Pushing the docs to dev/ for branch: main, commit e24c13ca756a490b0044326c82cb6ecaeb0aa750

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # 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: 5744e0ab801bd7d341c57909164479c2
+config: 2150ee48d6d48ae5340fd1f0d8a787fb
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -84,7 +84,7 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
 # rescaled versions of the dataset.
 fig, axes = plt.subplots(3, 1, figsize=(10, 12))
 dbs = DBSCAN(eps=0.3)
-for idx, scale in enumerate((1, 0.5, 3)):
+for idx, scale in enumerate([1, 0.5, 3]):
     dbs.fit(X * scale)
     plot(X * scale, dbs.labels_, parameters={"scale": scale, "eps": 0.3}, ax=axes[idx])
 
@@ -105,9 +105,15 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
 # One immediate advantage is that HDBSCAN is scale-invariant.
 fig, axes = plt.subplots(3, 1, figsize=(10, 12))
 hdb = HDBSCAN()
-for idx, scale in enumerate((1, 0.5, 3)):
-    hdb.fit(X)
-    plot(X, hdb.labels_, hdb.probabilities_, ax=axes[idx], parameters={"scale": scale})
+for idx, scale in enumerate([1, 0.5, 3]):
+    hdb.fit(X * scale)
+    plot(
+        X * scale,
+        hdb.labels_,
+        hdb.probabilities_,
+        ax=axes[idx],
+        parameters={"scale": scale},
+    )
 # %%
 # Multi-Scale Clustering
 # ----------------------