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https://github.com/lindermanlab/ssm
Bayesian learning and inference for state space models
https://github.com/lindermanlab/ssm
Last synced: 3 months ago
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Bayesian learning and inference for state space models
- Host: GitHub
- URL: https://github.com/lindermanlab/ssm
- Owner: lindermanlab
- License: mit
- Created: 2018-06-07T15:03:39.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-08-14T21:36:30.000Z (6 months ago)
- Last Synced: 2024-09-23T13:39:39.747Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 52.6 MB
- Stars: 558
- Watchers: 38
- Forks: 198
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- Awesome-state-space-models - GitHub
README
# SSM: Bayesian learning and inference for state space models
[](https://travis-ci.org/lindermanlab/ssm)
**Note: We're working full time on a [JAX](https://github.com/google/jax) refactor of SSM that will take advantage of JIT compilation, GPU and TPU support, automatic differentation, etc. You can keep track of our progress [here](https://github.com/probml/ssm-jax/). We're hoping to make an official release soon!**
This package has fast and flexible code for simulating, learning, and performing inference in a variety of state space models.
Currently, it supports:
- Hidden Markov Models (HMM)
- Auto-regressive HMMs (ARHMM)
- Input-output HMMs (IOHMM)
- Hidden Semi-Markov Models (HSMM)
- Linear Dynamical Systems (LDS)
- Switching Linear Dynamical Systems (SLDS)
- Recurrent SLDS (rSLDS)
- Hierarchical extensions of the above
- Partial observations and missing data
We support the following observation models:
- Gaussian
- Student's t
- Bernoulli
- Poisson
- Categorical
- Von Mises
HMM inference is done with either expectation maximization (EM) or stochastic gradient descent (SGD). For SLDS, we use stochastic variational inference (SVI).
# Examples
Here's a snippet to illustrate how we simulate from an HMM.
```
import ssm
T = 100 # number of time bins
K = 5 # number of discrete states
D = 2 # dimension of the observations
# make an hmm and sample from it
hmm = ssm.HMM(K, D, observations="gaussian")
z, y = hmm.sample(T)
```
Fitting an HMM is simple.
```
test_hmm = ssm.HMM(K, D, observations="gaussian")
test_hmm.fit(y)
zhat = test_hmm.most_likely_states(y)
```
The notebooks folder has more thorough, complete examples of HMMs, SLDS, and recurrent SLDS.
# Installation
```
git clone https://github.com/lindermanlab/ssm
cd ssm
pip install numpy cython
pip install -e .
```
This will install "from source" and compile the Cython code for fast message passing and gradients.
To install with some parallel support via OpenMP, first make sure that your compiler supports it. OS X's default Clang compiler does not, but you can install GNU gcc and g++ with conda. Once you've set these as your default, you can install with OpenMP support using
```
USE_OPENMP=True pip install -e .
```