distclassipy 0.1.2

A python package for a distance-based classifier which can use several different distance metrics.

A python package for a distance-based classifier which can use several different distance metrics.

Installation

To install DistClassiPy, run the following command:

pip install distclassipy

Usage

Here's a quick example to get you started with DistClassiPy:

import distclassipy as dcpy
from sklearn.datasets import make_classification

X, y = make_classification(
    n_samples=1000,
    n_features=4,
    n_informative=2,
    n_redundant=0,
    random_state=0,
    shuffle=False,
)
clf = dcpy.DistanceMetricClassifier(metric="canberra")
clf.fit(X, y)
print(clf.predict([[0, 0, 0, 0]]))

Features

• Multiple distance metrics support
• Easy integration with existing data processing pipelines
• Efficient and scalable for large datasets

Documentation

For more detailed information about the package and its functionalities, please refer to the official documentation.

Contributing

Contributions are welcome! If you have suggestions for improvements or bug fixes, please feel free to open an issue or submit a pull request.

License

DistClassiPy is released under the GNU General Public License v3.0. See the LICENSE file for more details.

Citation

If you use DistClassiPy in your research or project, please consider citing the paper:

Light Curve Classification with DistClassiPy: a new distance-based classifier (submitted to A&C)

Authors

Siddharth Chaini, Ashish Mahabal, Ajit Kembhavi and Federica B. Bianco.