BNEngineNDB-class: Class '"BNEngineNDB"'

In ralmond/EABN: Evidence Accumulation Bayes Net Engine

Class "BNEngineNDB"

Description

A BNEngine instance which is not connected to a database.

Extends

Class "BNEngine", directly.

All reference classes extend and inherit methods from "envRefClass".

Methods

evidence

signature(x = "BNEngineNDB"): Returns list of EvidenceSets in the queue.

evidence

signature(x = "BNEngineNDB", value="list"): Sets the list of EvidenceSets in the queue.

Activation

At the start of each iteration of the mainLoop, it checks eng$shouldHalt() method. If this returns TRUE, then execution is immediately halted. When the queue is empty, it checks the eng$stopWhenFinished() method. If this returns true, then the main loop also terminates.

In the no database version, the process communicates with the rest of the system by checking the file referenced in the activeTest field. The eng$activate() creates this file with the extension ‘.running’. Renaming the file to have the extension .finish will cause eng$stopWhenFinished() to return true, that is the mainLoop will finish when the queue is empty. Renaming the file to have the extension .halt will cause eng$shouldHalt() to return true, and mainLoop will stop when it finishes processing the current event.

Fields

app:

Object of class character giving an globally unique identifier for the application

srs:

Object of class StudentRecordSet of NULL giving the student record set for the application.

profModel:

Object of class character giving the name of the proficiency model (for the default student record) in the warehouse manifest.

listenerSet:

Object of class ListenerSet giving a set of listeners who will listen for new statistics.

statistics:

Object of class list containing Statistic objects to be run on every update cycle.

histNodes:

Object of class character giving the names of the nodes in the proficiency model whose history will be recorded.

warehouseObj:

Object of class PnetWarehouse which stores the Bayes nets, both evidence models and student models are stored here.

waittime:

Object of class numeric giving the time in seconds the main event loop should wait before checking again for messages.

processN:

Object of class numeric giving the number of times that the main loop should run before stopping. If Inf, then the main loop will run without stopping.

manifest:

Object of class data.frame which provides the manifest for the PnetWarehouse

histnodes:

Object of class character which gives the names of the nodes for whom history will be recorded.

evidenceQueue:

A list of EvidenceSet events to be processed.

statmat:

Object of class data.frame which gives the descriptions of the Statistic objects to be used with the net.

activeTest:

A pathname to the file whose existance will be checked to determine whether or not the engine should be considered active.

Class-Based Methods

activate():

Creates the activeTest to indicate that the process is running.

deactivate():

Deletes the activeTest file to indicate that the process is no longer running.

shouldHalt():

This function checks the activeTest file to see whether or not the flag is set to cause the process to halt after processing the current record..

stopWhenFinished():

This function checks the activeTest file database to see whether or not the flag is set to cause the process to stop when the event queue is empty.

studentRecords():

Returns the StudentRecordSet associated with this engine.

fetchStats():

Fetches the statistics marked in the database configuration.

fetchStats():

Fetches the statistics or information in the statmat field.

initialize(app, warehouse, listeners, profModel, waittime, statistics, histNodes, evidenceQueue, processN, statmat, ...):

Initializes this class

saveManifest(manifest):

This sets the internal manifest field.

fetchManifest():

This returns the internal manifest field.

fetchNextEvidence():

This returns the first evidence set from the evidenceQueue field, and removes that element from the queue.

saveStats(statmat):

This saves the statistic table to the internal field.

evidenceSets():

This returns NULL

show():

This produces a printable summary.

The following methods are inherited (from the corresponding class): evidenceSets ("BNEngine"), stats ("BNEngine"), setProcessed ("BNEngine"), setManifest ("BNEngine"), activate ("BNEngine"), isActivated ("BNEngine"), saveManifest ("BNEngine"), setHistNodes ("BNEngine"), studentRecords ("BNEngine"), saveStats ("BNEngine"), fetchNextEvidence ("BNEngine"), setError ("BNEngine"), getHistNodes ("BNEngine"), warehouse ("BNEngine"), show ("BNEngine"), fetchManifest ("BNEngine"), fetchStats ("BNEngine")

Note

The assumption of this engine is that the serialized student model will be passed in along with the evidence and will be returned along with the updated statistics.

Author(s)

Russell Almond

References

Almond, Mislevy, Steinberg, Yan and Williamson (2015). Bayesian Networks in Educational Assessment. Springer. Especially Chapter 13.

See Also

Classes: BNEngine, BNEngineMongo

Constituent parts: StudentRecordSet, PnetWarehouse

Setup Functions: loadManifest, setupDefaultSR, configStats, baselineHist,

Main Loop Functions: mainLoop, accumulateEvidence, handleEvidence, getRecordForUser, logEvidence, updateSM, updateStats, updateHist, announceStats,

Examples

showClass("BNEngineNDB")

ralmond/EABN documentation built on Aug. 30, 2023, 12:52 p.m.