samim0088
diff --git a/‎dev/_downloads/auto_examples_jupyter.zip
52 Bytes b/‎dev/_downloads/auto_examples_jupyter.zip
52 Bytes
diff --git a/‎dev/_downloads/auto_examples_python.zip
51 Bytes b/‎dev/_downloads/auto_examples_python.zip
51 Bytes
diff --git a/‎dev/_downloads/plot_confusion_matrix.ipynb
Lines changed: 1 addition & 1 deletion b/‎dev/_downloads/plot_confusion_matrix.ipynb
Lines changed: 1 addition & 1 deletion
diff --git a/‎dev/_downloads/plot_confusion_matrix.py
Lines changed: 9 additions & 8 deletions b/‎dev/_downloads/plot_confusion_matrix.py
Lines changed: 9 additions & 8 deletions
diff --git a/‎dev/_downloads/scikit-learn-docs.pdf
12.6 KB b/‎dev/_downloads/scikit-learn-docs.pdf
12.6 KB
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
-113 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
-113 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png
-113 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png
-113 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
51 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
51 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png
51 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png
51 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
27 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
27 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png
27 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png
27 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
155 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
155 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png
155 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png
155 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
-14 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
-14 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png
5.95 KB b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png
5.95 KB
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png
5.95 KB b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png
5.95 KB
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.png
-38 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.png
-38 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png
-38 Bytes b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png
-38 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_thumb.png
4.92 KB b/‎dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_thumb.png
4.92 KB
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
479 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
479 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_0011.png
479 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_0011.png
479 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_cluster_comparison_thumb.png
15 Bytes b/‎dev/_images/sphx_glr_plot_cluster_comparison_thumb.png
15 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_001.png
-457 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_001.png
-457 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_0011.png
-457 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_0011.png
-457 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_001_carousel.png
-35 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_001_carousel.png
-35 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_thumb.png
-28 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_thumb.png
-28 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_methods_thumb1.png
-28 Bytes b/‎dev/_images/sphx_glr_plot_compare_methods_thumb1.png
-28 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_reduction_001.png
41 Bytes b/‎dev/_images/sphx_glr_plot_compare_reduction_001.png
41 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_compare_reduction_thumb.png
-73 Bytes b/‎dev/_images/sphx_glr_plot_compare_reduction_thumb.png
-73 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_confusion_matrix_002.png
2.16 KB b/‎dev/_images/sphx_glr_plot_confusion_matrix_002.png
2.16 KB
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_001.png
-4 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_001.png
-4 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_0011.png
-4 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_0011.png
-4 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_002.png
80 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_002.png
80 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_face_segmentation_0021.png
80 Bytes b/‎dev/_images/sphx_glr_plot_face_segmentation_0021.png
80 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_004.png
122 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_004.png
122 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_faces_decomposition_0041.png
122 Bytes b/‎dev/_images/sphx_glr_plot_faces_decomposition_0041.png
122 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_001.png
-54 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_001.png
-54 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_0011.png
-54 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_0011.png
-54 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_003.png
78 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_003.png
78 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_005.png
380 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_005.png
380 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_image_denoising_thumb.png
59 Bytes b/‎dev/_images/sphx_glr_plot_image_denoising_thumb.png
59 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_003.png
1.71 KB b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_003.png
1.71 KB
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_004.png
24 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_004.png
24 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_005.png
-2.49 KB b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_005.png
-2.49 KB
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_006.png
453 Bytes b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_006.png
453 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_007.png
3.09 KB b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_007.png
3.09 KB
diff --git a/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_008.png
1.1 KB b/‎dev/_images/sphx_glr_plot_johnson_lindenstrauss_bound_008.png
1.1 KB
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_001.png
-405 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_001.png
-405 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_002.png
-13 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_002.png
-13 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_0021.png
-13 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_0021.png
-13 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_approximation_thumb.png
-239 Bytes b/‎dev/_images/sphx_glr_plot_kernel_approximation_thumb.png
-239 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_001.png
-616 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_001.png
-616 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0011.png
-616 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0011.png
-616 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_002.png
15 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_002.png
15 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0021.png
15 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_0021.png
15 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_thumb.png
-111 Bytes b/‎dev/_images/sphx_glr_plot_kernel_ridge_regression_thumb.png
-111 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lasso_model_selection_002.png
-20 Bytes b/‎dev/_images/sphx_glr_plot_lasso_model_selection_002.png
-20 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lasso_model_selection_0021.png
-20 Bytes b/‎dev/_images/sphx_glr_plot_lasso_model_selection_0021.png
-20 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_006.png
164 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_006.png
164 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0061.png
164 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0061.png
164 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_007.png
-151 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_007.png
-151 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0071.png
-151 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0071.png
-151 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_008.png
-39 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_008.png
-39 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0081.png
-39 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0081.png
-39 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_009.png
45 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_009.png
45 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0091.png
45 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0091.png
45 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_010.png
164 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_010.png
164 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0101.png
164 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0101.png
164 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_011.png
167 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_011.png
167 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_012.png
-233 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_012.png
-233 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_013.png
106 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_013.png
106 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_lle_digits_0131.png
106 Bytes b/‎dev/_images/sphx_glr_plot_lle_digits_0131.png
106 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_manifold_sphere_001.png
-549 Bytes b/‎dev/_images/sphx_glr_plot_manifold_sphere_001.png
-549 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_manifold_sphere_thumb.png
147 Bytes b/‎dev/_images/sphx_glr_plot_manifold_sphere_thumb.png
147 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_001.png
732 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_001.png
732 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_0011.png
732 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_0011.png
732 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_002.png
-1.31 KB b/‎dev/_images/sphx_glr_plot_model_complexity_influence_002.png
-1.31 KB
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_0021.png
-1.31 KB b/‎dev/_images/sphx_glr_plot_model_complexity_influence_0021.png
-1.31 KB
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_003.png
-147 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_003.png
-147 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_0031.png
-147 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_0031.png
-147 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_model_complexity_influence_thumb.png
105 Bytes b/‎dev/_images/sphx_glr_plot_model_complexity_influence_thumb.png
105 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_multioutput_face_completion_001.png
-18 Bytes b/‎dev/_images/sphx_glr_plot_multioutput_face_completion_001.png
-18 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_multioutput_face_completion_0011.png
-18 Bytes b/‎dev/_images/sphx_glr_plot_multioutput_face_completion_0011.png
-18 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_multioutput_face_completion_thumb.png
-40 Bytes b/‎dev/_images/sphx_glr_plot_multioutput_face_completion_thumb.png
-40 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_002.png
-242 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_002.png
-242 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_003.png
358 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_003.png
358 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_0031.png
358 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_0031.png
358 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_004.png
99 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_004.png
99 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_out_of_core_classification_0041.png
99 Bytes b/‎dev/_images/sphx_glr_plot_out_of_core_classification_0041.png
99 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_001.png
59 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_001.png
59 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0011.png
59 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_0011.png
59 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_002.png
124 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_002.png
124 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0021.png
124 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_0021.png
124 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_003.png
480 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_003.png
480 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0031.png
480 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_0031.png
480 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_004.png
-40 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_004.png
-40 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_0041.png
-40 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_0041.png
-40 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_prediction_latency_thumb.png
41 Bytes b/‎dev/_images/sphx_glr_plot_prediction_latency_thumb.png
41 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_stock_market_001.png
-1 Bytes b/‎dev/_images/sphx_glr_plot_stock_market_001.png
-1 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_stock_market_thumb.png
-2 Bytes b/‎dev/_images/sphx_glr_plot_stock_market_thumb.png
-2 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_001.png
57 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_001.png
57 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_0011.png
57 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_0011.png
57 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_002.png
-109 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_002.png
-109 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_theilsen_thumb.png
-30 Bytes b/‎dev/_images/sphx_glr_plot_theilsen_thumb.png
-30 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_002.png
79 Bytes b/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_002.png
79 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_0021.png
79 Bytes b/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_0021.png
79 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png
-15 Bytes b/‎dev/_images/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png
-15 Bytes
diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.txt
Lines changed: 14 additions & 14 deletions b/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.txt
Lines changed: 14 additions & 14 deletions
@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nclass_names = iris.target_names\n\n# Split the data into a training set and a test set\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\n# Run classifier, using a model that is too regularized (C too low) to see\n# the impact on the results\nclassifier = svm.SVC(kernel='linear', C=0.01)\ny_pred = classifier.fit(X_train, y_train).predict(X_test)\n\n\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, cm[i, j],\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, y_pred)\nnp.set_printoptions(precision=2)\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names,\n                      title='Confusion matrix, without normalization')\n\n# Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n                      title='Normalized confusion matrix')\n\nplt.show()"
+        "print(__doc__)\n\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nclass_names = iris.target_names\n\n# Split the data into a training set and a test set\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\n# Run classifier, using a model that is too regularized (C too low) to see\n# the impact on the results\nclassifier = svm.SVC(kernel='linear', C=0.01)\ny_pred = classifier.fit(X_train, y_train).predict(X_test)\n\n\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, y_pred)\nnp.set_printoptions(precision=2)\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names,\n                      title='Confusion matrix, without normalization')\n\n# Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n                      title='Normalized confusion matrix')\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {
 
