# -*- coding: utf-8 -*-
#
-# Copyright (C) 2008-2019 EDF R&D
+# Copyright (C) 2008-2023 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
# Author: Jean-Philippe Argaud, jean-philippe.argaud@edf.fr, EDF R&D
"Verification de la disponibilite de l'ensemble des algorithmes"
-# ==============================================================================
-import numpy, sys
+import sys
+import unittest
+import numpy
from adao import adaoBuilder
-def test1():
- """Verification de la disponibilite de l'ensemble des algorithmes\n(Utilisation d'un operateur matriciel)"""
- print(test1.__doc__)
- Xa = {}
- for algo in ("3DVAR", "Blue", "ExtendedBlue", "LinearLeastSquares", "NonLinearLeastSquares", "DerivativeFreeOptimization"):
- print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
+
+# ==============================================================================
+class Test_Adao(unittest.TestCase):
+ def test1(self):
+ """Verification de la disponibilite de l'ensemble des algorithmes\n(Utilisation d'un operateur matriciel)"""
+ print(self.test1.__doc__.strip()+"\n")
+ Xa = {}
+ for algo in ("3DVAR", "Blue", "ExtendedBlue", "LinearLeastSquares", "NonLinearLeastSquares", "DerivativeFreeOptimization"):
+ print("")
+ msg = "Algorithme en test : %s"%algo
+ print(msg+"\n"+"-"*len(msg))
+ #
+ adaopy = adaoBuilder.New()
+ adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "Bounds":[[-1,10.],[-1,10.],[-1,10.]]})
+ adaopy.setBackground (Vector = [0,1,2])
+ adaopy.setBackgroundError (ScalarSparseMatrix = 1.)
+ adaopy.setObservation (Vector = [0.5,1.5,2.5])
+ adaopy.setObservationError (DiagonalSparseMatrix = "1 1 1")
+ adaopy.setObservationOperator(Matrix = "1 0 0;0 2 0;0 0 3")
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
#
- adaopy = adaoBuilder.New()
- adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "Bounds":[[-1,10.],[-1,10.],[-1,10.]]})
- adaopy.setBackground (Vector = [0,1,2])
- adaopy.setBackgroundError (ScalarSparseMatrix = 1.)
- adaopy.setObservation (Vector = [0.5,1.5,2.5])
- adaopy.setObservationError (DiagonalSparseMatrix = "1 1 1")
- adaopy.setObservationOperator(Matrix = "1 0 0;0 2 0;0 0 3")
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- for algo in ("ExtendedKalmanFilter", "KalmanFilter", "UnscentedKalmanFilter", "EnsembleKalmanFilter", "4DVAR"):
- print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
+ for algo in ("ExtendedKalmanFilter", "KalmanFilter", "UnscentedKalmanFilter", "EnsembleKalmanFilter", "4DVAR"):
+ print("")
+ msg = "Algorithme en test : %s"%algo
+ print(msg+"\n"+"-"*len(msg))
+ #
+ adaopy = adaoBuilder.New()
+ adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "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 1 1")
+ adaopy.setObservationOperator(Matrix = "1 0 0;0 2 0;0 0 3")
+ adaopy.setEvolutionError (ScalarSparseMatrix = 1.)
+ adaopy.setEvolutionModel (Matrix = "1 0 0;0 1 0;0 0 1")
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
#
- adaopy = adaoBuilder.New()
- adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "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 1 1")
- adaopy.setObservationOperator(Matrix = "1 0 0;0 2 0;0 0 3")
- adaopy.setEvolutionError (ScalarSparseMatrix = 1.)
- adaopy.setEvolutionModel (Matrix = "1 0 0;0 1 0;0 0 1")
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- for algo in ("ParticleSwarmOptimization", "QuantileRegression", ):
- print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
+ for algo in ("ParticleSwarmOptimization", "QuantileRegression", ):
+ print("")
+ msg = "Algorithme en test : %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(Matrix = "1 0 0;0 1 0;0 0 1")
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
#
- 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(Matrix = "1 0 0;0 1 0;0 0 1")
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- for algo in ("EnsembleBlue", ):
- print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
+ for algo in ("EnsembleBlue", ):
+ print("")
+ msg = "Algorithme en test : %s"%algo
+ print(msg+"\n"+"-"*len(msg))
+ #
+ adaopy = adaoBuilder.New()
+ adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"SetSeed":1000, })
+ adaopy.setBackground (VectorSerie = 100*[[0,1,2]])
+ adaopy.setBackgroundError (ScalarSparseMatrix = 1.)
