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src/a/z/AZOrange-HEAD/azorange/AZutilities/getUnbiasedAccuracy.py   AZOrange(Download)
                    self.__log(logTxt)
                    if trainData.domain.classVar.varType == orange.VarTypes.Discrete:
                        res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
                        CA = evalUtilities.CA(res)[0]
                        optAcc[ml].append(CA)
                    else:
                        res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
                                    optAcc[ml].append(optInfo["Acc"])
                            else:
                                    res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
                                    R2 = evalUtilities.R2(res)[0]
                                    optAcc[ml].append(R2)
                                    optAcc[ml].append(tunedPars[0])
                                else:
                                    res = evalUtilities.crossValidation([MLmethods[ml]], trainData, folds=5, stratified=orange.MakeRandomIndices.StratifiedIfPossible, random_generator = random.randint(0, 100))
                                    R2 = evalUtilities.R2(res)[0]
                                    optAcc[ml].append(R2)

src/a/z/AZOrange-HEAD/tests/source/AZevalUtilitiesTest.py   AZOrange(Download)
        self.assert_(evalUtilities.CA(res)[0] == 1.0)
 
        res = evalUtilities.crossValidation(learners, data, rep, testAttrFilter="Data Origin", testFilterVal=["SRC1"])
        self.assert_(len(res.results) == 36)
        self.assert_(evalUtilities.ConfMat(res) == [[[26.0, 0.0], [0.0, 10.0]]])