Pushing the docs to dev/ for branch: master, commit 61e722aa126207efcdbc1ddcd4453854ad44ea09

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.neighbors import LocalOutlierFactor\n\nnp.random.seed(42)\n\n# Generate train data\nX = 0.3 * np.random.randn(100, 2)\n# Generate some abnormal novel observations\nX_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\nX = np.r_[X + 2, X - 2, X_outliers]\n\n# fit the model\nclf = LocalOutlierFactor(n_neighbors=20)\ny_pred = clf.fit_predict(X)\ny_pred_outliers = y_pred[200:]\n\n# plot the level sets of the decision function\nxx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))\nZ = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\nplt.title(\"Local Outlier Factor (LOF)\")\nplt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)\n\na = plt.scatter(X[:200, 0], X[:200, 1], c='white',\n                edgecolor='k', s=20)\nb = plt.scatter(X[200:, 0], X[200:, 1], c='red',\n                edgecolor='k', s=20)\nplt.axis('tight')\nplt.xlim((-5, 5))\nplt.ylim((-5, 5))\nplt.legend([a, b],\n           [\"normal observations\",\n            \"abnormal observations\"],\n           loc=\"upper left\")\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import LocalOutlierFactor\n\nnp.random.seed(42)\n\n# Generate train data\nX_inliers = 0.3 * np.random.randn(100, 2)\nX_inliers = np.r_[X_inliers + 2, X_inliers - 2]\n\n# Generate some abnormal novel observations\nX_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\nX = np.r_[X_inliers, X_outliers]\n\n# fit the model\nclf = LocalOutlierFactor(n_neighbors=20)\ny_pred = clf.fit_predict(X)\n\n# plot the level sets of the decision function\nxx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))\nZ = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\nplt.title(\"Local Outlier Factor (LOF)\")\nplt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)\n\na = plt.scatter(X_inliers[:, 0], X_inliers[:, 1], c='white',\n                edgecolor='k', s=20)\nb = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red',\n                edgecolor='k', s=20)\nplt.axis('tight')\nplt.xlim((-5, 5))\nplt.ylim((-5, 5))\nplt.legend([a, b],\n           [\"normal observations\",\n            \"abnormal observations\"],\n           loc=\"upper left\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -26,15 +26,16 @@
 np.random.seed(42)
 
 # Generate train data
-X = 0.3 * np.random.randn(100, 2)
+X_inliers = 0.3 * np.random.randn(100, 2)
+X_inliers = np.r_[X_inliers + 2, X_inliers - 2]
+
 # Generate some abnormal novel observations
 X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
-X = np.r_[X + 2, X - 2, X_outliers]
+X = np.r_[X_inliers, X_outliers]
 
 # fit the model
 clf = LocalOutlierFactor(n_neighbors=20)
 y_pred = clf.fit_predict(X)
-y_pred_outliers = y_pred[200:]
 
 # plot the level sets of the decision function
 xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
@@ -44,9 +45,9 @@
 plt.title("Local Outlier Factor (LOF)")
 plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
 
-a = plt.scatter(X[:200, 0], X[:200, 1], c='white',
+a = plt.scatter(X_inliers[:, 0], X_inliers[:, 1], c='white',
                 edgecolor='k', s=20)
-b = plt.scatter(X[200:, 0], X[200:, 1], c='red',
+b = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red',
                 edgecolor='k', s=20)
 plt.axis('tight')
 plt.xlim((-5, 5))