Tools Class 
Namespace: Accord.Statistics
The Tools type exposes the following members.
Name  Description  

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 RSquared (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.
Expands a grouped data into a full observation matrix.
 
Expand(Int32, Int32)  Obsolete.
Expands a grouped data into a full observation matrix.
 
Expand(Int32, Double, Double)  Obsolete.
Expands a grouped data into a full observation matrix.
 
Expand(Int32, Int32, Int32)  Obsolete.
Extends a grouped data into a full observation matrix.
 
Expand(Int32, Int32, Double, Double)  Obsolete.
Expands a grouped data into a full observation matrix.
 
Expand(Int32, Int32, Int32, Int32)  Obsolete.
Expands a grouped data into a full observation matrix.
 
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.
Groups the occurrences contained in data matrix of binary (dichotomous) data.
 
InnerFence 
Creates Tukey's box plot inner fence.
 
OuterFence 
Creates Tukey's box plot outer fence.
 
Proportions(Int32, Int32)  Obsolete.
Calculates the prevalence of a class for each variable.
 
Proportions(Int32, Int32, Int32)  Obsolete.
Calculates the prevalence of a class.
 
Random  Obsolete.
Returns a random permutation of size n.
 
RandomCovariance 
Generates a random Covariance(Double, Double, Boolean) matrix.
 
RandomGroups(Int32, Double)  Obsolete.
Returns a random group assignment for a sample
into two mutually exclusive groups.
 
RandomGroups(Int32, Int32)  Obsolete.
Returns a random group assignment for a sample.
 
RandomGroups(Int32, Int32, Int32)  Obsolete.
Returns a random group assignment for a sample, making
sure different class labels are distributed evenly among
the groups.
 
RandomSample  Obsolete.
Returns a random sample of size k from a population of size n.
 
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.
Shuffles a collection.
 
ShuffleT(T)  Obsolete.
Shuffles an array.
 
SplitInformation 
Computes the split information measure.
 
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 
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 ZScores, from the given data.
 
ZScores(Double) 
Generates the Standard Scores, also known as ZScores, from the given data.
 
ZScores(Double, Double, Double) 
Generates the Standard Scores, also known as ZScores, from the given data.
 
ZScores(Double, Double, Double) 
Generates the Standard Scores, also known as ZScores, from the given data.
