cvLabMATLAB/kmeansKOMSM.m at main · ComputerVisionLaboratory/cvLabMATLAB · GitHub

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classdef kmeansKOMSM
	properties (SetAccess = public)
		nDim;
		nNum1;
		nClass;
		nSubDim1;
		nOrthDim;
		nSigma;
		nAlpha;

		X1;
		A1;
		B1;
		E1;
		C1;

		Lc;

		D;
		O;
		W;
		useRFF;
		numRandFeats;
	end% properties

	methods
		function OB = kmeansKOMSM(X1,Lc,nSubDim1,nSigma,varargin)

			% if nargin == 5
			% 	OB.nAlpha = 0.999;
			% else
			% 	OB.nAlpha = varargin{1};
			% end
			useRFF = false;
			if nargin > 4 && strcmp(varargin{1},'RFF') && isnumeric(varargin{2})
				useRFF = true;
				numRandFeats = varargin{2};
			end

			OB.nAlpha = 0.999;

			if useRFF
				OB.numRandFeats = numRandFeats;
			end
			OB.nSubDim1 = nSubDim1;
			OB.nSigma = nSigma;
			OB.Lc = Lc;
			OB.X1 = X1;
			OB.useRFF = useRFF;


			[OB.nDim , OB.nNum1, OB.nClass] = size(X1);

			OB.A1 = zeros(OB.nNum1,OB.nSubDim1,OB.nClass);
			OB.B1 = zeros(OB.nNum1,OB.nSubDim1,OB.nClass);
			OB.E1 = zeros(OB.nSubDim1,OB.nClass);
			OB.C1 = zeros(1,OB.nClass);
			for I=1:OB.nClass
				if OB.useRFF
					OB.A1(:,:,I) = kmeansKPCA(OB.X1(:,:,I),OB.Lc(I,:),OB.nSubDim1,OB.nSigma,...
						'RFF', OB.numRandFeats);
				else
					OB.A1(:,:,I) = kmeansKPCA(OB.X1(:,:,I),OB.Lc(I,:),OB.nSubDim1,OB.nSigma);
				end
			end

			OB.D = zeros(OB.nSubDim1, OB.nSubDim1, OB.nClass,OB.nClass);
			for I1 = 1:OB.nClass
				for I2 = I1:OB.nClass
					% if useRFF
					% 	K = cvtKernelGramMatrix(OB.X1(:, :, I1), OB.X1(:, :, I2), OB.numRandFeats, nSigma);
					% else
					% 	K = cvtKernelGramMatrix(OB.X1(:, :, I1), OB.X1(:, :, I2), nSigma);
					% end
					K = cvtKernelGramMatrix(X1(:, :, I1), X1(:, :, I2), nSigma);
					K = diag(sqrt(OB.Lc(I1,:)))*K*diag(sqrt(OB.Lc(I2,:)));
					if I1 == I2
						OB.D(:,:,I1,I2) = eye(OB.nSubDim1,OB.nSubDim1);
					else
						OB.D(:,:,I1,I2) =  OB.A1(:,:,I1)'*K* OB.A1(:,:,I2);
						OB.D(:,:,I2,I1) = OB.D(:,:,I1,I2)';
					end
				end
			end
			OB.D = reshape(permute(OB.D,[1,3,2,4]), OB.nSubDim1 * OB.nClass, OB.nSubDim1 * OB.nClass);
			if numel(find(isinf(OB.D)))
				fprintf('Error: OB.D includes Inf or NaN\n');
				return
			end

			[B,OB.W] = svd(OB.D);
			OB.W=diag(OB.W/trace(OB.W));


			OB.nOrthDim = find(cumsum(OB.W)/sum(OB.W)>=OB.nAlpha, 1 );
			B=B(:,1:OB.nOrthDim);
			OB.W = OB.W(1:OB.nOrthDim);
			OB.O = diag(1./(OB.W))*B';

			for I1 = 1:OB.nClass
				OB.B1(:,:,I1) = diag(sqrt(OB.Lc(I1,:)))*OB.A1(:,:,I1);
			end

		end
		function [V2 E2 C2 A2 ] = TransformS(OB, X2,nSubDim2)

			nSize = size(X2);
			X2=X2(:,:,:);
			[tmp, nNum2,nSet2] = size(X2);

			A2 = zeros(nNum2,nSubDim2,nSet2);
			E2 = zeros(nSubDim2,nSet2);
			C2 = zeros(1,nSet2);
			for I=1:nSet2
				[A2(:,:,I) E2(:,I) C2(I)] = orzKPCA(X2(:,:,I),nSubDim2,OB.nSigma,'R');
			end

			V2 = zeros(OB.nOrthDim,nSubDim2,nSet2);
			for J = 1:nSet2
				a = zeros( OB.nSubDim1, nNum2,OB.nClass);
				for I = 1:OB.nClass

					Z = cvtKernelGramMatrix(OB.X1(:, : ,I), X2(:, :, J), OB.nSigma);
					a(:,:,I) = OB.A1(:,:,I)' * Z;
				end
				a = permute(a,[1,3,2]);
				a = reshape(a,size(a,1)*size(a,2),size(a,3));
				V2(:,:,J) = cvtGramSchmidt(OB.O*a*A2(:,:,J));
			end
			V2 = reshape(V2,[OB.nOrthDim,nSubDim2,nSize(3:end),1]);
		end

		function [Y2] = Transform(OB, X2)
			% ƒpƒ^[ƒ�?ƒxƒNƒgƒ�?‚É�?Î�?·‚é’¼Œð‰»•Ï�?�‚¾‚¯
			% ”ñüŒ`’¼Œð‰»•Ï�?�
			nSize = size(X2);
			X2=X2(:,:,:);
			[tmp nNum2,nSet2] = size(X2);

			Y2 = zeros(OB.nOrthDim,nNum2,nSet2);
			for J=1:nSet2
				a = zeros( OB.nSubDim1,nNum2,OB.nClass);
				for I = 1:OB.nClass
					if OB.useRFF
						Z = cvtKernelGramMatrix(OB.X1(:, :, I), X2(:, :, J), OB.numRandFeats, OB.nSigma);
					else
						Z = cvtKernelGramMatrix(OB.X1(:, :, I), X2(:, :, J), OB.nSigma);
					end
					% Z = cvtKernelGramMatrix(OB.X1(:, :, I), X2(:, :, J), OB.nSigma);

					%Z = diag(sqrt(OB.Lc(I,:)))*Z;
					%a(:,:,I) = OB.A1(:,:,I)' * Z;
					a(:,:,I) = OB.B1(:,:,I)' * Z;
				end
				a = permute(a,[1,3,2]);
				a = reshape(a,OB.nSubDim1*OB.nClass,nNum2);
				Y2(:,:,J) = OB.O*a;
			end
			Y2 = reshape(Y2,[OB.nOrthDim,nNum2,nSize(3:end),1]);
		end

	end% methods
end% classdef