https://github.com/x-tabdeveloping/rvfln

A Python implementation of random vector functional networks and broad learning systems using Sklearn's Regressor and classifier APIs
https://github.com/x-tabdeveloping/rvfln

broad-learning data-science deep-learning machine-learning scikit-learn sklearn sklearn-compatible

Last synced: 11 months ago
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A Python implementation of random vector functional networks and broad learning systems using Sklearn's Regressor and classifier APIs

• Host: GitHub
• URL: https://github.com/x-tabdeveloping/rvfln
• Owner: x-tabdeveloping
• License: mit
• Created: 2022-03-21T15:43:29.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-03-25T12:49:28.000Z (almost 4 years ago)
• Last Synced: 2025-03-18T14:06:19.850Z (11 months ago)
• Topics: broad-learning, data-science, deep-learning, machine-learning, scikit-learn, sklearn, sklearn-compatible
• Language: Python
• Homepage:
• Size: 16.6 KB
• Stars: 7
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: License.txt

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README

          
# RVFLN



A Python implementation of Random Vector Functional Link Networks and Broad Learning Systems using the Sklearn API



## Installation

    pip install rvfln




## Usage

Since the API is based on that of sklearn the usage is very similar.

The package contains:

- An rvfln module containing a regressor and a classifier estimator

- A bls module containing a regressor and a classifier estimator




### Example:




    from rvfln.bls import BLSClassifier

    clf = BLSClassifier(

        n_z = 10,

        n_z_features = 400,

        n_h = 2000,

        alpha = 1

    ).fit(x_train, y_train)

    print(clf.score(x_test, y_test))




YOH-HAN PAO, STEPHEN M. PHILLIPS & DEJAN J. SOBAJIC (1992) Neural-net computing and the intelligent control of systems, International Journal of Control, 56:2, 263-289, DOI: 10.1080/00207179208934315