lambdaU = 1. / 10. lambdaV = 1. / 10. priors = {'alpha': alpha, 'beta': beta, 'lambdaU': lambdaU, 'lambdaV': lambdaV} # Load in the Sanger dataset (_, X_min, M, _, _, _, _) = load_Sanger(standardised=standardised) # Run the cross-validation framework random.seed(42) numpy.random.seed(9000) nested_crossval = LineSearchCrossValidation(classifier=bnmf_vb_optimised, R=X_min, M=M, values_K=K_range, folds=no_folds, priors=priors, init_UV=init_UV, iterations=iterations, restarts=restarts, quality_metric=quality_metric, file_performance=output_file) nested_crossval.run() """ All model fits for fold 1, metric AIC: [255811.96901730512, 252136.60278205923, 250856.47342754889, 250226.77983334119, 248494.30974350378]. Best K for fold 1: 10. Performance: {'R^2': 0.8062985415424776, 'MSE': 2.2509307850276188, 'Rp': 0.89865828998196062}. All model fits for fold 2, metric AIC: [254648.23575785581, 252738.04961239398, 251251.00158825511, 249894.09518978855, 249008.72134400008]. Best K for fold 2: 10. Performance: {'R^2': 0.8034479975799519, 'MSE': 2.2875509346902207, 'Rp': 0.8971303533111219}.