Improvement of internal pre run

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

diff --git a/src/daComposant/daAlgorithms/QuantileRegression.py b/src/daComposant/daAlgorithms/QuantileRegression.py
index 218efb4fdb8a663515c8f80debd0ee4158697292..671bfa5ee2e5f69de7a7b67f098e634706a7a787 100644 (file)
--- a/src/daComposant/daAlgorithms/QuantileRegression.py
+++ b/src/daComposant/daAlgorithms/QuantileRegression.py
@@ -1,24 +1,24 @@
 #-*-coding:iso-8859-1-*-
 #
-#  Copyright (C) 2008-2015 EDF R&D
+# Copyright (C) 2008-2017 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
-#  License as published by the Free Software Foundation; either
-#  version 2.1 of the License.
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
 #
-#  This library is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-#  Lesser General Public License for more details.
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
 #
-#  You should have received a copy of the GNU Lesser General Public
-#  License along with this library; if not, write to the Free Software
-#  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
 #
-#  See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
 #
-#  Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
 from daCore import BasicObjects
@@ -67,7 +67,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -75,26 +75,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
-        self._pre_run()
+        self._pre_run(Parameters)
         #
-        # Paramètres de pilotage
-        # ----------------------
-        self.setParameters(Parameters)
-        #
-        if self._parameters.has_key("Bounds") and (type(self._parameters["Bounds"]) is type([]) or type(self._parameters["Bounds"]) is type(())) and (len(self._parameters["Bounds"]) > 0):
-            Bounds = self._parameters["Bounds"]
-            logging.debug("%s Prise en compte des bornes effectuee"%(self._name,))
-        else:
-            Bounds = None
-        #
-        # Opérateur d'observation
-        # -----------------------
         Hm = HO["Direct"].appliedTo
         #
         # Utilisation éventuelle d'un vecteur H(Xb) précalculé
         # ----------------------------------------------------
         if HO["AppliedToX"] is not None and HO["AppliedToX"].has_key("HXb"):
-            HXb = HO["AppliedToX"]["HXb"]
+            HXb = Hm( Xb, HO["AppliedToX"]["HXb"])
         else:
             HXb = Hm( Xb )
         HXb = numpy.asmatrix(numpy.ravel( HXb )).T
@@ -111,15 +99,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # ------------------------------
         def CostFunction(x):
             _X  = numpy.asmatrix(numpy.ravel( x )).T
+            if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["CurrentState"].store( _X )
             _HX = Hm( _X )
             _HX = numpy.asmatrix(numpy.ravel( _HX )).T
+            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             Jb  = 0.
             Jo  = 0.
             J   = Jb + Jo
-            if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["CurrentState"].store( _X )
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
@@ -145,7 +133,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 func        = CostFunction,
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,
-                bounds      = Bounds,
+                bounds      = self._parameters["Bounds"],
                 quantile    = self._parameters["Quantile"],
                 maxfun      = self._parameters["MaximumNumberOfSteps"],
                 toler       = self._parameters["CostDecrementTolerance"],