Minor dev documentation corrections

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

diff --git a/src/daComposant/daAlgorithms/QuantileRegression.py b/src/daComposant/daAlgorithms/QuantileRegression.py
index 0d0cf2ae1f9e98afc0342e9e644423951058a4f1..4b0dd223528f1ad1c12dab21206bc9de2e779e0c 100644 (file)
--- a/src/daComposant/daAlgorithms/QuantileRegression.py
+++ b/src/daComposant/daAlgorithms/QuantileRegression.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# Copyright (C) 2008-2017 EDF R&D
+# Copyright (C) 2008-2019 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
@@ -21,7 +21,7 @@
 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
 import logging
-from daCore import BasicObjects
+from daCore import BasicObjects, NumericObjects
 import numpy
 
 # ==============================================================================
@@ -67,7 +67,20 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
             default  = [],
             typecast = tuple,
             message  = "Liste de calculs supplémentaires à stocker et/ou effectuer",
-            listval  = ["BMA", "OMA", "OMB", "CurrentState", "CostFunctionJ", "CostFunctionJb", "CostFunctionJo", "Innovation", "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum"]
+            listval  = [
+                "Analysis",
+                "BMA",
+                "CostFunctionJ",
+                "CostFunctionJb",
+                "CostFunctionJo",
+                "CurrentState",
+                "Innovation",
+                "OMA",
+                "OMB",
+                "SimulatedObservationAtBackground",
+                "SimulatedObservationAtCurrentState",
+                "SimulatedObservationAtOptimum",
+                ]
             )
         self.defineRequiredParameter( # Pas de type
             name     = "Bounds",
@@ -78,7 +91,7 @@ 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(Parameters, R, B, Q)
+        self._pre_run(Parameters, Xb, Y, R, B, Q)
         #
         Hm = HO["Direct"].appliedTo
         #
@@ -102,11 +115,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # ------------------------------
         def CostFunction(x):
             _X  = numpy.asmatrix(numpy.ravel( x )).T
-            if self._parameters["StoreInternalVariables"] or "CurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._parameters["StoreInternalVariables"] or self._toStore("CurrentState"):
                 self.StoredVariables["CurrentState"].store( _X )
             _HX = Hm( _X )
             _HX = numpy.asmatrix(numpy.ravel( _HX )).T
-            if "SimulatedObservationAtCurrentState" in self._parameters["StoreSupplementaryCalculations"]:
+            if self._toStore("SimulatedObservationAtCurrentState"):
                 self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             Jb  = 0.
             Jo  = 0.
@@ -131,8 +144,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         # Minimisation de la fonctionnelle
         # --------------------------------
         if self._parameters["Minimizer"] == "MMQR":
-            import mmqr
-            Minimum, J_optimal, Informations = mmqr.mmqr(
+            Minimum, J_optimal, Informations = NumericObjects.mmqr(
                 func        = CostFunction,
                 x0          = Xini,
                 fprime      = GradientOfCostFunction,
@@ -153,23 +165,23 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         self.StoredVariables["Analysis"].store( Xa.A1 )
         #
-        if "OMA"                           in self._parameters["StoreSupplementaryCalculations"] or \
-           "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMA") or \
+            self._toStore("SimulatedObservationAtOptimum"):
             HXa = Hm(Xa)
         #
         # Calculs et/ou stockages supplémentaires
         # ---------------------------------------
-        if "Innovation" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("Innovation"):
             self.StoredVariables["Innovation"].store( numpy.ravel(d) )
-        if "BMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("BMA"):
             self.StoredVariables["BMA"].store( numpy.ravel(Xb - Xa) )
-        if "OMA" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMA"):
             self.StoredVariables["OMA"].store( numpy.ravel(Y - HXa) )
-        if "OMB" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("OMB"):
             self.StoredVariables["OMB"].store( numpy.ravel(d) )
-        if "SimulatedObservationAtBackground" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtBackground"):
             self.StoredVariables["SimulatedObservationAtBackground"].store( numpy.ravel(HXb) )
-        if "SimulatedObservationAtOptimum" in self._parameters["StoreSupplementaryCalculations"]:
+        if self._toStore("SimulatedObservationAtOptimum"):
             self.StoredVariables["SimulatedObservationAtOptimum"].store( numpy.ravel(HXa) )
         #
         self._post_run(HO)
@@ -177,4 +189,4 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
 
 # ==============================================================================
 if __name__ == "__main__":
-    print('\n AUTODIAGNOSTIC \n')
+    print('\n AUTODIAGNOSTIC\n')