https://github.com/Arnie0426/FastLDA

https://github.com/Arnie0426/FastLDA

Last synced: 16 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/Arnie0426/FastLDA
• Owner: Arnie0426
• License: mit
• Created: 2018-02-10T20:19:26.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-04-23T04:50:40.000Z (over 5 years ago)
• Last Synced: 2024-08-01T00:38:29.679Z (3 months ago)
• Language: C++
• Size: 2.49 MB
• Stars: 17
• Watchers: 2
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-topic-models - FastLDA - C++ implementation of LDA [:page_facing_up:](https://dl.acm.org/doi/pdf/10.1145/1401890.1401960) (Models / Latent Dirichlet Allocation (LDA) [:page_facing_up:](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf))

README

        
# FastLDA -- A minimal and fast implementation of Latent Dirichlet Allocation

## Still a work in progress, but currently fully usable!

## Prerequisites

**On Unix (Linux, OS X)**

* A compiler with C++11 support

* CMake >= 2.8.12

**On Windows**

* Visual Studio 2015 (required for all Python versions, see notes below)

* CMake >= 3.1

## Installation

### Building from source

1. Fork/clone this repo.

2. In fastlda/ please run

```bash

git submodule init

git submodule update

pip install --upgrade .  # recommend doing this in a virtualenv

```

This should install all the dependencies needed.

3. Run

```bash

python src/python/lda_example.py

```

for an end-to-end LDA run (from training to inference using NIPS dataset from https://archive.ics.uci.edu/ml/datasets/bag+of+words).

### Alternatives

- [ ] Pypi version soon to come!

## Usage

A Python class has been exposed with all the relevant training/inference functions.

For an example, see: [this simple example](https://github.com/Arnie0426/FastLDA/blob/master/src/python/lda_example.py#L26-L51)

## License

MIT

This code is written in C++ with python modules exposed using pybind11.