+ adaopy.setObservation (Vector = [0.5,1.5,2.5])
+ adaopy.setObservationError (DiagonalSparseMatrix = "1 2 3")
+ adaopy.setObservationOperator(Matrix = "1 0 0;0 1 0;0 0 1")
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
#
- adaopy = adaoBuilder.New()
- adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"SetSeed":1000, })
- adaopy.setBackground (VectorSerie = 100*[[0,1,2]])
- adaopy.setBackgroundError (ScalarSparseMatrix = 1.)
- adaopy.setObservation (Vector = [0.5,1.5,2.5])
- adaopy.setObservationError (DiagonalSparseMatrix = "1 2 3")
- adaopy.setObservationOperator(Matrix = "1 0 0;0 1 0;0 0 1")
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- print("")
- msg = "Tests des ecarts attendus :"
- print(msg+"\n"+"="*len(msg))
- verify_similarity_of_algo_results(("3DVAR", "Blue", "ExtendedBlue", "4DVAR", "DerivativeFreeOptimization"), Xa, 5.e-5)
- verify_similarity_of_algo_results(("LinearLeastSquares", "NonLinearLeastSquares"), Xa, 5.e-7)
- verify_similarity_of_algo_results(("KalmanFilter", "ExtendedKalmanFilter", "UnscentedKalmanFilter"), Xa, 1.e-14)
- verify_similarity_of_algo_results(("KalmanFilter", "EnsembleKalmanFilter"), Xa, 5.e-2)
- print(" Les resultats obtenus sont corrects.")
- print("")
- #
- return 0
-
-def test2():
- """Verification de la disponibilite de l'ensemble des algorithmes\n(Utilisation d'un operateur fonctionnel)"""
- print(test2.__doc__)
- Xa = {}
- M = numpy.matrix("1 0 0;0 2 0;0 0 3")
- def H(x): return M * numpy.asmatrix(numpy.ravel( x )).T
- for algo in ("3DVAR", "Blue", "ExtendedBlue", "NonLinearLeastSquares", "DerivativeFreeOptimization"):
print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
- #
- adaopy = adaoBuilder.New()
- adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "Bounds":[[-1,10.],[-1,10.],[-1,10.]]})
- adaopy.setBackground (Vector = [0,1,2])
- adaopy.setBackgroundError (ScalarSparseMatrix = 1.)
- adaopy.setObservation (Vector = [0.5,1.5,2.5])
- adaopy.setObservationError (DiagonalSparseMatrix = "1 1 1")
- adaopy.setObservationOperator(OneFunction = H)
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- M = numpy.matrix("1 0 0;0 2 0;0 0 3")
- def H(x): return M * numpy.asmatrix(numpy.ravel( x )).T
- for algo in ("ExtendedKalmanFilter", "KalmanFilter", "EnsembleKalmanFilter", "UnscentedKalmanFilter", "4DVAR"):
+ msg = "Tests des ecarts attendus :"
+ print(msg+"\n"+"="*len(msg))
+ verify_similarity_of_algo_results(("3DVAR", "Blue", "ExtendedBlue", "4DVAR", "DerivativeFreeOptimization"), Xa, 5.e-5)
+ verify_similarity_of_algo_results(("LinearLeastSquares", "NonLinearLeastSquares"), Xa, 5.e-7)
+ verify_similarity_of_algo_results(("KalmanFilter", "ExtendedKalmanFilter", "UnscentedKalmanFilter"), Xa, 1.e-14)
+ verify_similarity_of_algo_results(("KalmanFilter", "EnsembleKalmanFilter"), Xa, 2.e-1)
+ print(" Les resultats obtenus sont corrects.")
print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
+
+ def test2(self):
+ """Verification de la disponibilite de l'ensemble des algorithmes\n(Utilisation d'un operateur fonctionnel)"""
+ print(self.test2.__doc__)
+ Xa = {}
+ M = numpy.diag([1.,2.,3.])
+ def H(x): return M @ numpy.ravel( x )
+ for algo in ("3DVAR", "Blue", "ExtendedBlue", "NonLinearLeastSquares", "DerivativeFreeOptimization"):
+ print("")
+ msg = "Algorithme en test : %s"%algo
+ print(msg+"\n"+"-"*len(msg))
+ #
+ adaopy = adaoBuilder.New()
+ adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "Bounds":[[-1,10.],[-1,10.],[-1,10.]]})
+ adaopy.setBackground (Vector = [0,1,2])
+ adaopy.setBackgroundError (ScalarSparseMatrix = 1.)
