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…is a very lightweight Python API for simulating sample spaces, events, random variables, and (conditional) distributions.

Check out the iPython notebook or read the following:

from sample_space import * class NCoinTosses(Experiment): def __init__(self, n, p): self.n = n self.p = p def rerun(self): self.tosses = [Bern(self.p) for _ in range(self.n)] def heads(self): return sum(self.tosses) def there_are_at_least_two_heads(self): return self.heads() >= 2 def first_toss_heads(self): return self.tosses[0] space = SampleSpace(NCoinTosses(10, 0.5), iters=20000) # ask for probability of any truthy method print(' P(#H>=2):', space.probability_that('there_are_at_least_two_heads')) # alias for the above, if it's more grammatical print(' P(H1):', space.probability_of('first_toss_heads')) # change the number of iterations print(' P(H1), 1K iters:', space.probability_of('first_toss_heads', iters=1000)) # ask for probabilities of functions of random variables print(' P(#H>5):', space.probability_that(['heads', is_greater_than(5)])) # ask for conditional probabilities print(' P(#H>5|H1):', space.probability_that(['heads', is_greater_than(5)], given=['first_toss_heads'])) print(' P(H1|#H>5):', space.probability_of('first_toss_heads', given=[['heads', is_greater_than(5)]])) print(' P(#H>5|H1,H>=2):', space.probability_that(['heads', is_greater_than(5)], given=['first_toss_heads', 'there_are_at_least_two_heads'])) # ask for expectations, variances, and moments, conditionally or absolutely print(' E(#H):', space.expected_value_of('heads')) print(' E(#H|H1):', space.expected_value_of('heads', given=['first_toss_heads'])) print(' Var(#H):', space.variance_of('heads')) print(' Var(#H|H1):', space.variance_of('heads', given=['first_toss_heads'])) print('1st moment of #H:', space.nth_moment_of('heads', 1)) print('2nd moment of #H:', space.nth_moment_of('heads', 2)) print('3rd moment of #H:', space.nth_moment_of('heads', 3)) print('4th moment of #H:', space.nth_moment_of('heads', 4)) print(' Skewness of #H:', space.nth_moment_of('heads', 3, central=True, normalized=True), '(using nth_moment_of w/ central=True, normalized=True)') print(' Skewness of #H:', space.skewness_of('heads'), '(using skewness_of)') print(' Kurtosis of #H:', space.kurtosis_of('heads')) # some plots fig = plt.figure(figsize=(14,3)) # plot distribution histograms fig.add_subplot(121) space.plot_distribution_of('heads') # pass kwargs plt.legend() # plot conditional distribution histograms fig.add_subplot(122) space.plot_distribution_of('heads', given=['first_toss_heads'], bins=10) # can pass kwargs plt.legend() plt.show()

Which should output (plus some plots):

P(#H>=2): 0.98975 P(H1): 0.502 P(H1), 1K iters: 0.48 P(#H>5): 0.37665 P(#H>5|H1): 0.5076294006183305 P(H1|#H>5): 0.6580109757729888 P(#H>5|H1,H>=2): 0.49361831442463533 E(#H): 4.9983 E(#H|H1): 5.48924623116 Var(#H): 2.4486457975 Var(#H|H1): 2.31806506582 1st moment of #H: 4.99245 2nd moment of #H: 27.5097 3rd moment of #H: 163.13055 4th moment of #H: 1015.54155 Skewness of #H: -0.00454435802967 (using nth_moment_of w/ central=True, normalized=True) Skewness of #H: 0.00414054522343 (using skewness_of) Kurtosis of #H: 2.78225928171

Mostly to avoid bugs / reduce boilerplate in statistical simulations for sanity-checking homework solutions. But also to get a better understanding of probability theory.

Sample spaces are a core concept in probability theory. They encapsulate the idea of repeatedly running an experiment with random results. Almost every important statistical quantity – the probability of an event, or any moment of a random variable – is always defined relative to a sample space. So if you’re trying to program meaningful simulations (and if you’re more concerned with expressiveness than performance), you might as well organize your code by explicitly defining one.

First run

pip install sample_space

and `import` the library. Then define a subclass of `Experiment`
that responds to `rerun(self)`. `rerun` should perform a random
experiment and store one or more basic results as instance variables. If
you want to define more complex events or random variables, you can
express them as instance methods.

Then, initialize a `SampleSpace` with an instance of your
`Experiment`. You can query your sample space for the
`probability_that`/`probability_of` an event, or you can query it
for the `distribution_of`, `expected_value_of`, `variance_of`,
`skewness_of`, `kurtosis_of`, or `nth_moment_of` of random
variable (which can also just be an event, in which case it will be
interpreted as an indicator). Finally, for any of these methods, you can
pass a `given` keyword argument with a list of events, which will make
any results you obtain conditional on all of those events occurring.
Behind the scenes, `SampleSpace` will just `rerun` your experiment
10000 times and average your random variable or count how often an event
occurs (conditionally). You can pass an `iters` keyword argument to
any method or to `SampleSpace.__init__` to increase the number of
iterations.

To reference events or random variable, pass the string name of an instance variable or instance method of your experiment, or pass an array with a variable/method name and a lambda function. For example:

space = SampleSpace(CoinTossExperiment(10)) space.probability_that('first_toss_is_heads') space.probability_that(['n_heads', lambda h: h > 5]) space.expected_value_of('n_heads') space.expected_value_of('n_heads', given=['first_toss_is_heads']) space.probability_that('first_toss_is_heads, given=[['n_heads', lambda h: h > 3], 'last_toss_is_heads'])

Additionally, `sample_space` defines a few helpful lambda-returning
methods (`is_greater_than(x)`, `is_less_than(x)`,
`is_at_least(x)`, `is_at_most(x)`, `equals(x)`) for convenience.
Of course, you could also define instance methods on your `Experiment`
to accomplish the same goal.

The library also exposes a few basic sampling functions (`Bern(p)`,
`Bin(n,p)`, `RandomSign(p)`, and `Categ(categories, weights)`) to
assist with defining experiments.

If you’d prefer not to define a full `Experiment` class, you can also
just define a random event / random variable function that returns
either a boolean or a number, and call
`probability_that`/`expected_value_of`:

import sample_space as ss def weighted_coin_flip_is_heads(p=0.4): return ss.Bern(p) def n_weighted_heads(n=100, p=0.4): return sum(weighted_coin_flip_is_heads(p) for _ in range(n)) print(ss.probability_that(weighted_coin_flip_is_heads)) print(ss.probability_that(lambda: weighted_coin_flip_is_heads(0.5)) print(ss.expected_value_of(n_weighted_heads)) print(ss.expected_value_of(lambda: n_weighted_heads(200, 0.3)))

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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