CREClass.m

@@ -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)
+        function Calc(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. 
             if isempty(obj.Data)
                 warning('nothing to do');
                 return;

TestCREClass.m

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