Improvement of internal pre run

[modules/adao.git] / src / daComposant / daAlgorithms / 3DVAR.py

diff --git a/src/daComposant/daAlgorithms/3DVAR.py b/src/daComposant/daAlgorithms/3DVAR.py
index 2c80c6d032b83ae8c8e9b9b377cc135952aff0e1..d9a1187535dcec15b907cf1078f71947a2f0c34c 100644 (file)
--- a/src/daComposant/daAlgorithms/3DVAR.py
+++ b/src/daComposant/daAlgorithms/3DVAR.py
@@ -107,23 +107,7 @@ 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()
-        if logging.getLogger().level < logging.WARNING:
-            self.__iprint, self.__disp = 1, 1
-            self.__message = scipy.optimize.tnc.MSG_ALL
-        else:
-            self.__iprint, self.__disp = -1, 0
-            self.__message = scipy.optimize.tnc.MSG_NONE
-        #
-        # 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
+        self._pre_run(Parameters)
         #
         # Correction pour pallier a un bug de TNC sur le retour du Minimum
         if self._parameters.has_key("Minimizer") == "TNC":
@@ -168,9 +152,9 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             if "InnovationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["InnovationAtCurrentState"].store( _Innovation )
             #
-            Jb  = 0.5 * (_X - Xb).T * BI * (_X - Xb)
-            Jo  = 0.5 * _Innovation.T * RI * _Innovation
-            J   = float( Jb ) + float( Jo )
+            Jb  = float( 0.5 * (_X - Xb).T * BI * (_X - Xb) )
+            Jo  = float( 0.5 * _Innovation.T * RI * _Innovation )
+            J   = Jb + Jo
             #
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
@@ -215,11 +199,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,
                 args        = (),
-                bounds      = Bounds,
+                bounds      = self._parameters["Bounds"],
                 maxfun      = self._parameters["MaximumNumberOfSteps"]-1,
                 factr       = self._parameters["CostDecrementTolerance"]*1.e14,
                 pgtol       = self._parameters["ProjectedGradientTolerance"],
-                iprint      = self.__iprint,
+                iprint      = self._parameters["optiprint"],
                 )
             nfeval = Informations['funcalls']
             rc     = Informations['warnflag']
@@ -229,11 +213,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,
                 args        = (),
-                bounds      = Bounds,
+                bounds      = self._parameters["Bounds"],
                 maxfun      = self._parameters["MaximumNumberOfSteps"],
                 pgtol       = self._parameters["ProjectedGradientTolerance"],
                 ftol        = self._parameters["CostDecrementTolerance"],
-                messages    = self.__message,
+                messages    = self._parameters["optmessages"],
                 )
         elif self._parameters["Minimizer"] == "CG":
             Minimum, fopt, nfeval, grad_calls, rc = scipy.optimize.fmin_cg(
@@ -243,7 +227,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 args        = (),
                 maxiter     = self._parameters["MaximumNumberOfSteps"],
                 gtol        = self._parameters["GradientNormTolerance"],
-                disp        = self.__disp,
+                disp        = self._parameters["optdisp"],
                 full_output = True,
                 )
         elif self._parameters["Minimizer"] == "NCG":
@@ -254,7 +238,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 args        = (),
                 maxiter     = self._parameters["MaximumNumberOfSteps"],
                 avextol     = self._parameters["CostDecrementTolerance"],
-                disp        = self.__disp,
+                disp        = self._parameters["optdisp"],
                 full_output = True,
                 )
         elif self._parameters["Minimizer"] == "BFGS":
@@ -265,7 +249,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 args        = (),
                 maxiter     = self._parameters["MaximumNumberOfSteps"],
                 gtol        = self._parameters["GradientNormTolerance"],
-                disp        = self.__disp,
+                disp        = self._parameters["optdisp"],
                 full_output = True,
                 )
         else: