mchmm | Markov Chains and Hidden Markov Models in Python | Bot library

by maximtrp Python Version: 0.4.3 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | mchmm Summary

mchmm is a Python library typically used in Automation, Bot applications. mchmm has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can install using 'pip install mchmm' or download it from GitHub, PyPI.

Markov Chains and Hidden Markov Models in Python

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Quality

Security

License

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Support

mchmm has a low active ecosystem.

It has 51 star(s) with 11 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 4 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mchmm is 0.4.3

Quality

mchmm has 0 bugs and 0 code smells.

Security

mchmm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

mchmm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

mchmm is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

mchmm releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

mchmm saves you 148 person hours of effort in developing the same functionality from scratch.

It has 462 lines of code, 28 functions and 7 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed mchmm and discovered the below as its top functions. This is intended to give you an instant insight into mchmm implemented functionality, and help decide if they suit your requirements.

Generate a Gaussian from an observation sequence .
Simulate the model .
Compute the viterbi transition matrix .
Create the graph .
Initialize the state transition matrix .
Compute the transition matrix .
Initialize the model .
Calculate the chisquare .
Convert probability matrix to frequency matrix .
Calculate the n - order n - order matrix .

Get all kandi verified functions for this library.

mchmm Key Features

No Key Features are available at this moment for mchmm.

mchmm Examples and Code Snippets

No Code Snippets are available at this moment for mchmm.

Community Discussions

Trending Discussions on mchmm

generating a Markov chain simulation using a transition matrix of specific size and with a given seed, using the mchmm library

QUESTION

generating a Markov chain simulation using a transition matrix of specific size and with a given seed, using the mchmm library

Asked 2022-Feb-23 at 20:48

I am trying to generate a Markov simulation using a specific sequence as start, using the mchmm library coded with scipy and numpy. I am not sure if I am using it correctly, since the library also has Viterbi and Baum-Welch algorithms in the context of Markov, which I am not familiar with.

To illustrate, I will continue with an example.

...

ANSWER

Answered 2022-Feb-23 at 20:48

The states in the MarkovChain instance a are 'A', 'B' and 'C'. When the simulate method is given a string for state, it expects it to be the name of one of the states, i.e. either 'A', 'B' or 'C'. You get that error because data[-3:] is not one of the states.

For example, in the following I use start='A' in the call of simulate(), and it generates a sequence of 10 states, starting at 'A':

Source https://stackoverflow.com/questions/71239580

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install mchmm

You can install using 'pip install mchmm' or download it from GitHub, PyPI.
You can use mchmm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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