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diff --git a/test/test6903/Verification_des_mono_et_multi_fonctions_C.py b/test/test6903/Verification_des_mono_et_multi_fonctions_C.py
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+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2008-2018 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
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307 USA
+#
+# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
+#
+# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
+"Verification du fonctionnement correct d'entrees en mono ou multi-fonctions"
+
+# ==============================================================================
+import numpy, sys
+from adao import adaoBuilder
+
+M = numpy.matrix("1 0 0;0 2 0;0 0 3")
+def MonoFonction( x ):
+    return M * numpy.asmatrix(numpy.ravel( x )).T
+
+def MultiFonction( xserie ):
+    _mulHX = []
+    for _subX in xserie:
+        _mulHX.append( M * numpy.asmatrix(numpy.ravel( _subX )).T )
+    return _mulHX
+
+# ==============================================================================
+def test1():
+    """
+    Verification du fonctionnement identique pour les algorithmes autres
+    en utilisant une fonction lineaire et carree
+    """
+    print(test1.__doc__)
+    Xa = {}
+    #
+    for algo in ("ParticleSwarmOptimization", "QuantileRegression", ):
+        print("")
+        msg = "Algorithme en test en MonoFonction : %s"%algo
+        print(msg+"\n"+"-"*len(msg))
+        #
+        adaopy = adaoBuilder.New()
+        adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"BoxBounds":3*[[-1,3]], "SetSeed":1000})
+        adaopy.setBackground         (Vector = [0,1,2])
+        adaopy.setBackgroundError    (ScalarSparseMatrix = 1.)
+        adaopy.setObservation        (Vector = [0.5,1.5,2.5])
+        adaopy.setObservationError   (DiagonalSparseMatrix = "1 2 3")
+        adaopy.setObservationOperator(OneFunction = MonoFonction)
+        adaopy.setObserver("Analysis",Template="ValuePrinter")
+        adaopy.execute()
+        Xa["Mono/"+algo] = adaopy.get("Analysis")[-1]
+        del adaopy
+    #
+    for algo in ("ParticleSwarmOptimization", "QuantileRegression", ):
+        print("")
+        msg = "Algorithme en test en MultiFonction : %s"%algo
+        print(msg+"\n"+"-"*len(msg))
+        #
+        adaopy = adaoBuilder.New()
+        adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"BoxBounds":3*[[-1,3]], "SetSeed":1000})
+        adaopy.setBackground         (Vector = [0,1,2])
+        adaopy.setBackgroundError    (ScalarSparseMatrix = 1.)
+        adaopy.setObservation        (Vector = [0.5,1.5,2.5])
+        adaopy.setObservationError   (DiagonalSparseMatrix = "1 2 3")
+        adaopy.setObservationOperator(OneFunction = MultiFonction, InputAsMF = True)
+        adaopy.setObserver("Analysis",Template="ValuePrinter")
+        adaopy.execute()
+        Xa["Multi/"+algo] = adaopy.get("Analysis")[-1]
+        del adaopy
+    #
+    print("")
+    msg = "Tests des ecarts attendus :"
+    print(msg+"\n"+"="*len(msg))
+    for algo in ("ParticleSwarmOptimization", "QuantileRegression"):
+        verify_similarity_of_algo_results(("Multi/"+algo, "Mono/"+algo), Xa, 1.e-20)
+    print("  Les resultats obtenus sont corrects.")
+    print("")
+    #
+    return 0
+
+# ==============================================================================
+def almost_equal_vectors(v1, v2, precision = 1.e-15, msg = ""):
+    """Comparaison de deux vecteurs"""
+    print("    Difference maximale %s: %.2e"%(msg, max(abs(v2 - v1))))
+    return max(abs(v2 - v1)) < precision
+
+def verify_similarity_of_algo_results(serie = [], Xa = {}, precision = 1.e-15):
+    print("  Comparaisons :")
+    for algo1 in serie:
+        for algo2 in serie:
+            if algo1 is algo2: break
+            assert almost_equal_vectors( Xa[algo1], Xa[algo2], precision, "entre %s et %s "%(algo1, algo2) )
+    print("  Algorithmes dont les resultats sont similaires a %.0e : %s\n"%(precision, serie,))
+    sys.stdout.flush()
+
+#===============================================================================
+if __name__ == "__main__":
+    print('\nAUTODIAGNOSTIC\n')
+    test1()