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 learn_svm_classifier(dataset, radius):
if not dataset.is_marked():
return None
learn_dataset, learn_results = [], []
for record, markup in zip(dataset.data, dataset.markup):
keys = svm_tools.get_sequence_dataset(record.seq, radius, dataset.alphabet)
results = svm_tools.markup_sequence_dataset(keys, markup)
learn_dataset.extend(keys)
learn_results.extend(results)
classifier = svm.SVC(kernel='rbf')
classifier.fit(np.asarray(learn_dataset, float), np.asarray(learn_results, float))
return classifier
def learn_svm_classifier(dataset, radius):
if not dataset.is_marked():
return None
learn_dataset, learn_results = [], []
for record, markup in zip(dataset.data, dataset.markup):
keys = svm_tools.get_sequence_dataset(record.seq, radius,
dataset.alphabet)
results = svm_tools.markup_sequence_dataset(keys, markup)
learn_dataset.extend(keys)
learn_results.extend(results)
classifier = svm.SVC(kernel='rbf')
classifier.fit(np.asarray(learn_dataset, float),
np.asarray(learn_results, float))
return classifier
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))
