Cyclic Ordering with Feature Extraction

This package (COFE - kaa·fee) implements nonlinear dimensionality reduction with a circular constraint on the (dependent) principal components.

Features

Assigns time-labels to high-dimensional data representing an underlying rhythmic process
Identifies features in the data that contribute to the temporal reordering
Regularized unsupervised machine learning approach with automated choice of hyperparameters.

Prerequisites
- Python 3.9 or better installed on your system. You can download and install Python from the official Python website.
- Git installed on your system. You can download and install Git from the official Git website.
Clone the COFE Repository
1. Open a terminal or command prompt.
2. Navigate to the directory where you want to install COFE.
3. Clone the COFE repository from GitHub by running the following command:
```
git clone https://github.com/bharathananth/COFE.git
```
Installation
1. Navigate to the COFE directory:
  cd COFE
2. You can install COFE and its dependencies by running the following command:
  python -m pip install .
Verify Installation
To verify that COFE is installed correctly, you can try importing it in a Python environment. Open a Python interpreter or create a new Python script, and then try importing COFE:
```
import COFE.analyse
import COFE.plot
import COFE.scpca
```

Once installed, you can start using COFE in your Python projects. Refer to the docstrings of COFE functions for usage.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.