From b29e272c337155fb6f87a9fb347f1b160cfc858c Mon Sep 17 00:00:00 2001
From: Pustalorc <pustalorc@gmail.com>
Date: Wed, 8 Jul 2020 18:35:12 +0000
Subject: [PATCH] Add Sample Matlab Code

---
 MATLAB_DDC_Algorithm.m | 214 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 214 insertions(+)
 create mode 100644 MATLAB_DDC_Algorithm.m

diff --git a/MATLAB_DDC_Algorithm.m b/MATLAB_DDC_Algorithm.m
new file mode 100644
index 0000000..65e2d22
--- /dev/null
+++ b/MATLAB_DDC_Algorithm.m
@@ -0,0 +1,214 @@
+function [ Clusters, Results ] = DDC_ver01_1_CAMS( varargin )
+%DDC_VER01.1 Data Density Based Clustering
+% Copyright R Hyde 2017
+% Released under the GNU GPLver3.0
+% You should have received a copy of the GNU General Public License
+% along with this program.  If not, see <http://www.gnu.org/licenses/
+% If you use this file please acknowledge the author and cite as a
+% reference:
+% Hyde, R.; Angelov, P., "Data density based clustering," Computational
+% Intelligence (UKCI), 2014 14th UK Workshop on , vol., no., pp.1,7, 8-10 Sept. 2014
+% doi: 10.1109/UKCI.2014.6930157
+% Downloadable from: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6930157&isnumber=6930143
+%
+% ACCMIP revision: *Try clusters with no distance in any dimension set grid
+% dimension instead of remaining as initial radii (Line 132)
+%
+% Data Density Based Clustering with Manual Radii
+% Useage:
+% [Clusters, Results]=DDC_ver01(DataIn, InitR, Merge, Verbose]
+% Inputs:
+% DataIn:   m x n array of data for clustering m rows of samples, data
+%           should be normalised 0-1 or scaled appropriately
+% InitR:    initial radii array with radius in each diension, if a single
+%           value is provided the same value will be used for all.
+% Verbose:  1 - plot output of progress, 0 - silent
+% Merge:    Flag=1 to merge clusters if centre is within the ellipse of
+%           another
+% Outputs:
+% Results:  array of data with cluster number appended as last column
+% Clusters: array of cluster centre co-ords and radii
+
+%% Start Main Function
+if size(varargin,2)<4
+    sprintf('Input error - not enough inputs.')
+    return
+end
+DataIn=varargin{1}; % Read Array of Data
+InitR=varargin{2}; % Read Initial Radius
+Verbose=varargin{4}; % Read flag for plotting during the run
+Merge=varargin{3}; % Read flag for merging final clusters
+% defaults to use instead of initial radii, 1 grid in lat, lon, scaled O3 std dev in case of cluster radii = 0
+% DefaultRadii = [getappdata(CatacombePaper,'GridDistance'),...
