1 #-*-coding:iso-8859-1-*-
3 # Copyright (C) 2008-2009 EDF R&D
5 # This library is free software; you can redistribute it and/or
6 # modify it under the terms of the GNU Lesser General Public
7 # License as published by the Free Software Foundation; either
8 # version 2.1 of the License.
10 # This library is distributed in the hope that it will be useful,
11 # but WITHOUT ANY WARRANTY; without even the implied warranty of
12 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
13 # Lesser General Public License for more details.
15 # You should have received a copy of the GNU Lesser General Public
16 # License along with this library; if not, write to the Free Software
17 # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
19 # See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
22 Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs
23 independants supposes de variances egales au sens du test de Student.
24 En input : la tolerance
25 En output : le resultat du diagnostic est une reponse booleenne au test :
26 True si les moyennes sont egales au sens du Test de Student
27 False dans le cas contraire.
29 __author__ = "Sophie RICCI - Octobre 2008"
31 import sys ; sys.path.insert(0, "../daCore")
35 from BasicObjects import Diagnostic
36 from ComputeStudent import IndependantVectorsEqualVariance
39 # ==============================================================================
40 class ElementaryDiagnostic(Diagnostic,Persistence.OneScalar):
42 Diagnostic qui effectue le test d egalite des moyennes de 2 vecteurs independants supposes de variances egales au sens du test de Student.
43 En input : la tolerance
44 En output : le resultat du diagnostic est une reponse booleenne au test :
45 True si les moyennes sont egales au sens du Test de Student
46 False dans le cas contraire.
48 def __init__(self, name="", unit="", basetype = None, parameters = {} ):
49 Diagnostic.__init__(self, name, parameters)
50 Persistence.OneScalar.__init__( self, name, unit, basetype = bool)
51 if not self.parameters.has_key("tolerance"):
52 raise ValueError("A parameter named \"tolerance\" is required.")
54 def formula(self, V1, V2):
56 Effectue le calcul de la p-value de Student pour deux vecteurs
57 independants supposes de variances egales.
59 [aire, Q, reponse, message] = IndependantVectorsEqualVariance(
62 tolerance = self.parameters["tolerance"],
64 logging.info( message )
65 answerStudentTest = False
66 if (aire < (100.*self.parameters["tolerance"])) :
67 answerStudentTest = False
69 answerStudentTest = True
70 return answerStudentTest
72 def calculate(self, vector1 = None, vector2 = None, step = None):
74 Active la formule de calcul
76 if (vector1 is None) or (vector2 is None) :
77 raise ValueError("Two vectors must be given to calculate the Student value")
78 V1 = numpy.array(vector1)
79 V2 = numpy.array(vector2)
80 if (V1.size < 1) or (V2.size < 1):
81 raise ValueError("The given vectors must not be empty")
82 if V1.size != V2.size:
83 raise ValueError("The two given vectors must have the same size, or the vector types are incompatible")
84 value = self.formula( V1, V2 )
85 self.store( value = value, step = step)
87 # ==============================================================================
88 if __name__ == "__main__":
89 print '\n AUTODIAGNOSTIC \n'
91 print " Test d'égalite des moyennes au sens de Student pour deux vecteurs"
92 print " indépendants supposés de variances égales"
95 # Initialisation des inputs et appel du diagnostic
96 # --------------------------------------------------------------------
98 D = ElementaryDiagnostic("ComputeMeanStudent_IndepVect_EgalVar", parameters = {
99 "tolerance":tolerance,
102 # Tirage de l'echantillon aleatoire
103 # --------------------------------------------------------------------
104 x1 = numpy.array(([-0.23262176, 1.36065207, 0.32988102, 0.24400551, -0.66765848, -0.19088483, -0.31082575, 0.56849814, 1.21453443, 0.99657516]))
105 x2 = numpy.array(([-0.23, 1.36, 0.32, 0.24, -0.66, -0.19, -0.31, 0.56, 1.21, 0.99]))
108 # --------------------------------------------------------------------
112 print " L'hypothèse d'égalité des moyennes est valide."
115 raise ValueError("The egality of the means is NOT valid")