minor typos

Author: MarcusMNoack

examples/SingleTaskTest.ipynb

@@ -291,13 +291,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "a4441014",
+   "cell_type": "markdown",
+   "id": "256bd1e7",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# HGDL"
+    "#### HGDL"
    ]
   },
   {
@@ -346,6 +344,14 @@
     "my_gp1.stop_training(opt_obj) ##this leaves the dask client alive, kill_client() will shut it down. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dc1b996b",
+   "metadata": {},
+   "source": [
+    "#### ADAM"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 21,
@@ -392,6 +398,14 @@
     "my_gp1.stop_training(opt_obj) ##this leaves the dask client alive, kill_client() will shut it down."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "285da8c2",
+   "metadata": {},
+   "source": [
+    "#### MCMC"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 22,

fvgp/fvgp.py

@@ -60,7 +60,7 @@ class fvGP(GP):
         computation relies on this: when a hyperparameter index belongs to the mean
         function its kernel derivative is assumed zero, and vice versa.
     noise_variances : np.ndarray, optional
-        An numpy array defining the uncertainties/noise in the
+        A numpy array defining the uncertainties/noise in the
         ``y_data`` in form of a point-wise variance. Shape (V, No).
         If ``y_data`` has np.nan entries, the corresponding
         ``noise_variances`` have to be np.nan.
@@ -388,7 +388,7 @@ def update_gp_data(
             It is possible that not every entry in ``x_new``
             has all corresponding tasks available. In that case ``y_new`` may contain np.nan entries.
         noise_variances_new : np.ndarray, optional
-            An numpy array or list defining the uncertainties/noise in the
+            A numpy array or list defining the uncertainties/noise in the
             ``y_data`` in form of a point-wise variance. Shape (V, No).
             If ``y_data`` has np.nan entries, the corresponding
             ``noise_variances`` have to be np.nan.

fvgp/gp.py

@@ -62,7 +62,7 @@ class GP:
         computation relies on this: when a hyperparameter index belongs to the mean
         function its kernel derivative is assumed zero, and vice versa.
     noise_variances : np.ndarray, optional
-        An numpy array defining the uncertainties/noise in the
+        A numpy array defining the uncertainties/noise in the
         ``y_data`` in form of a point-wise variance. Shape (V).
         Note: if no noise_variances are provided here, the noise_function
         callable will be used; if the callable is not provided, the noise variances
@@ -455,7 +455,7 @@ def update_gp_data(
         y_new : np.ndarray
             The values of the data points. Shape (V).
         noise_variances_new : np.ndarray, optional
-            An numpy array defining the uncertainties in the data ``y_data`` in form of a point-wise variance.
+            A numpy array defining the uncertainties in the data ``y_data`` in form of a point-wise variance.
             Shape(y_new)==Shape(noise_variances_new).
             Note: if no variances are provided here, the noise_covariance
             callable will be used; if the callable is not provided the noise variances