From: Jean-Philippe ARGAUD Date: Tue, 25 Jun 2013 20:31:32 +0000 (+0200) Subject: Improving output for function repetition algorithm X-Git-Tag: V7_3_0~24 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=143a4e3cd5734eb02bfa10610203d41b4ddc302a;p=modules%2Fadao.git Improving output for function repetition algorithm --- diff --git a/doc/en/reference.rst b/doc/en/reference.rst index f2261fb..8260b75 100644 --- a/doc/en/reference.rst +++ b/doc/en/reference.rst @@ -739,9 +739,13 @@ each algorithm, the required commands/keywords are given, being described in *"CheckingPoint", "ObservationOperator"* + NumberOfPrintedDigits + This key indicates the number of digits of precision for floating point + printed output. The default is 8, with a minimum of 0. + NumberOfRepetition This key indicates the number of time to repeat the function evaluation. The - default is 2. + default is 2, with a minimum of 1. SetDebug This key requires the activation, or not, of the debug mode during the diff --git a/src/daComposant/daAlgorithms/FunctionRepetitionTest.py b/src/daComposant/daAlgorithms/FunctionRepetitionTest.py index 53eefef..080bdef 100644 --- a/src/daComposant/daAlgorithms/FunctionRepetitionTest.py +++ b/src/daComposant/daAlgorithms/FunctionRepetitionTest.py @@ -24,12 +24,19 @@ import logging from daCore import BasicObjects, PlatformInfo m = PlatformInfo.SystemUsage() -import numpy +import numpy, copy # ============================================================================== class ElementaryAlgorithm(BasicObjects.Algorithm): def __init__(self): BasicObjects.Algorithm.__init__(self, "FUNCTIONREPETITIONTEST") + self.defineRequiredParameter( + name = "NumberOfPrintedDigits", + default = 8, + typecast = int, + message = "Nombre de chiffres affichés pour les impressions de réels", + minval = 0, + ) self.defineRequiredParameter( name = "NumberOfRepetition", default = 2, @@ -61,6 +68,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): Xn = numpy.asmatrix(numpy.ravel( Xb )).T # # ---------- + _p = self._parameters["NumberOfPrintedDigits"] if len(self._parameters["ResultTitle"]) > 0: msg = " ====" + "="*len(self._parameters["ResultTitle"]) + "====\n" msg += " " + self._parameters["ResultTitle"] + "\n" @@ -69,14 +77,14 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # msg = "===> Information before launching:\n" msg += " -----------------------------\n" - msg += " Characteristics of input parameter X, internally converted:\n" + msg += " Characteristics of input vector X, internally converted:\n" msg += " Type...............: %s\n"%type( Xn ) msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Xn ).shape) - msg += " Minimum value......: %.5e\n"%numpy.min( Xn ) - msg += " Maximum value......: %.5e\n"%numpy.max( Xn ) - msg += " Mean of vector.....: %.5e\n"%numpy.mean( Xn ) - msg += " Standard error.....: %.5e\n"%numpy.std( Xn ) - msg += " L2 norm of vector..: %.5e\n"%numpy.linalg.norm( Xn ) + msg += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Xn ) + msg += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Xn ) + msg += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Xn ) + msg += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Xn ) + msg += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Xn ) print(msg) # if self._parameters["SetDebug"]: @@ -87,6 +95,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): print("===> Beginning of evaluation, without activating debug\n") # # ---------- + Ys = [] for i in range(self._parameters["NumberOfRepetition"]): print(" %s\n"%("-"*75,)) print("===> Repetition step number %i on a total of %i\n"%(i+1,self._parameters["NumberOfRepetition"])) @@ -98,21 +107,49 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # msg = "===> Information after launching:\n" msg += " ----------------------------\n" - msg += " Characteristics of output parameter Y, to compare to other calculations:\n" + msg += " Characteristics of output vector Y, to compare to other calculations:\n" msg += " Type...............: %s\n"%type( Y ) msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Y ).shape) - msg += " Minimum value......: %.5e\n"%numpy.min( Y ) - msg += " Maximum value......: %.5e\n"%numpy.max( Y ) - msg += " Mean of vector.....: %.5e\n"%numpy.mean( Y ) - msg += " Standard error.....: %.5e\n"%numpy.std( Y ) - msg += " L2 norm of vector..: %.5e\n"%numpy.linalg.norm( Y ) + msg += (" Minimum value......: %."