dit is a Python package for information theory.
- Documentation:
Coming soon.
- Downloads:
Coming soon.
- Dependencies:
- Python 2.6, 2.7, 3.2, or 3.3
- numpy
- iterutils
- six
- Optional Dependencies:
- cython
- Install:
Until
ditis available on PyPI, the easiest way to install is:pip install git+https://github.com/dit/dit/#egg=ditAlternatively, you can clone this repository, move into the newly created
ditdirectory, and then install the package:git clone https://github.com/dit/dit.git cd dit pip install .- Mailing list:
None
- Code and bug tracker:
- License:
BSD 2-Clause, see LICENSE.txt for details.
Basic usage is as follows:
>>> import ditCreate a biased coin and print it. :
>>> d = dit.Distribution(['H', 'T'], [.4, .6])
>>> print d
Class: Distribution
Alphabet: ('H', 'T') for all rvs
Base: linear
Outcome Class: str
Outcome Length: 1
RV Names: None
x p(x)
H 0.4
T 0.6Calculate the probability of 'H' and also of 'H' or 'T'. :
>>> d['H']
0.4
>>> d.event_probability(['H','T'])
1.0Create a distribution representing the XOR logic function. Here, we have two inputs, X and Y, and then an output 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 0.25Calculate the Shannon entropy and extropy of the joint distribution. :
>>> dit.algorithms.entropy(d)
0.97095059445466858
>>> dit.algorithms.extropy(d)
1.2451124978365313Calculate the Shannon mutual informations I[X:Z], I[Y:Z], I[X,Y:Z]. :
>>> dit.algorithms.mutual_information(d, ['X'], ['Z'])
0.0
>>> dit.algorithms.mutual_information(d, ['Y'], ['Z'])
0.0
>>> dit.algorithms.mutual_information(d, ['X', 'Y'], ['Z'])
1.0Calculate 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 1/4Convert the distribution probabilities to log (base 3.5) probabilities, and access its pmf. :
>>> d2.set_base(3.5)
>>> d2.pmf
array([-1.10658951, -1.10658951, -1.10658951, -1.10658951])Draw 5 random samples from this distribution. :
>>> d2.rand(5)
['10', '11', '00', '01', '10']