Pushing the docs to dev/ for branch: master, commit 03c3af5bdec29903567fa3d63f2e6776a2b3041b

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\n\nX_train = np.c_[.5, 1].T\ny_train = [.5, 1]\nX_test = np.c_[0, 2].T\n\nnp.random.seed(0)\n\nclassifiers = dict(ols=linear_model.LinearRegression(),\n                   ridge=linear_model.Ridge(alpha=.1))\n\nfignum = 1\nfor name, clf in classifiers.items():\n    fig = plt.figure(fignum, figsize=(4, 3))\n    plt.clf()\n    plt.title(name)\n    ax = plt.axes([.12, .12, .8, .8])\n\n    for _ in range(6):\n        this_X = .1 * np.random.normal(size=(2, 1)) + X_train\n        clf.fit(this_X, y_train)\n\n        ax.plot(X_test, clf.predict(X_test), color='gray')\n        ax.scatter(this_X, y_train, s=3, c='gray', marker='o', zorder=10)\n\n    clf.fit(X_train, y_train)\n    ax.plot(X_test, clf.predict(X_test), linewidth=2, color='blue')\n    ax.scatter(X_train, y_train, s=30, c='red', marker='+', zorder=10)\n\n    ax.set_xticks(())\n    ax.set_yticks(())\n    ax.set_ylim((0, 1.6))\n    ax.set_xlabel('X')\n    ax.set_ylabel('y')\n    ax.set_xlim(0, 2)\n    fignum += 1\n\nplt.show()"
+        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\n\nX_train = np.c_[.5, 1].T\ny_train = [.5, 1]\nX_test = np.c_[0, 2].T\n\nnp.random.seed(0)\n\nclassifiers = dict(ols=linear_model.LinearRegression(),\n                   ridge=linear_model.Ridge(alpha=.1))\n\nfor name, clf in classifiers.items():\n    fig, ax = plt.subplots(figsize=(4, 3))\n\n    for _ in range(6):\n        this_X = .1 * np.random.normal(size=(2, 1)) + X_train\n        clf.fit(this_X, y_train)\n\n        ax.plot(X_test, clf.predict(X_test), color='gray')\n        ax.scatter(this_X, y_train, s=3, c='gray', marker='o', zorder=10)\n\n    clf.fit(X_train, y_train)\n    ax.plot(X_test, clf.predict(X_test), linewidth=2, color='blue')\n    ax.scatter(X_train, y_train, s=30, c='red', marker='+', zorder=10)\n\n    ax.set_title(name)\n    ax.set_xlim(0, 2)\n    ax.set_ylim((0, 1.6))\n    ax.set_xlabel('X')\n    ax.set_ylabel('y')\n\n    fig.tight_layout()\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -42,12 +42,8 @@
 classifiers = dict(ols=linear_model.LinearRegression(),
                    ridge=linear_model.Ridge(alpha=.1))
 
-fignum = 1
 for name, clf in classifiers.items():
-    fig = plt.figure(fignum, figsize=(4, 3))
-    plt.clf()
-    plt.title(name)
-    ax = plt.axes([.12, .12, .8, .8])
+    fig, ax = plt.subplots(figsize=(4, 3))
 
     for _ in range(6):
         this_X = .1 * np.random.normal(size=(2, 1)) + X_train
@@ -60,12 +56,12 @@
     ax.plot(X_test, clf.predict(X_test), linewidth=2, color='blue')
     ax.scatter(X_train, y_train, s=30, c='red', marker='+', zorder=10)
 
-    ax.set_xticks(())
-    ax.set_yticks(())
+    ax.set_title(name)
+    ax.set_xlim(0, 2)
     ax.set_ylim((0, 1.6))
     ax.set_xlabel('X')
     ax.set_ylabel('y')
-    ax.set_xlim(0, 2)
-    fignum += 1
+
+    fig.tight_layout()
 
 plt.show()