Pushing the docs to dev/ for branch: main, commit dbde1da1954be91b9a0a12c9a109c17cd109bc76

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans\nfrom sklearn.feature_extraction.image import extract_patches_2d\n\nprint(\"Learning the dictionary... \")\nrng = np.random.RandomState(0)\nkmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True, n_init=3)\npatch_size = (20, 20)\n\nbuffer = []\nt0 = time.time()\n\n# The online learning part: cycle over the whole dataset 6 times\nindex = 0\nfor _ in range(6):\n    for img in faces.images:\n        data = extract_patches_2d(img, patch_size, max_patches=50, random_state=rng)\n        data = np.reshape(data, (len(data), -1))\n        buffer.append(data)\n        index += 1\n        if index % 10 == 0:\n            data = np.concatenate(buffer, axis=0)\n            data -= np.mean(data, axis=0)\n            data /= np.std(data, axis=0)\n            kmeans.partial_fit(data)\n            buffer = []\n        if index % 100 == 0:\n            print(\"Partial fit of %4i out of %i\" % (index, 6 * len(faces.images)))\n\ndt = time.time() - t0\nprint(\"done in %.2fs.\" % dt)"
+        "import time\n\nimport numpy as np\n\nfrom sklearn.cluster import MiniBatchKMeans\nfrom sklearn.feature_extraction.image import extract_patches_2d\n\nprint(\"Learning the dictionary... \")\nrng = np.random.RandomState(0)\nkmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)\npatch_size = (20, 20)\n\nbuffer = []\nt0 = time.time()\n\n# The online learning part: cycle over the whole dataset 6 times\nindex = 0\nfor _ in range(6):\n    for img in faces.images:\n        data = extract_patches_2d(img, patch_size, max_patches=50, random_state=rng)\n        data = np.reshape(data, (len(data), -1))\n        buffer.append(data)\n        index += 1\n        if index % 10 == 0:\n            data = np.concatenate(buffer, axis=0)\n            data -= np.mean(data, axis=0)\n            data /= np.std(data, axis=0)\n            kmeans.partial_fit(data)\n            buffer = []\n        if index % 100 == 0:\n            print(\"Partial fit of %4i out of %i\" % (index, 6 * len(faces.images)))\n\ndt = time.time() - t0\nprint(\"done in %.2fs.\" % dt)"
       ]
     },
     {

dev/_downloads/0af0092c704518874f82d38d725bb97f/plot_dict_face_patches.ipynb

@@ -42,7 +42,7 @@
 
 print("Learning the dictionary... ")
 rng = np.random.RandomState(0)
-kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True, n_init=3)
+kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)
 patch_size = (20, 20)
 
 buffer = []

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -145,7 +145,7 @@
       },
       "outputs": [],
       "source": [
-        "md_scaling = manifold.MDS(\n    n_components=n_components,\n    max_iter=50,\n    n_init=4,\n    random_state=0,\n    normalized_stress=False,\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
+        "md_scaling = manifold.MDS(\n    n_components=n_components, max_iter=50, n_init=4, random_state=0\n)\nS_scaling = md_scaling.fit_transform(S_points)\n\nplot_2d(S_scaling, S_color, \"Multidimensional scaling\")"
       ]
     },
     {

dev/_downloads/7f205ae026a8c21fcab1e6a86cfadb7d/plot_dict_face_patches.py

@@ -163,11 +163,7 @@ def add_2d_scatter(ax, points, points_color, title=None):
 # Read more in the :ref:`User Guide <multidimensional_scaling>`.
 
 md_scaling = manifold.MDS(
-    n_components=n_components,
-    max_iter=50,
-    n_init=4,
-    random_state=0,
-    normalized_stress=False,
+    n_components=n_components, max_iter=50, n_init=4, random_state=0
 )
 S_scaling = md_scaling.fit_transform(S_points)