very small changes to the example notebook

learner.ipynb

@@ -747,7 +747,7 @@
     "\n",
     "# The `cdims` will automatically be set when using `from_product`, so\n",
     "# `plot()` will return a HoloMap with correctly labeled sliders.\n",
-    "learner.plot().overlay('beta').grid()"
+    "learner.plot().overlay('beta').grid().select(y=(-1, 3))"
    ]
   },
   {
@@ -859,7 +859,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def g(x, noise_level=0.1):\n",
+    "def F(x, noise_level=0.1):\n",
     "    return (np.sin(5 * x) * (1 - np.tanh(x ** 2))\n",
     "            + np.random.randn() * noise_level)"
    ]
@@ -870,7 +870,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "learner = adaptive.SKOptLearner(g, dimensions=[(-2., 2.)],\n",
+    "learner = adaptive.SKOptLearner(F, dimensions=[(-2., 2.)],\n",
     "                                base_estimator=\"GP\",\n",
     "                                acq_func=\"gp_hedge\",\n",
     "                                acq_optimizer=\"lbfgs\",\n",
@@ -887,7 +887,7 @@
    "source": [
     "%%opts Overlay [legend_position='top']\n",
     "xs = np.linspace(*learner.space.bounds[0])\n",
-    "to_learn = hv.Curve((xs, [g(x, 0) for x in xs]), label='to learn')\n",
+    "to_learn = hv.Curve((xs, [F(x, 0) for x in xs]), label='to learn')\n",
     "\n",
     "runner.live_plot().relabel('prediction', depth=2) * to_learn"
    ]