rmatam
diff --git a/‎dev/_downloads/auto_examples_jupyter.zip
473 Bytes b/‎dev/_downloads/auto_examples_jupyter.zip
473 Bytes
diff --git a/‎dev/_downloads/auto_examples_python.zip
464 Bytes b/‎dev/_downloads/auto_examples_python.zip
464 Bytes
diff --git a/‎dev/_downloads/face_recognition.ipynb
Lines changed: 2 additions & 2 deletions b/‎dev/_downloads/face_recognition.ipynb
Lines changed: 2 additions & 2 deletions
diff --git a/‎dev/_downloads/face_recognition.py
Lines changed: 3 additions & 2 deletions b/‎dev/_downloads/face_recognition.py
Lines changed: 3 additions & 2 deletions
diff --git a/‎dev/_downloads/plot_faces_decomposition.ipynb
Lines changed: 2 additions & 2 deletions b/‎dev/_downloads/plot_faces_decomposition.ipynb
Lines changed: 2 additions & 2 deletions
diff --git a/‎dev/_downloads/plot_faces_decomposition.py
Lines changed: 11 additions & 3 deletions b/‎dev/_downloads/plot_faces_decomposition.py
Lines changed: 11 additions & 3 deletions
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
96 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
96 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png
96 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png
96 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
-508 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
-508 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png
-508 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png
-508 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
-253 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
-253 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png
-253 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png
-253 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
5 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
5 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png
-10 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png
-10 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png
-10 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png
-10 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.png
-878 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.png
-878 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png
-878 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png
-878 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_thumb.png
61 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_thumb.png
61 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
-441 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
-441 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_0011.png
-441 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_0011.png
-441 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_thumb.png
55 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_thumb.png
55 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_001.png
90 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_001.png
90 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_0011.png
90 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_0011.png
90 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_001_carousel.png
9 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_001_carousel.png
9 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_thumb.png
47 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_thumb.png
47 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_thumb1.png
47 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_thumb1.png
47 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_reduction_001.png
-49 Bytes b/‎dev/_images/sphx_glr_plot_compare_reduction_001.png
-49 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_reduction_thumb.png
5 Bytes b/‎dev/_images/sphx_glr_plot_compare_reduction_thumb.png
5 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_dict_face_patches_001.png
-178 Bytes b/‎dev/_images/sphx_glr_plot_dict_face_patches_001.png
-178 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_dict_face_patches_0011.png
-178 Bytes b/‎dev/_images/sphx_glr_plot_dict_face_patches_0011.png
-178 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_dict_face_patches_thumb.png
-25 Bytes b/‎dev/_images/sphx_glr_plot_dict_face_patches_thumb.png
-25 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_001.png
12 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_001.png
12 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_0011.png
12 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_0011.png
12 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_002.png
-169 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_002.png
-169 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_0021.png
-169 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_0021.png
-169 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_thumb.png
37 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_thumb.png
37 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_002.png
942 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_002.png
942 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_0021.png
942 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_0021.png
942 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_004.png
-706 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_004.png
-706 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_0041.png
-706 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_0041.png
-706 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_005.png
-111 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_005.png
-111 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_0051.png
-111 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_0051.png
-111 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_001.png
112 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_001.png
112 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_0011.png
112 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_0011.png
112 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_003.png
129 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_003.png
129 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_004.png
-362 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_004.png
-362 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_005.png
-368 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_005.png
-368 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_006.png
-125 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_006.png
-125 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_thumb.png
60 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_thumb.png
60 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_003.png
-908 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_003.png
-908 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_004.png
342 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_004.png
342 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_005.png
-936 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_005.png
-936 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_006.png
-390 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_006.png
-390 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_007.png
288 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_007.png
288 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_008.png
-2.17 KB b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_008.png
-2.17 KB
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_001.png
-197 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_001.png
-197 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_002.png
7 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_002.png
7 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_0021.png
7 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_0021.png
7 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_thumb.png
51 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_thumb.png
51 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_001.png
99 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_001.png
99 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0011.png
99 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0011.png
99 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_002.png
216 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_002.png
216 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0021.png
216 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0021.png
216 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_thumb.png
48 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_thumb.png
48 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lasso_model_selection_002.png
9 Bytes b/‎dev/_images/sphx_glr_plot_lasso_model_selection_002.png
9 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lasso_model_selection_0021.png
9 Bytes b/‎dev/_images/sphx_glr_plot_lasso_model_selection_0021.png
9 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_006.png
-180 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_006.png
