Skip to main content

dit is a Python package for information theory on discrete random variables.

Project description

dit is a Python package for information theory.

Continuous Integration Status Continuous Integration Status (windows) Test Coverage Status Code Health Documentation Status Requirements Status DOI Join the Chat Say Thanks!

Documentation:

http://docs.dit.io

Downloads:

Coming soon.

Dependencies:
  • Python 2.7, 3.3, 3.4, 3.5, or 3.6

  • numpy

  • iterutils

  • six

  • contextlib2

  • prettytable

  • networkx

Optional Dependencies:
  • cython

  • cvxopt

  • numdifftools

  • scipy

Note:

The cython extensions are currently not supported on windows. Please install using the --nocython option.

Install:

Until dit is available on PyPI, the easiest way to install is:

pip install git+https://github.com/dit/dit/#egg=dit

Alternatively, you can clone this repository, move into the newly created dit directory, and then install the package. Be sure to include the period (.) in the install command:

git clone https://github.com/dit/dit.git
cd dit
pip install .
Mailing list:

None

Code and bug tracker:

https://github.com/dit/dit

License:

BSD 2-Clause, see LICENSE.txt for details.

Quickstart

The basic usage of dit corresponds to creating distributions, modifying them if need be, and then computing properties of those distributions. First, we import:

>>> import dit

Suppose we have a really thick coin, one so thick that there is a reasonable chance of it landing on its edge. Here is how we might represent the coin in dit.

>>> d = dit.Distribution(['H', 'T', 'E'], [.4, .4, .2])
>>> print d
Class:          Distribution
Alphabet:       ('E', 'H', 'T') for all rvs
Base:           linear
Outcome Class:  str
Outcome Length: 1
RV Names:       None

x   p(x)
E   0.2
H   0.4
T   0.4

Calculate the probability of H and also of the combination H or T.

>>> d['H']
0.4
>>> d.event_probability(['H','T'])
0.8

Calculate the Shannon entropy and extropy of the joint distribution.

>>> dit.shannon.entropy(d)
1.5219280948873621
>>> dit.other.extropy(d)
1.1419011889093373

Create a distribution where Z = xor(X, Y).

>>> import dit.example_dists
>>> d = dit.example_dists.Xor()
>>> d.set_rv_names(['X', 'Y', 'Z'])
>>> print d
Class:          Distribution
Alphabet:       ('0', '1') for all rvs
Base:           linear
Outcome Class:  str
Outcome Length: 3
RV Names:       ('X', 'Y', 'Z')

x     p(x)
000   0.25
011   0.25
101   0.25
110   0.25

Calculate the Shannon mutual informations I[X:Z], I[Y:Z], and I[X,Y:Z].

>>> dit.shannon.mutual_information(d, ['X'], ['Z'])
0.0
>>> dit.shannon.mutual_information(d, ['Y'], ['Z'])
0.0
>>> dit.shannon.mutual_information(d, ['X', 'Y'], ['Z'])
1.0

Calculate the marginal distribution P(X,Z). Then print its probabilities as fractions, showing the mask.

>>> d2 = d.marginal(['X', 'Z'])
>>> print d2.to_string(show_mask=True, exact=True)
Class:          Distribution
Alphabet:       ('0', '1') for all rvs
Base:           linear
Outcome Class:  str
Outcome Length: 2 (mask: 3)
RV Names:       ('X', 'Z')

x     p(x)
0*0   1/4
0*1   1/4
1*0   1/4
1*1   1/4

Convert the distribution probabilities to log (base 3.5) probabilities, and access its probability mass function.

>>> d2.set_base(3.5)
>>> d2.pmf
array([-1.10658951, -1.10658951, -1.10658951, -1.10658951])

Draw 5 random samples from this distribution.

>>> dit.math.prng.seed(1)
>>> d2.rand(5)
['01', '10', '00', '01', '00']

Enjoy!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

dit-1.0.0.dev6-py2.py3-none-any.whl (1.9 MB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page