PyDTMC is a lightweight Python library focused on discrete-time Markov chains and hidden Markov models. It provides a comprehensive set of classes and functions for creating, manipulating, analyzing, simulating, and visualizing Markovian stochastic processes.
| Status: |
|
| Info: |
|
| PyPI: |
|
| Conda: |
|
| Donation: |
|
Installation
PyDTMC requires Python 3.12 or greater and depends on NumPy, SciPy, NetworkX, and Matplotlib.
For the PyPI installation:
pip install pydtmc
For the Conda installation:
conda install -c conda-forge pydtmc
For the latest development version:
pip install git+https://github.com/TommasoBelluzzo/PyDTMC.git
Note: Graphviz and pydot are recommended for graph visualization using the plot_graph function.
Quick Start
Import the classes from PyDTMC and create instances of a discrete-time Markov chain and a hidden Markov model:
from pydtmc import MarkovChain, HiddenMarkovModel
# Markov Chain
p = [
[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'])
# Hidden Markov Model
p = [
[0.4, 0.6],
[0.8, 0.2]
]
e = [
[0.5, 0.0, 0.0, 0.5],
[0.2, 0.2, 0.2, 0.4]
]
hmm = HiddenMarkovModel(p, e, ['A', 'B'], ['H1', 'H2', 'H3', 'H4'])
Usage: MarkovChain Class
Using the mc instance created in the Quick Start section, the following examples showcase some of the class properties, methods, and visualization functions:
Properties
print(mc.is_absorbing)
print(mc.is_ergodic)
print(mc.is_irreducible)
print(mc.recurrent_states)
print(mc.transient_states)
print(mc.communicating_classes)
print(mc.period)
print(mc.pi)
print(mc.fundamental_matrix)
print(mc.entropy_rate)
Methods
print(mc.absorption_probabilities())
print(mc.expected_rewards(10, [2.0, -3.0, 8.0, -7.0]))
print(mc.expected_transitions(2))
print(mc.first_passage_probabilities(5, 3))
print(mc.hitting_probabilities([0, 1]))
print(mc.mean_number_visits())
print(mc.simulate(10, seed=32))
Visualization
from pydtmc import plot_eigenvalues, plot_graph, plot_redistributions, plot_sequence, plot_trellis
plot_eigenvalues(mc, dpi=300)
plot_graph(mc, dpi=300)
plot_sequence(mc, 10, plot_type='histogram', dpi=300)
plot_sequence(mc, 10, plot_type='heatmap', dpi=300)
plot_sequence(mc, 10, plot_type='matrix', dpi=300)
plot_redistributions(mc, 10, plot_type='heatmap', dpi=300)
plot_redistributions(mc, 10, plot_type='projection', dpi=300)
Usage: HiddenMarkovModel Class
Using the hmm instance created in the Quick Start section, the following examples showcase some of the class properties, methods, and visualization functions:
Properties
print(hmm.is_ergodic)
print(hmm.is_regular)
print(hmm.pi)
Methods
print(hmm.emission_probability('H1', 'A'))
print(hmm.transition_probability('A', 'B'))
print(hmm.decode(['H1', 'H2', 'H3']))
print(hmm.predict('viterbi', ['H1', 'H2', 'H3']))
print(hmm.simulate(12, seed=1488))
Visualization
from pydtmc import plot_graph, plot_sequence, plot_trellis
plot_graph(hmm, dpi=300)
plot_sequence(hmm, 10, plot_type='histogram', dpi=300)
plot_sequence(hmm, 10, plot_type='heatmap', dpi=300)
plot_sequence(hmm, 10, plot_type='matrix', dpi=300)
plot_trellis(hmm, 10, dpi=300)
References
For additional functionality, mathematical definitions, and usage examples, refer to the documentation.
If you use PyDTMC in your work, please cite the software using its Zenodo DOI.
1 maintainer
Metadata
1 maintainer
Release files for PyDTMC 10.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pydtmc-10.0.0.tar.gz | 173.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pydtmc-10.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 269.8 kB
Release files / pydtmc-10.0.0.tar.gz
| Download URL | pydtmc-10.0.0.tar.gz |
|---|---|
| Size | 173.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
7ab673d6e82f59b17d4588e2683ea672fb2a3f603eb798d3f8b05ecb681f80bb
|
|
BLAKE2b-256 checksum How to use checksums |
bd531108a85e58d7b07b95f129166137e181b8dbe296fe1f03aa5d52add7a671
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
Release files / pydtmc-10.0.0-py3-none-any.whl
| Download URL | pydtmc-10.0.0-py3-none-any.whl |
|---|---|
| Size | 96.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5e7799a45d924a38945c80e9f7193a9bf234d60960d93089a0d700a018e1ff99
|
|
BLAKE2b-256 checksum How to use checksums |
d106d1fffe72d9c340abb6fd1ddaf7346a11735a59f4235ebf6358fa2b7ba22f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.16
|
