CombinationKernel.m

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namespace kernels{


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class CombinationKernel
  :public kernels.BaseKernel {
    public: /* ( setObservable ) */

        CombOp = @("a,b")a.*b;
    private:

        Kernels = {""};
        Weights = "[]";

        
    public:


        CombinationKernel() {
            this = this@kernels.BaseKernel;
            this.registerProps(" CombOp ");
        }


        function K = evaluate(colvec<double> x,varargin) {
            if isempty(this.Kernels)
                error(" No Kernels here to combine. Use addKernel for setup. ");
            end
            K = this.Weights(1)*this.Kernels[1].evaluate(x,varargin[:]);
            for n = 2:length(this.Kernels)
                K = this.CombOp(K, this.Weights(n)*this.Kernels[n].evaluate(x,varargin[:]));
            end
        }


        function c = getGlobalLipschitz() {
            error(" Not implemented yet ");
        }
        function Nabla = getNabla(colvec<double> x,matrix<double> y) {
            error(" Not implemented yet ");
        }
        function this = addKernel(kernel,weight) {
            if nargin == 2
                weight = 1;
            end
            if ~isa(kernel," kernels.BaseKernel ")
                error(" kernel param must be a kernels.BaseKernel! ");
            end
            this.Kernels[end+1] = kernel;
            this.Weights(end+1) = weight;
        }

    
    public: /* ( Static ) */

        static function  test_CombinationKernels() {
            k1 = kernels.CombinationKernel;
            k1.addKernel(kernels.GaussKernel(.7));
            k1.addKernel(kernels.GaussKernel(.5));
            /* k1.addKernel(kernels.PolyKernel(3),3); */
            k1.addKernel(kernels.PolyKernel(2),2);
            k1.CombOp= @(a,b)a.*b;
            k2 = kernels.CombinationKernel;
            k2.addKernel(kernels.LinearKernel,4);
            k2.addKernel(kernels.PolyKernel(2));
            k2.CombOp= @(a,b)a+b;
            k = kernels.CombinationKernel;
            k.addKernel(k1);
            k.addKernel(k2);

            x = -1:.05:1;
            fx = sinc(x);

            svr = general.regression.ScalarEpsSVR;
            svr.Eps= .3;
            svr.Lambda= 1/4;
            svr.K= k.evaluate(x,x);

            figure(1);
            plot(x,fx," r ",x,[fx-svr.Eps; fx+svr.Eps]," r-- ");

            [ai,svidx] = svr.computeKernelCoefficients(fx,[]);
            sv = x(svidx);
            svfun = @(x)ai^t * k.evaluate(sv,x);

            fsvr = svfun(x);

            hold on;

            /*  Plot approximated function */
            plot(x,fsvr," b ",x,[fsvr-svr.Eps; fsvr+svr.Eps]," b-- ");
            skipped = setdiff(1:length(x),svidx);
            plot(sv,fx(svidx)," .r ",x(skipped),fx(skipped)," xr ");

            hold off;
        }

    

};
}