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@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "# visualize the decision surface, projected down to the first\n# two principal components of the dataset\npca = PCA(n_components=8).fit(data_train)\n\nX = pca.transform(data_train)\n\n# Generate grid along first two principal components\nmultiples = np.arange(-2, 2, 0.1)\n# steps along first component\nfirst = multiples[:, np.newaxis] * pca.components_[0, :]\n# steps along second component\nsecond = multiples[:, np.newaxis] * pca.components_[1, :]\n# combine\ngrid = first[np.newaxis, :, :] + second[:, np.newaxis, :]\nflat_grid = grid.reshape(-1, data.shape[1])\n\n# title for the plots\ntitles = [\n    \"SVC with rbf kernel\",\n    \"SVC (linear kernel)\\n with Fourier rbf feature map\\nn_components=100\",\n    \"SVC (linear kernel)\\n with Nystroem rbf feature map\\nn_components=100\",\n]\n\nplt.figure(figsize=(18, 7.5))\nplt.rcParams.update({\"font.size\": 14})\n# predict and plot\nfor i, clf in enumerate((kernel_svm, nystroem_approx_svm, fourier_approx_svm)):\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    plt.subplot(1, 3, i + 1)\n    Z = clf.predict(flat_grid)\n\n    # Put the result into a color plot\n    Z = Z.reshape(grid.shape[:-1])\n    plt.contourf(multiples, multiples, Z, cmap=plt.cm.Paired)\n    plt.axis(\"off\")\n\n    # Plot also the training points\n    plt.scatter(\n        X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired, edgecolors=(0, 0, 0)\n    )\n\n    plt.title(titles[i])\nplt.tight_layout()\nplt.show()"
+        "# visualize the decision surface, projected down to the first\n# two principal components of the dataset\npca = PCA(n_components=8).fit(data_train)\n\nX = pca.transform(data_train)\n\n# Generate grid along first two principal components\nmultiples = np.arange(-2, 2, 0.1)\n# steps along first component\nfirst = multiples[:, np.newaxis] * pca.components_[0, :]\n# steps along second component\nsecond = multiples[:, np.newaxis] * pca.components_[1, :]\n# combine\ngrid = first[np.newaxis, :, :] + second[:, np.newaxis, :]\nflat_grid = grid.reshape(-1, data.shape[1])\n\n# title for the plots\ntitles = [\n    \"SVC with rbf kernel\",\n    \"SVC (linear kernel)\\n with Fourier rbf feature map\\nn_components=100\",\n    \"SVC (linear kernel)\\n with Nystroem rbf feature map\\nn_components=100\",\n]\n\nplt.figure(figsize=(18, 7.5))\nplt.rcParams.update({\"font.size\": 14})\n# predict and plot\nfor i, clf in enumerate((kernel_svm, nystroem_approx_svm, fourier_approx_svm)):\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    plt.subplot(1, 3, i + 1)\n    Z = clf.predict(flat_grid)\n\n    # Put the result into a color plot\n    Z = Z.reshape(grid.shape[:-1])\n    levels = np.arange(10)\n    lv_eps = 0.01  # Adjust a mapping from calculated contour levels to color.\n    plt.contourf(\n        multiples,\n        multiples,\n        Z,\n        levels=levels - lv_eps,\n        cmap=plt.cm.tab10,\n        vmin=0,\n        vmax=10,\n        alpha=0.7,\n    )\n    plt.axis(\"off\")\n\n    # Plot also the training points\n    plt.scatter(\n        X[:, 0],\n        X[:, 1],\n        c=targets_train,\n        cmap=plt.cm.tab10,\n        edgecolors=(0, 0, 0),\n        vmin=0,\n        vmax=10,\n    )\n\n    plt.title(titles[i])\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],
 
@@ -223,12 +223,29 @@
 
     # Put the result into a color plot
     Z = Z.reshape(grid.shape[:-1])
-    plt.contourf(multiples, multiples, Z, cmap=plt.cm.Paired)
+    levels = np.arange(10)
+    lv_eps = 0.01  # Adjust a mapping from calculated contour levels to color.
+    plt.contourf(
+        multiples,
+        multiples,
+        Z,
+        levels=levels - lv_eps,
+        cmap=plt.cm.tab10,
+        vmin=0,
+        vmax=10,
+        alpha=0.7,
+    )
     plt.axis("off")
 
