# labels = list(unzipped_data[1])
# fraser_samples = []
# for sample in samples:
# features = {}
# for k, v in sample.iteritems():
# if k in fraser_feature_dict_mix.keys():
# features[fraser_feature_dict_mix[k]] = v
# fraser_samples.append(features)
# samples = zip(fraser_samples, labels)
# make_arff_file(file_name, samples)
if __name__ == "__main__":
op = dvr.get_optima_feature_data()
db = dvr.get_dementiabank_feature_data()
make_arff_file("optima_all_features",op)
make_arff_file("dbank_all_features",db)
# # Load clinical data sets.
# clinical_samples = dvr.get_clinical_feature_data()
# # Train on clinical and test on clinical with all features
# train_clinical_test_clinical(clinical_samples)
# # Train on clinical and test on clinical with fraser features
# train_clinical_test_clinical_fraser_features(clinical_samples)
# # Load the clinical and non-clinical data sets
