Documentation corrections for outputs

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

diff --git a/src/daComposant/daAlgorithms/SamplingTest.py b/src/daComposant/daAlgorithms/SamplingTest.py
index 7f2edf4ce70cc397e115cb03238169d1e3d62a5a..7856b236df20a796e1a2e57518b49e1ceec80026 100644 (file)
--- a/src/daComposant/daAlgorithms/SamplingTest.py
+++ b/src/daComposant/daAlgorithms/SamplingTest.py
@@ -1,6 +1,6 @@
 #-*-coding:iso-8859-1-*-
 #
-#  Copyright (C) 2008-2014 EDF R&D
+#  Copyright (C) 2008-2015 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
@@ -31,21 +31,27 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         self.defineRequiredParameter(
             name     = "SampleAsnUplet",
             default  = [],
-            typecast = list,
+            typecast = tuple,
             message  = "Points de calcul définis par une liste de n-uplet",
             )
         self.defineRequiredParameter(
             name     = "SampleAsExplicitHyperCube",
             default  = [],
-            typecast = list,
+            typecast = tuple,
             message  = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable comme une liste",
             )
         self.defineRequiredParameter(
             name     = "SampleAsMinMaxStepHyperCube",
             default  = [],
-            typecast = list,
+            typecast = tuple,
             message  = "Points de calcul définis par un hyper-cube dont on donne la liste des échantillonages de chaque variable par un triplet [min,max,step]",
             )
+        self.defineRequiredParameter(
+            name     = "SampleAsIndependantRandomVariables",
+            default  = [],
+            typecast = tuple,
+            message  = "Points de calcul définis par un hyper-cube dont les points sur chaque axe proviennent de l'échantillonage indépendant de la variable selon la spécification ['distribution',[parametres],nombre]",
+            )
         self.defineRequiredParameter(
             name     = "QualityCriterion",
             default  = "AugmentedWeightedLeastSquares",
@@ -68,7 +74,12 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["CostFunctionJ","CurrentState","Innovation","ObservedState"]
+            listval  = ["CostFunctionJ","CurrentState","Innovation","SimulatedObservationAtCurrentState"]
+            )
+        self.defineRequiredParameter(
+            name     = "SetSeed",
+            typecast = numpy.random.seed,
+            message  = "Graine fixée pour le générateur aléatoire",
             )
 
     def run(self, Xb=None, Y=None, U=None, HO=None, EM=None, CM=None, R=None, B=None, Q=None, Parameters=None):
@@ -92,10 +103,22 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             coordinatesList = []
             for i,dim in enumerate(self._parameters["SampleAsMinMaxStepHyperCube"]):
                 if len(dim) != 3:
-                    raise ValueError("For dimension %i, the variable definition %s is incorrect, it should be [min,max,step]."%(i,dim))
+                    raise ValueError("For dimension %i, the variable definition \"%s\" is incorrect, it should be [min,max,step]."%(i,dim))
                 else:
                     coordinatesList.append(numpy.linspace(*dim))
             sampleList = itertools.product(*coordinatesList)
+        elif len(self._parameters["SampleAsIndependantRandomVariables"]) > 0:
+            coordinatesList = []
+            for i,dim in enumerate(self._parameters["SampleAsIndependantRandomVariables"]):
+                if len(dim) != 3:
+                    raise ValueError("For dimension %i, the variable definition \"%s\" is incorrect, it should be ('distribution',(parameters),length) with distribution in ['normal'(mean,std),'lognormal'(mean,sigma),'uniform'(low,high),'weibull'(shape)]."%(i,dim))
+                elif not( str(dim[0]) in ['normal','lognormal','uniform','weibull'] and hasattr(numpy.random,dim[0]) ):
+                    raise ValueError("For dimension %i, the distribution name \"%s\" is not allowed, please choose in ['normal'(mean,std),'lognormal'(mean,sigma),'uniform'(low,high),'weibull'(shape)]"%(i,dim[0]))
+                else:
+                    distribution = getattr(numpy.random,str(dim[0]),'normal')
+                    parameters   = dim[1]
+                    coordinatesList.append(distribution(*dim[1], size=max(1,int(dim[2]))))
+            sampleList = itertools.product(*coordinatesList)
         else:
             sampleList = iter([Xn,])
         # ----------
@@ -134,8 +157,8 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 self.StoredVariables["CurrentState"].store( _X )
             if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
                 self.StoredVariables["Innovation"].store( Y - _HX )
-            if "ObservedState" in self._parameters["StoreSupplementaryCalculations"]:
-                self.StoredVariables["ObservedState"].store( _HX )
+            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 )