https://github.com/sandialabs/chmmpp
Analysis of multivariate time series data to detect patterns using a Hidden Markov Model (HMM).
https://github.com/sandialabs/chmmpp
scr-3025 snl-data-analysis snl-science-libs
Last synced: 3 months ago
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Analysis of multivariate time series data to detect patterns using a Hidden Markov Model (HMM).
- Host: GitHub
- URL: https://github.com/sandialabs/chmmpp
- Owner: sandialabs
- Created: 2024-06-05T16:28:11.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-07-22T22:29:38.000Z (10 months ago)
- Last Synced: 2025-01-11T15:24:26.217Z (4 months ago)
- Topics: scr-3025, snl-data-analysis, snl-science-libs
- Language: C++
- Homepage:
- Size: 438 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# chmmpp
The chmmpp software supports the analysis of multivariate time series
data to detect patterns using a Hidden Markov Model (HMM).
Many applications involve the detection and characterization of hidden or
latent states in a complex system, using observable states and variables.
The chmmpp software supports inference of latent states integrating
both (1) a HMM and (2) application-specific constraints that reflect
known relationships amongst hidden states. For example, HMMs have been
widely used in natural language processing to tag the part of speech
of words in a sentence (e.g. noun, verb, adjective, etc.). But in many
applications there are known relationships that need to be enforced,
such as the fact that a simple English sentence must contain at least
one noun and exactly one verb.
The chmmpp software supports both application-specific and generic methods
for constrained inference. This includes a framework for customized
Viterbi methods, constrained inference of hidden states with A-star and
integer programming methods, and various contraint-informed methods for
learning HMM model parameters. A focus of chmmpp is support for generic
methods that enable the agile expression of complex sets of constraints
that naturally arise in many real-world applications. Optimization
constraints can be expressed in chmmpp directly in C++ or using the
coek modeling framework. A variety of commercial and open source source
optimization solvers can be used to ensure that maximum likelihood
solutions are found for hidden states.
## Setup
To install the library create a directory called build, navigate to this directory, and run cmake ..
A libary will be created in build/library and executables of the examples will be found in build/examples