Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Bindings for EM machines and trainers of Bob

Project Description

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.

Release History

Release History

This version
History Node

2.1.0

History Node

2.0.13

History Node

2.0.12

History Node

2.0.11

History Node

2.0.10

History Node

2.0.9

History Node

2.0.8

History Node

2.0.7

History Node

2.0.6

History Node

2.0.5

History Node

2.0.4

History Node

2.0.3

History Node

2.0.2

History Node

2.0.1

History Node

2.0.0

History Node

2.0.0b5

History Node

2.0.0b4

History Node

2.0.0b3

History Node

2.0.0b2

Download Files

Download Files

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

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
bob.learn.em-2.1.0.zip (2.1 MB) Copy SHA256 Checksum SHA256 Source Sep 15, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting