| Name | markovm JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | Python library for Markov Models. |
| upload_time | 2023-06-10 22:39:31 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
markov model
markov chain
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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No coveralls.
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<p align="center">
<a href="https://github.com/HH-MWB/timerun">
<img src="https://user-images.githubusercontent.com/50187675/244896432-f28357ec-2ced-4095-aef1-fca6a73dd983.png" alt="MarkovM">
</a>
</p>
<p align="center"><strong>MarkovM</strong> - <em>Python library for Markov Models.</em></p>
<p align="center">
<a href="https://github.com/HH-MWB/markovm/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/pypi/l/markovm.svg"></a>
<a href="https://pypi.org/project/markovm/"><img alt="PyPI Latest Release" src="https://img.shields.io/pypi/v/markovm.svg"></a>
<a href="https://pypi.org/project/markovm/"><img alt="Package Status" src="https://img.shields.io/pypi/status/markovm.svg"></a>
<a href="https://github.com/psf/black/"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
<a href="https://pycqa.github.io/isort/"><img alt="Imports: isort" src="https://img.shields.io/badge/%20imports-isort-%231674b1"></a>
</p>
## Installation
Install from [Python Package Index](https://pypi.org/project/markovm/):
```sh
pip install markovm
```
Install from [Source Code](https://github.com/HH-MWB/markovm):
```sh
pip install .
```
## Quickstart
### Create Model
You can use `markovm.create_markov_model` to create a Markov model. Please provide all valid states and an n-by-n matrix describing the probabilities of transitions, where n is the number of states. If two states `i` and `j` do not have a connection in between, set `matrix[i][j]` to `0`. For example,
```python
>>> import markovm
>>> import numpy
>>> m = markovm.create_markov_model(
... states=("A", "B", "C"),
... transitions=numpy.array([
... [0.0, 1.0, 0.0], # A must goto B
... [0.2, 0.0, 0.8], # B can goto A (20%) or C (80%)
... [0.0, 0.5, 0.5], # C can goto B or stay
... ]),
... )
```
### Random Walk
You can use `markovm.random_walk` to randomly walk through a Markov model. By default, it will start with the first state. If you want it to start with another state, please provide the index of the expected starting state to `index` in the function call. You can also set a seed to the function call, and it uses `None` by default. For example,
```python
>>> import itertools
>>> for state in itertools.islice(
... markovm.random_walk(m, seed=0), 5
... ):
... print(state)
...
A
B
C
B
A
```
Raw data
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