8000 DOC fix typo in KMeans's param. (#25649) · scikit-learn/scikit-learn@6169cc9 · GitHub
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DOC fix typo in KMeans's param. (#25649)
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‎examples/cluster/plot_kmeans_digits.py

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@@ -114,7 +114,7 @@ def bench_k_means(kmeans, name, data, labels):
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#
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# We will compare three approaches:
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#
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# * an initialization using `kmeans++`. This method is stochastic and we will
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# * an initialization using `k-means++`. This method is stochastic and we will
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# run the initialization 4 times;
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# * a random initialization. This method is stochastic as well and we will run
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# the initialization 4 times;

‎examples/cluster/plot_kmeans_plusplus.py

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@@ -23,7 +23,7 @@
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)
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X = X[:, ::-1]
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# Calculate seeds from kmeans++
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# Calculate seeds from k-means++
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centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)
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# Plot init seeds along side sample data

‎examples/cluster/plot_kmeans_stability_low_dim_dense.py

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The first plot shows the best inertia reached for each combination
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of the model (``KMeans`` or ``MiniBatchKMeans``), and the init method
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(``init="random"`` or ``init="kmeans++"``) for increasing values of the
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(``init="random"`` or ``init="k-means++"``) for increasing values of the
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``n_init`` parameter that controls the number of initializations.
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The second plot demonstrates one single run of the ``MiniBatchKMeans``

‎sklearn/cluster/_kmeans.py

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@@ -345,7 +345,7 @@ def k_means(
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n_init consecutive runs in terms of inertia.
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When `n_init='auto'`, the number of runs depends on the value of init:
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10 if using `init='random'`, 1 if using `init='kmeans++'`.
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10 if using `init='random'`, 1 if using `init='k-means++'`.
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.. versionadded:: 1.2
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Added 'auto' option for `n_init`.
@@ -1206,7 +1206,7 @@ class KMeans(_BaseKMeans):
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high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).
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When `n_init='auto'`, the number of runs depends on the value of init:
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10 if using `init='random'`, 1 if using `init='kmeans++'`.
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10 if using `init='random'`, 1 if using `init='k-means++'`.
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.. versionadded:: 1.2
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Added 'auto' option for `n_init`.
@@ -1734,7 +1734,7 @@ class MiniBatchKMeans(_BaseKMeans):
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:ref:`kmeans_sparse_high_dim`).
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When `n_init='auto'`, the number of runs depends on the value of init:
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3 if using `init='random'`, 1 if using `init='kmeans++'`.
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3 if using `init='random'`, 1 if using `init='k-means++'`.
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.. versionadded:: 1.2
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Added 'auto' option for `n_init`.

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