New docs

.github/workflows/documentation.yml

@@ -16,7 +16,7 @@ jobs:
           pip install sphinx sphinx_rtd_theme myst_parser
       - name: Sphinx build
         run: |
-          sphinx-build doc _build
+          sphinx-build docs _build
       - name: Deploy to GitHub Pages
         uses: peaceiris/actions-gh-pages@v3
         if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}

_build/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: a550d150b6a93eef7b25c5c1c1b7749e
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_build/_sources/classifier.rst.txt

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+DistanceMetricClassifier module
+===============================
+
+.. automodule:: distclassipy.classifier
+   :members:
+   :undoc-members:
+   :show-inheritance:

_build/_sources/distances.rst.txt

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+Distance module
+===============
+
+.. automodule:: distclassipy.distances
+   :members:
+   :undoc-members:
+   :show-inheritance:

_build/_sources/index.rst.txt

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+.. DistClassiPy documentation master file, created by
+   sphinx-quickstart on Wed Feb  7 16:42:22 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to DistClassiPy's documentation!
+========================================
+
+DistClassiPy is a Python package for a distance-based classifier which can use several different distance metrics.
+
+Installation
+------------
+
+To install DistClassiPy, run the following command:
+
+.. code-block:: bash
+
+    pip install distclassipy
+
+Usage
+-----
+
+Here's a quick example to get you started with DistClassiPy:
+
+.. code-block:: python
+
+    import distclassipy as dcpy
+    clf = dcpy.DistanceMetricClassifier()
+    # Add your data and labels
+    clf.fit(data, labels)
+    # Predict new instances
+    predictions = clf.predict(new_data)
+
+Features
+--------
+
+- Multiple distance metrics support
+- Easy integration with existing data processing pipelines
+- Efficient and scalable for large datasets
+
+Contact
+-------
+
+For any queries, please reach out to Siddharth Chaini at [email protected].
+
+
+
+Indices and tables
+------------------
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
+API Documentation
+-----------------
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API Reference
+
+   classifier
+   distances