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Accord.NET (logo) Tools Class
Set of statistics functions.
Inheritance Hierarchy
SystemObject
  Accord.StatisticsTools

Namespace:  Accord.Statistics
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.4.0
Syntax
public static class Tools
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The Tools type exposes the following members.

Methods
  NameDescription
Public methodStatic memberCenter(Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Public methodStatic memberCenter(Double, Double)
Centers an observation, subtracting the empirical mean from each element in the observation vector.
Public methodStatic memberCenter(Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Public methodStatic memberCenter(Double, Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Public methodStatic memberCenter(Double, Double, Double)
Centers an observation, subtracting the empirical mean from each element in the observation vector.
Public methodStatic memberCenter(Double, Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Public methodStatic memberDetermination
Gets the coefficient of determination, as known as the R-Squared (R²)
Public methodStatic memberDistance
Computes the kernel distance for a kernel function even if it doesn't implement the IDistance interface. Can be used to check the proper implementation of the distance function.
Public methodStatic memberExpand(Int32) Obsolete.
Expands a grouped data into a full observation matrix.
Public methodStatic memberExpand(Int32, Int32) Obsolete.
Expands a grouped data into a full observation matrix.
Public methodStatic memberExpand(Int32, Double, Double) Obsolete.
Expands a grouped data into a full observation matrix.
Public methodStatic memberExpand(Int32, Int32, Int32) Obsolete.
Extends a grouped data into a full observation matrix.
Public methodStatic memberExpand(Int32, Int32, Double, Double) Obsolete.
Expands a grouped data into a full observation matrix.
Public methodStatic memberExpand(Int32, Int32, Int32, Int32) Obsolete.
Expands a grouped data into a full observation matrix.
Public methodStatic memberGroup Obsolete.
Groups the occurrences contained in data matrix of binary (dichotomous) data.
Public methodStatic memberInnerFence
Creates Tukey's box plot inner fence.
Public methodStatic memberOuterFence
Creates Tukey's box plot outer fence.
Public methodStatic memberProportions(Int32, Int32) Obsolete.
Calculates the prevalence of a class for each variable.
Public methodStatic memberProportions(Int32, Int32, Int32) Obsolete.
Calculates the prevalence of a class.
Public methodStatic memberRandom Obsolete.
Returns a random permutation of size n.
Public methodStatic memberRandomCovariance
Public methodStatic memberRandomGroups(Int32, Double) Obsolete.
Returns a random group assignment for a sample into two mutually exclusive groups.
Public methodStatic memberRandomGroups(Int32, Int32) Obsolete.
Returns a random group assignment for a sample.
Public methodStatic memberRandomGroups(Int32, Int32, Int32) Obsolete.
Returns a random group assignment for a sample, making sure different class labels are distributed evenly among the groups.
Public methodStatic memberRandomSample Obsolete.
Returns a random sample of size k from a population of size n.
Public methodStatic memberRank
Gets the rank of a sample, often used with order statistics.
Public methodStatic memberShuffleT(IListT) Obsolete.
Shuffles a collection.
Public methodStatic memberShuffleT(T) Obsolete.
Shuffles an array.
Public methodStatic memberSplitInformation
Computes the split information measure.
Public methodStatic memberStandardize(Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Public methodStatic memberStandardize(Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Public methodStatic memberStandardize(Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Public methodStatic memberStandardize(Double, Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Public methodStatic memberStandardize(Double, Double, Boolean, Double)
Standardizes column data, removing the empirical standard deviation from each variable.
Public methodStatic memberStandardize(Double, Double, Boolean, Double)
Standardizes column data, removing the empirical standard deviation from each variable.
Public methodStatic memberWhitening(Double, Double)
Computes the whitening transform for the given data, making its covariance matrix equals the identity matrix.
Public methodStatic memberWhitening(Double, Double)
Computes the whitening transform for the given data, making its covariance matrix equals the identity matrix.
Public methodStatic memberZScores(Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
Public methodStatic memberZScores(Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
Public methodStatic memberZScores(Double, Double, Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
Public methodStatic memberZScores(Double, Double, Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
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Remarks
This class represents collection of common functions used in statistics. Every Matrix function assumes data is organized in a table-like model, where Columns represents variables and Rows represents a observation of each variable.
See Also