maximum: 400
 Mean: 37.028
 Std Deviation: 82.80369356295446
Distribution of 100000 samples:
 1: 13.846%
 2: 13.803%
 3: 13.966%
 4: 13.927%
 10: 8.353%
 20: 8.358%
 30: 8.293%
 40: 8.401%
 100: 2.767%
 200: 2.8%
 300: 2.74%
 400: 2.746%

Output Analysis: flex_cat("C")
Typical Timing: 524 ± 21 ns
Statistics of 1000 samples:
 Minimum: 100
 Median: 200
 Maximum: 400
 Mean: 249
 Std Deviation: 112.03370721149751
Distribution of 100000 samples:
 100: 25.115%
 200: 25.051%
 300: 24.905%
 400: 24.929%


Random Integers:

Base Case
Output Analysis: Random.randrange(10)
Typical Timing: 453 ± 27 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.517
 Std Deviation: 2.8840993445145107
Distribution of 100000 samples:
 0: 9.866%
 1: 10.067%
 2: 9.872%
 3: 10.074%
 4: 10.093%
 5: 9.962%
 6: 10.073%
 7: 10.057%
 8: 9.996%
 9: 9.94%

Output Analysis: random_below(10)
Typical Timing: 65 ± 6 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.494
 Std Deviation: 2.846061149038099
Distribution of 100000 samples:
 0: 9.877%
 1: 9.981%
 2: 9.846%
 3: 10.033%
 4: 9.905%
 5: 10.169%
 6: 10.022%
 7: 10.026%
 8: 10.136%
 9: 10.005%

Output Analysis: random_index(10)
Typical Timing: 71 ± 9 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.518
 Std Deviation: 2.842491117973063
Distribution of 100000 samples:
 0: 10.019%
 1: 10.146%
 2: 9.887%
 3: 9.87%
 4: 10.029%
 5: 10.061%
 6: 9.882%
 7: 9.97%
 8: 10.098%
 9: 10.038%

Output Analysis: random_range(10)
Typical Timing: 87 ± 13 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.603
 Std Deviation: 2.8466637480556947
Distribution of 100000 samples:
 0: 9.908%
 1: 9.851%
 2: 10.271%
 3: 9.917%
 4: 9.991%
 5: 10.006%
 6: 9.974%
 7: 10.068%
 8: 10.04%
 9: 9.974%

Output Analysis: random_below(-10)
Typical Timing: 88 ± 22 ns
Statistics of 1000 samples:
 Minimum: -9
 Median: -5
 Maximum: 0
 Mean: -4.57
 Std Deviation: 2.891619176423732
Distribution of 100000 samples:
 -9: 10.062%
 -8: 10.037%
 -7: 9.903%
 -6: 9.924%
 -5: 10.042%
 -4: 9.936%
 -3: 10.103%
 -2: 10.025%
 -1: 9.868%
 0: 10.1%

Output Analysis: random_index(-10)
Typical Timing: 80 ± 10 ns
Statistics of 1000 samples:
 Minimum: -10
 Median: -5
 Maximum: -1
 Mean: -5.411
 Std Deviation: 2.9323501969713095
Distribution of 100000 samples:
 -10: 10.029%
 -9: 10.07%
 -8: 9.895%
 -7: 9.933%
 -6: 10.057%
 -5: 10.032%
 -4: 10.02%
 -3: 9.969%
 -2: 9.92%
 -1: 10.075%

Output Analysis: random_range(-10)
Typical Timing: 97 ± 11 ns
Statistics of 1000 samples:
 Minimum: -10
 Median: -5
 Maximum: -1
 Mean: -5.466
 Std Deviation: 2.8253896769873754
Distribution of 100000 samples:
 -10: 10.019%
 -9: 9.984%
 -8: 9.993%
 -7: 9.974%
 -6: 10.021%
 -5: 10.041%
 -4: 9.998%
 -3: 10.013%
 -2: 10.064%
 -1: 9.893%

