PyDTMC 1.4.0

A framework for discrete-time Markov chains analysis.

PyDTMC is a full-featured, lightweight library for discrete-time Markov chains analysis. It provides classes and functions for creating, manipulating and simulating markovian stochastic processes.

Requirements

PyDTMC supports only Python 3 and the minimum version required is 3.6. In addition, the environment must include the following libraries:

• Matplotlib
• NetworkX
• Numpy

In order to use the plot_graph function, Graphviz and PyDot must be installed too.

Installation & Upgrade

Via PyPI:

$ pip install PyDTMC
$ pip install --upgrade PyDTMC

Via GitHub:

$ pip install git+https://github.com/TommasoBelluzzo/PyDTMC.git@master#egg=PyDTMC
$ pip install --upgrade git+https://github.com/TommasoBelluzzo/PyDTMC.git@master#egg=PyDTMC

Usage

The core element of the library is the MarkovChain class, which can be instantiated as follows:

>>> import numpy as np
>>> p = np.array([[0.2, 0.7, 0.0, 0.1], [0.0, 0.6, 0.3, 0.1], [0.0, 0.0, 1.0, 0.0], [0.5, 0.0, 0.5, 0.0]])
>>> mc = MarkovChain(p, ['A', 'B', 'C', 'D'])
>>> print(mc)

DISCRETE-TIME MARKOV CHAIN

 - TRANSITION MATRIX:

            A       B       C       D
      ------- ------- ------- -------
  A | 0.20000 0.70000 0.00000 0.10000
  B | 0.00000 0.60000 0.30000 0.10000
  C | 0.00000 0.00000 1.00000 0.00000
  D | 0.50000 0.00000 0.50000 0.00000

 - PROPERTIES:

  ABSORBING:   YES
  APERIODIC:   YES
  IRREDUCIBLE: NO
  ERGODIC:     NO

 - COMMUNICATING CLASSES:

          [A,B,D] | [C]
  TYPE:         T |   R
  PERIOD:       1 |   1

Static values of a MarkovChain instance can be retrieved through its properties:

>>> print(mc.size)
4

>>> print(mc.recurrent_states)
['C']

>>> print(mc.transient_states)
['A', 'B', 'D']

>>> print(mc.steady_states)
[array([0., 0., 1., 0.])]

>>>print(mc.fundamental_matrix)
[[1.50943396 2.64150943 0.41509434]
 [0.18867925 2.83018868 0.30188679]
 [0.75471698 1.32075472 1.20754717]]

>>> print(mc.absorption_times)
[4.56603774 3.32075472 3.28301887]

>>> print(mc.topological_entropy)
0.6931471805599457

Dynamic computations on a MarkovChain instance can be performed through its methods:

>>> print(mc.expected_rewards(10, [2, -3, 8, -7]))
[-2.76071635 -12.01665113  23.23460025  -8.45723276]

>>> print(mc.expected_transitions(2))
[[0.085  0.2975 0.     0.0425]
 [0.     0.345  0.1725 0.0575]
 [0.     0.     0.7    0.    ]
 [0.15   0.     0.15   0.    ]]
 
>>> print(mc.walk(10))
['D', 'A', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'C']

Plotting functions can provide a visual representation of a MarkovChain instance and its attributes. In order to immediately display function outputs, the interactive mode of Matplotlib must be turned on.

>>> plot_eigenvalues(mc)

>>> plot_graph(mc)

>>> plot_walk(mc, 10, 'sequence')