Did I find the right examples for you? yes no      Crawl my project      Python Jobs

All Samples(5)  |  Call(5)  |  Derive(0)  |  Import(0)

src/a/z/AZOrange-HEAD/azorange/AZutilities/getUnbiasedAccuracy.py   AZOrange(Download)
        else:
            #compute Q2
            res["Q2"] = evalUtilities.calcRsqrt(exp_pred)
            #compute RMSE
            res["RMSE"] = evalUtilities.calcRMSE(exp_pred)
                    for n,ex in enumerate(testData):
                        local_exp_pred.append((ex.getclass().value, predictions[n].value))
                    results[ml].append((evalUtilities.calcRMSE(local_exp_pred), evalUtilities.calcRsqrt(local_exp_pred) ) )
                    #Save the experimental value and correspondent predicted value
                    exp_pred[ml] += local_exp_pred
                        for n,ex in enumerate(testData):
                            local_exp_pred.append((ex.getclass().value, predictions[n].value))
                        Cresults.append((evalUtilities.calcRMSE(local_exp_pred), evalUtilities.calcRsqrt(local_exp_pred) ) )
                        #Save the experimental value and correspondent predicted value
                        Cexp_pred += local_exp_pred

src/a/z/AZOrange-HEAD/azorange/AZutilities/competitiveWorkflow.py   AZOrange(Download)
        else:
            #compute Q2
            res["Q2"] = evalUtilities.calcRsqrt(exp_pred)
            #compute RMSE
            res["RMSE"] = evalUtilities.calcRMSE(exp_pred)
                        for ex in testData:
                            local_exp_pred.append((ex.getclass(), model(ex)))
                        results[ml].append((evalUtilities.calcRMSE(local_exp_pred), evalUtilities.calcRsqrt(local_exp_pred) ) )
                        #Save the experimental value and correspondent predicted value
                        exp_pred[ml] += local_exp_pred