Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Bindings for EM machines and trainers of Bob

Project description

http://img.shields.io/badge/docs-v2.1.4-yellow.svg http://img.shields.io/badge/docs-latest-orange.svg https://gitlab.idiap.ch/bob/bob.learn.em/badges/v2.1.4/build.svg https://gitlab.idiap.ch/bob/bob.learn.em/badges/v2.1.4/coverage.svg https://img.shields.io/badge/gitlab-project-0000c0.svg http://img.shields.io/pypi/v/bob.learn.em.svg

Expectation Maximization Machine Learning Tools

This package is part of the signal-processing and machine learning toolbox Bob. It contains routines for learning probabilistic models via Expectation Maximization (EM).

The EM algorithm is an iterative method that estimates parameters for statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

The package includes the machine definition per se and a selection of different trainers for specialized purposes:

  • Maximum Likelihood (ML)
  • Maximum a Posteriori (MAP)
  • K-Means
  • Inter Session Variability Modelling (ISV)
  • Joint Factor Analysis (JFA)
  • Total Variability Modeling (iVectors)
  • Probabilistic Linear Discriminant Analysis (PLDA)
  • EM Principal Component Analysis (EM-PCA)

Installation

Complete Bob’s installation instructions. Then, to install this package, run:

$ conda install bob.learn.em

Contact

For questions or reporting issues to this software package, contact our development mailing list.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for bob.learn.em, version 2.1.4
Filename, size File type Python version Upload date Hashes
Filename, size bob.learn.em-2.1.4.zip (2.1 MB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page