Update and documentation of InputValuesTest

[modules/adao.git] / src / daComposant / daAlgorithms / DifferentialEvolution.py

diff --git a/src/daComposant/daAlgorithms/DifferentialEvolution.py b/src/daComposant/daAlgorithms/DifferentialEvolution.py
index 381d4134e021e72d31287a333b937e7c15b41131..8c2b09aeb08232c2e418eb98a5e8e7677f504f8c 100644 (file)
--- a/src/daComposant/daAlgorithms/DifferentialEvolution.py
+++ b/src/daComposant/daAlgorithms/DifferentialEvolution.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2018 EDF R&D
+# Copyright (C) 2008-2020 EDF R&D
 #
 # This library is free software; you can redistribute it and/or
 # modify it under the terms of the GNU Lesser General Public
@@ -38,11 +38,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 "BEST1EXP",
                 "RAND1EXP",
                 "RANDTOBEST1EXP",
-                "CURRENTTOBEST1EXP",
+                # "CURRENTTOBEST1EXP",
                 "BEST2EXP",
                 "RAND2EXP",
                 "RANDTOBEST1BIN",
-                "CURRENTTOBEST1BIN",
+                # "CURRENTTOBEST1BIN",
                 "BEST2BIN",
                 "RAND2BIN",
                 "RAND1BIN",
@@ -108,6 +108,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
             listval  = [
+                "Analysis",
                 "BMA",
                 "CostFunctionJ",
                 "CostFunctionJb",
@@ -166,10 +167,10 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             _HX = Hm( _X )
             _HX = numpy.asmatrix(numpy.ravel( _HX )).T
             _Innovation = Y - _HX
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"] or \
-               "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtCurrentState") or \
+                self._toStore("SimulatedObservationAtCurrentOptimum"):
                 self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
-            if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("InnovationAtCurrentState"):
                 self.StoredVariables["InnovationAtCurrentState"].store( _Innovation )
             #
             if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","DA"]:
@@ -197,24 +198,24 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
-            if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"] or \
-               "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("IndexOfOptimum") or \
+                self._toStore("CurrentOptimum") or \
+                self._toStore("CostFunctionJAtCurrentOptimum") or \
+                self._toStore("CostFunctionJbAtCurrentOptimum") or \
+                self._toStore("CostFunctionJoAtCurrentOptimum") or \
+                self._toStore("SimulatedObservationAtCurrentOptimum"):
                 IndexMin = numpy.argmin( self.StoredVariables["CostFunctionJ"][nbPreviousSteps:] ) + nbPreviousSteps
-            if "IndexOfOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("IndexOfOptimum"):
                 self.StoredVariables["IndexOfOptimum"].store( IndexMin )
-            if "CurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("CurrentOptimum"):
                 self.StoredVariables["CurrentOptimum"].store( self.StoredVariables["CurrentState"][IndexMin] )
-            if "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtCurrentOptimum"):
                 self.StoredVariables["SimulatedObservationAtCurrentOptimum"].store( self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin] )
-            if "CostFunctionJAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("CostFunctionJAtCurrentOptimum"):
                 self.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( self.StoredVariables["CostFunctionJ" ][IndexMin] )
-            if "CostFunctionJbAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("CostFunctionJbAtCurrentOptimum"):
                 self.StoredVariables["CostFunctionJbAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJb"][IndexMin] )
-            if "CostFunctionJoAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("CostFunctionJoAtCurrentOptimum"):
                 self.StoredVariables["CostFunctionJoAtCurrentOptimum"].store( self.StoredVariables["CostFunctionJo"][IndexMin] )
             return J
         #
@@ -249,28 +250,27 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
-        if "OMA"                           in self._parameters["StoreSupplementaryCalculations"] or \
-           "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMA") or self._toStore("SimulatedObservationAtOptimum"):
+            if self._toStore("SimulatedObservationAtCurrentState"):
                 HXa = self.StoredVariables["SimulatedObservationAtCurrentState"][IndexMin]
-            elif "SimulatedObservationAtCurrentOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+            elif self._toStore("SimulatedObservationAtCurrentOptimum"):
                 HXa = self.StoredVariables["SimulatedObservationAtCurrentOptimum"][-1]
             else:
                 HXa = Hm(Xa)
-        if "Innovation" in self._parameters["StoreSupplementaryCalculations"] or \
-            "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("Innovation") or \
+           self._toStore("OMB"):
             d  = Y - HXb
-        if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("Innovation"):
             self.StoredVariables["Innovation"].store( numpy.ravel(d) )
-        if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMB"):
             self.StoredVariables["OMB"].store( numpy.ravel(d) )
-        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("BMA"):
             self.StoredVariables["BMA"].store( numpy.ravel(Xb) - numpy.ravel(Xa) )
-        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMA"):
             self.StoredVariables["OMA"].store( numpy.ravel(Y) - numpy.ravel(HXa) )
-        if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtBackground"):
             self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(Hm(Xb)) )
-        if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtOptimum"):
             self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
         #
         self._post_run()
@@ -278,4 +278,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')