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src/a/z/AZOrange-HEAD/tests/source/AZorngParamOptTest.py   AZOrange(Download)
        """
        #Create  the appspack instance
        opt=paramOptUtilities.Appspack()
        #Learner to be optimized
        learner=AZorngRF.RFLearner()
        # Run the appspack which will configure the input learner and aditionaly return
        #[<minimum of objective function found>, <optimized parameters>]
        tunedPars = opt(learner=learner,\
                        dataSet=dataSet,\
                        evaluateMethod = evalM,\
    def test_PLSAdvanced_Usage(self):
        """PLS - Test of optimizer with advanced configuration
        """
        #Create  the appspack instance
        opt=paramOptUtilities.Appspack()
        # Run the appspack which will configure the input learner and aditionaly return 
        #[<minimum of objective function found>, <optimized parameters>]
        tunedPars = opt(learner=learner,\
                        dataSet=dataSet,\
                        evaluateMethod = evalM,\
        expectedAcc = [0.57999999999999996, 0.58999999999999997] #Ver 0.3 - Artifact: The second value can be expected on other Systems
        #Create  the appspack instance
        opt=paramOptUtilities.Appspack()
        #Learner to be optimized
        learner=AZorngPLS.PLSLearner()

src/a/z/AZOrange-HEAD/tests/source/AZorngParamOptMPITest.py   AZOrange(Download)
        ExpectedCA = [0.612] #opencv1.1: 0.90480000000000005
 
        optimizer = paramOptUtilities.Appspack()
 
        learner = AZorngRF.RFLearner()
 
        # Calculate the optimal parameters. This can take a long period of time!
        tunedPars = optimizer(learner=learner,\
                        dataSet=trainFile,\
                        evaluateMethod = evalM,\
        ExpectedCA = [0.585]
 
        optimizer = paramOptUtilities.Appspack()
 
        learner = AZorngCvANN.CvANNLearner()
 
        # Calculate the optimal parameters. This can take a long period of time!
        tunedPars = optimizer(learner=learner,\
                        dataSet=trainFile,\
                        evaluateMethod = evalM,\
        ExpectedCA = [0.585] #Should be the result of the default point
 
        optimizer = paramOptUtilities.Appspack()
 
        learner = AZorngCvSVM.CvSVMLearner()

src/a/z/AZOrange-HEAD/orange/OrangeWidgets/Classify/OWParamOpt.py   AZOrange(Download)
	self.dataset = None
	self.learner = None
        self.optimizer = paramOptUtilities.Appspack()
        self.verbose = 0
        self.tunedPars = None