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Scikit-learn compatible Locality Preserving Projections in Python

lpproj 최신버젼 다운로드

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// repository documentation

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# Locality Preserving Projections in Python

This is a Python implementation of Locality Preserving Projections (LPP), compatible with scikit-learn.

[![version status](http://img.shields.io/pypi/v/lpproj.svg?style=flat)](https://pypi.python.org/pypi/lpproj)
[![downloads](http://img.shields.io/pypi/dm/lpproj.svg?style=flat)](https://pypi.python.org/pypi/lpproj)
[![build status](http://img.shields.io/travis/jakevdp/lpproj/master.svg?style=flat)](https://travis-ci.org/jakevdp/lpproj)
[![license](http://img.shields.io/badge/license-BSD-blue.svg?style=flat)](https://github.com/jakevdp/lpproj/blob/master/LICENSE)

## Installation and Use

This package is pure Python, and depends only on [numpy](http://numpy.org/), [scipy](http://scipy.org/), and [scikit-learn](http://scikit-learn.org/).
The released version can be installed via pip:

$ pip install lpproj

A basic example of its use can be found in [Example.ipynb](Example.ipynb); the library follows scikit-learn's patterns for ``fit()``, ``transform()``, and ``fit_transform()``. The simplest example for a feature matrix ``X``:

```python
from lpproj import LocalityPreservingProjection 
lpp = LocalityPreservingProjection(n_components=2)

X_2D = lpp.fit_transform(X)
```

## More Information

For information on the LPP algorithm, see the paper, [Locality Preserving Projections (pdf)](http://papers.nips.cc/paper/2359-locality-preserving-projections.pdf).

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