DOC fix typo in KMeans's param. (#25649)

rickiepark · jeremiedbb · commit 43b8e020bc49 · 2023-03-08T17:21:14.000+01:00
diff --git a/examples/cluster/plot_kmeans_digits.py b/examples/cluster/plot_kmeans_digits.py
@@ -114,7 +114,7 @@ def bench_k_means(kmeans, name, data, labels):
 #
 # We will compare three approaches:
 #
-# * an initialization using `kmeans++`. This method is stochastic and we will
+# * an initialization using `k-means++`. This method is stochastic and we will
 #   run the initialization 4 times;
 # * a random initialization. This method is stochastic as well and we will run
 #   the initialization 4 times;
diff --git a/examples/cluster/plot_kmeans_plusplus.py b/examples/cluster/plot_kmeans_plusplus.py
@@ -23,7 +23,7 @@
 )
 X = X[:, ::-1]
 
-# Calculate seeds from kmeans++
+# Calculate seeds from k-means++
 centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)
 
 # Plot init seeds along side sample data
diff --git a/examples/cluster/plot_kmeans_stability_low_dim_dense.py b/examples/cluster/plot_kmeans_stability_low_dim_dense.py
@@ -10,7 +10,7 @@
 
 The first plot shows the best inertia reached for each combination
 of the model (``KMeans`` or ``MiniBatchKMeans``), and the init method
-(``init="random"`` or ``init="kmeans++"``) for increasing values of the
+(``init="random"`` or ``init="k-means++"``) for increasing values of the
 ``n_init`` parameter that controls the number of initializations.
 
 The second plot demonstrates one single run of the ``MiniBatchKMeans``
diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
@@ -346,7 +346,7 @@ def k_means(
         n_init consecutive runs in terms of inertia.
 
         When `n_init='auto'`, the number of runs depends on the value of init:
-        10 if using `init='random'`, 1 if using `init='kmeans++'`.
+        10 if using `init='random'`, 1 if using `init='k-means++'`.
 
         .. versionadded:: 1.2
            Added 'auto' option for `n_init`.
@@ -1197,7 +1197,7 @@ class KMeans(_BaseKMeans):
         high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).
 
         When `n_init='auto'`, the number of runs depends on the value of init:
-        10 if using `init='random'`, 1 if using `init='kmeans++'`.
+        10 if using `init='random'`, 1 if using `init='k-means++'`.
 
         .. versionadded:: 1.2
            Added 'auto' option for `n_init`.
@@ -1725,7 +1725,7 @@ class MiniBatchKMeans(_BaseKMeans):
         :ref:`kmeans_sparse_high_dim`).
 
         When `n_init='auto'`, the number of runs depends on the value of init:
-        3 if using `init='random'`, 1 if using `init='kmeans++'`.
+        3 if using `init='random'`, 1 if using `init='k-means++'`.
 
         .. versionadded:: 1.2
            Added 'auto' option for `n_init`.

Original file line number	Diff line number	Diff line change
`@@ -114,7 +114,7 @@ def bench_k_means(kmeans, name, data, labels):`
`114`	`114`	`#`
`115`	`115`	`# We will compare three approaches:`
`116`	`116`	`#`
`117`		-# * an initialization using `kmeans++`. This method is stochastic and we will
	`117`	+# * an initialization using `k-means++`. This method is stochastic and we will
`118`	`118`	`# run the initialization 4 times;`
`119`	`119`	`# * a random initialization. This method is stochastic as well and we will run`
`120`	`120`	`# the initialization 4 times;`
Original file line number	Diff line number	Diff line change
`@@ -23,7 +23,7 @@`
`23`	`23`	`)`
`24`	`24`	`X = X[:, ::-1]`
`25`	`25`
`26`		`-# Calculate seeds from kmeans++`
	`26`	`+# Calculate seeds from k-means++`
`27`	`27`	`centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)`
`28`	`28`
`29`	`29`	`# Plot init seeds along side sample data`