Name

adams.flow.source.WekaDataGenerator

Synopsis

Generates artificial data using a Weka data generator.

Additional information

Flow input/output:
- output: weka.core.Instances

Options

• loggingLevel
  

  The logging level for outputting errors and debugging output.

  command-line	-logging-level <OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST>
  default	WARNING
  min-user-mode	Expert

  
  
• name
  

  The name of the actor.

  command-line	-name <java.lang.String>
  default	WekaDataGenerator

  
  
• annotations
  

  The annotations to attach to this actor.

  command-line	-annotation <adams.core.base.BaseAnnotation>
  default

  
  
• skip
  

  If set to true, transformation is skipped and the input token is just forwarded as it is.

  command-line	-skip <boolean>
  default	false

  
  
• stopFlowOnError
  

  If set to true, the flow execution at this level gets stopped in case this actor encounters an error; the error gets propagated; useful for critical actors.

  command-line	-stop-flow-on-error <boolean>
  default	false
  min-user-mode	Expert

  
  
• silent
  

  If enabled, then no errors are output in the console; Note: the enclosing actor handler must have this enabled as well.

  command-line	-silent <boolean>
  default	false
  min-user-mode	Expert

  
  
• properties
  

  The properties to update with the values associated with the specified values.

  command-line	-property <adams.core.base.BaseString> [-property ...]
  default

  
  
• variableNames
  

  The names of the variables to update the properties with.

  command-line	-variable <adams.core.VariableName> [-variable ...]
  default

  
  
• dataGenerator
  

  The data generator to use for generating the weka.core.Instances object.

  command-line	-generator <weka.datagenerators.DataGenerator>
  default	weka.datagenerators.classifiers.classification.Agrawal -r weka.datagenerators.classifiers.classification.Agrawal-S_1_-n_100_-F_1_-P_0.05 -S 1 -n 100 -F 1 -P 0.05