Pushing the docs to dev/ for branch: main, commit 6169cc9b383c7dc15d9039767ed28eb6fd944f06

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -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``

dev/_downloads/4b8d8f0d50e5aa937ac9571a35eadc28/plot_kmeans_stability_low_dim_dense.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;

dev/_downloads/5a87b25ba023ee709595b8d02049f021/plot_kmeans_digits.py

@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Run the benchmark\n\nWe will compare three approaches:\n\n* an initialization using `kmeans++`. This method is stochastic and we will\n  run the initialization 4 times;\n* a random initialization. This method is stochastic as well and we will run\n  the initialization 4 times;\n* an initialization based on a :class:`~sklearn.decomposition.PCA`\n  projection. Indeed, we will use the components of the\n  :class:`~sklearn.decomposition.PCA` to initialize KMeans. This method is\n  deterministic and a single initialization suffice.\n\n"
+        "## Run the benchmark\n\nWe will compare three approaches:\n\n* an initialization using `k-means++`. This method is stochastic and we will\n  run the initialization 4 times;\n* a random initialization. This method is stochastic as well and we will run\n  the initialization 4 times;\n* an initialization based on a :class:`~sklearn.decomposition.PCA`\n  projection. Indeed, we will use the components of the\n  :class:`~sklearn.decomposition.PCA` to initialize KMeans. This method is\n  deterministic and a single initialization suffice.\n\n"
       ]
     },
     {

dev/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.cluster import kmeans_plusplus\nfrom sklearn.datasets import make_blobs\nimport matplotlib.pyplot as plt\n\n# Generate sample data\nn_samples = 4000\nn_components = 4\n\nX, y_true = make_blobs(\n    n_samples=n_samples, centers=n_components, cluster_std=0.60, random_state=0\n)\nX = X[:, ::-1]\n\n# Calculate seeds from kmeans++\ncenters_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)\n\n# Plot init seeds along side sample data\nplt.figure(1)\ncolors = [\"#4EACC5\", \"#FF9C34\", \"#4E9A06\", \"m\"]\n\nfor k, col in enumerate(colors):\n    cluster_data = y_true == k\n    plt.scatter(X[cluster_data, 0], X[cluster_data, 1], c=col, marker=\".\", s=10)\n\nplt.scatter(centers_init[:, 0], centers_init[:, 1], c=\"b\", s=50)\nplt.title(\"K-Means++ Initialization\")\nplt.xticks([])\nplt.yticks([])\nplt.show()"
+        "from sklearn.cluster import kmeans_plusplus\nfrom sklearn.datasets import make_blobs\nimport matplotlib.pyplot as plt\n\n# Generate sample data\nn_samples = 4000\nn_components = 4\n\nX, y_true = make_blobs(\n    n_samples=n_samples, centers=n_components, cluster_std=0.60, random_state=0\n)\nX = X[:, ::-1]\n\n# Calculate seeds from k-means++\ncenters_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)\n\n# Plot init seeds along side sample data\nplt.figure(1)\ncolors = [\"#4EACC5\", \"#FF9C34\", \"#4E9A06\", \"m\"]\n\nfor k, col in enumerate(colors):\n    cluster_data = y_true == k\n    plt.scatter(X[cluster_data, 0], X[cluster_data, 1], c=col, marker=\".\", s=10)\n\nplt.scatter(centers_init[:, 0], centers_init[:, 1], c=\"b\", s=50)\nplt.title(\"K-Means++ Initialization\")\nplt.xticks([])\nplt.yticks([])\nplt.show()"
       ]
     }
   ],

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Empirical evaluation of the impact of k-means initialization\n\nEvaluate the ability of k-means initializations strategies to make\nthe algorithm convergence robust, as measured by the relative standard\ndeviation of the inertia of the clustering (i.e. the sum of squared\ndistances to the nearest cluster center).\n\nThe first plot shows the best inertia reached for each combination\nof the model (``KMeans`` or ``MiniBatchKMeans``), and the init method\n(``init=\"random\"`` or ``init=\"kmeans++\"``) for increasing values of the\n``n_init`` parameter that controls the number of initializations.\n\nThe second plot demonstrates one single run of the ``MiniBatchKMeans``\nestimator using a ``init=\"random\"`` and ``n_init=1``. This run leads to\na bad convergence (local optimum), with estimated centers stuck\nbetween ground truth clusters.\n\nThe dataset used for evaluation is a 2D grid of isotropic Gaussian\nclusters widely spaced.\n"
+        "\n# Empirical evaluation of the impact of k-means initialization\n\nEvaluate the ability of k-means initializations strategies to make\nthe algorithm convergence robust, as measured by the relative standard\ndeviation of the inertia of the clustering (i.e. the sum of squared\ndistances to the nearest cluster center).\n\nThe first plot shows the best inertia reached for each combination\nof the model (``KMeans`` or ``MiniBatchKMeans``), and the init method\n(``init=\"random\"`` or ``init=\"k-means++\"``) for increasing values of the\n``n_init`` parameter that controls the number of initializations.\n\nThe second plot demonstrates one single run of the ``MiniBatchKMeans``\nestimator using a ``init=\"random\"`` and ``n_init=1``. This run leads to\na bad convergence (local optimum), with estimated centers stuck\nbetween ground truth clusters.\n\nThe dataset used for evaluation is a 2D grid of isotropic Gaussian\nclusters widely spaced.\n"
       ]
     },
     {

dev/_downloads/77b640d8771f7ecdeb2dbc948ebce90a/plot_kmeans_plusplus.ipynb

@@ -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