SVR.m

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namespace demos{


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class SVR {
    public: /* ( Static ) */

        static function PlotManagerpm = EpsLoss(double ep) {

            if nargin == 0
                ep = .1;
            end

            x = -5*ep:ep/10:5*ep;
            lx = max(0, abs(x)-ep);
            pm = PlotManager;
            h = pm.nextPlot(" eps_loss ",...
                sprintf(" \\epsilon-insensitive loss function for \\epsilon=%g ",ep),...
                " value "," epsilon-norm ");
            plot(h,x,zeros(size(x))," k-- ",x,lx," b ",[0 0+eps],[-ep/3 max(lx)]," black-- ");

            /*  Create textbox */
            annotation(gcf," textbox ",[0.4258 0.113 0.0461 0.07664],...
                " String ",[" -\epsilon "],...
                " FontWeight "," bold ",...
                " FontSize ",20," LineStyle "," none ");


            annotation(gcf," textbox ",[0.5858 0.1229 0.0394 0.06729],...
                " String ",[" \epsilon "],...
                " FontWeight "," bold ",...
                " FontSize ",20," LineStyle "," none ");

            axis([x(1) x(end) -ep/3 max(lx)]);

            if nargout < 1
                pm.done;
                pm.LeaveOpen= true;
            end
        }
        static function  ScalarEpsSVR_SMO(integer version,PlotManager pm) {

            if nargin < 2
                pm = PlotManager;
                pm.LeaveOpen= true;
                if nargin < 1
                    version = 2;
                end
            end

            x = -5:.1:5;
/*              fx = sin(x)+.2*x; */
            fx = sin(x-.5).*x; 
            fx = fx ./ max(abs(fx));

            svr = general.regression.ScalarEpsSVR_SMO;
            svr.Version= version;
            svr.Eps= .1;
            svr.Lambda= 1/20;/* 1/20; % i.e. C=10 as in ScalarEpsSVR */

            svr.Vis= 1;

            kernel = kernels.GaussKernel(.8);
            svr.K= kernel.evaluate(x,x);
            kexp = kernels.KernelExpansion;
            kexp.Kernel= kernel;
            [ai, svidx] = svr.computeKernelCoefficients(fx,[]);    
            kexp.Centers.xi= x(:,svidx);
            kexp.Ma= ai^t;
            afx = kexp.evaluate(x);

            h = pm.nextPlot(" svr_smo ",sprintf(" It: %d, #SV=%d, eps=%f ",...
                svr.LastIterations,length(svidx),svr.Eps)," x "," f(x) ");
            demos.SVR.plotSVR(h, x, fx, afx, svr.Eps, svidx);
        }
        static function  ScalarEpsSVR_SMO_SingleSteps(varargin) {
            
            ip.addParamValue('Version',2);
            ip.addParamValue('Steps',8);
            pmd = PlotManager(false,2,2);
            pmd.LeaveOpen= true;
            ip.addParamValue('PM',pmd);
            ip.addParamValue('Gamma',.8);
            ip.addParamValue('Eps',.1);
            ip.parse(varargin[:]);
            r = ip.Results;

            x = -5:.1:5;
            fx = sin(x-.5).*x;
            fx = fx ./ max(abs(fx));

            svr = general.regression.ScalarEpsSVR_SMO;
            svr.Version= r.Version;
            svr.Eps= r.Eps;
            svr.Lambda= 1/20;
            svr.Vis= 1;

            kernel = kernels.GaussKernel(r.Gamma);
            svr.K= kernel.evaluate(x,x);
            kexp = kernels.KernelExpansion;
            kexp.Kernel= kernel;

            for l = 1:r.Steps
                svr.MaxCount= l;

                [ai, svidx] = svr.computeKernelCoefficients(fx,[]);    
                kexp.Centers.xi= x(:,svidx);
                kexp.Ma= ai^t;
                afx = kexp.evaluate(x);

                h = r.PM.nextPlot(sprintf(" iter%d ",l),...
                sprintf(" Iteration %d ",l)," x "," f(x) and approximation ");
                demos.SVR.plotSVR(h, x, fx, afx, svr.Eps, svidx);
            end
        }
    private: /* ( Static ) */

        static function  plotSVR(h,colvec<double> x,fx,afx,eps,svidx) {
            plot(h,x,fx," r ",x,[fx-eps; fx+eps]," r-- ");
            hold(h," on ");
            plot(h,x,afx," b ");
            /*  No need for eps-margin on approximation
 *             plot(h,x,[afx-eps; afx+eps],'b--'); */
            skipped = setdiff(1:length(x),svidx);
            plot(h,x(svidx),fx(svidx)," k. "," MarkerSize ",16);
/*              plot(h,x(skipped),fx(skipped),'xr'); */
        }

    

};
}