+%     getappdata(CatacombePaper,  'RadO3')]; 
+
+%% Initialise
+if size(InitR,2)==1 % create equal radii across each domension if only one provided
+    if Verbose==1
+    sprintf('Using equal radii')
+    end
+    InitR=ones(size(DataIn,2),1)*InitR;
+elseif size(InitR,2)<size(DataIn,2)
+    sprintf('Radii not single or equal to data dimensions')
+    return
+end
+NumClusters=0; % initial number of clusters
+Results=zeros(size(DataIn,1),size(DataIn,2)+1); % initiate empty array
+
+%% ### DDC routine ###
+Glob_Mean=mean(DataIn,1); % array of means of data dim
+Glob_Scalar=sum(sum((DataIn.*DataIn),2),1)/size(DataIn,1); % array of scalar products for each data dim
+
+while size(DataIn,1)>0
+    % size(DataIn,1) % uncomment to trace remaining data
+    NumClusters=NumClusters+1;
+    Clusters.Rad(NumClusters,:)=InitR;
+    %% Find Cluster Centre
+    Glob_Mean=mean(DataIn,1); % array of means of data dim
+    Glob_Scalar=sum(sum((DataIn.*DataIn),2),1)/size(DataIn,1); % array of scalar products for each data dim
+  % full calculations
+%     GDensity=1./(1+(pdist2(DataIn,Glob_Mean,'euclidean').^2)+Glob_Scalar-(sum(Glob_Mean.^2))); % calculate global densities
+%     [~, CentreIndex]=max(GDensity); % find index of max densest point
+    % slim calculations
+    GDensity=pdist2(DataIn,Glob_Mean,'euclidean').^2 + Glob_Scalar - sum(Glob_Mean.^2); % calculate global densities
+    [~, CentreIndex]=min(GDensity); % find index of max densest point
+  
+    %% Find points belonging to cluster
+    Include=bsxfun(@minus,DataIn,DataIn(CentreIndex,:)).^2; % sum square of distances from centre
+    RadSq=Clusters.Rad(NumClusters,:).^2; % square radii
+    Include=sum(bsxfun(@rdivide,Include,RadSq),2); % divide by radii and add terms
+    Include=find(Include<1);
+    
+    %% Remove outliers >3sigma
+    Dist=pdist2(DataIn(Include,:),DataIn(CentreIndex,:)); % distances to all potential members
+    Include=Include(abs(Dist - mean(Dist) <= 3*std(Dist))==1,:); % keep only indices of samples with 3 sigma
+
+    %% Move cluster centre to local densest point
+    LocMean=mean(DataIn(Include,:),1);
+    LocScalar=sum((DataIn(Include,:).^2),2)/size(Include,1); % array of scalar products of data dims
+    % full calculations
+%     LocDens=1./(1+(pdist2(DataIn(Include,:),LocMean,'euclidean').^2)+LocScalar-(sum(LocMean.^2))); % calculate local densities
+%     [~,CentreIndex]=max(LocDens);
+    % slim calculations
+    LocDens=pdist2(DataIn(Include,:),LocMean,'euclidean').^2 + LocScalar - sum(LocMean.^2); % calculate local densities
+    [~,CentreIndex]=min(LocDens);
+    CentreIndex=Include(CentreIndex);
+    Clusters.Centre(NumClusters,:)=DataIn(CentreIndex,:); % assign cluster centre
+    
+    %% Assign data to new centre
+    Include=bsxfun(@minus,DataIn,Clusters.Centre(NumClusters,:)).^2; % sum square of distances from centre
+    RadSq=Clusters.Rad(NumClusters,:).^2; % square radii
+    Include=sum(bsxfun(@rdivide,Include,RadSq),2); % divide by radii and add terms
+    Include=find(Include<1);
+
+    %% Remove outliers >3sigma
+    Dist=pdist2(Clusters.Centre(NumClusters,:),DataIn(Include,:)); % distances to all potential members
+    Include=Include(abs(Dist - mean(Dist) <= 3*std(Dist))==1,:); % keep only indices of samples with 3 sigma
+
+    %% Update radii to maximum distances
+    for idx=1:size(DataIn,2)
+        value01=pdist2(DataIn(Include,idx),Clusters.Centre(NumClusters,idx),'Euclidean');
+        if max(value01)>0
+            Clusters.Rad(NumClusters,idx)=max(value01);
+        end
+    end
+    
+    %% Assign data to cluster based on new radii
+    Include=bsxfun(@minus,DataIn,Clusters.Centre(NumClusters,:)).^2; % sum square of distances from centre
+    RadSq=Clusters.Rad(NumClusters,:).^2; % square radii
