C# Class Accord.Statistics.Tools

Set of statistics functions

This class represents collection of 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.

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Méthodes publiques

Méthode	Description
Center ( this matrix, bool inPlace = false ) : ].double[	Centers column data, subtracting the empirical mean from each variable.
Center ( this matrix, double means, bool inPlace = false ) : ].double[	Centers column data, subtracting the empirical mean from each variable.
Center ( this observation, double result = null ) : double[]	Centers an observation, subtracting the empirical mean from each element in the observation vector.
Center ( this values, double mean, double result = null ) : double[]	Centers an observation, subtracting the empirical mean from each element in the observation vector.
Center ( this matrix, bool inPlace = false ) : double[][]	Centers column data, subtracting the empirical mean from each variable.
Center ( this matrix, double means, bool inPlace = false ) : double[][]	Centers column data, subtracting the empirical mean from each variable.
Distance ( this kernel, double x, double y ) : double	Computes the kernel distance for a kernel function even if it doesn't implement the Accord.Math.Distances.IDistance interface. Can be used to check the proper implementation of the distance function.
InnerFence ( this quartiles ) : DoubleRange	Creates Tukey's box plot inner fence.
OuterFence ( this quartiles ) : DoubleRange	Creates Tukey's box plot outer fence.
RandomCovariance ( int size, double minValue, double maxValue ) : ].double[	Generates a random Measures.Covariance(double[], double[], bool) matrix.
Rank ( this samples, bool alreadySorted = false ) : double[]	Gets the rank of a sample, often used with order statistics.
SplitInformation ( int samples, int partitions ) : double	Computes the split information measure.
Standardize ( this matrix, bool inPlace = false ) : ].double[	Standardizes column data, removing the empirical standard deviation from each variable. This method does not remove the empirical mean prior to execution.
Standardize ( this matrix, double standardDeviations, bool inPlace = false, double tol = 1e-12 ) : ].double[	Standardizes column data, removing the empirical standard deviation from each variable. This method does not remove the empirical mean prior to execution.
Standardize ( this values, bool inPlace = false ) : double[]	Standardizes column data, removing the empirical standard deviation from each variable. This method does not remove the empirical mean prior to execution.
Standardize ( this values, double standardDeviation, bool inPlace = false ) : double[]	Standardizes column data, removing the empirical standard deviation from each variable. This method does not remove the empirical mean prior to execution.
Standardize ( this matrix, bool inPlace = false ) : double[][]	Standardizes column data, removing the empirical standard deviation from each variable. This method does not remove the empirical mean prior to execution.
Standardize ( this matrix, double standardDeviations, bool inPlace = false, double tol = 1e-12 ) : double[][]	Standardizes column data, removing the empirical standard deviation from each variable. This method does not remove the empirical mean prior to execution.
ZScores ( this matrix ) : ].double[	Generates the Standard Scores, also known as Z-Scores, from the given data.
ZScores ( this matrix, double means, double standardDeviations ) : ].double[	Generates the Standard Scores, also known as Z-Scores, from the given data.
ZScores ( this matrix ) : double[][]	Generates the Standard Scores, also known as Z-Scores, from the given data.
ZScores ( this matrix, double means, double standardDeviations ) : double[][]	Generates the Standard Scores, also known as Z-Scores, from the given data.

Method Details

Center() public static méthode

Centers column data, subtracting the empirical mean from each variable.

public static Center ( this matrix, bool inPlace = false ) : ].double[
matrix	this	A matrix where each column represent a variable and each row represent a observation.
inPlace	bool	True to perform the operation in place, altering the original input matrix.
Résultat	].double[

Center() public static méthode

Centers column data, subtracting the empirical mean from each variable.

public static Center ( this matrix, double means, bool inPlace = false ) : ].double[
matrix	this	A matrix where each column represent a variable and each row represent a observation.
means	double	The mean value of the given values, if already known.
inPlace	bool	True to perform the operation in place, altering the original input matrix.
Résultat	].double[

Center() public static méthode

Centers an observation, subtracting the empirical mean from each element in the observation vector.

public static Center ( this observation, double result = null ) : double[]
observation	this	An array of double precision floating-point numbers.
result	double	The destination array where the result of this operation should be stored.
Résultat	double[]

Center() public static méthode

Centers an observation, subtracting the empirical mean from each element in the observation vector.

public static Center ( this values, double mean, double result = null ) : double[]
values	this	An array of double precision floating-point numbers.
mean	double	The mean of the , if already known.
result	double	The destination array where the result of this operation should be stored.
Résultat	double[]

Center() public static méthode

Centers column data, subtracting the empirical mean from each variable.

public static Center ( this matrix, bool inPlace = false ) : double[][]
matrix	this	A matrix where each column represent a variable and each row represent a observation.
inPlace	bool	True to perform the operation in place, altering the original input matrix.
Résultat	double[][]

Center() public static méthode

Centers column data, subtracting the empirical mean from each variable.

public static Center ( this matrix, double means, bool inPlace = false ) : double[][]
matrix	this	A matrix where each column represent a variable and each row represent a observation.
means	double	The mean value of the given values, if already known.
inPlace	bool	True to perform the operation in place, altering the original input matrix.
Résultat	double[][]

Distance() public static méthode

Computes the kernel distance for a kernel function even if it doesn't implement the Accord.Math.Distances.IDistance interface. Can be used to check the proper implementation of the distance function.

public static Distance ( this kernel, double x, double y ) : double
kernel	this	The kernel function whose distance needs to be evaluated.
x	double	An input point x given in input space.
y	double	An input point y given in input space.
Résultat	double

InnerFence() public static méthode

Creates Tukey's box plot inner fence.

public static InnerFence ( this quartiles ) : DoubleRange
quartiles	this
Résultat	AForge.DoubleRange

OuterFence() public static méthode

Creates Tukey's box plot outer fence.

public static OuterFence ( this quartiles ) : DoubleRange
quartiles	this
Résultat	AForge.DoubleRange

RandomCovariance() public static méthode

Generates a random Measures.Covariance(double[], double[], bool) matrix.

public static RandomCovariance ( int size, double minValue, double maxValue ) : ].double[
size	int	The size of the square matrix.
minValue	double	The minimum value for a diagonal element.
maxValue	double	The maximum size for a diagonal element.
Résultat	].double[

Rank() public static méthode

Gets the rank of a sample, often used with order statistics.

public static Rank ( this samples, bool alreadySorted = false ) : double[]
samples	this
alreadySorted	bool
Résultat	double[]