def predict_dataset(classifier, dataset, radius, alphabet): result = [] for data in dataset.data: predict_data = svm_tools.get_sequence_dataset(data.seq, radius, alphabet) predicted_result = svm_tools.filter_predicted_result(classifier.predict(predict_data)) result.append((str(data.seq), predicted_result)) return result
def predict_dataset(classifier, dataset, radius, alphabet): result = [] for data in dataset.data: predict_data = svm_tools.get_sequence_dataset(data.seq, radius, alphabet) predicted_result = svm_tools.filter_predicted_result( classifier.predict(predict_data)) result.append((str(data.seq), predicted_result)) return result
def predict_sequence(classifier, sequence, radius, alphabet): dataset = svm_tools.get_sequence_dataset(sequence, radius, alphabet) return svm_tools.filter_predicted_result(classifier.predict(dataset))