def __init__(self, instance):
AnomalyProcessor.__init__(self, instance)
# dimension_match -> anom vlaues
self._anom_values = {}
# dimension_match -> norm_values
self._norm_values = {}
#params
self.anom_threshold = self._instance_conf.anom_threshold
self.normal_threshold = self._instance_conf.normal_threshold
self.fault_ittr = self._instance_conf.fault_ittr
self.normal_ittr = self._instance_conf.normal_ittr
# what dimension to match the samples on - LIST
self.dimension_match = self._instance_conf.dimension_match
# what to name the sample when publishing back - log file!
self.sample_name = self._instance_conf.sample_name
#metric to aggregate - should put these in config and load in
self.metrics = self._instance_conf.sample_metrics
#normalize?
self.normalized = self._instance_conf.normalized
#implement the additional features in AD3?
self.ad3 = self._instance_conf.ad3
# dimension_match -> sample
self._sample_buffer = {}
def __init__(self):
AnomalyProcessor.__init__(self, cfg.CONF.ks.kafka_group)
ks_config = cfg.CONF.ks
self._reference_duration = ks_config.reference_duration
self._probe_duration = ks_config.probe_duration
self._ks_d = ks_config.ks_d
self._min_samples = ks_config.min_samples
self._timeseries = {}
def __init__(self):
AnomalyProcessor.__init__(self, cfg.CONF.ks.kafka_group)
ks_config = cfg.CONF.ks
self._reference_duration = ks_config.reference_duration
self._probe_duration = ks_config.probe_duration
self._ks_d = ks_config.ks_d
self._min_samples = ks_config.min_samples
self._timeseries = {}
def __init__(self):
AnomalyProcessor.__init__(self, cfg.CONF.nupic.kafka_group)
self._models = {}
self._shifters = {}
self._anomaly_likelihood = {}
# Load the model params JSON
with open(cfg.CONF.nupic.model_params) as fp:
self.model_params = json.load(fp)
def __init__(self):
AnomalyProcessor.__init__(self, cfg.CONF.nupic.kafka_group)
self._models = {}
self._shifters = {}
self._anomaly_likelihood = {}
# Load the model params JSON
with open(cfg.CONF.nupic.model_params) as fp:
self.model_params = json.load(fp)