-180 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0061.png
-180 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0061.png
-180 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_007.png
-138 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_007.png
-138 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0071.png
-138 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0071.png
-138 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_008.png
-76 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_008.png
-76 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0081.png
-76 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0081.png
-76 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_009.png
-67 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_009.png
-67 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0091.png
-67 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0091.png
-67 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_010.png
-272 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_010.png
-272 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0101.png
-272 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0101.png
-272 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_011.png
-166 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_011.png
-166 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_012.png
-36 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_012.png
-36 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_013.png
282 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_013.png
282 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0131.png
282 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0131.png
282 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_manifold_sphere_001.png
707 Bytes b/‎dev/_images/sphx_glr_plot_manifold_sphere_001.png
707 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_manifold_sphere_thumb.png
-80 Bytes b/‎dev/_images/sphx_glr_plot_manifold_sphere_thumb.png
-80 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_001.png
-6 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_001.png
-6 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_0011.png
-6 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_0011.png
-6 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_002.png
-267 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_002.png
-267 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_0021.png
-267 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_0021.png
-267 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_003.png
3.05 KB b/‎dev/_images/sphx_glr_plot_model_complexity_influence_003.png
3.05 KB
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_0031.png
3.05 KB b/‎dev/_images/sphx_glr_plot_model_complexity_influence_0031.png
3.05 KB
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_thumb.png
84 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_thumb.png
84 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_multioutput_face_completion_001.png
-2 Bytes b/‎dev/_images/sphx_glr_plot_multioutput_face_completion_001.png
-2 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_multioutput_face_completion_0011.png
-2 Bytes b/‎dev/_images/sphx_glr_plot_multioutput_face_completion_0011.png
-2 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_multioutput_face_completion_thumb.png
-64 Bytes b/‎dev/_images/sphx_glr_plot_multioutput_face_completion_thumb.png
-64 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_002.png
-928 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_002.png
-928 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_003.png
-838 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_003.png
-838 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_0031.png
-838 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_0031.png
-838 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_004.png
-452 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_004.png
-452 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_0041.png
-452 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_0041.png
-452 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_001.png
1.42 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_001.png
1.42 KB
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0011.png
1.42 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_0011.png
1.42 KB
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_002.png
670 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_002.png
670 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0021.png
670 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_0021.png
670 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_003.png
-4.72 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_003.png
-4.72 KB
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0031.png
-4.72 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_0031.png
-4.72 KB
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_004.png
-1.3 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_004.png
-1.3 KB
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0041.png
-1.3 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_0041.png
-1.3 KB
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_thumb.png
-1.09 KB b/‎dev/_images/sphx_glr_plot_prediction_latency_thumb.png
-1.09 KB
diff --git a/‎dev/_images/sphx_glr_plot_sparse_coding_001.png
-121 Bytes b/‎dev/_images/sphx_glr_plot_sparse_coding_001.png
-121 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_sparse_coding_thumb.png
-22 Bytes b/‎dev/_images/sphx_glr_plot_sparse_coding_thumb.png
-22 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_001.png
522 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_001.png
522 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_0011.png
522 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_0011.png
522 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_002.png
-190 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_002.png
-190 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_thumb.png
59 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_thumb.png
59 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_001.png
-258 Bytes b/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_001.png
-258 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_0011.png
-258 Bytes b/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_0011.png
-258 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png
-17 Bytes b/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png
-17 Bytes
diff --git a/‎dev/_sources/auto_examples/applications/face_recognition.txt
Lines changed: 3 additions & 2 deletions b/‎dev/_sources/auto_examples/applications/face_recognition.txt
Lines changed: 3 additions & 2 deletions
diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.txt
Lines changed: 15 additions & 15 deletions b/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.txt
Lines changed: 15 additions & 15 deletions
@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "from __future__ import print_function\n\nfrom time import time\nimport logging\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.datasets import fetch_lfw_people\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.decomposition import RandomizedPCA\nfrom sklearn.svm import SVC\n\n\nprint(__doc__)\n\n# Display progress logs on stdout\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')"
+        "from __future__ import print_function\n\nfrom time import time\nimport logging\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.datasets import fetch_lfw_people\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\n\n\nprint(__doc__)\n\n# Display progress logs on stdout\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')"
       ], 
       "outputs": [], 
       "metadata": {
@@ -78,7 +78,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "n_components = 150\n\nprint(\"Extracting the top %d eigenfaces from %d faces\"\n      % (n_components, X_train.shape[0]))\nt0 = time()\npca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)\nprint(\"done in %0.3fs\" % (time() - t0))\n\neigenfaces = pca.components_.reshape((n_components, h, w))\n\nprint(\"Projecting the input data on the eigenfaces orthonormal basis\")\nt0 = time()\nX_train_pca = pca.transform(X_train)\nX_test_pca = pca.transform(X_test)\nprint(\"done in %0.3fs\" % (time() - t0))"
+        "n_components = 150\n\nprint(\"Extracting the top %d eigenfaces from %d faces\"\n      % (n_components, X_train.shape[0]))\nt0 = time()\npca = PCA(n_components=n_components, svd_solver='randomized',\n          whiten=True).fit(X_train)\nprint(\"done in %0.3fs\" % (time() - t0))\n\neigenfaces = pca.components_.reshape((n_components, h, w))\n\nprint(\"Projecting the input data on the eigenfaces orthonormal basis\")\nt0 = time()\nX_train_pca = pca.transform(X_train)\nX_test_pca = pca.transform(X_test)\nprint(\"done in %0.3fs\" % (time() - t0))"
       ], 
       "outputs": [], 
       "metadata": {
 
