Extreme Optimization™: Complexity made simple.

Math and Statistics
Libraries for .NET

  • Home
  • Features
    • Math Library
    • Vector and Matrix Library
    • Statistics Library
    • Performance
    • Usability
  • Documentation
    • Introduction
    • Math Library User's Guide
    • Vector and Matrix Library User's Guide
    • Data Analysis Library User's Guide
    • Statistics Library User's Guide
    • Reference
  • Resources
    • Downloads
    • QuickStart Samples
    • Sample Applications
    • Frequently Asked Questions
    • Technical Support
  • Blog
  • Order
  • Company
    • About us
    • Testimonials
    • Customers
    • Press Releases
    • Careers
    • Partners
    • Contact us
Introduction
Deployment Guide
Configuration
Using Parallelism
Expand Mathematics Library User's GuideMathematics Library User's Guide
Expand Vector and Matrix Library User's GuideVector and Matrix Library User's Guide
Expand Data Analysis Library User's GuideData Analysis Library User's Guide
Expand Statistics Library User's GuideStatistics Library User's Guide
Expand Data Access Library User's GuideData Access Library User's Guide
Expand ReferenceReference
  • Extreme Optimization
    • Features
    • Solutions
    • Documentation
    • QuickStart Samples
    • Sample Applications
    • Downloads
    • Technical Support
    • Download trial
    • How to buy
    • Blog
    • Company
    • Resources
  • Documentation
    • Introduction
    • Deployment Guide
    • Configuration
    • Using Parallelism
    • Mathematics Library User's Guide
    • Vector and Matrix Library User's Guide
    • Data Analysis Library User's Guide
    • Statistics Library User's Guide
    • Data Access Library User's Guide
    • Reference
  • Reference
    • Extreme
    • Extreme.Collections
    • Extreme.Data
    • Extreme.Data.Json
    • Extreme.Data.Matlab
    • Extreme.Data.R
    • Extreme.Data.Stata
    • Extreme.Data.Text
    • Extreme.DataAnalysis
    • Extreme.DataAnalysis.Linq
    • Extreme.Mathematics
    • Extreme.Mathematics.Algorithms
    • Extreme.Mathematics.Calculus
    • Extreme.Mathematics.Calculus.OrdinaryDifferentialEquations
    • Extreme.Mathematics.Curves
    • Extreme.Mathematics.Curves.Nonlinear
    • Extreme.Mathematics.Distributed
    • Extreme.Mathematics.Distributed.Cuda
    • Extreme.Mathematics.EquationSolvers
    • Extreme.Mathematics.FSharp
    • Extreme.Mathematics.Generic
    • Extreme.Mathematics.Generic.LinearAlgebra
    • Extreme.Mathematics.Generic.LinearAlgebra.Implementation
    • Extreme.Mathematics.Generic.LinearAlgebra.Providers
    • Extreme.Mathematics.Generic.SignalProcessing
    • Extreme.Mathematics.Implementation
    • Extreme.Mathematics.LinearAlgebra
    • Extreme.Mathematics.LinearAlgebra.Complex
    • Extreme.Mathematics.LinearAlgebra.Complex.Decompositions
    • Extreme.Mathematics.LinearAlgebra.Implementation
    • Extreme.Mathematics.LinearAlgebra.IO
    • Extreme.Mathematics.LinearAlgebra.IterativeSolvers
    • Extreme.Mathematics.LinearAlgebra.IterativeSolvers.Preconditioners
    • Extreme.Mathematics.LinearAlgebra.Providers
    • Extreme.Mathematics.LinearAlgebra.Sparse
    • Extreme.Mathematics.Optimization
    • Extreme.Mathematics.Optimization.Genetic
    • Extreme.Mathematics.Optimization.LineSearches
    • Extreme.Mathematics.Random
    • Extreme.Mathematics.SignalProcessing
    • Extreme.Numerics.FSharp
    • Extreme.Statistics
    • Extreme.Statistics.Distributions
    • Extreme.Statistics.IO