@@ -57,13 +57,6 @@ def plot_confusion_matrix(cm, classes,
     This function prints and plots the confusion matrix.
     Normalization can be applied by setting `normalize=True`.
     """
-    plt.imshow(cm, interpolation='nearest', cmap=cmap)
-    plt.title(title)
-    plt.colorbar()
-    tick_marks = np.arange(len(classes))
-    plt.xticks(tick_marks, classes, rotation=45)
-    plt.yticks(tick_marks, classes)
-
     if normalize:
         cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
         print("Normalized confusion matrix")
@@ -72,9 +65,17 @@ def plot_confusion_matrix(cm, classes,
 
     print(cm)
 
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
+    fmt = '.2f' if normalize else 'd'
     thresh = cm.max() / 2.
     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
-        plt.text(j, i, cm[i, j],
+        plt.text(j, i, format(cm[i, j], fmt),
                  horizontalalignment="center",
                  color="white" if cm[i, j] > thresh else "black")
 
 
@@ -226,53 +226,53 @@ 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: 0.027280s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.026130s
 
     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: 0.020358s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.020463s
 
     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: 0.016954s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016073s
 
     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: 0.014675s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.014042s
 
     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: 0.000371s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000365s
 
     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: 0.000650s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000654s
 
     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: 0.001114s
+    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: 0.001567s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001583s
 
     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: 0.001803s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001810s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -288,7 +288,7 @@ main code
                  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: 0.000207s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000199s
 
     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: 0.000282s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000283s
 
     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: 0.000447s
+    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000451s
 
     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: 0.000977s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000985s
 
 
-**Total running time of the script:** ( 0 minutes  25.380 seconds)
+**Total running time of the script:** ( 0 minutes  24.747 seconds)