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src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
                Acc = evalUtilities.getClassificationAccuracy(validationSet, model)
            else:
                Acc = -evalUtilities.getRMSE(validationSet, model)
            if bestModel == None or (Acc > bestAcc) or (Acc == bestAcc and model.nIter < bestNiter):
                bestSeed = seed

src/a/z/AZOrange-HEAD/tests/source/AZorngCvSVMTest.py   AZOrange(Download)
        svmL = AZorngCvSVM.CvSVMLearner(scaleData = False,svm_type = 103,gamma=0.01, C = 1,nu=0.5,p=1,eps=0.001, coef0=0, degree=3)
        svm = svmL(self.inDataC)
        trainedAcc = evalUtilities.getRMSE(self.inDataC, svm)
 
        self.assertEqual(round(trainedAcc,7),round(2.8525863999999999,7))# ver 0.3
        # Load the saved model
        loadedsvm = AZorngCvSVM.CvSVMread(self.modelPath)
        loadedAcc = evalUtilities.getRMSE(self.inDataC, loadedsvm)
        # Assure equal accuracy
        self.assertEqual(trainedAcc, loadedAcc)
 
        newSVM = svmLearner(self.inDataC)
        trainedAcc = evalUtilities.getRMSE(self.inDataC, newSVM)
        # Save model 
        rc = newSVM.write(self.modelPath)
        self.assertEqual(rc,True)
        # Load the saved model
        loadedsvm = AZorngCvSVM.CvSVMread(self.modelPath)
        loadedAcc = evalUtilities.getRMSE(self.inDataC, loadedsvm)

src/a/z/AZOrange-HEAD/tests/source/AZevalUtilitiesTest.py   AZOrange(Download)
        testData = data[int(len(data)/2)+1:]
        classifier = RFlearner(data)
        RMSE = evalUtilities.getRMSE(testData,classifier)
        self.assert_(RMSE-2.07396535555 < 0.05, "Got:"+str(RMSE))
 

src/a/z/AZOrange-HEAD/tests/source/AZorngPLSTest.py   AZOrange(Download)
 
        # Calculate RMSE 
        RegressorRMSE = evalUtilities.getRMSE(self.contTest, PLSRegressor)
 
        # Check that RMSE is what it used to be

src/a/z/AZOrange-HEAD/tests/source/AZorngCvANNTest.py   AZOrange(Download)
        CvANNmodel = CvANNlearner(contTrain)
        # Calculate classification accuracy 
        Acc = evalUtilities.getRMSE(contTest, CvANNmodel)
 
        # Check that the accuracy is what it used to be

src/a/z/AZOrange-HEAD/tests/source/AZorngRFTest.py   AZOrange(Download)
 
        # Calculate classification accuracy 
        Acc = evalUtilities.getRMSE(self.testDataReg, RFmodel)
 
        # Check that the accuracy is what it used to be

src/a/z/AZOrange-HEAD/tests/source/AZorngParamOptTest.py   AZOrange(Download)
        #The learner is now with its optimized parameters already set, so we can now make a classifier out of it
        classifier = learner(self.contTrain)
        RMSE = evalUtilities.getRMSE(self.contTest,classifier)
        self.assertEqual(round(RMSE,2),round(2.8900000000000001,2)) #Ver 0.3
 
        #The learner is now with its optimized parameters already set, so we can now make a classifier out of it
        classifier = learner(self.contTrain)
        RMSE = evalUtilities.getRMSE(self.contTest,classifier)
        self.assertEqual(round(RMSE,2),round(2.02,2)) #Ver 0.3