HMM classifier

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states.

HMM-classifier is hmmlearn mini-wrapper for classification problems. It build separate HMMs for every label.

Requirements

scipy
hmmlearn
numpy

Installation

pip install hmm-classifier

Based on hmmlearn project

https://hmmlearn.readthedocs.io/en/latest/

Supported HMMs:

• MultinomialHMM - Hidden Markov Model with multinomial (discrete) emissions
• GaussianHMM - Hidden Markov Model with Gaussian (continues) emissions.

Example

from hmmlearn import hmm
import numpy as np
from hmm_classifier import HMM_classifier

x = np.random.randint(0, 10, size=(300, 10, 2))
y = np.random.randint(0, 10, size=(300))

model = HMM_classifier(hmm.MultinomialHMM())
model.fit(x,y)

# Predict probability per label
pred = model.predict_proba(np.random.randint(0, 10, size=(10, 2)))

# Get label with the most high probability
pred = model.predict(np.random.randint(0, 10, size=(100, 2)))