Hidden Markov Models (HMMs) are powerful, flexible methods for representing and classifying data with trends over time, and have been a key component in speech recognition systems, bioinformatics and more.
In this blog post, we will briefly understand the theory behind Hidden Markov Models and implement it in Python.
A Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable ("hidden") states.
An HMM allows us to talk about both observed events (like words that we see in the input) and hidden events (like parts of speech), and model how they are related to each other.
Let's start with an example implementation of Hidden Markov Models in Python. We will use the hmmlearn library which is easy to use and very efficient.
For this example, we will generate a synthetic dataset, as real-world datasets for use with HMMs are often proprietary and difficult to find.
In Python you can install libraries using pip:
!pip install hmmlearn
Next, let's import the necessary libraries:
from hmmlearn import hmm import numpy as np
Now, let's create a Gaussian HMM. Note that we set two states in the HMM.
np.random.seed(42) model = hmm.GaussianHMM(n_components=2, covariance_type="full")
Next, we will fit the generated data(as this is a synthetic dataset, we will use random integers) to our HMM:
X = np.random.randint(1, 50, size = (100, 1)) model.fit(X)
We can now predict the hidden states:
hidden_states = model.predict(X) print(hidden_states)
And voila! you have successfully implemented a Hidden Markov Model in Python!
Hidden Markov Models, while being a bit more involved than other predictive models, offer a lot of power and flexibility for dealing with time-series data. They have a rich theory with many off-the-shelf tools and libraries supporting them, making an HMM a great addition to your data science toolbox.