https://github.com/angeloskath/supervised-lda

A flexible variational inference LDA library.
https://github.com/angeloskath/supervised-lda

Last synced: 11 days ago
JSON representation

A flexible variational inference LDA library.

• Host: GitHub
• URL: https://github.com/angeloskath/supervised-lda
• Owner: angeloskath
• License: mit
• Created: 2016-02-21T14:11:47.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-03-15T16:03:29.000Z (over 5 years ago)
• Last Synced: 2024-08-03T18:20:48.725Z (3 months ago)
• Language: C++
• Homepage:
• Size: 5.46 MB
• Stars: 22
• Watchers: 2
• Forks: 5
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-topic-models - lda++ - C++ library for LDA and (fast) supervised LDA (sLDA/fsLDA) using variational inference  (Libraries & Toolkits)

README

        
LDA++

=====

LDA++ is a C++ library and a set of accompanying console applications that

enable the inference of various [Latent Dirichlet

Allocation](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) models.

The project provides three [console applications](http://ldaplusplus.com/console-applications/)

* **lda** implementing **LDA**

* **slda** implementing **Supervised LDA**

* **fslda** implementing **Fast Supervised LDA**

and a library that can be used from your own C++ projects.

You can read the documentation site at

[ldaplusplus.com](http://ldaplusplus.com/) and there is of course an [API

documentation](http://ldaplusplus.com/api/) as well.

How to get it

-------------

We use CMake for building the project and currently only provide the option to

build from source. The [LDA++

installation](http://ldaplusplus.com/installation/) process is straightforward

and documented at our site.

Console applications

--------------------

We expect that the preferred way of using LDA++ will be through the provided

console applications. You can read thorough [documentation for

them](http://ldaplusplus.com/console-applications/) as well. All our console

applications are designed to read matrix files serialized in numpy format so

that one can easily create files in a python session.

It suffices to say that the following shell session runs the Fast Supervised

LDA (fsLDA) on the scikit learn digits dataset (provided you have installed

LDA++).

    $ python

    Python 2.7.12 (default, Jul  1 2016, 15:12:24) 

    [GCC 5.4.0 20160609] on linux2

    Type "help", "copyright", "credits" or "license" for more information.

    >>> from sklearn.datasets import load_digits

    >>> import numpy as np

    >>> d = load_digits()

    >>> with open("digits.npy", "wb") as f:

    ...     np.save(f, d.data.astype(np.int32).T)

    ...     np.save(f, d.target.astype(np.int32))

    ... 

    >>> exit()

    $ fslda train digits.npy model.npy

    E-M Iteration 1

    100

    200

    300

    400

    500

    600

    700

    800

    900

    1000

    1100

    1200

    1300

    1400

    1500

    1600

    1700

    log p(y | \bar{z}, eta): -4137.75

    log p(y | \bar{z}, eta): -3230.67

    log p(y | \bar{z}, eta): -2758.81

    log p(y | \bar{z}, eta): -2498.32

    log p(y | \bar{z}, eta): -2341.4

    log p(y | \bar{z}, eta): -2240.48

    log p(y | \bar{z}, eta): -2172.38

    log p(y | \bar{z}, eta): -2124.71

    log p(y | \bar{z}, eta): -2090.4

    log p(y | \bar{z}, eta): -2065.15

    ...

    $ python

    Python 2.7.12 (default, Jul  1 2016, 15:12:24) 

    [GCC 5.4.0 20160609] on linux2

    Type "help", "copyright", "credits" or "license" for more information.

    >>> import numpy as np

    >>> with open("model.npy") as f:

    ...     alpha = np.load(f)

    ...     beta = np.load(f)

    ...     eta = np.load(f)

    ...

    >>> import matplotlib.pyplot as plt

    >>> plt.imshow(beta[0].reshape(8, 8), interpolation='nearest', cmap='gray')

    

    >>> plt.show()

    >>> exit()

License

-------

MIT license found in the LICENSE file.