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A Python implementation of random vector functional networks and broad learning systems using Sklearn's Regressor and classifier APIs

License: MIT License

Python 100.00%
machine-learning deep-learning broad-learning data-science sklearn scikit-learn sklearn-compatible

rvfln's Introduction

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

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