+    Include=sum(bsxfun(@rdivide,Include,RadSq),2); % divide by radii and add terms
+    Include=find(Include<1);
+ 
+    %% Remove outliers >3sigma
+    Dist=pdist2(Clusters.Centre(NumClusters,:),DataIn(Include,:)); % distances to all potential members
+    Include=Include(abs(Dist - mean(Dist) <= 3*std(Dist))==1,:); % keep only indices of samples with 3 sigma
+    
+    %% Update radii to maximum distances
+    
+    for idx=1:size(DataIn,2)
+        value01=pdist2(DataIn(Include,idx),Clusters.Centre(NumClusters,idx),'Euclidean');
+        if max(value01)>0
+            Clusters.Rad(NumClusters,idx)=max(value01);
+        else
+%             Clusters.Rad(NumClusters,idx)=DefaultRadii(idx);
+        end
+    end
+ 
+    %% Plot
+    if Verbose==1
+        hold off;scatter(DataIn(:,1),DataIn(:,2));hold on
+        scatter(DataIn(CentreIndex,1),DataIn(CentreIndex,2),'r')
+        scatter(DataIn(Include,1),DataIn(Include,2),'g');
+        scatter(Clusters.Centre(NumClusters,1),Clusters.Centre(NumClusters,2),'*','r')
+        title(sprintf('Clustered: %i, Remaining: %i',size(Results,1)-size(DataIn,1), size(DataIn,1)))
+        axis([0 1 0 1])
+        drawnow
+        for zz=1:size(Clusters.Centre,1)
+            rectangle('Position',[Clusters.Centre(zz,1)-Clusters.Rad(zz,1), Clusters.Centre(zz,2)-Clusters.Rad(zz,2), 2*Clusters.Rad(zz,1), 2*Clusters.Rad(zz,2)],'Curvature',[1,1])
+        end
+    end
+    %% Assign data to final clusters
+    StartIdx=find(all(Results==0,2),1,'first');
+    EndIdx=StartIdx+size(Include,1)-1;
+    Results(StartIdx:EndIdx,:)=[DataIn(Include,:),ones(size(Include,1),1)*NumClusters];
+    DataIn(Include,:)=[]; % remove clustered data
+end
+
+%% Merge clusters if centre is within another cluster
+if Merge==1;
+MergeAny=1;
+    while MergeAny==1
+        if Verbose==1
+            figure(2)
+            clf
+            for zz=1:size(Clusters.Centre,1)
+                rectangle('Position',[Clusters.Centre(zz,1)-Clusters.Rad(zz,1),...
+                    Clusters.Centre(zz,2)-Clusters.Rad(zz,2), 2*Clusters.Rad(zz,1),...
+                    2*Clusters.Rad(zz,2)],'Curvature',[1,1])
+            end
+            hold on
+            scatter(Clusters.Centre(:,1),Clusters.Centre(:,2),'*','r')
+            drawnow
+        end
+
+        MergeAny=0;
+        Merges=[];
+        % for each cluster & find if cluster centre is within other clusters
+        for idx1=1:size(Clusters.Centre,1); 
+            InEll=bsxfun(@minus,Clusters.Centre,Clusters.Centre(idx1,1:end)).^2;
+            InEll=sum(bsxfun(@rdivide,InEll,Clusters.Rad(idx1,:).^2),2); % divide by rad^2 & add
+            InEll=(InEll<1);
+            Merges(idx1,:)=InEll.';
+        end
+        Merges(logical(eye(size(Merges))))=0;
+        % Merge clusters
+        for idx=1:size(Clusters.Centre,1)
+            [~,idx1]=find(Merges(idx,:),1);
+            Results(ismember(Results(:,end),idx1),end)=idx;
+            if idx1
+                MergeAny=1;
+            end
+        end
+        %% renumber clusters
+        [C,~,ic]=unique(Results(:,end));
+        C=1:size(C,1);
+        Results(:,end)=C(ic);
+        %% Re-create cluster data
+        Clusters.Centre=[];
+        Clusters.Rad=[];
+        for idx1=1:max(Results(:,end))
+            Clusters.Centre(idx1,:)=mean(Results(Results(:,3)==idx1,1:end-1),1);
+            for idx2=1:size(Results,2)-1
+                value01=pdist2(Results(Results(:,3)==idx1,idx2),Clusters.Centre(idx1,idx2),'Euclidean');
+                if max(value01)>0
+                    Clusters.Rad(idx1,idx2)=max(value01);
+                else
+                    Clusters.Rad(idx1,idx2)=0;
+                end
+            end
+        end
+
+    end
+end
+
+end % end function
\ No newline at end of file