minor bug fixes

lbl-camera · Sep 22, 2023 · 1ec275c · 1ec275c
1 parent 5d754b8
commit 1ec275c
Showing 1 changed file with 10 additions and 4 deletions.
diff --git a/fvgp/gp.py b/fvgp/gp.py
@@ -1637,6 +1637,9 @@ def mutual_information(self,joint,m1,m2):
             The first marginal distribution
         m2 : np.ndarray
             The second marginal distribution
+        Return:
+        -------
+        mutual information : float
         """
         return self.entropy(m1) + self.entropy(m2) - self.entropy(joint)
     ###########################################################################
@@ -1670,7 +1673,8 @@ def gp_mutual_information(self,x_pred, x_out = None):
         joint_covariance = \
                 np.asarray(np.block([[K,k],\
                                      [k.T,  kk]]))
-        return self.mutual_information(joint_covariance, kk, K)
+        return {"x":x_pred,
+                "mutual information":self.mutual_information(joint_covariance, kk, K)}
 
     ###########################################################################
     def gp_total_correlation(self,x_pred, x_out = None):
@@ -1705,8 +1709,10 @@ def gp_total_correlation(self,x_pred, x_out = None):
 
         prod_covariance = np.asarray(np.block([[K, k * 0.],\
                                                [k.T * 0.,   kk * np.identity(len(kk))]]))
-        return self.kl_div(np.zeros((len(joint_covariance))),np.zeros((len(joint_covariance))),joint_covariance,prod_covariance)
 
+
+        return {"x":x_pred,
+                "total correlation":self.kl_div(np.zeros((len(joint_covariance))),np.zeros((len(joint_covariance))),joint_covariance,prod_covariance)}
     ###########################################################################
     def shannon_information_gain(self, x_pred, x_out = None):
         """
@@ -1735,7 +1741,7 @@ def shannon_information_gain(self, x_pred, x_out = None):
         if x_out is not None: x_pred = self._cartesian_product(x_pred,x_out)
 
         return {"x": x_pred,
-                "sig":np.exp(-self.gp_total_correlation(x_pred, x_out = None))}
+                "sig":np.exp(-self.gp_total_correlation(x_pred, x_out = None)["total correlation"])}
 
     ###########################################################################
     def shannon_information_gain_vec(self, x_pred, x_out = None):
@@ -1759,7 +1765,7 @@ def shannon_information_gain_vec(self, x_pred, x_out = None):
 
         sig = np.zeros((len(x_pred)))
         for i in range(len(x_pred)):
-            sig[i] = np.exp(-self.gp_mutual_information(x_pred[i].reshape(1,len(x_pred[i])), x_out = None))
+            sig[i] = np.exp(-self.gp_mutual_information(x_pred[i].reshape(1,len(x_pred[i])), x_out = None)["mutual information"])
 
         return {"x": x_pred,
                 "sig(x)":sig}