+ adaopy.setObservation (Vector = [0.5,1.5,2.5])
+ adaopy.setObservationError (DiagonalSparseMatrix = "1 1 1")
+ adaopy.setObservationOperator(OneFunction = H)
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
#
- adaopy = adaoBuilder.New()
- adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "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 1 1")
- adaopy.setObservationOperator(OneFunction = H)
- adaopy.setEvolutionError (ScalarSparseMatrix = 1.)
- adaopy.setEvolutionModel (Matrix = "1 0 0;0 1 0;0 0 1")
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- M = numpy.matrix("1 0 0;0 1 0;0 0 1")
- def H(x): return M * numpy.asmatrix(numpy.ravel( x )).T
- for algo in ("ParticleSwarmOptimization", "QuantileRegression", ):
- print("")
- msg = "Algorithme en test : %s"%algo
- print(msg+"\n"+"-"*len(msg))
+ M = numpy.diag([1.,2.,3.])
+ def H(x): return M @ numpy.ravel( x )
+ for algo in ("ExtendedKalmanFilter", "KalmanFilter", "EnsembleKalmanFilter", "UnscentedKalmanFilter", "4DVAR"):
+ print("")
+ msg = "Algorithme en test : %s"%algo
+ print(msg+"\n"+"-"*len(msg))
+ #
+ adaopy = adaoBuilder.New()
+ adaopy.setAlgorithmParameters(Algorithm=algo, Parameters={"EpsilonMinimumExponent":-10, "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 1 1")
+ adaopy.setObservationOperator(OneFunction = H)
+ adaopy.setEvolutionError (ScalarSparseMatrix = 1.)
+ adaopy.setEvolutionModel (Matrix = "1 0 0;0 1 0;0 0 1")
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
#
- 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 = H)
- adaopy.setObserver("Analysis",Template="ValuePrinter")
- adaopy.execute()
- Xa[algo] = adaopy.get("Analysis")[-1]
- del adaopy
- #
- print("")
- msg = "Tests des ecarts attendus :"
- print(msg+"\n"+"="*len(msg))
- verify_similarity_of_algo_results(("3DVAR", "Blue", "ExtendedBlue", "4DVAR", "DerivativeFreeOptimization"), Xa, 5.e-5)
- verify_similarity_of_algo_results(("KalmanFilter", "ExtendedKalmanFilter", "UnscentedKalmanFilter"), Xa, 1.e14)
- verify_similarity_of_algo_results(("KalmanFilter", "EnsembleKalmanFilter"), Xa, 5.e-2)
- print(" Les resultats obtenus sont corrects.")
- print("")
- #
- return 0
+ M = numpy.identity(3)
+ def H(x): return M @ numpy.ravel( x )
+ for algo in ("ParticleSwarmOptimization", "QuantileRegression", ):
+ print("")
+ msg = "Algorithme en test : %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 = H)
+ adaopy.setObserver("Analysis",Template="ValuePrinter")
+ adaopy.execute()
+ Xa[algo] = adaopy.get("Analysis")[-1]
+ del adaopy
+ #
+ print("")
+ msg = "Tests des ecarts attendus :"
+ print(msg+"\n"+"="*len(msg))
+ verify_similarity_of_algo_results(("3DVAR", "Blue", "ExtendedBlue", "4DVAR", "DerivativeFreeOptimization"), Xa, 5.e-5)
+ verify_similarity_of_algo_results(("KalmanFilter", "ExtendedKalmanFilter", "UnscentedKalmanFilter"), Xa, 2.e-14)
+ verify_similarity_of_algo_results(("KalmanFilter", "EnsembleKalmanFilter"), Xa, 2e-1)
+ print(" Les resultats obtenus sont corrects.")
+ print("")
def almost_equal_vectors(v1, v2, precision = 1.e-15, msg = ""):
"""Comparaison de deux vecteurs"""
#===============================================================================
if __name__ == "__main__":
- print('\nAUTODIAGNOSTIC\n')
- test1()
- test2()
+ print("\nAUTODIAGNOSTIC\n==============")
+ sys.stderr = sys.stdout
+ unittest.main(verbosity=2)
