https://github.com/sylvchev/simple_esn

simple Echo State Networks integrated with scikit-learn
https://github.com/sylvchev/simple_esn

esn reservoir scikit-learn

Last synced: about 1 month ago
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simple Echo State Networks integrated with scikit-learn

• Host: GitHub
• URL: https://github.com/sylvchev/simple_esn
• Owner: sylvchev
• License: gpl-3.0
• Created: 2015-06-03T15:00:13.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2020-02-09T23:38:41.000Z (almost 5 years ago)
• Last Synced: 2023-12-16T15:16:45.409Z (about 1 year ago)
• Topics: esn, reservoir, scikit-learn
• Language: Python
• Homepage:
• Size: 150 KB
• Stars: 26
• Watchers: 5
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Simple Echo State Network

[![Coverage Status](https://coveralls.io/repos/sylvchev/simple_esn/badge.svg?branch=master&service=github)](https://coveralls.io/github/sylvchev/simple_esn?branch=master)

[![Travis CI](https://travis-ci.org/sylvchev/simple_esn.svg?branch=master)](https://travis-ci.org/sylvchev/simple_esn)

[![Code Climate](https://codeclimate.com/github/sylvchev/simple_esn/badges/gpa.svg)](https://codeclimate.com/github/sylvchev/simple_esn)

## Simple ESN

**simple_esn** implement a Python class of simple Echo State Network models

within the Scikit-learn framework. It is intended to be a fast-and-easy

transformation of an input signal in a reservoir of neurons. The classification

or regression could be done with any scikit-learn classifier/regressor.

The `SimpleESN` object could be part of a `Pipeline` and its parameter space could

be explored with a `GridSearchCV`, for example.

The code is inspired by the "minimalistic ESN example" proposed by Mantas

Lukoševičius. It is licenced under GPLv3.

## Useful links

-   Code from Mantas Lukoševičius: http://organic.elis.ugent.be/software/minimal

-   Code from Mantas Lukoševičius: http://minds.jacobs-university.de/mantas/code

-   More serious reservoir computing softwares: http://organic.elis.ugent.be/software

-   Scikit-learn, indeed: http://scikit-learn.org/

## Dependencies

The only dependencies are scikit-learn, numpy and scipy.

## Installation

Install with `python setup.py install` or `python setup.py develop`

## Examples

Using the SimpleESN class is easy as:

```python

from simple_esn.simple_esn import SimpleESN

import numpy as np

n_samples, n_features = 10, 5

np.random.seed(0)

X = np.random.randn(n_samples, n_features)

esn = SimpleESN(n_readout = 2)

echoes = esn.fit_transform(X)

```

It could also be part of a Pipeline:

```python

from simple_esn.simple_esn import SimpleESN

# Pick your classifier

pipeline = Pipeline([('esn', SimpleESN(n_readout=1000)),

                     ('svr', svm.SVR())])

parameters = {

    'esn__weight_scaling': [0.5, 1.0],

    'svr__C': [1, 10]

}

grid_search = GridSearchCV(pipeline, parameters)

grid_search.fit(X_train, y_train)

```