vtkStatisticsAlgorithm

Section: Visualization Toolkit Infovis Classes

Usage

All statistics algorithms can conceptually be operated with several options: * Learn: given an input data set, calculate a minimal statistical model (e.g., sums, raw moments, joint probabilities). * Derive: given an input minimal statistical model, derive the full model (e.g., descriptive statistics, quantiles, correlations, conditional probabilities). NB: It may be, or not be, a problem that a full model was not derived. For instance, when doing parallel calculations, one only wants to derive the full model after all partial calculations have completed. On the other hand, one can also directly provide a full model, that was previously calculated or guessed, and not derive a new one. * Assess: given an input data set, input statistics, and some form of threshold, assess a subset of the data set. * Test: perform at least one statistical test. Therefore, a vtkStatisticsAlgorithm has the following vtkTable ports * 3 input ports: * Data (mandatory) * Parameters to the learn phase (optional) * Input model (optional) * 3 output port (called Output): * Data (annotated with assessments when the Assess option is ON). * Output model (identical to the the input model when Learn option is OFF). * Meta information about the model and/or the overall fit of the data to the model; is filled only when the Assess option is ON.

.SECTION Thanks Thanks to Philippe Pebay and David Thompson from Sandia National Laboratories for implementing this class.

To create an instance of class vtkStatisticsAlgorithm, simply invoke its constructor as follows

  obj = vtkStatisticsAlgorithm

Methods

The class vtkStatisticsAlgorithm has several methods that can be used. They are listed below. Note that the documentation is translated automatically from the VTK sources, and may not be completely intelligible. When in doubt, consult the VTK website. In the methods listed below, obj is an instance of the vtkStatisticsAlgorithm class.