probability_calculator
Calculate with and analyze random variables / probability densities.
Usage
Install the package, e.g.: pip install probability_calculator.
Initialization and plotting
The package provides simple ways to define random variables with discrete outcomes, for instance a die:
from probability_calculator import FairDie
density = FairDie(6) # initialize a fair die with 6 sides
fig, ax = density.plot_outcomes() # plot the outcomes using matplotlib
For the general case, the class RandomVariables can be used:
from probability_calculator import RandomVariable
from fractions import Fraction
density = RandomVariable(outcomes=[
{ "value": 0, "p": Fraction(2, 10) },
{ "value": 1, "p": Fraction(3, 10) },
{ "value": Fraction(5, 2), "p": Fraction(1, 10) },
{ "value": 3, "p": Fraction(4, 10) },
]) # initialize a discrete density with 4 different outcomes
fig, ax = density.plot_outcomes() # plot the density using matplotlib
Note that only integers and fractions can be used to assure exact calculations.
Combine random variables
The discrete density of throwing a die two times can be modelled by adding the random variable with itself:
density_for_one_throw = FairDie(6)
density_sum_of_two_throws = density_for_one_throw + density_for_one_throw # same as density_for_one_throw*2
fig, ax = density_sum_of_two_throws.plot_outcomes()
Note that the operations (+ and *) on random variables are always assuming independed random variables, even when using the same variable multiple times.
Limitations
Continuous densities / random variables are not supported at the moment.
Adding many random variables together might get stuck due to a lot of possible outcomes. In general, adding 10 densities with 10 outcomes each lead to Die(10)*100 is no problem.
Contributing
We greatly appreciate fixes and new features for probability_calculator. All contributions to this project should be sent as pull requests on github.
