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))
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))