     # Plot also the training points
     plt.scatter(
-        X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired, edgecolors=(0, 0, 0)
+        X[:, 0],
+        X[:, 1],
+        c=targets_train,
+        cmap=plt.cm.tab10,
+        edgecolors=(0, 0, 0),
+        vmin=0,
+        vmax=10,
     )
 
     plt.title(titles[i])
Original file line number	Diff line number	Diff line change
`@@ -69,7 +69,7 @@`
`69`	`69`	`},`
`70`	`70`	`"outputs": [],`
`71`	`71`	`"source": [`
`72`		- "# visualize the decision surface, projected down to the first\n# two principal components of the dataset\npca = PCA(n_components=8).fit(data_train)\n\nX = pca.transform(data_train)\n\n# Generate grid along first two principal components\nmultiples = np.arange(-2, 2, 0.1)\n# steps along first component\nfirst = multiples[:, np.newaxis] * pca.components_[0, :]\n# steps along second component\nsecond = multiples[:, np.newaxis] * pca.components_[1, :]\n# combine\ngrid = first[np.newaxis, :, :] + second[:, np.newaxis, :]\nflat_grid = grid.reshape(-1, data.shape[1])\n\n# title for the plots\ntitles = [\n \"SVC with rbf kernel\",\n \"SVC (linear kernel)\\n with Fourier rbf feature map\\nn_components=100\",\n \"SVC (linear kernel)\\n with Nystroem rbf feature map\\nn_components=100\",\n]\n\nplt.figure(figsize=(18, 7.5))\nplt.rcParams.update({\"font.size\": 14})\n# predict and plot\nfor i, clf in enumerate((kernel_svm, nystroem_approx_svm, fourier_approx_svm)):\n # Plot the decision boundary. For that, we will assign a color to each\n # point in the mesh [x_min, x_max]x[y_min, y_max].\n plt.subplot(1, 3, i + 1)\n Z = clf.predict(flat_grid)\n\n # Put the result into a color plot\n Z = Z.reshape(grid.shape[:-1])\n plt.contourf(multiples, multiples, Z, cmap=plt.cm.Paired)\n plt.axis(\"off\")\n\n # Plot also the training points\n plt.scatter(\n X[:, 0], X[:, 1], c=targets_train, cmap=plt.cm.Paired, edgecolors=(0, 0, 0)\n )\n\n plt.title(titles[i])\nplt.tight_layout()\nplt.show()"
	`72`	+ "# visualize the decision surface, projected down to the first\n# two principal components of the dataset\npca = PCA(n_components=8).fit(data_train)\n\nX = pca.transform(data_train)\n\n# Generate grid along first two principal components\nmultiples = np.arange(-2, 2, 0.1)\n# steps along first component\nfirst = multiples[:, np.newaxis] * pca.components_[0, :]\n# steps along second component\nsecond = multiples[:, np.newaxis] * pca.components_[1, :]\n# combine\ngrid = first[np.newaxis, :, :] + second[:, np.newaxis, :]\nflat_grid = grid.reshape(-1, data.shape[1])\n\n# title for the plots\ntitles = [\n \"SVC with rbf kernel\",\n \"SVC (linear kernel)\\n with Fourier rbf feature map\\nn_components=100\",\n \"SVC (linear kernel)\\n with Nystroem rbf feature map\\nn_components=100\",\n]\n\nplt.figure(figsize=(18, 7.5))\nplt.rcParams.update({\"font.size\": 14})\n# predict and plot\nfor i, clf in enumerate((kernel_svm, nystroem_approx_svm, fourier_approx_svm)):\n # Plot the decision boundary. For that, we will assign a color to each\n # point in the mesh [x_min, x_max]x[y_min, y_max].\n plt.subplot(1, 3, i + 1)\n Z = clf.predict(flat_grid)\n\n # Put the result into a color plot\n Z = Z.reshape(grid.shape[:-1])\n levels = np.arange(10)\n lv_eps = 0.01 # Adjust a mapping from calculated contour levels to color.\n plt.contourf(\n multiples,\n multiples,\n Z,\n levels=levels - lv_eps,\n cmap=plt.cm.tab10,\n vmin=0,\n vmax=10,\n alpha=0.7,\n )\n plt.axis(\"off\")\n\n # Plot also the training points\n plt.scatter(\n X[:, 0],\n X[:, 1],\n c=targets_train,\n cmap=plt.cm.tab10,\n edgecolors=(0, 0, 0),\n vmin=0,\n vmax=10,\n )\n\n plt.title(titles[i])\nplt.tight_layout()\nplt.show()"
`73`	`73`	`]`
`74`	`74`	`}`
`75`	`75`	`],`