+str(_p)+"e\n")%numpy.min( Y ) + msg += (" Maximum value......: %."+str(_p)+"e\n")%numpy.max( Y ) + msg += (" Mean of vector.....: %."+str(_p)+"e\n")%numpy.mean( Y ) + msg += (" Standard error.....: %."+str(_p)+"e\n")%numpy.std( Y ) + msg += (" L2 norm of vector..: %."+str(_p)+"e\n")%numpy.linalg.norm( Y ) print(msg) + # + Ys.append( copy.copy( numpy.ravel(Y) ) ) # print(" %s\n"%("-"*75,)) if self._parameters["SetDebug"]: print("===> End evaluation, deactivating debug if necessary\n") logging.getLogger().setLevel(CUR_LEVEL) # + msg = (" %s\n"%("-"*75,)) + msg += ("\n===> Statistical analysis of the outputs obtained throught repeated evaluations\n") + Yy = numpy.array( Ys ) + msg += ("\n Characteristics of the whole set of outputs Y:\n") + msg += (" Number of evaluations.........................: %i\n")%len( Ys ) + msg += (" Minimum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.min( Yy ) + msg += (" Maximum value of the whole set of outputs.....: %."+str(_p)+"e\n")%numpy.max( Yy ) + msg += (" Mean of vector of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.mean( Yy ) + msg += (" Standard error of the whole set of outputs....: %."+str(_p)+"e\n")%numpy.std( Yy ) + Ym = numpy.mean( numpy.array( Ys ), axis=0 ) + msg += ("\n Characteristics of the vector Ym, mean of the outputs Y:\n") + msg += (" Size of the mean of the outputs...............: %i\n")%Ym.size + msg += (" Minimum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.min( Ym ) + msg += (" Maximum value of the mean of the outputs......: %."+str(_p)+"e\n")%numpy.max( Ym ) + msg += (" Mean of the mean of the outputs...............: %."+str(_p)+"e\n")%numpy.mean( Ym ) + msg += (" Standard error of the mean of the outputs.....: %."+str(_p)+"e\n")%numpy.std( Ym ) + Ye = numpy.mean( numpy.array( Ys ) - Ym, axis=0 ) + msg += "\n Characteristics of the mean of the differences between the outputs Y and their mean Ym:\n" + msg += (" Size of the mean of the differences...........: %i\n")%Ym.size + msg += (" Minimum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.min( Ye ) + msg += (" Maximum value of the mean of the differences..: %."+str(_p)+"e\n")%numpy.max( Ye ) + msg += (" Mean of the mean of the differences...........: %."+str(_p)+"e\n")%numpy.mean( Ye ) + msg += (" Standard error of the mean of the differences.: %."+str(_p)+"e\n")%numpy.std( Ye ) + msg += ("\n %s\n"%("-"*75,)) + print(msg) + # logging.debug("%s Taille mémoire utilisée de %.1f Mo"%(self._name, m.getUsedMemory("M"))) logging.debug("%s Terminé"%self._name) # diff --git a/src/daComposant/daAlgorithms/FunctionTest.py b/src/daComposant/daAlgorithms/FunctionTest.py index f19ec16..059a6b3 100644 --- a/src/daComposant/daAlgorithms/FunctionTest.py +++ b/src/daComposant/daAlgorithms/FunctionTest.py @@ -62,7 +62,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # msg = "===> Information before launching:\n" msg += " -----------------------------\n" - msg += " Characteristics of input parameter X, internally converted:\n" + msg += " Characteristics of input vector X, internally converted:\n" msg += " Type...............: %s\n"%type( Xn ) msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Xn ).shape) msg += " Minimum value......: %.5e\n"%numpy.min( Xn ) @@ -86,7 +86,7 @@ class ElementaryAlgorithm(BasicObjects.Algorithm): # msg = "===> Information after launching:\n" msg += " ----------------------------\n" - msg += " Characteristics of output parameter Y, to compare to observation:\n" + msg += " Characteristics of output vector Y, to compare to observation:\n" msg += " Type...............: %s\n"%type( Y ) msg += " Lenght of vector...: %i\n"%max(numpy.matrix( Y ).shape) msg += " Minimum value......: %.5e\n"%numpy.min( Y )