From: Jean-Philippe ARGAUD Date: Sun, 24 Jan 2021 17:08:08 +0000 (+0100) Subject: Minor improvements for internal variables X-Git-Tag: V9_7_0b1~40 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=2201c84e2047882b9bd918b3e62de45711d514d5;p=modules%2Fadao.git Minor improvements for internal variables --- diff --git a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py index 122b6e0..d322058 100644 --- a/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py +++ b/src/daComposant/daAlgorithms/EnsembleKalmanFilter.py @@ -47,6 +47,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): "ETKF-N-15", "ETKF-N-16", "MLEF-T", + "MLEF-B", ], ) self.defineRequiredParameter( @@ -189,11 +190,11 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): NumericObjects.etkf(self, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="FiniteSize16") # #-------------------------- - # Default MLEF = MLEF-B - elif self._parameters["Minimizer"] in ["MLEF-B", "MLEF"]: + # Default MLEF = MLEF-T + elif self._parameters["Minimizer"] in ["MLEF-T", "MLEF"]: NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False) # - elif self._parameters["Minimizer"] == "MLEF-T": + elif self._parameters["Minimizer"] == "MLEF-B": NumericObjects.mlef(self, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=True) # #-------------------------- diff --git a/src/daComposant/daCore/NumericObjects.py b/src/daComposant/daCore/NumericObjects.py index 176f329..d2b02a6 100644 --- a/src/daComposant/daCore/NumericObjects.py +++ b/src/daComposant/daCore/NumericObjects.py @@ -1215,7 +1215,8 @@ def etkf(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="KalmanFilterFormula"): return 0 # ============================================================================== -def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False, _epsilon=1.e-1, _e=1.e-7, _jmax=15000): +def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, VariantM="MLEF13", + BnotT=False, _epsilon=1.e-3, _e=1.e-7, _jmax=15000): """ Maximum Likelihood Ensemble Filter (EnKF/MLEF Zupanski 2005, Bocquet 2013) @@ -1280,9 +1281,9 @@ def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False, _epsilon=1.e-1, _e=1 Xn_predicted = numpy.zeros((__n,__m)) for step in range(duration-1): if hasattr(Y,"store"): - Ynpu = numpy.asmatrix(numpy.ravel( Y[step+1] )).T + Ynpu = numpy.ravel( Y[step+1] )[:,None] else: - Ynpu = numpy.asmatrix(numpy.ravel( Y )).T + Ynpu = numpy.ravel( Y )[:,None] # if U is not None: if hasattr(U,"store") and len(U)>1: @@ -1301,10 +1302,10 @@ def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False, _epsilon=1.e-1, _e=1 ) # if selfA._parameters["EstimationOf"] == "State": # Forecast + Q and observation of forecast - EMX = M( [(Xn[:,i], Un) for i in range(__m)], argsAsSerie = True ) + EMX = M( [(Xn[:,i,numpy.newaxis], Un) for i in range(__m)], argsAsSerie = True ) for i in range(__m): qi = numpy.random.multivariate_normal(numpy.zeros(__n), Qn) - Xn_predicted[:,i] = (numpy.ravel( EMX[i] ) + qi).reshape((__n,-1)) + Xn_predicted[:,i] = numpy.ravel( EMX[i] ) + qi if Cm is not None and Un is not None: # Attention : si Cm est aussi dans M, doublon ! Cm = Cm.reshape(__n,Un.size) # ADAO & check shape Xn_predicted = Xn_predicted + Cm * Un @@ -1312,49 +1313,52 @@ def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False, _epsilon=1.e-1, _e=1 # --- > Par principe, M = Id, Q = 0 Xn_predicted = Xn # - # Mean of forecast and observation of forecast - Xfm = Xn_predicted.mean(axis=1, dtype=mfp).astype('float') - # - EaX = (Xn_predicted - Xfm.reshape((__n,-1))) / numpy.sqrt(__m-1) - # #-------------------------- - Ua = numpy.eye(__m) - Ta = numpy.eye(__m) - # - __j = 0 # 4: - vw = numpy.zeros(__m) # 4: - Deltaw = 1 - while numpy.linalg.norm(Deltaw) >= _e or __j >= _jmax: # 5: et 19: - vx = numpy.ravel(Xfm) + EaX @ vw # 6: - # - if BnotT: - EE = vx.reshape((__n,-1)) + _epsilon * EaX # 7: - else: - EE = vx.reshape((__n,-1)) + numpy.sqrt(__m-1) * EaX @ Ta # 8: - # - EZ = H( [(EE[:,i], Un) for i in range(__m)], - argsAsSerie = True, - returnSerieAsArrayMatrix = True ) - # - ybar = EZ.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1)) # 10: Observation mean + if VariantM == "MLEF13": + Xfm = numpy.asarray(Xn_predicted.mean(axis=1, dtype=mfp).astype('float')) + EaX = numpy.asarray((Xn_predicted - Xfm.reshape((__n,-1))) / numpy.sqrt(__m-1)) + Ua = numpy.eye(__m) + __j = 0 + Deltaw = 1 + if not BnotT: + Ta = numpy.eye(__m) + vw = numpy.zeros(__m) + while numpy.linalg.norm(Deltaw) >= _e and __j <= _jmax: + vx1 = numpy.ravel(Xfm) + EaX @ vw + # + if BnotT: + E1 = vx1.reshape((__n,-1)) + _epsilon * EaX + else: + E1 = vx1.reshape((__n,-1)) + numpy.sqrt(__m-1) * EaX @ Ta + # + HE2 = H( [(E1[:,i,numpy.newaxis], Un) for i in range(__m)], + argsAsSerie = True, + returnSerieAsArrayMatrix = True ) + vy2 = HE2.mean(axis=1, dtype=mfp).astype('float').reshape((__p,-1)) + # + if BnotT: + EaY = (HE2 - vy2) / _epsilon + else: + EaY = ( (HE2 - vy2) @ numpy.linalg.inv(Ta) ) / numpy.sqrt(__m-1) + # + GradJ = numpy.ravel(vw[:,None] - EaY.transpose() @ (RI * ( Ynpu - vy2 ))) + mH = numpy.eye(__m) + EaY.transpose() @ (RI * EaY) + Deltaw = - numpy.linalg.solve(mH,GradJ) + # + vw = vw + Deltaw + # + if not BnotT: + Ta = numpy.real(scipy.linalg.sqrtm(numpy.linalg.inv( mH ))) + # + __j = __j + 1 # if BnotT: - EY = (EZ - ybar) / _epsilon # 11: - else: - EY = ( (EZ - ybar) @ numpy.linalg.inv(Ta) ) / numpy.sqrt(__m-1) # 12: + Ta = numpy.real(scipy.linalg.sqrtm(numpy.linalg.inv( mH ))) # - GradJ = numpy.ravel(vw.reshape((__m,1)) - EY.transpose() @ (RI * (Ynpu - ybar))) # 13: - mH = numpy.eye(__m) + EY.transpose() @ (RI * EY) # 14: - Deltaw = numpy.linalg.solve(mH,GradJ) # 15: - vw = vw - Deltaw # 16: - if not BnotT: - Ta = numpy.linalg.inv(numpy.real(scipy.linalg.sqrtm( mH ))) # 17: - __j = __j + 1 # 18: - # - if BnotT: - Ta = numpy.linalg.inv(numpy.real(scipy.linalg.sqrtm( mH ))) # 20: - # - Xn = vx.reshape((__n,-1)) + numpy.sqrt(__m-1) * EaX @ Ta @ Ua # 21: + Xn = vx1.reshape((__n,-1)) + numpy.sqrt(__m-1) * EaX @ Ta @ Ua + #-------------------------- + else: + raise ValueError("VariantM has to be chosen in the authorized methods list.") # if selfA._parameters["InflationType"] == "MultiplicativeOnAnalysisAnomalies": Xn = CovarianceInflation( Xn, @@ -1362,7 +1366,7 @@ def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False, _epsilon=1.e-1, _e=1 selfA._parameters["InflationFactor"], ) # - Xa = Xn.mean(axis=1, dtype=mfp).astype('float') + Xa = Xn.mean(axis=1, dtype=mfp).astype('float').reshape((__n,-1)) #-------------------------- # if selfA._parameters["StoreInternalVariables"] \ @@ -1430,7 +1434,7 @@ def mlef(selfA, Xb, Y, U, HO, EM, CM, R, B, Q, BnotT=False, _epsilon=1.e-1, _e=1 if selfA._toStore("CostFunctionJAtCurrentOptimum"): selfA.StoredVariables["CostFunctionJAtCurrentOptimum" ].store( selfA.StoredVariables["CostFunctionJ" ][IndexMin] ) if selfA._toStore("APosterioriCovariance"): - Eai = (1/numpy.sqrt(__m-1)) * (Xn - Xa.reshape((__n,-1))) # Anomalies + Eai = numpy.asarray((Xn - Xa.reshape((__n,-1))) / numpy.sqrt(__m-1)) # Anomalies Pn = Eai @ Eai.T Pn = 0.5 * (Pn + Pn.T) selfA.StoredVariables["APosterioriCovariance"].store( Pn )