def _predict_failure(self, ip_feature_db):
"""
Turn the ip_feature_db into two dimensional array and feed it to
all classifiers.
"""
failList = list()
ip_set = TrainingSet()
for cur_ip in ip_feature_db:
ip_set.add_ip(cur_ip, ip_feature_db[cur_ip])
for fail_classifier in self.__fail_classifiers:
bad_ip_prediction = fail_classifier.predict(ip_set._ip_feature_list)
failList.extend([ip_set._ip_index[i] for i in range(0, len(bad_ip_prediction)) if bad_ip_prediction[i] == ip_set.BAD_TARGET])
return failList
def _predict_failure(self, ip_feature_db):
"""
Turn the ip_feature_db into two dimensional array and feed it to
all classifiers.
"""
failList = list()
ip_set = TrainingSet()
for cur_ip in ip_feature_db:
ip_set.add_ip(cur_ip, ip_feature_db[cur_ip])
for fail_classifier in self.__fail_classifiers:
bad_ip_prediction = fail_classifier.predict(
ip_set._ip_feature_list)
failList.extend([
ip_set._ip_index[i] for i in range(0, len(bad_ip_prediction))
if bad_ip_prediction[i] == ip_set.BAD_TARGET
])
return failList
def _predict_failure(self, ip_feature_db):
"""
Turn the ip_feature_db into two dimensional array and feed it to
all classifiers.
"""
failList = list()
ip_set = TrainingSet()
for fail_model in self._fail_models:
ip_set._normalisation_data = fail_model.getNormalisationData()
ip_set._normalisation_function = ip_set.normalise_individual
if ip_set._normalisation_data[TrainingSet.NORMALISATION_TYPE] == 'sparse':
ip_set._normalisation_function = ip_set.normalise_sparse
ip_set = ip_set.precook_to_predict(ip_feature_db)
print ip_set._ip_feature_array
bad_ip_prediction = fail_model.getClassifier().predict(ip_set._ip_feature_array)
failList.extend([ip_set._ip_index[i] for i in range(0, len(bad_ip_prediction)) if bad_ip_prediction[i] == ip_set.BAD_TARGET])
return failList
def _predict_failure(self, ip_feature_db):
"""
Turn the ip_feature_db into two dimensional array and feed it to
all classifiers.
"""
failList = list()
ip_set = TrainingSet()
for fail_model in self._fail_models:
ip_set._normalisation_data = fail_model.getNormalisationData()
ip_set._normalisation_function = ip_set.normalise_individual
if ip_set._normalisation_data[
TrainingSet.NORMALISATION_TYPE] == 'sparse':
ip_set._normalisation_function = ip_set.normalise_sparse
ip_set = ip_set.precook_to_predict(ip_feature_db)
print ip_set._ip_feature_array
bad_ip_prediction = fail_model.getClassifier().predict(
ip_set._ip_feature_array)
failList.extend([
ip_set._ip_index[i] for i in range(0, len(bad_ip_prediction))
if bad_ip_prediction[i] == ip_set.BAD_TARGET
])
return failList
