Pushing the docs to dev/ for branch: main, commit b260100656093f24e8682c6c8c66ef520a0c7fa3

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,8 +15,9 @@
 # License: BSD 3 clause (C) INRIA
 
 
-# #############################################################################
+# %%
 # Generate sample data
+# --------------------
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn import neighbors
@@ -29,8 +30,9 @@
 # Add noise to targets
 y[::5] += 1 * (0.5 - np.random.rand(8))
 
-# #############################################################################
+# %%
 # Fit regression model
+# --------------------
 n_neighbors = 5
 
 for i, weights in enumerate(["uniform", "distance"]):

dev/_downloads/437df39fcde24ead7b91917f2133a53c/plot_regression.py

@@ -26,7 +26,43 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Alexandre Gramfort <[email protected]>\n#         Fabian Pedregosa <[email protected]>\n#\n# License: BSD 3 clause (C) INRIA\n\n\n# #############################################################################\n# Generate sample data\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import neighbors\n\nnp.random.seed(0)\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\nT = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nn_neighbors = 5\n\nfor i, weights in enumerate([\"uniform\", \"distance\"]):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X, y).predict(T)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X, y, color=\"darkorange\", label=\"data\")\n    plt.plot(T, y_, color=\"navy\", label=\"prediction\")\n    plt.axis(\"tight\")\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors, weights))\n\nplt.tight_layout()\nplt.show()"
+        "# Author: Alexandre Gramfort <[email protected]>\n#         Fabian Pedregosa <[email protected]>\n#\n# License: BSD 3 clause (C) INRIA"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate sample data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import neighbors\n\nnp.random.seed(0)\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\nT = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fit regression model\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_neighbors = 5\n\nfor i, weights in enumerate([\"uniform\", \"distance\"]):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X, y).predict(T)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X, y, color=\"darkorange\", label=\"data\")\n    plt.plot(T, y_, color=\"navy\", label=\"prediction\")\n    plt.axis(\"tight\")\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors, weights))\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],