Pushing the docs to dev/ for branch: master, commit 354c8c3bc3e36c69021713da66e7fa2f6cb07756

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nfrom sklearn.svm import SVR\nimport matplotlib.pyplot as plt\n\n# #############################################################################\n# Generate sample data\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\ny = np.sin(X).ravel()\n\n# #############################################################################\n# Add noise to targets\ny[::5] += 3 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nsvr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\nsvr_lin = SVR(kernel='linear', C=1e3)\nsvr_poly = SVR(kernel='poly', C=1e3, degree=2)\ny_rbf = svr_rbf.fit(X, y).predict(X)\ny_lin = svr_lin.fit(X, y).predict(X)\ny_poly = svr_poly.fit(X, y).predict(X)\n\n# #############################################################################\n# Look at the results\nlw = 2\nplt.scatter(X, y, color='darkorange', label='data')\nplt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')\nplt.plot(X, y_lin, color='c', lw=lw, label='Linear model')\nplt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')\nplt.xlabel('data')\nplt.ylabel('target')\nplt.title('Support Vector Regression')\nplt.legend()\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nfrom sklearn.svm import SVR\nimport matplotlib.pyplot as plt\n\n# #############################################################################\n# Generate sample data\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\ny = np.sin(X).ravel()\n\n# #############################################################################\n# Add noise to targets\ny[::5] += 3 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nsvr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)\nsvr_lin = SVR(kernel='linear', C=100, gamma='auto')\nsvr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,\n               coef0=1)\ny_rbf = svr_rbf.fit(X, y).predict(X)\ny_lin = svr_lin.fit(X, y).predict(X)\ny_poly = svr_poly.fit(X, y).predict(X)\n\n# #############################################################################\n# Look at the results\nlw = 2\n\nsvrs = [svr_rbf, svr_lin, svr_poly]\nkernel_label = ['RBF', 'Linear', 'Polynomial']\nmodel_color = ['m', 'c', 'g']\n\nfig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)\nfor ix, svr in enumerate(svrs):\n    axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw,\n                  label='{} model'.format(kernel_label[ix]))\n    axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor=\"none\",\n                     edgecolor=model_color[ix], s=50,\n                     label='{} support vectors'.format(kernel_label[ix]))\n    axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)],\n                     y[np.setdiff1d(np.arange(len(X)), svr.support_)],\n                     facecolor=\"none\", edgecolor=\"k\", s=50,\n                     label='other training data')\n    axes[ix].legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),\n                    ncol=1, fancybox=True, shadow=True)\n\nfig.text(0.5, 0.04, 'data', ha='center', va='center')\nfig.text(0.06, 0.5, 'target', ha='center', va='center', rotation='vertical')\nfig.suptitle(\"Support Vector Regression\", fontsize=14)\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -23,22 +23,37 @@
 
 # #############################################################################
 # Fit regression model
-svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
-svr_lin = SVR(kernel='linear', C=1e3)
-svr_poly = SVR(kernel='poly', C=1e3, degree=2)
+svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
+svr_lin = SVR(kernel='linear', C=100, gamma='auto')
+svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,
+               coef0=1)
 y_rbf = svr_rbf.fit(X, y).predict(X)
 y_lin = svr_lin.fit(X, y).predict(X)
 y_poly = svr_poly.fit(X, y).predict(X)
 
 # #############################################################################
 # Look at the results
 lw = 2
-plt.scatter(X, y, color='darkorange', label='data')
-plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')
-plt.plot(X, y_lin, color='c', lw=lw, label='Linear model')
-plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')
-plt.xlabel('data')
-plt.ylabel('target')
-plt.title('Support Vector Regression')
-plt.legend()
+
+svrs = [svr_rbf, svr_lin, svr_poly]
+kernel_label = ['RBF', 'Linear', 'Polynomial']
+model_color = ['m', 'c', 'g']
+
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
+for ix, svr in enumerate(svrs):
+    axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw,
+                  label='{} model'.format(kernel_label[ix]))
+    axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor="none",
+                     edgecolor=model_color[ix], s=50,
+                     label='{} support vectors'.format(kernel_label[ix]))
+    axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)],
+                     y[np.setdiff1d(np.arange(len(X)), svr.support_)],
+                     facecolor="none", edgecolor="k", s=50,
+                     label='other training data')
+    axes[ix].legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
+                    ncol=1, fancybox=True, shadow=True)
+
+fig.text(0.5, 0.04, 'data', ha='center', va='center')
+fig.text(0.06, 0.5, 'target', ha='center', va='center', rotation='vertical')
+fig.suptitle("Support Vector Regression", fontsize=14)
 plt.show()