    • Extreme.Statistics.Linq
    • Extreme.Statistics.Multivariate
    • Extreme.Statistics.Random
    • Extreme.Statistics.Tests
    • Extreme.Statistics.TimeSeriesAnalysis
  • Extreme.Statistics
    • Aggregator Class
    • AnovaModel Class
    • AnovaModelRow Class
    • AnovaRow Class
    • AnovaRowCollection Class
    • AnovaRowType Enumeration
    • AnovaTable Class
    • BoundaryIntervalBehavior Enumeration
    • CategoricalScale Class
    • CategoricalVariable Class
    • CategoricalVariable.CategoricalFilters Structure
    • Cell Structure
    • CellArray Class
    • ClassificationModel Class
    • ClusteringModel Class
    • CollectionSortOrder Class
    • ContingencyTable Class
    • ContingencyTableCell Structure
    • DataArray(T) Class
    • DataArrayElement(T) Class
    • DateTimeInterval Structure
    • DateTimeScale Class
    • DateTimeUnit Enumeration
    • DateTimeVariable Class
    • DateTimeVariable.DateTimeFilters Structure
    • Descriptives(T) Class
    • Filter Class
    • GeneralizedLinearModel Class
    • Histogram Class
    • HistogramBin Structure
    • HistogramBinCollection Class
    • HypothesisTests Class
    • InsufficientDataException Class
    • ITransformationModel Interface
    • Kernel Class
    • KernelDensity Class
    • KernelDensityBandwidthEstimator Enumeration
    • KeyVariable Class
    • KeyVariable(T) Class
    • LinearRegressionModel Class
    • LinkFunction Class
    • LogisticRegressionMethod Enumeration
    • LogisticRegressionModel Class
    • MissingValueAction Enumeration
    • MissingValueException Class
    • Model Class
    • ModelExtensions Class
    • ModelFamily Class
    • ModelFitOptions Class
    • ModelInput Class
    • ModelInputCategory Enumeration
    • ModelInputFormat Enumeration
    • ModelInputGroup Class
    • ModelSerialization Enumeration
    • ModelStatus Enumeration
    • ModelTerm Class
    • ModelTermCollection Class
    • ModelTermKind Enumeration
    • MultipleMissingValueAction Enumeration
    • NearestCorrelationMatrixAlgorithm Enumeration
    • NonlinearRegressionModel Class
    • NumericalScale Class
    • NumericalVariable Class
    • NumericalVariable.NumericalFilters Structure
    • NumericalVariable.NumericalVariableTransforms Structure
    • Observation Structure
    • ObservationCollection Class
    • OneWayAnovaModel Class
    • OneWayRAnovaModel Class
    • Parameter Class
    • ParameterCollection Class
    • ParameterVector Class
    • PolynomialRegressionModel Class
    • RankTiebreaker Enumeration
    • RegressionModel Class
    • RegularizedRegressionModel Class
    • ScaleFittingMethod Enumeration
    • SeriesExtensions Class
    • SimpleRegressionKind Enumeration
    • SimpleRegressionModel Class
    • SortOrder Enumeration
    • SpecialBins Enumeration
    • Stats Class
    • StepwiseCriterion Enumeration
    • StepwiseOptions Class
    • StepwiseRegressionMethod Enumeration
    • SumsOfSquaresType Enumeration
    • TestOfHomogeneityOfVariances Enumeration
    • TestOfNormality Enumeration
    • TransformationModel Class
    • TransformedParameter Class
    • TwoWayAnovaModel Class
    • UnivariateModel Class
    • Variable Class
    • VariableCollection Class
    • WindowFilter Class
  • CategoricalScale Class
    • CategoricalScale Constructors
    • Properties
    • Methods
CategoricalScale ClassExtreme Optimization Numerical Libraries for .NET Professional
Represents a discrete classification of a Variable.
Inheritance Hierarchy

