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datamicroscopes: Bayesian nonparametric models in Python
Datamicroscopes is developed by Qadium, with funding from the DARPA XDATA program. Copyright Qadium 2015.
datamicroscopes
0.1
- GitHub
- Qadium
-
Site
- Discovering structure in your data: an overview of clustering
- Finding the number of clusters with the Dirichlet Process
- Network Modeling with the Infinite Relational Model
- Bayesian Nonparametric Topic Modeling with the Daily Kos
- Datatypes and Bayesian Nonparametric Models
- Binary Data with the Beta Bernouli Distribution
- Categorical Data and the Dirichlet Discrete Distribution
- Real Valued Data and the Normal Inverse-Wishart Distribution
- Univariate Data with the Normal Inverse Chi-Square Distribution
- Count Data and Ordinal Data with the Gamma-Poisson Distribution
- Inferring Gaussians with the Dirichlet Process Mixture Model
- Digit recognition with the MNIST dataset
- Clustering the Enron e-mail corpus using the Infinite Relational Model
- Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process
- Tutorials
- Datatypes and likelihood models in datamicroscopes
- Datatypes and Bayesian Nonparametric Models
- Binary Data with the Beta Bernouli Distribution
- Categorical Data and the Dirichlet Discrete Distribution
- Real Valued Data and the Normal Inverse-Wishart Distribution
- Univariate Data with the Normal Inverse Chi-Square Distribution
- Count Data and Ordinal Data with the Gamma-Poisson Distribution
- Examples
- API Reference
- Contents
datamicroscopes: Bayesian nonparametric models in Python¶
datamicroscopes is a library for discovering structure in your data. It implements several Bayesian nonparametric models for clustering such as the Dirichlet Process Mixture Model (DPMM) , the Infinite Relational Model (IRM) , and the Hierarchichal Dirichlet Process (HDP) . These models rely on the Dirichlet Process, which allow for the automatic learning of the number of clusters in a datset. Additionally, our API provides users with a flexible set of likelihood models for various types of data, such as binary, ordinal, categorical, and real-valued variables( datatypes) .
Please read our introduction for an overview of clustering and structure discovery.
Tutorials
Datatypes and Likelihood Models
- Datatypes and Bayesian Nonparametric Models
- Binary Data with the Beta Bernouli Distribution
- Categorical Data and the Dirichlet Discrete Distribution
- Real Valued Data and the Normal Inverse-Wishart Distribution
- Univariate Data with the Normal Inverse Chi-Square Distribution
- Count Data and Ordinal Data with the Gamma-Poisson Distribution