Base Case
Output Analysis: Random.randrange(1, 10)
Typical Timing: 606 ± 44 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5
 Maximum: 9
 Mean: 4.946
 Std Deviation: 2.6026636082786148
Distribution of 100000 samples:
 1: 11.134%
 2: 11.176%
 3: 11.085%
 4: 11.212%
 5: 11.182%
 6: 11.06%
 7: 10.998%
 8: 10.923%
 9: 11.23%

Output Analysis: random_range(1, 10)
Typical Timing: 82 ± 4 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5
 Maximum: 9
 Mean: 4.969
 Std Deviation: 2.590125044809178
Distribution of 100000 samples:
 1: 11.186%
 2: 10.98%
 3: 11.033%
 4: 11.229%
 5: 11.141%
 6: 10.807%
 7: 11.049%
 8: 11.345%
 9: 11.23%

Output Analysis: random_range(10, 1)
Typical Timing: 83 ± 4 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5
 Maximum: 9
 Mean: 5.199
 Std Deviation: 2.552236844987728
Distribution of 100000 samples:
 1: 11.11%
 2: 11.193%
 3: 11.113%
 4: 11.16%
 5: 11.033%
 6: 11.022%
 7: 11.092%
 8: 11.176%
 9: 11.101%

Base Case
Output Analysis: Random.randint(-5, 5)
Typical Timing: 698 ± 58 ns
Statistics of 1000 samples:
 Minimum: -5
 Median: 0
 Maximum: 5
 Mean: -0.032
 Std Deviation: 3.237817082458405
Distribution of 100000 samples:
 -5: 9.034%
 -4: 9.092%
 -3: 9.134%
 -2: 9.195%
 -1: 9.119%
 0: 9.136%
 1: 9.059%
 2: 9.105%
 3: 9.111%
 4: 9.047%
 5: 8.968%

Output Analysis: random_int(-5, 5)
Typical Timing: 67 ± 9 ns
Statistics of 1000 samples:
 Minimum: -5
 Median: 0
 Maximum: 5
 Mean: -0.037
 Std Deviation: 3.18241398293163
Distribution of 100000 samples:
 -5: 9.144%
 -4: 9.091%
 -3: 9.183%
 -2: 9.078%
 -1: 9.08%
 0: 8.973%
 1: 9.204%
 2: 9.093%
 3: 9.038%
 4: 9.021%
 5: 9.095%

Base Case
Output Analysis: Random.randrange(1, 20, 2)
Typical Timing: 782 ± 67 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 11
 Maximum: 19
 Mean: 10.4
 Std Deviation: 5.673816369269646
Distribution of 100000 samples:
 1: 9.88%
 3: 10.029%
 5: 10.084%
 7: 9.982%
 9: 10.045%
 11: 10.059%
 13: 9.948%
 15: 9.799%
 17: 9.961%
 19: 10.213%

Output Analysis: random_range(1, 20, 2)
Typical Timing: 79 ± 4 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 11
 Maximum: 19
 Mean: 10.128
 Std Deviation: 5.846177708023747
Distribution of 100000 samples:
 1: 10.038%
 3: 9.957%
 5: 10.052%
 7: 10.115%
 9: 10.005%
 11: 9.94%
 13: 9.904%
 15: 10.025%
 17: 10.102%
 19: 9.862%

Output Analysis: random_range(1, 20, -2)
Typical Timing: 93 ± 19 ns
Statistics of 1000 samples:
 Minimum: 2
 Median: 10
 Maximum: 20
 Mean: 10.926
 Std Deviation: 5.7726811663080655
Distribution of 100000 samples:
 2: 9.912%
 4: 10.044%
 6: 10.005%
 8: 9.858%
 10: 10.223%
 12: 10.011%
 14: 9.96%
 16: 9.916%
 18: 9.904%
 20: 10.167%

Output Analysis: random_range(20, 1, -2)
Typical Timing: 86 ± 12 ns
Statistics of 1000 samples:
 Minimum: 2
 Median: 12
 Maximum: 20
 Mean: 11.144
 Std Deviation: 5.66916249576632
Distribution of 100000 samples:
 2: 9.92%
 4: 9.996%
 6: 9.986%
 8: 10.038%
 10: 9.937%
 12: 9.954%
 14: 9.892%
 16: 10.095%
 18: 10.226%
 20: 9.956%

