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PyDTMC-7.0.0
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مشاهده بیشترتوضیحات
A framework for discrete-time Markov chains analysis.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | PyDTMC-7.0.0 |
| نام | PyDTMC |
| نسخه کتابخانه | 7.0.0 |
| نگهدارنده | ['Tommaso Belluzzo'] |
| ایمیل نگهدارنده | ['tommaso.belluzzo@gmail.com'] |
| نویسنده | Tommaso Belluzzo |
| ایمیل نویسنده | tommaso.belluzzo@gmail.com |
| آدرس صفحه اصلی | https://github.com/TommasoBelluzzo/PyDTMC |
| آدرس اینترنتی | https://pypi.org/project/PyDTMC/ |
| مجوز | MIT |
PyDTMC is a full-featured, lightweight library for discrete-time Markov chains analysis. It provides classes and functions for creating, manipulating, simulating and visualizing Markov processes.
<table>
<tr>
<td align="right">Status:</td>
<td align="left">
<a href="https://github.com/TommasoBelluzzo/PyDTMC/actions/workflows/continuous_integration.yml"><img alt="Build" src="https://img.shields.io/github/workflow/status/TommasoBelluzzo/PyDTMC/Continuous%20Integration?style=flat&label=Build&color=1081C2"/></a>
<a href="https://pydtmc.readthedocs.io/"><img alt="Docs" src="https://img.shields.io/readthedocs/pydtmc?style=flat&label=Docs&color=1081C2"/></a>
<a href="https://coveralls.io/github/TommasoBelluzzo/PyDTMC?branch=master"><img alt="Coverage" src="https://img.shields.io/coveralls/github/TommasoBelluzzo/PyDTMC?style=flat&label=Coverage&color=1081C2"/></a>
</td>
</tr>
<tr>
<td align="right">Info:</td>
<td align="left">
<a href="#"><img alt="License" src="https://img.shields.io/github/license/TommasoBelluzzo/PyDTMC?style=flat&label=License&color=1081C2"/></a>
<a href="#"><img alt="Lines" src="https://img.shields.io/tokei/lines/github/TommasoBelluzzo/PyDTMC?style=flat&label=Lines&color=1081C2"/></a>
<a href="#"><img alt="Size" src="https://img.shields.io/github/repo-size/TommasoBelluzzo/PyDTMC?style=flat&label=Size&color=1081C2"/></a>
</td>
</tr>
<tr>
<td align="right">PyPI:</td>
<td align="left">
<a href="https://pypi.org/project/PyDTMC/"><img alt="Version" src="https://img.shields.io/pypi/v/PyDTMC?style=flat&label=Version&color=1081C2"/></a>
<a href="https://pypi.org/project/PyDTMC/"><img alt="Python" src="https://img.shields.io/pypi/pyversions/PyDTMC?style=flat&label=Python&color=1081C2"/></a>
<a href="https://pypi.org/project/PyDTMC/"><img alt="Wheel" src="https://img.shields.io/pypi/wheel/PyDTMC?style=flat&label=Wheel&color=1081C2"/></a>
<a href="https://pypi.org/project/PyDTMC/"><img alt="Downloads" src="https://img.shields.io/pypi/dm/PyDTMC?style=flat&label=Downloads&color=1081C2"/></a>
</td>
</tr>
<tr>
<td align="right">Conda:</td>
<td align="left">
<a href="https://anaconda.org/conda-forge/pydtmc/"><img alt="Version" src="https://img.shields.io/conda/vn/conda-forge/pydtmc?style=flat&label=Version"/></a>
<a href="https://anaconda.org/conda-forge/pydtmc/"><img alt="Python" src="https://img.shields.io/pypi/pyversions/PyDTMC?style=flat&label=Python&color=1081C2"/></a>
<a href="https://anaconda.org/conda-forge/pydtmc/"><img alt="Platforms" src="https://img.shields.io/conda/pn/conda-forge/pydtmc?style=flat&label=Platforms&color=1081C2"/></a>
<a href="https://anaconda.org/conda-forge/pydtmc/"><img alt="Downloads" src="https://img.shields.io/conda/dn/conda-forge/pydtmc?style=flat&label=Downloads&color=1081C2"/></a>
</td>
</tr>
<tr>
<td align="right">Donation:</td>
<td align="left">
<a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=D8LH6DNYN7EN8"><img alt="PayPal" src="https://www.paypalobjects.com/en_US/i/btn/btn_donate_LG.gif"/></a>
</td>
</tr>
</table>
## Requirements
The `Python` environment must include the following packages:
* [Matplotlib](https://matplotlib.org/)
* [NetworkX](https://networkx.github.io/)
* [NumPy](https://www.numpy.org/)
* [SciPy](https://www.scipy.org/)
*Notes:*
* It's recommended to install [Graphviz](https://www.graphviz.org/) and [pydot](https://pypi.org/project/pydot/) before using the `plot_graph` function.
* The packages [pytest](https://pytest.org/) and [pytest-benchmark](https://pypi.org/project/pytest-benchmark/) are required for performing unit tests.
* The package [Sphinx](https://www.sphinx-doc.org/) is required for building the package documentation.
