Minor improvements and fixes for internal variables

diff --git a/src/daComposant/daAlgorithms/SamplingTest.py b/src/daComposant/daAlgorithms/SamplingTest.py
index 6458d17124eee123944008bb32f4b7f93ae37730..279f789af1c489a77464a8b052a0926d8cd4cafa 100644 (file)
--- a/src/daComposant/daAlgorithms/SamplingTest.py
+++ b/src/daComposant/daAlgorithms/SamplingTest.py
@@ -20,9 +20,11 @@
 #
 # Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
 
-import logging
+import copy, logging, itertools
+import numpy
 from daCore import BasicObjects
-import numpy, copy, itertools
+from daCore.PlatformInfo import PlatformInfo
+mfp = PlatformInfo().MaximumPrecision()
 
 # ==============================================================================
 class ElementaryAlgorithm(BasicObjects.Algorithm):
@@ -100,14 +102,15 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         #
         Hm = HO["Direct"].appliedTo
         #
-        Xn = copy.copy( Xb )
+        X0 = numpy.ravel( Xb )
+        Y0 = numpy.ravel( Y  )
         #
         # ---------------------------
         if len(self._parameters["SampleAsnUplet"]) > 0:
             sampleList = self._parameters["SampleAsnUplet"]
             for i,Xx in enumerate(sampleList):
-                if numpy.ravel(Xx).size != Xn.size:
-                    raise ValueError("The size %i of the %ith state X in the sample and %i of the checking point Xb are different, they have to be identical."%(numpy.ravel(Xx).size,i+1,Xn.size))
+                if numpy.ravel(Xx).size != X0.size:
+                    raise ValueError("The size %i of the %ith state X in the sample and %i of the checking point Xb are different, they have to be identical."%(numpy.ravel(Xx).size,i+1,X0.size))
         elif len(self._parameters["SampleAsExplicitHyperCube"]) > 0:
             sampleList = itertools.product(*list(self._parameters["SampleAsExplicitHyperCube"]))
         elif len(self._parameters["SampleAsMinMaxStepHyperCube"]) > 0:
@@ -131,7 +134,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                     coordinatesList.append(distribution(*dim[1], size=max(1,int(dim[2]))))
             sampleList = itertools.product(*coordinatesList)
         else:
-            sampleList = iter([Xn,])
+            sampleList = iter([X0,])
         # ----------
         BI = B.getI()
         RI = R.getI()
@@ -141,33 +144,33 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
                 _HX = numpy.nan
                 Jb, Jo, J = numpy.nan, numpy.nan, numpy.nan
             else:
-                _X  = numpy.asmatrix(numpy.ravel( x )).T
-                _HX = numpy.asmatrix(numpy.ravel( HmX )).T
+                _X  = numpy.ravel( x )
+                _HX = numpy.ravel( HmX )
                 if QualityMeasure in ["AugmentedWeightedLeastSquares","AWLS","AugmentedPonderatedLeastSquares","APLS","DA"]:
                     if BI is None or RI is None:
                         raise ValueError("Background and Observation error covariance matrix has to be properly defined!")
-                    Jb  = 0.5 * (_X - Xb).T * BI * (_X - Xb)
-                    Jo  = 0.5 * (Y - _HX).T * RI * (Y - _HX)
+                    Jb  = float( 0.5 *  (_X - X0).T * (BI * (_X - X0))  )
+                    Jo  = float( 0.5 * (Y0 - _HX).T * (RI * (Y0 - _HX)) )
                 elif QualityMeasure in ["WeightedLeastSquares","WLS","PonderatedLeastSquares","PLS"]:
                     if RI is None:
                         raise ValueError("Observation error covariance matrix has to be properly defined!")
                     Jb  = 0.
-                    Jo  = 0.5 * (Y - _HX).T * RI * (Y - _HX)
+                    Jo  = float( 0.5 * (Y0 - _HX).T * (RI * (Y0 - _HX)) )
                 elif QualityMeasure in ["LeastSquares","LS","L2"]:
                     Jb  = 0.
-                    Jo  = 0.5 * (Y - _HX).T * (Y - _HX)
+                    Jo  = float( 0.5 * (Y0 - _HX).T @ (Y0 - _HX) )
                 elif QualityMeasure in ["AbsoluteValue","L1"]:
                     Jb  = 0.
-                    Jo  = numpy.sum( numpy.abs(Y - _HX) )
+                    Jo  = float( numpy.sum( numpy.abs(Y0 - _HX), dtype=mfp ) )
                 elif QualityMeasure in ["MaximumError","ME"]:
                     Jb  = 0.
-                    Jo  = numpy.max( numpy.abs(Y - _HX) )
+                    Jo  = numpy.max( numpy.abs(Y0 - _HX) )
                 #
-                J   = float( Jb ) + float( Jo )
+                J   = Jb + Jo
             if self._toStore("CurrentState"):
                 self.StoredVariables["CurrentState"].store( _X )
             if self._toStore("InnovationAtCurrentState"):
-                self.StoredVariables["InnovationAtCurrentState"].store( Y - _HX )
+                self.StoredVariables["InnovationAtCurrentState"].store( Y0 - _HX )
             if self._toStore("SimulatedObservationAtCurrentState"):
                 self.StoredVariables["SimulatedObservationAtCurrentState"].store( _HX )
             self.StoredVariables["CostFunctionJb"].store( Jb )
@@ -185,13 +188,12 @@ class ElementaryAlgorithm(BasicObjects.Algorithm):
         for i,Xx in enumerate(sampleList):
             if self._parameters["SetDebug"]:
                 print("===> Launching evaluation for state %i"%i)
-            __Xn = numpy.asmatrix(numpy.ravel( Xx )).T
             try:
-                Yn = Hm( __Xn )
+                Yy = Hm( numpy.ravel( Xx ) )
             except:
-                Yn = numpy.nan
+                Yy = numpy.nan
             #
-            J, Jb, Jo = CostFunction(__Xn,Yn,self._parameters["QualityCriterion"])
+            J, Jb, Jo = CostFunction( Xx, Yy,  self._parameters["QualityCriterion"])
         # ----------
         #
         if self._parameters["SetDebug"]: