include $(top_srcdir)/adm_local/make_common_starter.am
-EXTRA_DIST = daCore daAlgorithms daDiagnostics daExternals daMatrices daNumerics
+EXTRA_DIST = daCore daAlgorithms daDiagnostics daNumerics
DIR = $(top_srcdir)/src/daComposant/
--- /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
+
+__doc__ = """
+ Implémentation informatique de l'algorithme MMQR, basée sur la publication :
+ David R. Hunter, Kenneth Lange, "Quantile Regression via an MM Algorithm",
+ Journal of Computational and Graphical Statistics, 9, 1, pp.60-77, 2000.
+"""
+__author__ = "Jean-Philippe ARGAUD"
+
+import sys, math
+from numpy import array, matrix, asarray, asmatrix
+from numpy import sum, dot, linalg, ravel, max, min, hstack, argmin, argmax
+
+# ==============================================================================
+def mmqr(
+ func = None,
+ x0 = None,
+ fprime = None,
+ bounds = None,
+ quantile = 0.5,
+ maxfun = 15000,
+ toler = 1.e-06,
+ y = None,
+ ):
+ #
+ # Recuperation des donnees et informations initiales
+ # --------------------------------------------------
+ variables = asmatrix(ravel( x0 ))
+ mesures = asmatrix(ravel( y )).T
+ increment = sys.float_info[0]
+ p = len(variables.flat)
+ n = len(mesures.flat)
+ quantile = float(quantile)
+ #
+ # Calcul des parametres du MM
+ # ---------------------------
+ tn = float(toler) / n
+ e0 = -tn / math.log(tn)
+ epsilon = (e0-tn)/(1+math.log(e0))
+ #
+ # Calculs d'initialisation
+ # ------------------------
+ residus = ravel( mesures - func( variables ) )
+ poids = asarray( 1./(epsilon+abs(residus)) )
+ veps = 1. - 2. * quantile - residus * poids
+ lastsurrogate = -sum(residus*veps) - (1.-2.*quantile)*sum(residus)
+ iteration = 0
+ #
+ # Recherche iterative
+ # -------------------
+ while (increment > toler) and (iteration < maxfun) :
+ iteration += 1
+ #
+ Derivees = array(fprime(variables))
+ Derivees = Derivees.reshape(n,p) # Necessaire pour remettre en place la matrice si elle passe par des tuyaux YACS
+ DeriveesT = array(matrix(Derivees).T)
+ M = dot( DeriveesT , (array(matrix(p*[poids,]).T)*Derivees) )
+ SM = dot( DeriveesT , veps ).T
+ step = - linalg.lstsq( M, SM )[0]
+ #
+ variables = variables + step
+ if bounds is not None:
+ while( (variables < ravel(asmatrix(bounds)[:,0])).any() or (variables > ravel(asmatrix(bounds)[:,1])).any() ):
+ step = step/2.
+ variables = variables - step
+ residus = ravel( mesures - func(variables) )
+ surrogate = sum(residus**2 * poids) + (4.*quantile-2.) * sum(residus)
+ #
+ while ( (surrogate > lastsurrogate) and ( max(list(abs(step))) > 1.e-16 ) ) :
+ step = step/2.
+ variables = variables - step
+ residus = ravel( mesures-func(variables) )
+ surrogate = sum(residus**2 * poids) + (4.*quantile-2.) * sum(residus)
+ #
+ increment = lastsurrogate-surrogate
+ poids = 1./(epsilon+abs(residus))
+ veps = 1. - 2. * quantile - residus * poids
+ lastsurrogate = -sum(residus * veps) - (1.-2.*quantile)*sum(residus)
+ #
+ # Mesure d'écart : q*Sum(residus)-sum(residus negatifs)
+ # ----------------
+ Ecart = quantile * sum(residus) - sum( residus[residus<0] )
+ #
+ return variables, Ecart, [n,p,iteration,increment,0]
+
+# ==============================================================================
+if __name__ == "__main__":
+ print('\n AUTODIAGNOSTIC \n')
try:
if Concept in ("Background", "CheckingPoint", "ControlInput", "Observation", "Controls"):
commande = getattr(self,"set"+Concept)
- commande(Vector, VectorSerie, Script, Scheduler, Stored, Checked )
+ commande(Vector, VectorSerie, Script, Stored, Scheduler, Checked )
elif Concept in ("BackgroundError", "ObservationError", "EvolutionError"):
commande = getattr(self,"set"+Concept)
commande(Matrix, ScalarSparseMatrix, DiagonalSparseMatrix,
- ObjectMatrix, Script, Stored, Checked )
+ Script, Stored, ObjectMatrix, Checked )
elif Concept == "AlgorithmParameters":
self.setAlgorithmParameters( Algorithm, Parameters, Script )
elif Concept == "Debug":
elif Concept == "NoDebug":
self.setNoDebug()
elif Concept == "Observer":
- self.setObserver( Variable, Template, String, Script, ObjectFunction, Scheduler, Info )
+ self.setObserver( Variable, Template, String, Script, Info, ObjectFunction, Scheduler )
elif Concept == "Diagnostic":
self.setDiagnostic( Diagnostic, Identifier, Parameters, Script, Unit, BaseType )
elif Concept == "ObservationOperator":
self.setObservationOperator(
Matrix, OneFunction, ThreeFunctions, AppliedInXb,
- Parameters, Script, AvoidRC, Stored, Checked )
+ Parameters, Script, Stored, AvoidRC, Checked )
elif Concept in ("EvolutionModel", "ControlModel"):
commande = getattr(self,"set"+Concept)
commande(
Matrix, OneFunction, ThreeFunctions,
- Parameters, Script, Scheduler, AvoidRC, Stored, Checked )
+ Parameters, Script, Scheduler, Stored, AvoidRC, Checked )
else:
raise ValueError("the variable named '%s' is not allowed."%str(Concept))
Vector = None,
VectorSerie = None,
Script = None,
- Scheduler = None,
Stored = False,
+ Scheduler = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "Background"
Vector = None,
VectorSerie = None,
Script = None,
- Scheduler = None,
Stored = False,
+ Scheduler = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "CheckingPoint"
Vector = None,
VectorSerie = None,
Script = None,
- Scheduler = None,
Stored = False,
+ Scheduler = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "ControlInput"
Vector = None,
VectorSerie = None,
Script = None,
- Scheduler = None,
Stored = False,
+ Scheduler = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "Observation"
Vector = (), # Valeur par defaut pour un vecteur vide
VectorSerie = None,
Script = None,
- Scheduler = None,
Stored = False,
+ Scheduler = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "Controls"
Matrix = None,
ScalarSparseMatrix = None,
DiagonalSparseMatrix = None,
- ObjectMatrix = None,
Script = None,
Stored = False,
+ ObjectMatrix = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "BackgroundError"
Matrix = None,
ScalarSparseMatrix = None,
DiagonalSparseMatrix = None,
- ObjectMatrix = None,
Script = None,
Stored = False,
+ ObjectMatrix = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "ObservationError"
Matrix = None,
ScalarSparseMatrix = None,
DiagonalSparseMatrix = None,
- ObjectMatrix = None,
Script = None,
Stored = False,
+ ObjectMatrix = None,
Checked = False):
"Definition d'un concept de calcul"
Concept = "EvolutionError"
AppliedInXb = None,
Parameters = None,
Script = None,
- AvoidRC = True,
Stored = False,
+ AvoidRC = True,
Checked = False):
"Definition d'un concept de calcul"
Concept = "ObservationOperator"
ThreeFunctions = None,
Parameters = None,
Script = None,
+ Stored = False,
Scheduler = None,
AvoidRC = True,
- Stored = False,
Checked = False):
"Definition d'un concept de calcul"
Concept = "EvolutionModel"
ThreeFunctions = None,
Parameters = None,
Script = None,
+ Stored = False,
Scheduler = None,
AvoidRC = True,
- Stored = False,
Checked = False):
"Definition d'un concept de calcul"
Concept = "ControlModel"
Template = None,
String = None,
Script = None,
+ Info = None,
ObjectFunction = None,
- Scheduler = None,
- Info = None):
+ Scheduler = None):
"Definition d'un concept de calcul"
Concept = "Observer"
self.__case.register("set"+Concept, dir(), locals())
"""
files = []
for directory in sys.path:
- if os.path.isdir(os.path.join(directory,"daAlgorithms")):
- for fname in os.listdir(os.path.join(directory,"daAlgorithms")):
- root, ext = os.path.splitext(fname)
- if ext == '.py' and root != '__init__':
- files.append(root)
+ trypath = os.path.join(directory,"daAlgorithms")
+ if os.path.isdir(trypath):
+ for fname in os.listdir(trypath):
+ if os.path.isfile(os.path.join(trypath,fname)):
+ fc = open(os.path.join(trypath,fname)).read()
+ iselal = bool("class ElementaryAlgorithm" in fc)
+ root, ext = os.path.splitext(fname)
+ if iselal and ext == '.py' and root != '__init__':
+ files.append(root)
files.sort()
return files
"""
Permet de sélectionner l'algorithme à utiliser pour mener à bien l'étude
d'assimilation. L'argument est un champ caractère se rapportant au nom
- d'un fichier contenu dans "../daAlgorithms" et réalisant l'opération
- d'assimilation sur les arguments fixes.
+ d'un algorithme réalisant l'opération sur les arguments fixes.
"""
if choice is None:
raise ValueError("Error: algorithm choice has to be given")
if os.path.isfile(os.path.join(directory, daDirectory, str(choice)+'.py')):
module_path = os.path.abspath(os.path.join(directory, daDirectory))
if module_path is None:
- raise ImportError("No algorithm module named \"%s\" was found in a \"%s\" subdirectory\n The search path is %s"%(choice, daDirectory, sys.path))
+ raise ImportError("No algorithm module named \"%s\" has been found in the search path.\n The search path is %s"%(choice, sys.path))
#
# Importe le fichier complet comme un module
# ------------------------------------------
try:
sys_path_tmp = sys.path ; sys.path.insert(0,module_path)
self.__algorithmFile = __import__(str(choice), globals(), locals(), [])
+ if not hasattr(self.__algorithmFile, "ElementaryAlgorithm"):
+ raise ImportError("this module does not define a valid elementary algorithm.")
self.__algorithmName = str(choice)
sys.path = sys_path_tmp ; del sys_path_tmp
except ImportError as e:
GenericCaseViewer.__init__(self, __name, __objname, __content)
self.__internalSCD = _SCDViewer(__name, __objname, __content)
self._append = self.__internalSCD._append
- def dump(self, __filename=None):
+ def dump(self, __filename=None, __convertSCDinMemory=True):
"Restitution normalisée des commandes"
self.__internalSCD._finalize()
# -----
- if __filename is not None:
- __file = os.path.abspath(__filename)
- __SCDfile = __file[:__file.rfind(".")] + '_SCD.py'
- __SCDdump = self.__internalSCD.dump(__SCDfile)
- else:
+ if __filename is None:
raise ValueError("A file name has to be given for YACS XML output.")
# -----
if not PlatformInfo.has_salome or \
raise ImportError("\n\n"+\
"Unable to get SALOME or ADAO environnement variables.\n"+\
"Please load the right environnement before trying to use it.\n")
+ elif __convertSCDinMemory:
+ __file = os.path.abspath(__filename)
+ __SCDdump = self.__internalSCD.dump()
+ if os.path.isfile(__file) or os.path.islink(__file):
+ os.remove(__file)
+ from daYacsSchemaCreator.run import create_schema_from_content
+ create_schema_from_content(__SCDdump, __file)
else:
+ __file = os.path.abspath(__filename)
+ __SCDfile = __file[:__file.rfind(".")] + '_SCD.py'
+ __SCDdump = self.__internalSCD.dump(__SCDfile)
if os.path.isfile(__file) or os.path.islink(__file):
os.remove(__file)
__converterExe = os.path.join(os.environ["ADAO_ROOT_DIR"], "bin/salome", "AdaoYacsSchemaCreator.py")
import subprocess
__p = subprocess.Popen(__args)
(__stdoutdata, __stderrdata) = __p.communicate()
- if not os.path.exists(__file):
- __msg = "An error occured during the ADAO YACS Schema build.\n"
- __msg += "Creator applied on the input file:\n"
- __msg += " %s\n"%__SCDfile
- __msg += "If SALOME GUI is launched by command line, see errors\n"
- __msg += "details in your terminal.\n"
- raise ValueError(__msg)
+ __p.terminate()
os.remove(__SCDfile)
# -----
+ if not os.path.exists(__file):
+ # logging.debug("-- Error YacsSchemaCreator with convert SCD in memory=%s --"%__convertSCDinMemory)
+ # logging.debug("-- Content of the file : --")
+ # logging.debug(__SCDdump)
+ __msg = "An error occured during the ADAO YACS Schema build for\n"
+ __msg += "the target output file:\n"
+ __msg += " %s\n"%__file
+ __msg += "See errors details in your launching terminal log.\n"
+ raise ValueError(__msg)
+ # -----
__fid = open(__file,"r")
__text = __fid.read()
__fid.close()
"Déclaration des répertoires statiques"
parent = os.path.abspath(os.path.join(os.path.dirname(__file__),".."))
self.__paths = {}
- self.__paths["daExternals"] = os.path.join(parent,"daExternals")
- self.__paths["daMatrices"] = os.path.join(parent,"daMatrices")
self.__paths["daNumerics"] = os.path.join(parent,"daNumerics")
#
for v in self.__paths.values():
+++ /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
+++ /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
-
-__doc__ = """
- Implémentation informatique de l'algorithme MMQR, basée sur la publication :
- David R. Hunter, Kenneth Lange, "Quantile Regression via an MM Algorithm",
- Journal of Computational and Graphical Statistics, 9, 1, pp.60-77, 2000.
-"""
-__author__ = "Jean-Philippe ARGAUD"
-
-import sys, math
-from numpy import array, matrix, asarray, asmatrix
-from numpy import sum, dot, linalg, ravel, max, min, hstack, argmin, argmax
-
-# ==============================================================================
-def mmqr(
- func = None,
- x0 = None,
- fprime = None,
- bounds = None,
- quantile = 0.5,
- maxfun = 15000,
- toler = 1.e-06,
- y = None,
- ):
- #
- # Recuperation des donnees et informations initiales
- # --------------------------------------------------
- variables = asmatrix(ravel( x0 ))
- mesures = asmatrix(ravel( y )).T
- increment = sys.float_info[0]
- p = len(variables.flat)
- n = len(mesures.flat)
- quantile = float(quantile)
- #
- # Calcul des parametres du MM
- # ---------------------------
- tn = float(toler) / n
- e0 = -tn / math.log(tn)
- epsilon = (e0-tn)/(1+math.log(e0))
- #
- # Calculs d'initialisation
- # ------------------------
- residus = ravel( mesures - func( variables ) )
- poids = asarray( 1./(epsilon+abs(residus)) )
- veps = 1. - 2. * quantile - residus * poids
- lastsurrogate = -sum(residus*veps) - (1.-2.*quantile)*sum(residus)
- iteration = 0
- #
- # Recherche iterative
- # -------------------
- while (increment > toler) and (iteration < maxfun) :
- iteration += 1
- #
- Derivees = array(fprime(variables))
- Derivees = Derivees.reshape(n,p) # Necessaire pour remettre en place la matrice si elle passe par des tuyaux YACS
- DeriveesT = array(matrix(Derivees).T)
- M = dot( DeriveesT , (array(matrix(p*[poids,]).T)*Derivees) )
- SM = dot( DeriveesT , veps ).T
- step = - linalg.lstsq( M, SM )[0]
- #
- variables = variables + step
- if bounds is not None:
- while( (variables < ravel(asmatrix(bounds)[:,0])).any() or (variables > ravel(asmatrix(bounds)[:,1])).any() ):
- step = step/2.
- variables = variables - step
- residus = ravel( mesures - func(variables) )
- surrogate = sum(residus**2 * poids) + (4.*quantile-2.) * sum(residus)
- #
- while ( (surrogate > lastsurrogate) and ( max(list(abs(step))) > 1.e-16 ) ) :
- step = step/2.
- variables = variables - step
- residus = ravel( mesures-func(variables) )
- surrogate = sum(residus**2 * poids) + (4.*quantile-2.) * sum(residus)
- #
- increment = lastsurrogate-surrogate
- poids = 1./(epsilon+abs(residus))
- veps = 1. - 2. * quantile - residus * poids
- lastsurrogate = -sum(residus * veps) - (1.-2.*quantile)*sum(residus)
- #
- # Mesure d'écart : q*Sum(residus)-sum(residus negatifs)
- # ----------------
- Ecart = quantile * sum(residus) - sum( residus[residus<0] )
- #
- return variables, Ecart, [n,p,iteration,increment,0]
-
-# ==============================================================================
-if __name__ == "__main__":
- print('\n AUTODIAGNOSTIC \n')
