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src/a/z/AZOrange-HEAD/doc/openExampleScripts/buildOptParamModel.py   AZOrange(Download)
    # Get a learner object with optimized parameters (default settings)
    print "Optimizing model hyper-parameters"
    optLearner, isOptimized = paramOptUtilities.getOptParam(AZOrangeLearner, trainDataFile, verbose = 0, queueType = queueType, runPath = runPath)
 
    print "Parameters successfully optimized?"

src/a/z/AZOrange-HEAD/azorange/AZutilities/getUnbiasedAccuracy.py   AZOrange(Download)
                            trainData.save(os.path.join(runPath,"trainData.tab"))
                            tunedPars = paramOptUtilities.getOptParam(
                                learner = MLmethods[ml], 
                                trainDataFile = os.path.join(runPath,"trainData.tab"), 
                                paramList = self.paramList, 

src/a/z/AZOrange-HEAD/azorange/AZutilities/competitiveWorkflow.py   AZOrange(Download)
 
            tunedPars = paramOptUtilities.getOptParam(
                learner = learners[ML],
                trainDataFile = os.path.join(runPath,"trainData.tab"),
                useGrid = False,