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N/A __future__ print_function builtins str builtins range N/A pyGPs pyGPs.Validation valid N/A numpy np N/A N/A print N/A N/A print ---------------Cross-Validation DEMO------------------ N/A data_source ionosphere.data.txt N/A x N/A N/A y N/A N/A N/A open data_source f N/A N/A N/A index line N/A enumerate f N/A N/A feature N/A N/A line split , N/A attr N/A feature N/A -1 N/A attr N/A N/A float i N/A i attr N/A target N/A N/A feature N/A -1 N/A N/A N/A x append attr N/A N/A N/A y append target N/A x N/A N/A np array x N/A y N/A N/A np array y N/A N/A n D N/A x shape N/A i N/A range n N/A N/A N/A N/A N/A y N/A N/A i 0 N/A 0 g N/A N/A N/A y N/A N/A i 0 1 N/A N/A N/A y N/A N/A i 0 -1 N/A y N/A N/A np int8 y N/A K 10 N/A ACC N/A N/A RMSE N/A N/A cv_run 0 N/A N/A x_train x_test y_train y_test N/A N/A valid k_fold_validation x y K N/A N/A N/A print Run: cv_run N/A model N/A N/A pyGPs GPC N/A N/A N/A model optimize x_train y_train N/A N/A ymu ys2 fmu fs2 lp N/A N/A model predict x_test ys y_test N/A ymu_class N/A N/A np sign ymu N/A acc N/A N/A valid ACC ymu_class y_test N/A N/A print    accuracy = N/A round acc 2 N/A rmse N/A N/A valid RMSE ymu_class y_test N/A N/A print    rmse = N/A round rmse 2 N/A N/A N/A ACC append acc N/A N/A N/A RMSE append rmse N/A cv_run 1 N/A N/A print 
Accuracy:  N/A N/A np round N/A N/A np mean ACC 2 N/A N/A ( N/A str N/A N/A np round N/A N/A np std ACC 2 ) N/A N/A print Root-Mean-Square Error:  N/A N/A np round N/A N/A np mean RMSE 2 N/A 
We defined the following measures(@SEE pyGPs.Valid.valid):

    RMSE - Root mean squared error
    ACC - Classification accuracy
    Prec - Precision for class +1
    Recall - Recall for class +1
    NLPD - Negative log predictive density in transformed observation space
 N/A N/A print --------------------END OF DEMO-----------------------