tested the examples

docs/source/examples/MultiTaskTest.ipynb

@@ -17,7 +17,7 @@
    "outputs": [],
    "source": [
     "##first install the newest version of fvgp\n",
-    "#!pip install fvgp==4.0.0"
+    "#!pip install fvgp==4.0.5"
    ]
   },
   {
@@ -133,7 +133,7 @@
    "source": [
     "## A simple kernel definition\n",
     "It is vital in the multi-task case to think hard about kernel design. The kernel is now a function\n",
-    "over $\\mathcal{X} \\times \\mathcal{X} \\times T \\times T$, where $\\mathcal{X}$ is the input and $T$ is the output space. Print the input into kernel, it will have the dimensionality of this cartesian product space. \n",
+    "over X x X x T x T, where X is the input and T is the output space. Print the input into kernel, it will have the dimensionality of this cartesian product space. \n",
     "The default kernel in fvgp is a deep kernel, which can be good, but there is no guarantee.\n",
     "To use the default kernel, pytorch has to be installed manually (pip install torch)."
    ]
@@ -182,7 +182,7 @@
     "\n",
     "#my_gp2.update_gp_data(x_data3,y_data3)\n",
     "print(\"Global Training in progress\")\n",
-    "my_gp2.train(hps_bounds, max_iter = 200)"
+    "my_gp2.train(hps_bounds, max_iter = 20)"
    ]
   },
   {
@@ -272,6 +272,14 @@
     "fig.show()\n",
     "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80723910",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/source/examples/NonEuclideanInputSpaces.ipynb

@@ -8,7 +8,7 @@
     "# GPs on Non-Euclidean Input Spaces\n",
     "GPs on non-euclidean input spaces have become more and more relevant in recent years. fvgp can be used for that purpose as long as a correct kernel is defined. Of course if mean functions and noise functions are also provided, they have to operate on these non-Euclidean spaces. \n",
     "\n",
-    "In this example, we run a small GP on words. It's a proof of concept, the result are not super relevant"
+    "In this example, we run a small GP on words. It's a proof of concept, the result are not super relevant."
    ]
   },
   {
@@ -19,7 +19,7 @@
    "outputs": [],
    "source": [
     "#install the newest version of fvgp\n",
-    "#!pip install fvgp==4.0.0"
+    "#!pip install fvgp==4.0.5"
    ]
   },
   {

docs/source/examples/SingleTaskTest.ipynb

@@ -24,7 +24,7 @@
    "outputs": [],
    "source": [
     "##first install the newest version of fvgp\n",
-    "#!pip install fvgp==4.0.0"
+    "#!pip install fvgp==4.0.5"
    ]
   },
   {