@@ -38,7 +38,7 @@
 from sklearn.datasets import fetch_lfw_people
 from sklearn.metrics import classification_report
 from sklearn.metrics import confusion_matrix
-from sklearn.decomposition import RandomizedPCA
+from sklearn.decomposition import PCA
 from sklearn.svm import SVC
 
 
@@ -88,7 +88,8 @@
 print("Extracting the top %d eigenfaces from %d faces"
       % (n_components, X_train.shape[0]))
 t0 = time()
-pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
+pca = PCA(n_components=n_components, svd_solver='randomized',
+          whiten=True).fit(X_train)
 print("done in %0.3fs" % (time() - t0))
 
 eigenfaces = pca.components_.reshape((n_components, h, w))
 
@@ -71,7 +71,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "estimators = [\n    ('Eigenfaces - RandomizedPCA',\n     decomposition.RandomizedPCA(n_components=n_components, whiten=True),\n     True),\n\n    ('Non-negative components - NMF',\n     decomposition.NMF(n_components=n_components, init='nndsvda', tol=5e-3),\n     False),\n\n    ('Independent components - FastICA',\n     decomposition.FastICA(n_components=n_components, whiten=True),\n     True),\n\n    ('Sparse comp. - MiniBatchSparsePCA',\n     decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,\n                                      n_iter=100, batch_size=3,\n                                      random_state=rng),\n     True),\n\n    ('MiniBatchDictionaryLearning',\n        decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,\n                                                  n_iter=50, batch_size=3,\n                                                  random_state=rng),\n     True),\n\n    ('Cluster centers - MiniBatchKMeans',\n        MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,\n                        max_iter=50, random_state=rng),\n     True),\n\n    ('Factor Analysis components - FA',\n     decomposition.FactorAnalysis(n_components=n_components, max_iter=2),\n     True),\n]"
+        "estimators = [\n    ('Eigenfaces - PCA using randomized SVD',\n     decomposition.PCA(n_components=n_components, svd_solver='randomized',\n                       whiten=True),\n     True),\n\n    ('Non-negative components - NMF',\n     decomposition.NMF(n_components=n_components, init='nndsvda', tol=5e-3),\n     False),\n\n    ('Independent components - FastICA',\n     decomposition.FastICA(n_components=n_components, whiten=True),\n     True),\n\n    ('Sparse comp. - MiniBatchSparsePCA',\n     decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,\n                                      n_iter=100, batch_size=3,\n                                      random_state=rng),\n     True),\n\n    ('MiniBatchDictionaryLearning',\n        decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,\n                                                  n_iter=50, batch_size=3,\n                                                  random_state=rng),\n     True),\n\n    ('Cluster centers - MiniBatchKMeans',\n        MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,\n                        max_iter=50, random_state=rng),\n     True),\n\n    ('Factor Analysis components - FA',\n     decomposition.FactorAnalysis(n_components=n_components, max_iter=2),\n     True),\n]"
       ], 
       "outputs": [], 
       "metadata": {
@@ -107,7 +107,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "for name, estimator, center in estimators:\n    print(\"Extracting the top %d %s...\" % (n_components, name))\n    t0 = time()\n    data = faces\n    if center:\n        data = faces_centered\n    estimator.fit(data)\n    train_time = (time() - t0)\n    print(\"done in %0.3fs\" % train_time)\n    if hasattr(estimator, 'cluster_centers_'):\n        components_ = estimator.cluster_centers_\n    else:\n        components_ = estimator.components_\n    if hasattr(estimator, 'noise_variance_'):\n        plot_gallery(\"Pixelwise variance\",\n                     estimator.noise_variance_.reshape(1, -1), n_col=1,\n                     n_row=1)\n    plot_gallery('%s - Train time %.1fs' % (name, train_time),\n                 components_[:n_components])\n\nplt.show()"
+        "for name, estimator, center in estimators:\n    print(\"Extracting the top %d %s...\" % (n_components, name))\n    t0 = time()\n    data = faces\n    if center:\n        data = faces_centered\n    estimator.fit(data)\n    train_time = (time() - t0)\n    print(\"done in %0.3fs\" % train_time)\n    if hasattr(estimator, 'cluster_centers_'):\n        components_ = estimator.cluster_centers_\n    else:\n        components_ = estimator.components_\n\n    # Plot an image representing the pixelwise variance provided by the\n    # estimator e.g its noise_variance_ attribute. The Eigenfaces estimator,\n    # via the PCA decomposition, also provides a scalar noise_variance_\n    # (the mean of pixelwise variance) that cannot be displayed as an image\n    # so we skip it.\n    if (hasattr(estimator, 'noise_variance_') and\n            estimator.noise_variance_.ndim > 0):  # Skip the Eigenfaces case\n        plot_gallery(\"Pixelwise variance\",\n                     estimator.noise_variance_.reshape(1, -1), n_col=1,\n                     n_row=1)\n    plot_gallery('%s - Train time %.1fs' % (name, train_time),\n                 components_[:n_components])\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {
 
