Merge branch 'master' of github.com:lbl-camera/fvGP

fvgp/gp.py

@@ -38,9 +38,9 @@ class GP:
     ----------
     input_space_dim : int
         Dimensionality of the input space (D). If the input is non-Euclidean, the input dimensionality will be ignored.
-    x_data : np.ndarray or set or list
+    x_data : np.ndarray or list of tuples
         The input point positions. Shape (V x D), where D is the `input_space_dim`. If dealing with non-Euclidean inputs
-        x_data should be a set or iterable, not a numpy array.
+        x_data should be an iterable, not a numpy array.
     y_data : np.ndarray
         The values of the data points. Shape (V,1) or (V).
     init_hyperparameters : np.ndarray, optional
@@ -2001,8 +2001,10 @@ def gp_total_correlation(self, x_pred, x_out=None):
     ###########################################################################
     def gp_relative_information_entropy(self, x_pred, x_out=None):
         """
-        Function to compute KL divergence and therefore relative information entropy
-        of the prior distribution over-predicted function values and the posterior distribution.
+        Function to compute the KL divergence and therefore the relative information entropy
+        of the prior distribution over predicted function values and the posterior distribution.
+        The value is a reflection of how much information is predicted to be gained 
+        through observing a set of data points at x_pred.
 
         Parameters
         ----------
@@ -2033,8 +2035,12 @@ def gp_relative_information_entropy(self, x_pred, x_out=None):
     ###########################################################################
     def gp_relative_information_entropy_set(self, x_pred, x_out=None):
         """
-        Function to compute KL divergence and therefore relative information entropy
-        if the prior distribution over-predicted function values and the posterior distribution.
+        Function to compute the KL divergence and therefore the relative information entropy
+        of the prior distribution over predicted function values and the posterior distribution.
+        The value is a reflection of how much information is predicted to be gained 
+        through observing each data point in x_pred separately, not all 
+        at once as in `gp_relative_information_entrop`.
+
 
         Parameters
         ----------