SSF.csf_spatial compute CSF in R^K w/ groundtruth

src/MATLAB/+CSF/csf_spatial.m

+function [csf_mean,csf_std] = csf_spatial(Y,idxTrue)
+
+K = length(unique(idxTrue));
+mcdf = [];
+scdf = [];
+for ik=1:K
+    idxk=find(idxTrue==ik);
+    yk=Y(idxk,:);
+    [dk,sk] = CSF.od2d(yk);
+    mcdf(ik)=dk;
+    scdf(ik)=sk;
+end
+
+csf_mean = mean(mcdf);
+csf_std = std(mcdf);
+4;
\ No newline at end of file

src/MATLAB/+CSF/od2d.m

+function [dk,sk] = od2d(xk)
+
+cx = repmat(mean(xk),size(xk,1),1);
+dd = vecnorm(cx - xk, 2, 2);
+% dd = sqrt( (cx(:,1)-xk(:,1)).^2 + (cx(:,2)-xk(:,2)).^2 );
+idx=find( abs(dd-mean(dd))<std(dd));
+dd = dd(idx);
+dk = mean(dd) + log2(length(idx))/length(idx);
+sk = std(dd);
+4;
+
+% KX = NCD.Press(xk,[]);
+% dx = pdist2(xk,xk);
+% 
+% dk = KX - kx + log2(length(xk));
+
+% dx = pdist(xk);
+% dk = max(dx) - min(dx);
+% sk = norm(std(xk));
+
+% xx = (xk - mean(xk)) ./ std(xk);
+% [h,p] = kstest(xx);
+% dk = 1-p;
\ No newline at end of file

src/MATLAB/+CSF/pmean.m

+function mu=pmean(x)
+
+if length(x)<3
+    mu=mean(x);
+    return;
+end
+idx=find( abs(x-mean(x))<std(x));
+mu=mean(x(idx));
\ No newline at end of file

src/MATLAB/+Cluster/SpectralCluster.m

@@ -54,11 +54,11 @@
 function [idx,Y]= SpectralCluster(A,k)
 
 NREP=50;
-if (1==k)
-    idx=ones(size(A,2),1);
-    Y = []; % ack TODO need Y for 1?
-    return;
-end
+% if (1==k)
+%     idx=ones(size(A,2),1);
+%     Y = []; % ack TODO need Y for 1?
+%     return;
+% end
 for i=1:size(A,1)
     D(i,i)=sum(A(i,:));
 end
@@ -69,10 +69,13 @@ L=Cluster.Regularize(L); % remove the miniscule asymmetry from L
 [eVec eVal]=eig(L);
 
 X=[eVec(:,end) eVec(:,1:k-1)];
+if 1 == k
+    Y = X ./ max(X);
+else
     for i=1:size(X,1)
         Y(i,:)=X(i,:)./norm(X(i,:));
     end
-
+end
 idx=kmeans(Y,k,'emptyaction','singleton','replicates',NREP);
 end