SystemObject
  Extreme.StatisticsCategoricalScale
    Extreme.StatisticsDateTimeScale
    Extreme.StatisticsNumericalScale

Namespace: Extreme.Statistics
Assembly: Extreme.Numerics.Net40 (in Extreme.Numerics.Net40.dll) Version: 6.0.16073.0 (6.0.16312.0)
Syntax

C#
VB
C++
F#
Copy
public class CategoricalScale : IEnumerable
Public Class CategoricalScale
	Implements IEnumerable
public ref class CategoricalScale : IEnumerable
type CategoricalScale =  
    class
        interface IEnumerable
    end

The CategoricalScale type exposes the following members.

Constructors

  NameDescription
Public methodCategoricalScale(ICollection)
Constructs a categorical scale from an ICollection.
Public methodCategoricalScale(Type)
Constructs a factor from an enumeration type.
Protected methodCategoricalScale(SortOrder)
Constructs a new categorical scale.
Public methodCategoricalScale(Type, SortOrder)
Constructs a factor from an enumeration type.
Public methodCategoricalScale(ICollection, String, SortOrder)
Constructs a categorical scale from an ICollection.
Top
Properties

  NameDescription
Public propertyCount
Gets the number of levels in this CategoricalScale.
Public propertyIsOrdered
Gets whether the categorical scale is ordered or unordered.
Public propertyItem
Gets the level corresponding to the specified level index.

In C#, this property is the indexer for the DateTimeScale class.

Public propertySortOrder
Gets a value that indicates how the levels in the scale are sorted.
Top
Methods

  NameDescription
Public methodEquals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetCaption
Gets a textual representation of the level at the specified index.
Public methodGetCaptions
Gets an array containing the captions of the levels in this CategoricalScale.
Public methodGetComparer
Gets an IComparer object that can be used to compare the levels of this CategoricalScale.
Public methodGetEnumerator
Returns an IEnumerator object that can be used to iterate through the levels of the CategoricalScale.
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetLevelIndex
Gets the index of the specified level in this CategoricalScale.
Public methodGetLevels
Gets an array containing the levels of this CategoricalScale.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodMap(Object)
Gets the index of the specified level in this CategoricalScale.
Public methodMap(Object)
Returns an array of indexes corresponding to the
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Top
Remarks

Use the CategoricalScale class to represent the possible values for a categorical variable. The unique values are called levels. Every level is assigned an index, ranging from 0 to the number of distinct values minus 1.

The Map(Object) method finds the level index corresponding to an object.

Using a CategoricalScale, a categorical variable can be transformed into a numerical variable by replacing the level value by its level index. However, care should be taken that no new meaning is assigned to the numerical value of the level index. A variable that represents color using the categories 'red', 'green' and 'blue' can be converted into a numerical variable with possible values 0, 1, and 2. However, it is completely meaningless to talk about the mean or variance of this variable.

Several derived classes provide functionality tailored to specific kinds of data. These classes extend the Map(Object) methods:

ClassDescription
DateTimeScaleRepresents a class whose levels are contiguous time intervals.
NumericalScaleRepresents a class whose levels are contiguous numerical intervals.

CategoricalScale objects are used by CategoricalVariable objects to map levels to numerical values (level indexes).

Version Information

Numerical Libraries

Supported in: 5.x, 4.x
See Also

Reference

Extreme.Statistics Namespace

Copyright (c) 2004-2016 ExoAnalytics Inc.

Send comments on this topic to support@extremeoptimization.com

Copyright © 2004-2018, Extreme Optimization. All rights reserved.
Extreme Optimization, Complexity made simple, M#, and M Sharp are trademarks of ExoAnalytics Inc.
Microsoft, Visual C#, Visual Basic, Visual Studio, Visual Studio.NET, and the Optimized for Visual Studio logo
are registered trademarks of Microsoft Corporation.