## Installation & Upgrade
[PyPI](https://pypi.org/):
```sh
$ pip install PyDTMC
$ pip install --upgrade PyDTMC
```
[Git](https://git-scm.com/):
```sh
$ pip install https://github.com/TommasoBelluzzo/PyDTMC/tarball/master
$ pip install --upgrade https://github.com/TommasoBelluzzo/PyDTMC/tarball/master
$ pip install git+https://github.com/TommasoBelluzzo/PyDTMC.git#egg=PyDTMC
$ pip install --upgrade git+https://github.com/TommasoBelluzzo/PyDTMC.git#egg=PyDTMC
```
[Conda](https://docs.conda.io/):
```sh
$ conda install -c conda-forge pydtmc
$ conda update -c conda-forge pydtmc
$ conda install -c tommasobelluzzo pydtmc
$ conda update -c tommasobelluzzo pydtmc
```
## Usage
The core element of the library is the `MarkovChain` class, which can be instantiated as follows:
```console
>>> 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'])
>>> print(mc)
DISCRETE-TIME MARKOV CHAIN
SIZE: 4
RANK: 4
CLASSES: 2
> RECURRENT: 1
> TRANSIENT: 1
ERGODIC: NO
> APERIODIC: YES
> IRREDUCIBLE: NO
ABSORBING: YES
REGULAR: NO
REVERSIBLE: NO
```
Below a few examples of `MarkovChain` properties:
```console
>>> print(mc.is_ergodic)
False
>>> print(mc.recurrent_states)
['C']
>>> print(mc.transient_states)
['A', 'B', 'D']
>>> print(mc.steady_states)
[array([0.0, 0.0, 1.0, 0.0])]
>>> print(mc.is_absorbing)
True
>>> print(mc.fundamental_matrix)
[[1.50943396, 2.64150943, 0.41509434]
[0.18867925, 2.83018868, 0.30188679]
[0.75471698, 1.32075472, 1.20754717]]
>>> print(mc.kemeny_constant)
5.547169811320755
>>> print(mc.entropy_rate)
0.0
```
Below a few examples of `MarkovChain` methods:
```console
>>> print(mc.absorption_probabilities())
[1.0 1.0 1.0]
>>> 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, 0.0000, 0.0425]
[0.000, 0.3450, 0.1725, 0.0575]
[0.000, 0.0000, 0.7000, 0.0000]
[0.150, 0.0000, 0.1500, 0.0000]]
>>> print(mc.first_passage_probabilities(5, 3))
[[0.5000, 0.0000, 0.5000, 0.0000]
[0.0000, 0.3500, 0.0000, 0.0500]
[0.0000, 0.0700, 0.1300, 0.0450]
[0.0000, 0.0315, 0.1065, 0.0300]
[0.0000, 0.0098, 0.0761, 0.0186]]
>>> print(mc.hitting_probabilities([0, 1]))
[1.0, 1.0, 0.0, 0.5]
>>> print(mc.mean_absorption_times())
[4.56603774, 3.32075472, 3.28301887]
>>> print(mc.mean_number_visits())
[[0.50943396, 2.64150943, inf, 0.41509434]
[0.18867925, 1.83018868, inf, 0.30188679]
[0.00000000, 0.00000000, inf, 0.00000000]
[0.75471698, 1.32075472, inf, 0.20754717]]
>>> print(mc.walk(10, seed=32))
['D', 'A', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'C']
```
```console
>>> walk = ["A"]
>>> for i in range(1, 11):
... current_state = walk[-1]
... next_state = mc.next_state(current_state, seed=32)
... print(f'{i:02} {current_state} -> {next_state}')
... walk.append(next_state)
1) A -> B
2) B -> C
3) C -> C
4) C -> C
5) C -> C
6) C -> C
7) C -> C
8) C -> C
9) C -> C
10) C -> C
```
Plotting functions can provide a visual representation of `MarkovChain` instances; in order to display the output of plots immediately, the [interactive mode](https://matplotlib.org/stable/users/interactive.html#interactive-mode) of [Matplotlib](https://matplotlib.org/) must be turned on:
```console
>>> plot_eigenvalues(mc)
>>> plot_graph(mc)
>>> plot_walk(mc, 10, plot_type='histogram', dpi=300)
>>> plot_walk(mc, 10, plot_type='sequence', dpi=300)
>>> plot_walk(mc, 10, plot_type='transitions', dpi=300)
>>> plot_redistributions(mc, 10, plot_type='heatmap', dpi=300)
>>> plot_redistributions(mc, 10, plot_type='projection', dpi=300)
```
نیازمندی
| مقدار | نام |
|---|---|
| - | matplotlib |
| - | networkx |
| - | numpy |
| - | scipy |
| - | setuptools |
| - | wheel |
| - | twine |
| - | docutils |
| - | typing-extensions |
| - | sphinx |
| - | sphinx-autodoc-typehints |
| - | sphinx-rtd-theme |
| - | flake8 |
| - | pylint |
| - | defusedxml |
| - | numpydoc |
| - | pandas |
| - | pydot |
| - | coverage |
| - | pytest |
| - | pytest-benchmark |
| - | pytest-cov |
| - | coveralls |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl PyDTMC-7.0.0:
pip install PyDTMC-7.0.0.whl
نصب پکیج tar.gz PyDTMC-7.0.0:
pip install PyDTMC-7.0.0.tar.gz