Uses of Interface
adams.data.statistics.EqualLengthArrayStatistic

Packages that use EqualLengthArrayStatistic

Package	Description
adams.data.statistics

Uses of EqualLengthArrayStatistic in adams.data.statistics

Classes in adams.data.statistics that implement EqualLengthArrayStatistic

Modifier and Type	Class	Description
class	AbstractArrayDistance<T extends Number>	Ancestor for distance measures.
class	ArrayAngle<T extends Number>	Calculates the angle between the first array and the remaining arrays.
class	ArrayBinning<T extends Number>	Bins the data from given array using the specified algorithm and post-processing approach.
class	ArrayChebyshevDistance	Calculates the Chebyshev distance between the first array and the remaining arrays.
class	ArrayCorrelationCoefficient<T extends Number>	Calculates the correlation coefficient between the first array and the remaining arrays.
class	ArrayCovariance<T extends Number>	Calculates the covariance between the first array and the remaining arrays.
class	ArrayDifference<T extends Number>	Calculates the difference between the arrays.
class	ArrayEuclideanDistance	Calculates the Euclidean distance between the first array and the remaining arrays.
class	ArrayHistogram<T extends Number>	Generates a histogram from the given array. The formulas for the various width/#bin calculations can be found here: WikiPedia (2010).
class	ArrayKendallTheil<T extends Number>	Calculates the Kendall-Theil robust slope (also called Theil-Sen estimator) between the first array and the remaining arrays.
class	ArrayLinearRegression<T extends Number>	Calculates the slope and intercept of linear regression between two arrays (x and y). If more than two arrays supplied, then the linear regression is computed between the first (x) and all the other ones (y).
class	ArrayManhattanDistance	Calculates the Manhattan distance between the first array and the remaining arrays.
class	ArrayMeanAbsoluteError<T extends Number>	Calculates the mean absolute error (MAE) between the first array (actual) and the remaining arrays (predicted).
class	ArrayMinkowskiDistance	Calculates the Minkowski distance between the first array and the remaining arrays.
class	ArrayNormalize<T extends Number>	Normalizes the array(s), i.e., the sum of its/their values is 1.0.
class	ArrayNormalizeRange<T extends Number>	Normalizes the array(s) to the specified lower and upper bound.
class	ArrayRelativeAbsoluteError<T extends Number>	Calculates the relative absolute error (RAE) between the first array (actual) and the remaining arrays (predicted).
class	ArrayRootMeanSquaredError<T extends Number>	Calculates the root mean squared error (RMSE) between the first array (actual) and the remaining arrays (predicted).
class	ArrayRootRelativeSquaredError<T extends Number>	Calculates the root relative squared error (RRSE) between the first array (actual) and the remaining arrays (predicted).
class	ArrayRSquared<T extends Number>	Calculates the R^2 between the first array and the remaining arrays.
class	ArrayStandardize<T extends Number>	Standardizes the values in the array(s) to have mean 0 and stdev 1.
class	ArrayStandardScores<T extends Number>	Calculates the standard scores (or z scores) of the provided arrays.The arrays must be numeric, of course.