Metadata-Version: 2.1
Name: minihmm
Version: 0.3.3
Summary: Lightweight extensible HMM engine, supporting univariate or multivariate, continuous or discrete emissions
Home-page: http://github.com/joshuagryphon/minihmm
Author: Joshua Griffin Dunn
Maintainer: Joshua Griffin Dunn
License: BSD 3-Clause
Description: miniHMM
        =======
        
        Summary
        -------
        
        This is a toy library that implements first- through Nth-order hidden Markov
        models. 
        
        At present, `miniHMM` offers some benefits hard to find in other HMM libraries:
        
        - Its algorithms are numerically stable
        
        - It is able to compute high order hidden Markov models, which allow states
          to depend on the Nth previous states, rather than only on the immediate
          previous state. 
          
          Concretely, high-order models are implemented via a translation layer
          that converts high-order models of arbitrary degree into mathematically
          equivalent first-order models over a virtual state space. This implementation
          allows all algorithms developed for first-order models to be applied in 
          higher dimensions. See `minihmm.represent` for further detail.
        
        - Emissions may be univariate or multivariate (for multidimensional emissions),
          continuous or discrete. See `minihmm.factors` for examples of
          distributions that can be built out-of-the-box, and for hints on designing new
          ones,
          
        - Multiple distinct estimators are available for probability distributions,
          enabling e.g. addition of model noise, pseudocounts, et c during model
          training. See `minihmm.estimators` for details.
           
        - HMMs of all sorts can be trained via a Baum-Welch implementation with some
          bells & whistles (e.g.  noise scheduling, parallelization, parameter-tying
          (via estimator classes), et c)
        
        - In addition to the Viterbi algorithm (the maximum likelihood solution for a
          total sequence of states), states may be inferred by:
           
          - Probabilistically sampling valid sequences from their posterior
            distribution, given a sequence of emissions. This enables estimates of
            robustness and non-deterministic samples to be drawn
        
          - Labeling individual states by highest posterior probabilities (even
            though this doesn't guarantee a valid path)
        
        
        Running the tests
        -----------------
        
        Tests are currently written to run under `nose` separately under Python 3.6
        and 3.9, with the following virtual environments configured via `tox`:
        
        - `*-pinned` : run using versions of dependencies pinned in ``requirements.txt``
        
        - `*-latest` : run all tests using latest available versions of each dependency.
          This will enable us to catch breaking changes.
        
        By default, running ``tox`` from the shell will run all tests in all
        environments. To choose which environment(s) or test(s) to run, you can use
        standard `tox` or `nose` arguments (see their respective documentation
        for more details)::
        
            # run tests only under Python 3.6, with pinned requirements
            $ tox -e py36-pinned 
        
            # run tests under all environments, but only for estimator suite
            $ tox minihmm.test.test_estimators
        
            # run tests only for estimator suite, passing verbose mode to nose
            # note: nose args go after the double dash ('--')
            $ tox minihmm.test.test_estimators -- -v --nocapture
        
        
        As these environments assume you have Python 3.6, and 3.9 installed, we have
        defined a Dockerfile that contains all of them. This is the preferred
        environment for testing. Build the image with the following syntax::
        
            # build image from inside miniHMM folder
            $ docker build --pull -t minihmm .
        
            # start a container, mounting current folder as minihmm source
            $ docker run -it --rm minihmm
        
            # alternative if you are developing- mount your dev folder within
            # the container, then run tox inside the container
            $ docker run -it --rm $(pwd):/usr/src/minihmm minihmm
        
        
        Building the documentation
        --------------------------
        
        Documents may be built via Sphinx, either inside or outside the container.
        To build the docs, you must first install the package, as well as documentation
        dependencies. In the project folder::
        
            # install package
            $ pip install --user -e .
        
            # install doc dependencies
            $ pip install -r docs/requirements.txt
        
            # build docs & open in browser
            $ make -C docs html
            $ firefox docs/build/html/index.html
        
        
        Notes
        -----
        
        This library is in beta, and breaking changes are not uncommon. We try to be
        polite by announcing these in the changelog.
        
Keywords: HMM hidden Markov model machine learning modeling statistics
Platform: POSIX
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX
Description-Content-Type: text/x-rst