Output Analysis: d(10)
Typical Timing: 69 ± 11 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5
 Maximum: 10
 Mean: 5.401
 Std Deviation: 2.8796686426551736
Distribution of 100000 samples:
 1: 10.023%
 2: 10.094%
 3: 9.966%
 4: 9.93%
 5: 9.974%
 6: 10.049%
 7: 9.937%
 8: 9.954%
 9: 10.02%
 10: 10.053%

Output Analysis: dice(3, 6)
Typical Timing: 123 ± 17 ns
Statistics of 1000 samples:
 Minimum: 3
 Median: 10
 Maximum: 18
 Mean: 10.342
 Std Deviation: 2.9297814969071703
Distribution of 100000 samples:
 3: 0.489%
 4: 1.355%
 5: 2.82%
 6: 4.605%
 7: 6.931%
 8: 9.607%
 9: 11.644%
 10: 12.437%
 11: 12.731%
 12: 11.53%
 13: 9.805%
 14: 6.957%
 15: 4.558%
 16: 2.709%
 17: 1.338%
 18: 0.484%

Output Analysis: ability_dice(4)
Typical Timing: 202 ± 22 ns
Statistics of 1000 samples:
 Minimum: 3
 Median: 12
 Maximum: 18
 Mean: 12.121
 Std Deviation: 2.763692637938777
Distribution of 100000 samples:
 3: 0.079%
 4: 0.295%
 5: 0.789%
 6: 1.677%
 7: 2.931%
 8: 4.817%
 9: 7.018%
 10: 9.436%
 11: 11.408%
 12: 13.039%
 13: 13.267%
 14: 12.227%
 15: 10.086%
 16: 7.06%
 17: 4.257%
 18: 1.614%

Output Analysis: plus_or_minus(5)
Typical Timing: 61 ± 7 ns
Statistics of 1000 samples:
 Minimum: -5
 Median: 0
 Maximum: 5
 Mean: -0.133
 Std Deviation: 3.1615354349281466
Distribution of 100000 samples:
 -5: 9.077%
 -4: 9.077%
 -3: 9.009%
 -2: 9.085%
 -1: 9.214%
 0: 8.931%
 1: 9.108%
 2: 9.135%
 3: 9.061%
 4: 9.231%
 5: 9.072%

Output Analysis: plus_or_minus_linear(5)
Typical Timing: 90 ± 12 ns
Statistics of 1000 samples:
 Minimum: -5
 Median: 0
 Maximum: 5
 Mean: -0.059
 Std Deviation: 2.4358679053783794
Distribution of 100000 samples:
 -5: 2.806%
 -4: 5.521%
 -3: 8.218%
 -2: 11.096%
 -1: 13.836%
 0: 16.927%
 1: 13.824%
 2: 11.214%
 3: 8.389%
 4: 5.406%
 5: 2.763%

Output Analysis: plus_or_minus_gauss(5)
Typical Timing: 102 ± 8 ns
Statistics of 1000 samples:
 Minimum: -5
 Median: 0
 Maximum: 5
 Mean: 0
 Std Deviation: 1.62746694241347
Distribution of 100000 samples:
 -5: 0.19%
 -4: 1.214%
 -3: 4.388%
 -2: 11.437%
 -1: 20.473%
 0: 24.551%
 1: 20.365%
 2: 11.457%
 3: 4.528%
 4: 1.194%
 5: 0.203%


Random Floats:

Base Case
Output Analysis: Random.random()
Typical Timing: 51 ± 16 ns
Statistics of 1000 samples:
 Minimum: 0.0003702971209362804
 Median: (0.5285224456696601, 0.5293093733947216)
 Maximum: 0.9984423578948415
 Mean: 0.5130877819566628
 Std Deviation: 0.28931550321546284
Post-processor distribution of 100000 samples using round method:
 0: 50.114%
 1: 49.886%

