oadds package

Submodules

oadds.oadds module

Main module.

class oadds.oadds.Cluster

Bases: object

Cluster class. Contains the list of the micro-cluster and the number of micro-clusters.

insert(mc)

Inserts a micro-cluster into the cluster

Parameters:mc – the micro-cluster to be added to the list of the micro-clusters that make up the cluster.

Increases the counter of micro-clusters into the cluster by 1.

show()

class oadds.oadds.MicroCluster(currenttimestamp, lamb, clusterNumber)

Bases: object

Micro-Cluster class

Parameters:

• currenttimestamp – the timestamp in which the cluster is created.
• lamb – the lamb parameter used as decay factor.
• clusterNumber – the number of the micro-cluster.

getCenter()

Returns:the center of the micro-cluster.

getRadius()

Returns:the radius of the micro-cluster.

insertSample(sample, timestamp=0)

Adds a sample to a micro-cluster. Updates the variables of the micro-cluster with updateRealTimeWeight() and updateRealTimeLSandSS()

Parameters:

• sample – the sample object
• timestamp – deprecated, not needed anymore. Will be removed in the next versions.

noNewSamples()

Updates the Weighted Linear Sum (WLS), the Weighted Squared Sum (WSS) and the weight of the micro-cluster when no new samples are merged.

updateIncrementalRadiusTreshold()

Doc update incremental radius

updateRealTimeLSandSS(sample)

Updates the Weighted Linear Sum (WLS), the Weighted Squared Sum (WSS), the center and the radius of the micro-cluster when a new sample is merged.

Parameters:sample – the sample to merge into the micro-cluster.

updateRealTimeWeight()

Updates the Weight of the micro-cluster by the fading factor and increases it by 1.

class oadds.oadds.OADDS(lamb=0.1, epsilon='dynamic', minPts=1, beta=0.4, mu='auto', numberInitialSamples=None, startingBuffer=None, tp=60, k_std=3)

Bases: object

OADDS class.

Parameters:

• lamb – the lambda parameter - fading factor
• epsilon – the epsilon parameter
• beta – the beta parameter
• mu – the mu parameter
• numberInitialSamples – samples to use as initial buffer
• startgingBuffer – initial buffer on which apply DBScan or use it as unique class.
• tp – frequency at which to apply the pruning strategy and remove old micro-clusters.

initWithoutDBScan()

Produces a micro-cluster merging all the samples passed into the initial buffer

If epsilon is auto computes epsilon as the maxium radius obtained from these initial samples.

nearestCluster(sample, timestamp, kind)

resetLearningImpl()

Initializes two empty Cluster as a p-micro-cluter list and o-micro-cluster list.

If mu is auto computes the value

runInitialization()

Initializes the variables of the main algorithm with the methods resetLearningImpl() and initWithoutDBScan()

runOnNewSample(sample)

Performs the basic DenStream procedure for merging new samples.

• Try to merge the sample to the closest core-micro-cluster (or)
• Try to merge the sample to the closest outlier-micro-cluster (or)
• Generate new outlier-micro-cluster by the sample

Parameters:sample – the new available sample in the stream Returns:False if the sample is merged to an existing core-micro-cluster otherwise True meaning “anomalous” sample.

updateAll(mc)

class oadds.oadds.Sample(value, timestamp: int)

Bases: object

Each record of the stream has to be declared as a Sample class.

Parameters:

• value – the values of the current sample.
• timestamp – the timestamp of current sample.

getValue()

Returns:value

setMicroClusterNumber(microClusterNumber: int)

Assign to each sample the microClusterNumber in which was merged.

Set:microClusterNumber

setRealTimestamp(timestamp)

setTimestamp(timestamp: int)

Set:timestamp

oadds.oadds.computeReductionFactor(lamb, steps)

Module contents

Top-level package for oadds.