Computing Deviations

This package provides functions to compute various deviations between arrays in a variety of ways:

# StatsBase.counteq — Function

Count the number of indices at which the elements of the arrays a and b are equal.

# StatsBase.countne — Function

Count the number of indices at which the elements of the arrays a and b are not equal.

# StatsBase.sqL2dist — Function

Compute the squared L2 distance between two arrays: . Efficient equivalent of sum(abs2, a - b).

# StatsBase.L2dist — Function

Compute the L2 distance between two arrays: . Efficient equivalent of sqrt(sum(abs2, a - b)).

# StatsBase.L1dist — Function

Compute the L1 distance between two arrays: . Efficient equivalent of sum(abs, a - b).

# StatsBase.Linfdist — Function

Compute the L∞ distance, also called the Chebyshev distance, between two arrays: . Efficient equivalent of maxabs(a - b).

# StatsBase.gkldiv — Function

Compute the generalized Kullback-Leibler divergence between two arrays: . Efficient equivalent of sum(a*log(a/b)-a+b).

# StatsBase.meanad — Function

Return the mean absolute deviation between two arrays: mean(abs, a - b).

# StatsBase.maxad — Function

Return the maximum absolute deviation between two arrays: maxabs(a - b).

# StatsBase.msd — Function

Return the mean squared deviation between two arrays: mean(abs2, a - b).

# StatsBase.rmsd — Function

Return the root mean squared deviation between two optionally normalized arrays. The root mean squared deviation is computed as sqrt(msd(a, b)).

# StatsBase.psnr — Function

Compute the peak signal-to-noise ratio between two arrays a and b. maxv is the maximum possible value either array can take. The PSNR is computed as 10 * log10(maxv^2 / msd(a, b)).