Output Analysis: canonical()
Typical Timing: 44 ± 1 ns
Statistics of 1000 samples:
 Minimum: 0.001227269781981108
 Median: (0.514352637487454, 0.516711859646879)
 Maximum: 0.999883070340974
 Mean: 0.5060023084070058
 Std Deviation: 0.2888850931318481
Post-processor distribution of 100000 samples using round method:
 0: 49.78%
 1: 50.22%

Output Analysis: random_float(0.0, 10.0)
Typical Timing: 54 ± 7 ns
Statistics of 1000 samples:
 Minimum: 0.003669520808454968
 Median: (4.961322433075261, 4.970422268664564)
 Maximum: 9.987675193205668
 Mean: 4.961658864108672
 Std Deviation: 2.8485880494819167
Post-processor distribution of 100000 samples using floor method:
 0: 10.107%
 1: 9.972%
 2: 9.914%
 3: 10.073%
 4: 9.925%
 5: 9.942%
 6: 10.158%
 7: 9.829%
 8: 9.993%
 9: 10.087%

Base Case
Output Analysis: Random.triangular(0.0, 10.0, 5.0)
Typical Timing: 397 ± 26 ns
Statistics of 1000 samples:
 Minimum: 0.22534526234554247
 Median: (5.056597085718702, 5.058496349524351)
 Maximum: 9.714326876251489
 Mean: 4.940927055975845
 Std Deviation: 2.0331617814546883
Post-processor distribution of 100000 samples using round method:
 0: 0.525%
 1: 3.954%
 2: 8.033%
 3: 12.14%
 4: 16.144%
 5: 18.978%
 6: 15.985%
 7: 11.871%
 8: 7.907%
 9: 3.946%
 10: 0.517%

Output Analysis: triangular(0.0, 10.0, 5.0)
Typical Timing: 64 ± 9 ns
Statistics of 1000 samples:
 Minimum: 0.47316401319611423
 Median: (5.015437435780669, 5.0167357947766185)
 Maximum: 9.84615637754068
 Mean: 5.093149389635319
 Std Deviation: 2.051174580624293
Post-processor distribution of 100000 samples using round method:
 0: 0.529%
 1: 3.875%
 2: 7.957%
 3: 12.031%
 4: 16.05%
 5: 19.022%
 6: 15.947%
 7: 12.176%
 8: 7.919%
 9: 4.008%
 10: 0.486%


Random Booleans:

Output Analysis: percent_true(33.33)
Typical Timing: 52 ± 9 ns
Statistics of 1000 samples:
 Minimum: False
 Median: False
 Maximum: True
 Mean: 0.342
 Std Deviation: 0.47461696267754827
Distribution of 100000 samples:
 False: 66.369%
 True: 33.631%


Shuffle Performance:

some_small_list = [i for i in range(10)]
some_med_list = [i for i in range(100)]
some_large_list = [i for i in range(1000)]

Base Case:
Random.shuffle()  # fisher_yates in python
Typical Timing: 3812 ± 383 ns
Typical Timing: 31366 ± 80 ns
Typical Timing: 342115 ± 4401 ns

Fortuna.shuffle()  # knuth_b in cython
Typical Timing: 340 ± 54 ns
Typical Timing: 3771 ± 57 ns
Typical Timing: 37165 ± 165 ns

Fortuna.knuth_a()  # knuth_a in cython
Typical Timing: 892 ± 35 ns
Typical Timing: 7386 ± 100 ns
Typical Timing: 89712 ± 2855 ns

Fortuna.fisher_yates()  # fisher_yates in cython
Typical Timing: 885 ± 27 ns
Typical Timing: 7720 ± 434 ns
Typical Timing: 91236 ± 5951 ns


-------------------------------------------------------------------------
Total Test Time: 3.324 seconds

Legal Information

Fortuna is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. See online version of this license here: http://creativecommons.org/licenses/by-nc/3.0/

Other licensing options are available, please contact the author for details: Robert Sharp