@@ -66,8 +66,9 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row):
 # List of the different estimators, whether to center and transpose the
 # problem, and whether the transformer uses the clustering API.
 estimators = [
-    ('Eigenfaces - RandomizedPCA',
-     decomposition.RandomizedPCA(n_components=n_components, whiten=True),
+    ('Eigenfaces - PCA using randomized SVD',
+     decomposition.PCA(n_components=n_components, svd_solver='randomized',
+                       whiten=True),
      True),
 
     ('Non-negative components - NMF',
@@ -122,7 +123,14 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row):
         components_ = estimator.cluster_centers_
     else:
         components_ = estimator.components_
-    if hasattr(estimator, 'noise_variance_'):
+
+    # Plot an image representing the pixelwise variance provided by the
+    # estimator e.g its noise_variance_ attribute. The Eigenfaces estimator,
+    # via the PCA decomposition, also provides a scalar noise_variance_
+    # (the mean of pixelwise variance) that cannot be displayed as an image
+    # so we skip it.
+    if (hasattr(estimator, 'noise_variance_') and
+            estimator.noise_variance_.ndim > 0):  # Skip the Eigenfaces case
         plot_gallery("Pixelwise variance",
                      estimator.noise_variance_.reshape(1, -1), n_col=1,
                      n_row=1)
 
@@ -46,7 +46,7 @@ Gerhard Schroeder       0.76      0.76      0.76        25
     from sklearn.datasets import fetch_lfw_people
     from sklearn.metrics import classification_report
     from sklearn.metrics import confusion_matrix
-    from sklearn.decomposition import RandomizedPCA
+    from sklearn.decomposition import PCA
     from sklearn.svm import SVC
 
 
@@ -111,7 +111,8 @@ dataset): unsupervised feature extraction / dimensionality reduction
     print("Extracting the top %d eigenfaces from %d faces"
           % (n_components, X_train.shape[0]))
     t0 = time()
-    pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
+    pca = PCA(n_components=n_components, svd_solver='randomized',
+              whiten=True).fit(X_train)
     print("done in %0.3fs" % (time() - t0))
 
     eigenfaces = pca.components_.reshape((n_components, h, w))
 
@@ -226,69 +226,69 @@ main code
            learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
            penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
            verbose=0, warm_start=False)
-    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.026798s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.027413s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.5, learning_rate='optimal',
            loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
            power_t=0.5, random_state=None, shuffle=True, verbose=0,
            warm_start=False)
-    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.021841s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.020356s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.75,
            learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
            penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
            verbose=0, warm_start=False)
-    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.017555s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016345s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.9, learning_rate='optimal',
            loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
            power_t=0.5, random_state=None, shuffle=True, verbose=0,
            warm_start=False)
-    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.014737s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.014995s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000369s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000366s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.25, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000655s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000648s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001119s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001113s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.75, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001580s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001572s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.9, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001831s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001808s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
                  max_leaf_nodes=None, min_impurity_split=1e-07,
                  min_samples_leaf=1, min_samples_split=2,
                  min_weight_fraction_leaf=0.0, n_estimators=10, presort='auto',
                  random_state=None, subsample=1.0, verbose=0, warm_start=False)
-    Complexity: 10 | MSE: 28.9793 | Pred. Time: 0.000119s
+    Complexity: 10 | MSE: 28.9793 | Pred. Time: 0.000114s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
                  max_leaf_nodes=None, min_impurity_split=1e-07,
                  min_samples_leaf=1, min_samples_split=2,
                  min_weight_fraction_leaf=0.0, n_estimators=50, presort='auto',
                  random_state=None, subsample=1.0, verbose=0, warm_start=False)
-    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000197s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000191s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -297,7 +297,7 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=100,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000277s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000271s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -306,7 +306,7 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=200,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000430s
+    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000425s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -315,10 +315,10 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=500,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000936s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000922s
 
 
-**Total running time of the script:** ( 0 minutes  25.085 seconds)
+**Total running time of the script:** ( 0 minutes  25.481 seconds)