ITE / code / H_I_D / meta_estimators /
IShannon_HShannon_estimation.m  function [I] =
IShannon_HShannon_estimation(Y,ds,co) %Estimates Shannon mutual
information (I) making use of an estimator for Shannon
differential entropy; co is the cost object. %This is a  "meta"
method, using the relation: I(y^1,...,y^M) = \sum_{m=1}^MH(y^m)
- H([y^1,...,y^M]). %  ds: subspace dimensions. %  co: mutual
information estimator object. %       Thomas M. Cover, Joy A.
Thomas. Elements of Information Theory, John Wiley and Sons, New
York, USA (1991).      num_of_comps = length(ds);%number of
subspaces=:M     cum_ds =
cumsum([1;ds(1:end-1)]);%1,d_1+1,d_1+d_2+1,...,d_1+...+d_{M-1}+1
= starting indices of the subspaces  %I = - H([y^1,...,y^M]):
I = -H_estimation(Y,co.member_co); %I = I + \sum_{m=1}^MH(y^m):
for k = 1 : num_of_comps         idx = [cum_ds(k) :
cum_ds(k)+ds(k)-1];         I = I +
H_estimation(Y(idx,:),co.member_co);  Tip: Use camelCasing e.g.
ProjME to search for