Pushing the docs to dev/ for branch: master, commit 675f9f3a7cb2db4b5a3aecaedea72d2b4260fa76

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\n\n# we create 40 separable points\nrng = np.random.RandomState(0)\nn_samples_1 = 1000\nn_samples_2 = 100\nX = np.r_[1.5 * rng.randn(n_samples_1, 2),\n          0.5 * rng.randn(n_samples_2, 2) + [2, 2]]\ny = [0] * (n_samples_1) + [1] * (n_samples_2)\n\n# fit the model and get the separating hyperplane\nclf = svm.SVC(kernel='linear', C=1.0)\nclf.fit(X, y)\n\n# fit the model and get the separating hyperplane using weighted classes\nwclf = svm.SVC(kernel='linear', class_weight={1: 10})\nwclf.fit(X, y)\n\n# plot separating hyperplanes and samples\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors='k')\nplt.legend()\n\n# plot the decision functions for both classifiers\nax = plt.gca()\nxlim = ax.get_xlim()\nylim = ax.get_ylim()\n\n# create grid to evaluate model\nxx = np.linspace(xlim[0], xlim[1], 30)\nyy = np.linspace(ylim[0], ylim[1], 30)\nYY, XX = np.meshgrid(yy, xx)\nxy = np.vstack([XX.ravel(), YY.ravel()]).T\n\n# get the separating hyperplane\nZ = clf.decision_function(xy).reshape(XX.shape)\n\n# plot decision boundary and margins\na = ax.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5, linestyles=['-'])\n\n# get the separating hyperplane for weighted classes\nZ = wclf.decision_function(xy).reshape(XX.shape)\n\n# plot decision boundary and margins for weighted classes\nb = ax.contour(XX, YY, Z, colors='r', levels=[0], alpha=0.5, linestyles=['-'])\n\nplt.legend([a.collections[0], b.collections[0]], [\"non weighted\", \"weighted\"],\n           loc=\"upper right\")"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\n\n# we create clusters with 1000 and 100 points\nrng = np.random.RandomState(0)\nn_samples_1 = 1000\nn_samples_2 = 100\nX = np.r_[1.5 * rng.randn(n_samples_1, 2),\n          0.5 * rng.randn(n_samples_2, 2) + [2, 2]]\ny = [0] * (n_samples_1) + [1] * (n_samples_2)\n\n# fit the model and get the separating hyperplane\nclf = svm.SVC(kernel='linear', C=1.0)\nclf.fit(X, y)\n\n# fit the model and get the separating hyperplane using weighted classes\nwclf = svm.SVC(kernel='linear', class_weight={1: 10})\nwclf.fit(X, y)\n\n# plot separating hyperplanes and samples\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors='k')\nplt.legend()\n\n# plot the decision functions for both classifiers\nax = plt.gca()\nxlim = ax.get_xlim()\nylim = ax.get_ylim()\n\n# create grid to evaluate model\nxx = np.linspace(xlim[0], xlim[1], 30)\nyy = np.linspace(ylim[0], ylim[1], 30)\nYY, XX = np.meshgrid(yy, xx)\nxy = np.vstack([XX.ravel(), YY.ravel()]).T\n\n# get the separating hyperplane\nZ = clf.decision_function(xy).reshape(XX.shape)\n\n# plot decision boundary and margins\na = ax.contour(XX, YY, Z, colors='k', levels=[0], alpha=0.5, linestyles=['-'])\n\n# get the separating hyperplane for weighted classes\nZ = wclf.decision_function(xy).reshape(XX.shape)\n\n# plot decision boundary and margins for weighted classes\nb = ax.contour(XX, YY, Z, colors='r', levels=[0], alpha=0.5, linestyles=['-'])\n\nplt.legend([a.collections[0], b.collections[0]], [\"non weighted\", \"weighted\"],\n           loc=\"upper right\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -30,7 +30,7 @@
 import matplotlib.pyplot as plt
 from sklearn import svm
 
-# we create 40 separable points
+# we create clusters with 1000 and 100 points
 rng = np.random.RandomState(0)
 n_samples_1 = 1000
 n_samples_2 = 100
@@ -75,3 +75,4 @@
 
 plt.legend([a.collections[0], b.collections[0]], ["non weighted", "weighted"],
            loc="upper right")
+plt.show()