﻿ Tools Methods

# Tools Methods

The Tools type exposes the following members.

Methods
NameDescription
Center(Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Center(Double, Double)
Centers an observation, subtracting the empirical mean from each element in the observation vector.
Center(Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Center(Double, Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Center(Double, Double, Double)
Centers an observation, subtracting the empirical mean from each element in the observation vector.
Center(Double, Double, Boolean)
Centers column data, subtracting the empirical mean from each variable.
Determination
Gets the coefficient of determination, as known as the R-Squared (R²)
Distance
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.
Expand(Int32) Obsolete.
Expand(Int32, Int32) Obsolete.
Expand(Int32, Double, Double) Obsolete.
Expand(Int32, Int32, Int32) Obsolete.
Expand(Int32, Int32, Double, Double) Obsolete.
Expand(Int32, Int32, Int32, Int32) Obsolete.
FitTDistribution(Double, Double)
Creates a new distribution that has been fit to a given set of observations.
FitTDistribution(Double, Double)
Creates a new distribution that has been fit to a given set of observations.
FitTDistribution, TOptions(Double, TOptions, Double)
Creates a new distribution that has been fit to a given set of observations.
FitTDistribution, TOptions(Double, TOptions, Double)
Creates a new distribution that has been fit to a given set of observations.
FitNewTDistribution, TObservations(TDistribution, TObservations, Double)
Creates a new distribution that has been fit to a given set of observations.
FitNewTDistribution, TObservations, TOptions(TDistribution, TObservations, TOptions, Double)
Creates a new distribution that has been fit to a given set of observations.
Group Obsolete.
InnerFence
Creates Tukey's box plot inner fence.
OuterFence
Creates Tukey's box plot outer fence.
Proportions(Int32, Int32) Obsolete.
Proportions(Int32, Int32, Int32) Obsolete.
Random Obsolete.
RandomCovariance
RandomGroups(Int32, Double) Obsolete.
RandomGroups(Int32, Int32) Obsolete.
RandomGroups(Int32, Int32, Int32) Obsolete.
RandomSample Obsolete.
Rank(Double, Boolean, Boolean)
Gets the rank of a sample, often used with order statistics.
Rank(Double, Boolean, Boolean, Boolean)
Gets the rank of a sample, often used with order statistics.
ShuffleT(IListT) Obsolete.
ShuffleT(T) Obsolete.
Standardize(Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Standardize(Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Standardize(Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Standardize(Double, Double, Boolean)
Standardizes column data, removing the empirical standard deviation from each variable.
Standardize(Double, Double, Boolean, Double)
Standardizes column data, removing the empirical standard deviation from each variable.
Standardize(Double, Double, Boolean, Double)
Standardizes column data, removing the empirical standard deviation from each variable.
Ties(Double)
Gets the number of ties and distinct elements in a rank vector.
Ties(Double, SortedDictionaryDouble, Int32)
Gets the number of ties and distinct elements in a rank vector.
Whitening(Double, Double)
Computes the whitening transform for the given data, making its covariance matrix equals the identity matrix.
Whitening(Double, Double)
Computes the whitening transform for the given data, making its covariance matrix equals the identity matrix.
ZScores(Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
ZScores(Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
ZScores(Double, Double, Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
ZScores(Double, Double, Double)
Generates the Standard Scores, also known as Z-Scores, from the given data.
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