Pushing the docs to dev/ for branch: master, commit c0bb178389769a608ca76dff6b780a248e052d2a

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n\nimport numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import datasets, svm\n\ndigits = datasets.load_digits()\nX = digits.data\ny = digits.target\n\nsvc = svm.SVC(kernel='linear')\nC_s = np.logspace(-10, 0, 10)\n\nscores = list()\nscores_std = list()\nfor C in C_s:\n    svc.C = C\n    this_scores = cross_val_score(svc, X, y, cv=5, n_jobs=1)\n    scores.append(np.mean(this_scores))\n    scores_std.append(np.std(this_scores))\n\n# Do the plotting\nimport matplotlib.pyplot as plt\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.semilogx(C_s, scores)\nplt.semilogx(C_s, np.array(scores) + np.array(scores_std), 'b--')\nplt.semilogx(C_s, np.array(scores) - np.array(scores_std), 'b--')\nlocs, labels = plt.yticks()\nplt.yticks(locs, list(map(lambda x: \"%g\" % x, locs)))\nplt.ylabel('CV score')\nplt.xlabel('Parameter C')\nplt.ylim(0, 1.1)\nplt.show()"
+        "print(__doc__)\n\n\nimport numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import datasets, svm\n\ndigits = datasets.load_digits()\nX = digits.data\ny = digits.target\n\nsvc = svm.SVC(kernel='linear')\nC_s = np.logspace(-10, 0, 10)\n\nscores = list()\nscores_std = list()\nfor C in C_s:\n    svc.C = C\n    this_scores = cross_val_score(svc, X, y, cv=5, n_jobs=1)\n    scores.append(np.mean(this_scores))\n    scores_std.append(np.std(this_scores))\n\n# Do the plotting\nimport matplotlib.pyplot as plt\nplt.figure()\nplt.semilogx(C_s, scores)\nplt.semilogx(C_s, np.array(scores) + np.array(scores_std), 'b--')\nplt.semilogx(C_s, np.array(scores) - np.array(scores_std), 'b--')\nlocs, labels = plt.yticks()\nplt.yticks(locs, list(map(lambda x: \"%g\" % x, locs)))\nplt.ylabel('CV score')\nplt.xlabel('Parameter C')\nplt.ylim(0, 1.1)\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -32,8 +32,7 @@
 
 # Do the plotting
 import matplotlib.pyplot as plt
-plt.figure(1, figsize=(4, 3))
-plt.clf()
+plt.figure()
 plt.semilogx(C_s, scores)
 plt.semilogx(C_s, np.array(scores) + np.array(scores_std), 'b--')
 plt.semilogx(C_s, np.array(scores) - np.array(scores_std), 'b--')