sequence-modelling 0.1.6

sequence modelling using HMMs

sequence-modelling

Numerically optimized time-series and sequence modelling in Python.

Key features

• Hidden Markov Models and Quasi-Deterministic Hidden Markov Models
• Numerically stable: floating point arithmetic performed in log space to avoid underflow
• Easy to use (based on the scikit-learn API)
• Pure Python and Numpy based
• Open source and commercially usable (BSD license)
• Support for discrete and continuous (Gaussian) emissions

Installation

The easiest way to install sequence-modelling is using pip:

   pip install sequence-modelling

Example usage

   import numpy as np
   from sequence_modelling.emmissions import Gaussian
   from sequence_modelling.hmm import StandardHMM
   import sequence_modelling.hmmviz as plt

   # define a 2-state HMM estimator with one-dimensional Gaussian emissions

   # the transition matrix
   A = np.array([[0.6, 0.4],
                 [0.3, 0.7],
                 [0.5, 0.5]])

   # the emission object
   O = Gaussian(mu=np.array([[-100.0, 100.0]]),
             covar=np.array([[[10.0]], [[10.0]]]))

   # build the HMM model object
   hmm = StandardHMM(A, O)

   # sample 100 observations from the generative model
   obs, path = hmm.sample(dim=1, N=100)

    # fit the model to the data
   likelihood, ll, duration, rankn, res = hmm.fit([obs])

   # decode (predict) the most likely state sequence using the Viterbi algorithm
   decoded_path = hmm.viterbi(obs)

   # visualize the decoded state sequence
   from matplotlib.pyplot import figure, show
   fa = figure()
   plt.view_viterbi(fa.add_subplot(1, 1, 1), [obs], [decoded_path], hmm.O.mu, seq=0)
   fa.tight_layout()
   show()