Feature Extraction using Chaos
This repository contains optimized Python modules for transforming feature matrices into higher dimensions using estimates derived from chaotic skew-tent maps.
Video explanation on YouTube on the usage of chaotic maps as kernels and highlighting chief ideas and inspiration.
Reference Paper:
Balakrishnan, Harikrishnan Nellippallil, Aditi Kathpalia, Snehanshu Saha, and Nithin Nagaraj. “ChaosNet: A Chaos Based Artificial Neural Network Architecture for Classification.” ArXiv:1910.02423 [Nlin, Stat], October 6, 2019. http://arxiv.org/abs/1910.02423.
Python 3NumpyNumba
- Presently unpackaged
- Up-to-date conda environment with dependencies installed
git cloneinto a working directory
- Please check out
demo.pyto see ChaosFEX in action
- Add Jupyter notebook for detailed demo of trajectory & transformations
- Examples for showcasing performance as a kernel trick with SVC
- Integrate with
scikit-learn - Add tests and logging
- Packaging for PyPI
Copyright 2020 Harikrishnan N. B., Pranay S. Yadav and Nithin Nagaraj
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.