Chore: Replaced leftover artifacts of the template with proper content

Author: shivvor2

.github/workflows/python-app.yml

@@ -19,7 +19,7 @@ jobs:
       - uses: actions/checkout@v5
       - uses: prefix-dev/setup-pixi@v0.9.2
         with:
-          pixi-version: v0.23.0
+          pixi-version: v0.50.0
           environments: ${{ matrix.environment }}
           frozen: true
 
@@ -30,4 +30,4 @@ jobs:
         uses: codecov/codecov-action@v5.5.1
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-          slug: scikit-learn-contrib/project-template
+          slug: ${{ github.repository }}

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2016, Vighnesh Birodkar and scikit-learn-contrib contributors
+Copyright (c) 2016, Loo Jun Zing
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.md

@@ -31,15 +31,15 @@ This project was created to provide a version of DenMune that is:
 ### Installation
 
 ```bash
-pip install denmune-sklearn
+pip install denmune-skl
 ```
 
 ### Quick Usage
 
 ```python
 from sklearn.datasets import make_moons
 from sklearn.preprocessing import StandardScaler
-from denmune import DenMune
+from denmune-skl import DenMune
 import matplotlib.pyplot as plt
 import numpy as np
 

denmune/__init__.py

@@ -47,17 +47,15 @@ class DenMune(BaseEstimator, ClusterMixin):
 
     dim_reducer : str or estimator object, default='tsne'
         The dimensionality reduction method to use. Can be 'tsne', 'pca',
-        'umap' (if installed), or a pre-initialized scikit-learn compatible
-        estimator object.
-
-    dim_reducer_params : dict, default = None
-        Arguments provided to the inner dimension reducer object, ignored if
-        `dim_reducer` is an estimator object
+        or a pre-initialized scikit-learn compatible estimator object.
 
     metric : str, default='euclidean'
         The distance metric to use for the k-nearest neighbor search. See
         `sklearn.neighbors.NearestNeighbors` for valid options.
 
+    metric_params : dict, default=None
+        Additional keyword arguments for the metric function.
+
     n_jobs : int, default=None
         The number of parallel jobs to run for neighbors search and
         dimensionality reduction. `None` means 1, `-1` means using all
@@ -109,7 +107,7 @@ class DenMune(BaseEstimator, ClusterMixin):
     >>> from sklearn.preprocessing import StandardScaler
     >>> import numpy as np
     >>> # Assuming DenMune is defined in the current scope or imported
-    >>> # from denmune import DenMune
+    >>> from denmune_skl import DenMune
     >>>
     >>> # Generate sample data
     >>> X, y = make_moons(n_samples=250, noise=0.07, random_state=42)