Example 11: Multivariate Dispersion Entropy

Import a vector of 4096 uniformly distributed random integers in range [1 8] and convert it to a multivariate set of 4 sequences with 1024 samples each.

X = ExampleData("randintegers");
Data = reshape(X, 1024, 4);

1024×4 Matrix{Float64}:
 7.0  6.0  5.0  8.0
 1.0  2.0  5.0  3.0
 6.0  5.0  4.0  6.0
 6.0  7.0  3.0  3.0
 1.0  7.0  7.0  8.0
 8.0  3.0  7.0  3.0
 5.0  7.0  3.0  8.0
 2.0  1.0  2.0  3.0
 5.0  1.0  3.0  7.0
 6.0  7.0  2.0  6.0
 ⋮              
 7.0  4.0  2.0  7.0
 8.0  1.0  2.0  1.0
 1.0  2.0  8.0  1.0
 2.0  5.0  3.0  8.0
 1.0  4.0  1.0  3.0
 5.0  7.0  6.0  1.0
 4.0  7.0  4.0  5.0
 1.0  5.0  6.0  8.0
 7.0  5.0  1.0  8.0

Calculate the multivariate dispersion entropy and reverse dispersion entropy for embedding dimensions (m) = [1,1,2,3], using a 7-symbol transform.

MDisp, RDE = MvDispEn(Data, m = [1,1,2,3], c = 7);

6.922734508722953

0.0009856206548534644

Perform the same calculation but normalize the output entropy estimate w.r.t the number of unique dispersion patterns

MDisp, RDE = MvDispEn(Data, m = [1,1,2,3], c = 7, Norm = true);

0.5082259698140852

0.0009856218516602513

Compare the results above ($Methodx == 'v1'$) with those obtained using the mvDE method ($Methodx=='v2'$), returning estimates for each value from 1, ..., max(m)

MDisp, RDE = MvDispEn(Data, m = [1,1,2,3], c = 7, Norm = true, Methodx = "v2")

3-element Vector{Float64}:
 0.9543959541913541
 0.9407485371546896
 0.9301233396966886

3-element Vector{Float64}:
 0.026759488075314832
 0.008053236671745904
 0.0020161358982542705