magpie.utility.interfaces

Interface Commandable

All Known Implementing Classes:

AbstractNonlinearRegression, AbstractParsedNonlinearRegression, AllMetalsSplitter, APEAttributeGenerator, APRDFAttributeGenerator, AttributeIntervalSplitter, BaseAttributeGenerator, BaseAttributeSelector, BaseClassifier, BaseClusterer, BaseDatasetNormalizer, BaseDatasetSplitter, BaseModel, BaseRegression, BaseStatistics, BatchAttributeGenerator, BatchModelEvaluator, BestIndividualSelector, ChargeDependentAttributeGenerator, ChemicalOrderingAttributeGenerator, ClassificationRegression, ClassificationStatistics, ClustererSplitter, CompositeRegression, CompositionBasedCSPEngine, CompositionDataset, CoordinationNumberAttributeGenerator, CoulombEwaldMatrixRegression, CoulombMatrixAttributeGenerator, CoulombSineMatrixRegression, CrystalStructureDataset, CSPEngine, CumulantExpansionClassifier, Dataset, DMSPEngine, EffectiveCoordinationNumberAttributeGenerator, ElementalPropertyAttributeGenerator, ElementalPropertyAttributeGenerator, ElementDataset, ElementFractionAttributeGenerator, ElementPairPropertyAttributeGenerator, ExhaustiveAttributeSelector, FilterSplitter, GCLPAttributeGenerator, GuessMeanRegression, IndependentVariableNormalizer, InverseNormalizer, IonicCompoundFinder, IonicCompoundProximityAttributeGenerator, IonicityAttributeGenerator, LassoAttributeSelector, LASSORegression, LatticeSimilarityAttributeGenerator, LinearCorrectedRegression, LocalPropertyDifferenceAttributeGenerator, LocalPropertyVarianceAttributeGenerator, LookupTableAttributeGenerator, MeasuredClassSplitter, MeredigAttributeGenerator, MeredigAttributeSelector, MetallurgicalHeuristicRegression, MixedMetalsSplitter, MixingRuleRegression, ModelPredictionAttributeGenerator, MultiNormalizer, MultiObjectiveRegression, MultipleElementGroupsSplitter, MultiPropertyDataset, MultiPropertyRegression, NonlinearRegressionExpr, NonZeroClassificationSplitter, ObjectiveFunctionSplitter, OxidationStateGuesser, PackingEfficiencyAttributeGenerator, PairSiteAttributeGenerator, PhaseDiagramExclusionValidator, PolynomialRegression, PRDFAttributeGenerator, PRDFRegression, PredictedClassIntervalSplitter, PropertyAsAttributeGenerator, PrototypeDataset, PythonBasedAttributeSelector, RandomForestAttributeSelector, RandomGuessRegression, RegexAttributeSelector, RegressionStatistics, RegularSolutionNormalizer, RemoveCorrelatedAttributeSelector, RemoveDuplicateAttributeSelector, RemoveNaNAttributeSelector, RescalingNormalizer, ScikitLearnClassifier, ScikitLearnRegression, SimpleLinearModelExample, SingleElementGroupSplitter, SingleGuessRegression, SingleSiteAttributeGenerator, SoftmaxNormalizer, SplitClassifier, SplitModel, SplitRegression, StagedAttributeSelector, StagedRegression, StoichiometricAttributeGenerator, StructuralHeterogeneityAttributeGenerator, StructureKRRBasedRegression, TargetRegressionStatistics, UserSpecifiedAttributeSelector, UserSpecifiedExcludingAttributeSelector, ValenceShellAttributeGenerator, WekaAttributeSelector, WekaClassifier, WekaClusterer, WekaRegression, YangOmegaAttributeGenerator, ZScoreNormalizer

public interface Commandable

Provides interface for classes that can handle commands. Utilized by CommandHandler.

Commands are passed in the form of a list of objects. Under most conditions, these are simply text strings, but one must ensure that in order to be safe.

Anything can be output by a command. If nothing needs to be output, the function should simply return null.

Errors during command processing should throw Exceptions.

After you are done adding a command, make sure to document it in the class's Javadoc (see BaseClusterer for an example). Format should be:

<command><p><b>command usage</b> - Description of command
<br><pr><i>option name</i>: Description of option
<br>Details about command</command>

Surround names of a user option with <>'s. Signify that the parameter is optional by surrounding it with []'s. If the parameter must be an object, place a $ in front of the command name. As an example:

crossvalidate $<data> [<folds>] - Cross validate a model
data: Dataset used during cross-validation
folds: How many folds to use in validation process (default = 10)

Method Summary

Modifier and Type	Method and Description
java.lang.Object	runCommand(java.util.List<java.lang.Object> Command) Process some command described by a list of Objects.

Method Detail

runCommand

java.lang.Object runCommand(java.util.List<java.lang.Object> Command)
                     throws java.lang.Exception

Process some command described by a list of Objects.

Parameters:

Command - Command as a list of objects

Returns:

Any output (null if no output generated by command)

Throws:

java.lang.Exception - If something goes wrong