https://github.com/vzhong/torchlib

Data structures, algorithms, and ML/NLP tools in Lua.
https://github.com/vzhong/torchlib

deep-learning torch

Last synced: 2 months ago
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Data structures, algorithms, and ML/NLP tools in Lua.

• Host: GitHub
• URL: https://github.com/vzhong/torchlib
• Owner: vzhong
• Created: 2015-07-31T07:08:45.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2017-08-31T20:58:37.000Z (almost 7 years ago)
• Last Synced: 2024-04-23T00:12:35.747Z (2 months ago)
• Topics: deep-learning, torch
• Language: Lua
• Homepage:
• Size: 821 KB
• Stars: 23
• Watchers: 6
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-torch - [Code

README

        
# Torchlib

[![wercker status](https://app.wercker.com/status/c7bd97d06535598d96937e0cf5ace629/s/master "wercker status")](https://app.wercker.com/project/bykey/c7bd97d06535598d96937e0cf5ace629)

[![codecov](https://codecov.io/gh/vzhong/torchlib/branch/master/graph/badge.svg)](https://codecov.io/gh/vzhong/torchlib)

[View documentation](http://torchlib.github.io).

Data structures and libraries for Torch. All instances are Torch serializable with `torch.save` and `torch.load`.

## Installation

You can install `torchlib` as follows:

`git clone https://github.com/vzhong/torchlib.git && cd torchlib && luarocks make`

Torchlib is namespaced locally. To use it:

```lua

local tl = require 'torchlib'

local m = tl.DirectedGraph()

...

```

Examples and use cases are shown in the documentation.

## Documentation

The documentation is hosted [here](http://www.victorzhong.com/torchlib).

Alternatively you can build your own documentation with `docroc`, which you can get [here](https://github.com/vzhong/docroc).

## Overview

Torchlib's can be divided into categories based on usecases.

### Basic Datastructures and Algorithms

- Graphs

- Lists, heaps, queues and stacks

- Maps and counters

- Sets

- Trees

### Machine Learning

The machine learning package contains utilities that facilitate the training of and evaluation of machine learning models. These include:

- Dataset, which provides mechanisms for subsampling, shuffling, batching of arbitrary examples.

- Vocab, for mapping between indices and words.

- Model, an abstract class to facilitate the training of Torch based machine learning models.

- Scorer, for evaluating precision/recall metrics.

- ProbTable, for modeling probability distributions.

- Experiment, for logging experiment progress to a postgres instance.

### Utilities

- Downloader, for downloading content via http.

- Global, global convenience functions namespaced under `tl`.

- String, string convenience functions namespaced under `tl.string` and monkeypatched into `string`.

- Table, table convenience functions namespaced under `tl.table` and monkeypatched into `table`.

## Contribution

Pull requests are welcome! Torchlib is unit tested with the default Torch testing framework. Continuous integration is hosted on [Wercker](http://wercker.com/) which also automatically builds the documentations and deploys them on Github pages (of this repo).