Module analytic_results

Amber API Server

Boon Logic Amber API server

OpenAPI spec version: 2.0.0

Generated by: https://github.com/swagger-api/swagger-codegen.git

Classes

class AnalyticResults (ad=None, ah=None, aw=None, id=None, ni=None, ns=None, nw=None, cs=None, om=None, ri=None, si=None, pi=None)

NOTE: This class is auto generated by the swagger code generator program.

Do not edit the class manually.

AnalyticResults - a model defined in Swagger

Instance variables

var ad

Gets the ad of this AnalyticResults.

A binary array where 1 means a pattern was anomalous and 0 means normal. This value is derived by thresholding the anomalyIndex.

:return: The ad of this AnalyticResults. :rtype: list[int]

var ah

Gets the ah of this AnalyticResults.

The number of anomalous patterns in the last historyWindow samples. Specifically, this is a moving-window sum of the anomalyDetection array with window length historyWindow.

:return: The ah of this AnalyticResults. :rtype: list[int]

var aw

Gets the aw of this AnalyticResults.

Amber warning level at each sample, a measure of the compliance of recent behavior compared to behavior observed during Learning. This value is derived from recentAnomalies and a statistical model of expectations about the usual frequency of anomalies. - 0: OK - 1: asset changing - 2: asset critical

:return: The aw of this AnalyticResults. :rtype: list[int]

var cs

Gets the cs of this AnalyticResults.

See Boon Docs.

:return: The cs of this AnalyticResults. :rtype: list[int]

var id

Gets the id of this AnalyticResults.

The cluster to which each input pattern was assigned. The first pattern is assigned a clusterId of 1. Each pattern thereafter is either assigned to an existing cluster (if its distance from that cluster is less than percentVariation) or creates a new cluster (if its distance from all clusters exceeds percentVariation). The clusterId for each new cluster is the current maximum clusterId plus one. For example, a model with 10 clusters will have clusterIds 1-10, and the next new cluster will have clusterId 11. During Monitoring, the cluster model becomes frozen and no new clusters are formed. Patterns which cannot be assigned to any existing cluster will return a negative clusterId. These clusterIds start at -1 and decreasing strictly by 1 without repeating (they can be used for root cause analysis).

:return: The id of this AnalyticResults. :rtype: list[int]

var ni

Gets the ni of this AnalyticResults.

See Boon Docs.

:return: The ni of this AnalyticResults. :rtype: list[int]

var ns

Gets the ns of this AnalyticResults.

See Boon Docs.

:return: The ns of this AnalyticResults. :rtype: list[int]

var nw

Gets the nw of this AnalyticResults.

See Boon Docs.

:return: The nw of this AnalyticResults. :rtype: list[float]

var om

Gets the om of this AnalyticResults.

See Boon Docs.

:return: The om of this AnalyticResults. :rtype: list[float]

var pi

Gets the pi of this AnalyticResults.

An anomaly index that represents the probability within the model of getting that cluster. PI is scaled so that 0 is the most probable cluster (least anomalous) and values close to 1000 represent very improbable clusters, that is, that very rarely occurred during training.

:return: The pi of this AnalyticResults. :rtype: list[int]

var ri

Gets the ri of this AnalyticResults.

An integer between 0 and 1000 giving a measure of how anomalous this pattern is compared to patterns seen in the past. Values closer to 0 represent patterns which are ordinary given the data seen so far on this model, while values closer to 1000 represent anomalous patterns. Patterns with a high anomalyIndex belong to clusters with relatively few patterns compared to the other clusters.

:return: The ri of this AnalyticResults. :rtype: list[int]

var si

Gets the si of this AnalyticResults.

Exponentially smoothed anomalyIndex over the last 15 samples. The range remains between 0 and 1000.

:return: The si of this AnalyticResults. :rtype: list[int]

Methods

def to_dict(self)

Returns the model properties as a dict

def to_str(self)

Returns the string representation of the model