p147685/CRE_RB_and_CCRE
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p147685:EndPoint25Mar2022
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compare: p147685:e68267182d686971490247dd617de49e2696f373
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p147685:EndPoint25Mar2022

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2 changed files with 28 additions and 93 deletions
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60
CREClass.m
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@ -1,7 +1,4 @@
classdef CREClass < handle
% 23 and 24 Nov 2022 I added a new calculation for the RB based on
% Shanon entropy given a threshold \lambda. I also think there is a
% "mistake" in calculating the RB the classical way.
properties
Data; % the data set to get the CRE off
nBin; %number of bins
@ -114,62 +111,7 @@ classdef CREClass < handle
obj.nBin=numel(obj.Edges)-1;
end
end
function CalcRBShannon(obj)
% 23Nov22 Here I will try to implement a (hopefully better)
% definition of the relevance boundry. The basic idea is
% assume a dataset X= {x_1 ... x_i} X
% set a threshold \lambda NB \lambda \in of the data X
%Shanon entropy for this \lambda will be:
% H(\lamda)=p(x<\lambda)log(p(x<lambda))+p(x>=\lambda)log(p(x>=\lambda))
% Now the expectation value for H will be :
%E(H)=\int p(\lambda)*H(\lambda) d\lambda
if isempty(obj.Data)
warning('nothing to do');
return;
end
if isempty(obj.Edges);obj.CalcEdges;end
[CP,bin]=histcounts(obj.Data(:),transpose(obj.Edges),'Normalization','cdf');
dl=transpose(diff(bin)./sum(diff(bin))); % will capture the case with non-equidistant bins as well as equidistant bins
pdf_lambda=histcounts(obj.Data(:),transpose(obj.Edges),'Normalization','pdf');
CP=transpose(CP);
LogCP=log(CP);
LogCP(~isfinite(LogCP))=0; % see comment below
FC=1-CP;
FC(FC<0)=0; % due to rounding errors, small negative numbers can arrise. These are theoretically not possible.
LogFC=log(FC);
LogFC(~isfinite(LogFC))=0;%the log of 0 is -inf. however in Fc.*logFc it should end up as 0. to avoid conflicts removing the -inf
H=-1*(CP.*LogCP+FC.*LogFC); %Shannon Entropy when a theshold (lambda) is set to the lower bin edge
w=(dl.*pdf_lambda);
w=w./sum(w); % for one reason or the other, the sum is not always equal to 1.
EH=w*H; %calculate expectation value for H
RB_ind=[find(H>=EH,1,'first') find(H>=EH,1,'last')];
obj.RB=obj.Edges(RB_ind)+dl(RB_ind)/2;
obj.P_RB=FC(RB_ind);
if obj.Monitor
lambda=obj.Edges(1:end-1);
scatter(w,H);xlabel('weight');ylabel('Shannon Entropy');snapnow
plot(lambda,H);xlabel('lambda');ylabel('Shannon Entropy');
line(lambda(RB_ind(:)),[EH;EH],'linestyle',':');snapnow
histogram(obj.Data(:));snapnow
disp('sum of weights')
disp(sum(dl.*pdf_lambda))
disp('EH')
disp(EH)
disp('boundry index');
disp(RB_ind);
disp('boundry lambda value');
disp(obj.RB);
disp('boundry p-values');
disp(FC(RB_ind));
disp('fraction inside interval');
disp(abs(diff(FC(RB_ind))));
end
end
function Calc(obj) %calculate the CRE as well as a on-tailed definition of the RB.
% RR note: 23Nov22: I am not sure if I still like this RB
% definition. I keep having a hard time defending it
% theoretically. The code is retained, for now, however for backwards
% compatibility.
function Calc(obj)
if isempty(obj.Data)
warning('nothing to do');
return;

61
TestCREClass.m
View File

@ -2,36 +2,31 @@
% show some default behaviour
% and test the error handling.
%% set some constants
% A=randn(1000,1);
% A=rand(1000,1);
A=cat(1,randn(1000,1),randn(200,1)+10);
A=randn(100);
%% create instance of CRE class
% % % S1=CREClass;
% % % %% Calculate by setting labda equal to each of the (unique) values in the data.
% % % S1.UseHistProxyFlag=false;
% % % S1.EqualSizeBinFlag=false;
% % % S1.Data=A;% set gaussian data
% % % S1.Calc; %get RB based on the CRE
% % % S1.CalcRBShannon; %% get RB based on shannon entropy
% % % disp(S1)
% % % %% use histogram aproximation with unequal bin sizes in the histogram
% % % S2=CREClass;
% % % S2.UseHistProxyFlag=true;
% % % S2.EqualSizeBinFlag=false;
% % % S2.Data=A;% set gaussian data
% % % S2.nBin=numel(A); % set nbin to npoints; This is fine as we use the cummulative distribution and the formulas as defined in Zografos
% % % S2.Calc
% % % S2.CalcRBShannon;
% % % disp(S2)
% % % %% use histogram aproximation with equal bin sizes in the histogram
% % % S3=CREClass;
% % % S3.UseHistProxyFlag=true;
% % % S3.EqualSizeBinFlag=false;
% % % S3.Data=A;% set gaussian data
% % % S3.nBin=numel(A); % set nbin to npoints; This is fine as we use the cummulative distribution and the formulas as defined in Zografos
% % % S3.Calc
% % % S3.CalcRBShannon;
% % % disp(S3)
S1=CREClass;
%% Calculate by setting labda equal to each of the (unique) values in the data.
S1.UseHistProxyFlag=false;
S1.EqualSizeBinFlag=false;
S1.Data=A;% set gaussian data
S1.Calc;
disp(S1)
%% use histogram aproximation with unequal bin sizes in the histogram
S2=CREClass;
S2.UseHistProxyFlag=true;
S2.EqualSizeBinFlag=false;
S2.Data=A;% set gaussian data
S2.nBin=numel(A); % set nbin to npoints; This is fine as we use the cummulative distribution and the formulas as defined in Zografos
S2.Calc
disp(S2)
%% use histogram aproximation with equal bin sizes in the histogram
S3=CREClass;
S3.UseHistProxyFlag=true;
S3.EqualSizeBinFlag=false;
S3.Data=A;% set gaussian data
S3.nBin=numel(A); % set nbin to npoints; This is fine as we use the cummulative distribution and the formulas as defined in Zografos
S3.Calc
disp(S3)
%% Check recalculation of edges
S4=CREClass;
S4.UseHistProxyFlag=true;
@ -39,15 +34,13 @@ S4.EqualSizeBinFlag=true;
S4.Data=A;% set gaussian data
S4.nBin=numel(A); % set nbin to npoints; This is fine as we use the cummulative distribution and the formulas as defined in Zografos
S4.Calc
S4.CalcRBShannon;
disp(S4)
S4.nBin=round(numel(A)/10);
S4.nBin=numel(A)/10;
S4.CalcEdges ; % force a recalculation of the edges
S4.Calc;
S4.CalcRBShannon;
disp(S4)
%%
return;
%% Show effect of scaling or ofsett the data
% Scale
S.Data=100*A;