Pushing the docs to dev/ for branch: master, commit a31676b948e2abc7c5783d4bf2d4eb76660803ee

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Nelle Varoquaux <[email protected]>\n#         Alexandre Gramfort <[email protected]>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.isotonic import IsotonicRegression\nfrom sklearn.utils import check_random_state\n\nn = 100\nx = np.arange(n)\nrs = check_random_state(0)\ny = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))\n\n# #############################################################################\n# Fit IsotonicRegression and LinearRegression models\n\nir = IsotonicRegression()\n\ny_ = ir.fit_transform(x, y)\n\nlr = LinearRegression()\nlr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression\n\n# #############################################################################\n# Plot result\n\nsegments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]\nlc = LineCollection(segments, zorder=0)\nlc.set_array(np.ones(len(y)))\nlc.set_linewidths(np.full(n, 0.5))\n\nfig = plt.figure()\nplt.plot(x, y, 'r.', markersize=12)\nplt.plot(x, y_, 'g.-', markersize=12)\nplt.plot(x, lr.predict(x[:, np.newaxis]), 'b-')\nplt.gca().add_collection(lc)\nplt.legend(('Data', 'Isotonic Fit', 'Linear Fit'), loc='lower right')\nplt.title('Isotonic regression')\nplt.show()"
+        "print(__doc__)\n\n# Author: Nelle Varoquaux <[email protected]>\n#         Alexandre Gramfort <[email protected]>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.isotonic import IsotonicRegression\nfrom sklearn.utils import check_random_state\n\nn = 100\nx = np.arange(n)\nrs = check_random_state(0)\ny = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))\n\n# #############################################################################\n# Fit IsotonicRegression and LinearRegression models\n\nir = IsotonicRegression()\n\ny_ = ir.fit_transform(x, y)\n\nlr = LinearRegression()\nlr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression\n\n# #############################################################################\n# Plot result\n\nsegments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]\nlc = LineCollection(segments, zorder=0)\nlc.set_array(np.ones(len(y)))\nlc.set_linewidths(np.full(n, 0.5))\n\nfig = plt.figure()\nplt.plot(x, y, 'r.', markersize=12)\nplt.plot(x, y_, 'b.-', markersize=12)\nplt.plot(x, lr.predict(x[:, np.newaxis]), 'b-')\nplt.gca().add_collection(lc)\nplt.legend(('Data', 'Isotonic Fit', 'Linear Fit'), loc='lower right')\nplt.title('Isotonic regression')\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -50,7 +50,7 @@
 
 fig = plt.figure()
 plt.plot(x, y, 'r.', markersize=12)
-plt.plot(x, y_, 'g.-', markersize=12)
+plt.plot(x, y_, 'b.-', markersize=12)
 plt.plot(x, lr.predict(x[:, np.newaxis]), 'b-')
 plt.gca().add_collection(lc)
 plt.legend(('Data', 'Isotonic Fit', 'Linear Fit'), loc='lower right')