Metadata-Version: 2.1
Name: pohmm
Version: 0.5
Summary: Partially observable hidden Markov model
Home-page: https://github.com/vmonaco/pohmm
Author: Vinnie Monaco
Author-email: contact@vmonaco.com
License: MIT
Description: *pohmm* is an implementation of the partially observable hidden Markov model, a generalization of the hidden Markov model in which the underlying system state is partially observable through event metadata at each time step.
        
        An application that motivates usage of such a model is keystroke biometrics where the user can be in either a passive or active hidden state at each time step, and the time between key presses depends on the hidden state. In addition, the hidden state depends on the key that was pressed; thus the keys are observed symbols that partially reveal the hidden state of the user.
        
        For examples and documentation, see https://github.com/vmonaco/pohmm
        
Keywords: hidden Markov model data analysis
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Provides-Extra: testing
Provides-Extra: docs
