Slight corrections of parameters and variables treatment

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

diff --git a/src/daComposant/daAlgorithms/QuantileRegression.py b/src/daComposant/daAlgorithms/QuantileRegression.py
index 78a602fcfb96cc0313c5a112afd3b4aae9a112b8..616b41ae76c2a75c8e701f26d32edfd43b074a7c 100644 (file)
--- a/src/daComposant/daAlgorithms/QuantileRegression.py
+++ b/src/daComposant/daAlgorithms/QuantileRegression.py
@@ -49,7 +49,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = 15000,
             typecast = int,
             message  = "Nombre maximal de pas d'optimisation",
-            minval   = -1
+            minval   = 1,
             )
         self.defineRequiredParameter(
             name     = "CostDecrementTolerance",
@@ -57,6 +57,12 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             typecast = float,
             message  = "Maximum de variation de la fonction d'estimation lors de l'arrêt",
             )
+        self.defineRequiredParameter(
+            name     = "StoreInternalVariables",
+            default  = False,
+            typecast = bool,
+            message  = "Stockage des variables internes ou intermédiaires du calcul",
+            )
 
     def run(self, Xb=None, Y=None, H=None, M=None, R=None, B=None, Q=None, Parameters=None):
         """
@@ -105,7 +111,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             logging.debug("%s CostFunction Jb = %s"%(self._name, Jb))
             logging.debug("%s CostFunction Jo = %s"%(self._name, Jo))
             logging.debug("%s CostFunction J  = %s"%(self._name, J))
-            self.StoredVariables["CurrentState"].store( _X.A1 )
+            if self._parameters["StoreInternalVariables"]:
+                self.StoredVariables["CurrentState"].store( _X.A1 )
             self.StoredVariables["CostFunctionJb"].store( Jb )
             self.StoredVariables["CostFunctionJo"].store( Jo )
             self.StoredVariables["CostFunctionJ" ].store( J )
@@ -113,7 +120,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         def GradientOfCostFunction(x):
             _X      = numpy.asmatrix(x).flatten().T
-            logging.debug("%s GradientOfCostFunction X      = %s"%(self._name, numpy.asmatrix( _X ).flatten()))
+            logging.debug("%s GradientOfCostFunction X      = %s"%(self._name, _X.A1))
             Hg = H["Tangent"].asMatrix( _X )
             return Hg
         #
@@ -151,7 +158,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         # Obtention de l'analyse
         # ----------------------
-        Xa = numpy.asmatrix(Minimum).T
+        Xa = numpy.asmatrix(Minimum).flatten().T
         logging.debug("%s Analyse Xa = %s"%(self._name, Xa))
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )