Pushing the docs to dev/ for branch: main, commit c26000ed263c4c85b0959ec384d2ead3cf39e113

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,43 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Fabian Pedregosa -- <[email protected]>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import linear_model\n\n# X is the 10x10 Hilbert matrix\nX = 1.0 / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])\ny = np.ones(10)\n\n# #############################################################################\n# Compute paths\n\nn_alphas = 200\nalphas = np.logspace(-10, -2, n_alphas)\n\ncoefs = []\nfor a in alphas:\n    ridge = linear_model.Ridge(alpha=a, fit_intercept=False)\n    ridge.fit(X, y)\n    coefs.append(ridge.coef_)\n\n# #############################################################################\n# Display results\n\nax = plt.gca()\n\nax.plot(alphas, coefs)\nax.set_xscale(\"log\")\nax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\nplt.xlabel(\"alpha\")\nplt.ylabel(\"weights\")\nplt.title(\"Ridge coefficients as a function of the regularization\")\nplt.axis(\"tight\")\nplt.show()"
+        "# Author: Fabian Pedregosa -- <[email protected]>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import linear_model\n\n# X is the 10x10 Hilbert matrix\nX = 1.0 / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])\ny = np.ones(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute paths\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_alphas = 200\nalphas = np.logspace(-10, -2, n_alphas)\n\ncoefs = []\nfor a in alphas:\n    ridge = linear_model.Ridge(alpha=a, fit_intercept=False)\n    ridge.fit(X, y)\n    coefs.append(ridge.coef_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Display results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "ax = plt.gca()\n\nax.plot(alphas, coefs)\nax.set_xscale(\"log\")\nax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\nplt.xlabel(\"alpha\")\nplt.ylabel(\"weights\")\nplt.title(\"Ridge coefficients as a function of the regularization\")\nplt.axis(\"tight\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/6caa16249d07b4f3e57d5f3bf102b137/plot_ridge_path.ipynb

@@ -38,8 +38,9 @@
 X = 1.0 / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])
 y = np.ones(10)
 
-# #############################################################################
+# %%
 # Compute paths
+# -------------
 
 n_alphas = 200
 alphas = np.logspace(-10, -2, n_alphas)
@@ -50,8 +51,9 @@
     ridge.fit(X, y)
     coefs.append(ridge.coef_)
 
-# #############################################################################
+# %%
 # Display results
+# ---------------
 
 ax = plt.gca()