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This page explains the observables framework. It is designed to profile certain model parameter properties at certain timestamps during the adaptive modelling process.

Example timestamps are

  • A new best model has been constructed
  • A new model has been constructed for the sequential model builder
  • A complete generation has been built for the genetic model builder

The observables currently split into two groups, observables on a model, and observables on a batch (or generation) of models. Examples of the first kind are:

  • Weights, flags and percentage for polynomial models
  • Kernel parameter (=spread) for a RBFNN model

Examples of the second kind are

  • Model parameters of the best model in a generation
  • Spread (=largest/average/smallest value) of model parameters for a generation
  • Distribution of different model types in a generation when using the heterogeneous modelinterface

To accomodate this framework, a few key components have been implemented:

  • The getObservables() member function that each model interface should implement. This method returns a cell array of Observable objects.
  • The getBatchObservables() member function of BatchInterface (superclass of GeneticInterface). This method return a cell array of observable objects on a Batch/Generation of models.
  • The methods registerObserver() and observe() of the AdaptiveModelBuilder base class

The sequel will elaborate on all different classes and components.