Release of 28 September 2010 features the following changes to DataModeler:
- Modified SymbolicRegression to handle missing or non-numeric elements in the supplied input-response data.
- Introduced a new DataDistributionPlot function which facilitates examination of data sets. This builds upon BoxWhiskerPlot but is a mich more intelligent implementation for real-world assessment of multivariate data.
- Modified EvaluateModel and EvaluateEnsemble so that if non-numerics were in the evaluation data record, the model would still evaluate if those variables were not used in the model. Previously, any non-numeric entry would result in and Indeterminate result. A side-effect of this is that evaluation can be significantly faster for low-dimensional models which are derived from modeling with large numbers of possible input variables.
- Modified NoisePower and ScaleInvariantNoisePower to allow the use of fractional norms. Fractional norms can reduce the influence of data outliers.
- Added support for Max, Min and, Clip as modeling building blocks. These can be easily included by including the string "Bounds" in the BuildFunctionPatterns parameters and supplying that result to the FunctionPatterns option for SymbolicRegression.
- Removed Sigmoid and RBF from the "PowerMath" definition for BuildFunctionPatterns and moved them into the new "Bounds" predefined set.
- Modified SymbolicRegression so that supplied options are embedded in the ModelPersonality of returned models. This will be useful when, for example, custom FunctionPatterns are used during the modeling and, as a result, these definitions would be automatically transferred to future model evaluations. This capability could also be used to embed project info into the developed models (e.g., supplying "Project" -> "FormulationDesign" to the SymbolicRegression which would then be available for reference).
- Modified the ObjectiveOrder option behavior (used by ParetoFrontPlot, ParetoFrontLogPlot, ModelSelectionTable and ModelSelectionReport to allow integer values to specify the objectives to be displayed. This will be especially useful when looking at results from, for example, CascadeMonitor during a SymbolicRegression since the default behavior is to use a SecondaryModelingObjective during model development which is suppressed as an explicit objective prior to returning the final results.
- Modified RandomModel and RandomGenome to use the ModelingVariables option with that taking precedence over the DataVariables if there is a conflict.