Cast return values of JIDT estimators to float

Cast return values of JIDT estimators to float. Without this, return
values may be JPype types. This is inconvenient, for example, when
saving IDTxl results to disk and loading them in a new session, JPype
types require that a JVM is running to load the results. If the JVM
does not run, pickle fails to load the results with a hard to
understand error message.

idtxl/estimators_jidt.py

@@ -474,7 +474,7 @@ def estimate(self, var1, var2, conditional=None):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 class JidtDiscreteCMI(JidtDiscrete):
@@ -627,7 +627,7 @@ def estimate(self, var1, var2, conditional=None, return_calc=False):
                 jp.JArray(jp.JInt, 1)(var2.tolist()),
                 jp.JArray(jp.JInt, 1)(conditional.tolist())))
         else:
-            result = self.calc.computeAverageLocalOfObservations()
+            result = float(self.calc.computeAverageLocalOfObservations())
         if return_calc:
             return (result, self.calc)
         else:
@@ -784,7 +784,7 @@ def estimate(self, var1, var2, return_calc=False):
                 jp.JArray(jp.JInt, 1)(var1.tolist()),
                 jp.JArray(jp.JInt, 1)(var2.tolist())))
         else:
-            result = self.calc.computeAverageLocalOfObservations()
+            result = float(self.calc.computeAverageLocalOfObservations())
         if return_calc:
             return (result, self.calc)
         else:
@@ -908,7 +908,7 @@ def estimate(self, var1, var2):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 class JidtKraskovAIS(JidtKraskov):
@@ -999,7 +999,7 @@ def estimate(self, process):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 class JidtDiscreteAIS(JidtDiscrete):
@@ -1125,7 +1125,7 @@ def estimate(self, process, return_calc=False):
             result = np.array(self.calc.computeLocalFromPreviousObservations(
                                     jp.JArray(jp.JInt, 1)(process.tolist())))
         else:
-            result = self.calc.computeAverageLocalOfObservations()
+            result = float(self.calc.computeAverageLocalOfObservations())
         if return_calc:
             return (result, self.calc)
         else:
@@ -1228,7 +1228,7 @@ def estimate(self, process):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 class JidtGaussianMI(JidtGaussian):
@@ -1300,7 +1300,7 @@ def estimate(self, var1, var2):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 class JidtGaussianCMI(JidtGaussian):
@@ -1387,7 +1387,7 @@ def estimate(self, var1, var2, conditional=None):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
     def get_analytic_distribution(self, var1, var2, conditional=None):
         """Return a JIDT AnalyticNullDistribution object.
@@ -1507,7 +1507,7 @@ def estimate(self, source, target):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 class JidtDiscreteTE(JidtDiscrete):
@@ -1652,7 +1652,7 @@ def estimate(self, source, target, return_calc=False):
                 jp.JArray(jp.JInt, 1)(source.tolist()),
                 jp.JArray(jp.JInt, 1)(target.tolist())))
         else:
-            result = self.calc.computeAverageLocalOfObservations()
+            result = float(self.calc.computeAverageLocalOfObservations())
         if return_calc:
             return (result, self.calc)
         else:
@@ -1765,7 +1765,7 @@ def estimate(self, source, target):
         if self.settings['local_values']:
             return np.array(self.calc.computeLocalOfPreviousObservations())
         else:
-            return self.calc.computeAverageLocalOfObservations()
+            return float(self.calc.computeAverageLocalOfObservations())
 
 
 def common_estimate_surrogates_analytic(estimator, n_perm=200, **data):