[DOC ] Added examples to docstrings for K-Means and K-Medoids

.all-contributorsrc

@@ -2190,6 +2190,15 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "CTFallon",
+      "name": "Colin Fallon",
+      "avatar_url": "https://avatars.githubusercontent.com/u/19725980?v=4",
+      "profile": "https://github.com/CTFallon",
+      "contributions": [
+        "doc"
+      ]
     }
   ]
 }

sktime/clustering/k_means.py

@@ -64,6 +64,17 @@ class TimeSeriesKMeans(TimeSeriesLloyds):
         the sample weights if provided.
     n_iter_: int
         Number of iterations run.
+
+    Examples
+    --------
+    >>> from sktime.datasets import load_arrow_head
+    >>> from sktime.clustering.k_means import TimeSeriesKMeans
+    >>> X_train, y_train = load_arrow_head(split="train")
+    >>> X_test, y_test = load_arrow_head(split="test")
+    >>> clusterer = TimeSeriesKMeans(n_clusters=3)  # doctest: +SKIP
+    >>> clusterer.fit(X_train)  # doctest: +SKIP
+    TimeSeriesKMeans(n_clusters=3)
+    >>> y_pred = clusterer.predict(X_test)  # doctest: +SKIP
     """
 
     _tags = {"python_dependencies": "numba"}

sktime/clustering/k_medoids.py

@@ -58,6 +58,17 @@ class TimeSeriesKMedoids(TimeSeriesLloyds):
         the sample weights if provided.
     n_iter_: int
         Number of iterations run.
+
+    Examples
+    --------
+    >>> from sktime.datasets import load_arrow_head
+    >>> from sktime.clustering.k_medoids import TimeSeriesKMedoids
+    >>> X_train, y_train = load_arrow_head(split="train")
+    >>> X_test, y_test = load_arrow_head(split="test")
+    >>> clusterer = TimeSeriesKMedoids(n_clusters=3)  # doctest: +SKIP
+    >>> clusterer.fit(X_train)  # doctest: +SKIP
+    TimeSeriesKMedoids(n_clusters=3)
+    >>> y_pred = clusterer.predict(X_test)  # doctest: +SKIP
     """
 
     _tags = {"python_dependencies": "numba"}