--- /dev/null
+# -*- 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()
