BaseScalarSVR.m

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namespace general{
namespace regression{


/* (Autoinserted by mtoc++)
 * This source code has been filtered by the mtoc++ executable,
 * which generates code that can be processed by the doxygen documentation tool.
 *
 * On the other hand, it can neither be interpreted by MATLAB, nor can it be compiled with a C++ compiler.
 * Except for the comments, the function bodies of your M-file functions are untouched.
 * Consequently, the FILTER_SOURCE_FILES doxygen switch (default in our Doxyfile.template) will produce
 * attached source files that are highly readable by humans.
 *
 * Additionally, links in the doxygen generated documentation to the source code of functions and class members refer to
 * the correct locations in the source code browser.
 * However, the line numbers most likely do not correspond to the line numbers in the original MATLAB source files.
 */

class BaseScalarSVR
  :public KerMorObject,
   public ICloneable,
   public IKernelCoeffComp {
    public: /* ( setObservable ) */

        double AlphaRelMinValue = eps;
    /* (Transien*/
public:

                data.FileMatrix K;
    public: /* ( Dependent, setObservable ) */

        double Lambda;
    protected:

        double C = .5;
    private:

        fLambda = 1;
    public:

        BaseScalarSVR() {
            this = this@KerMorObject;
            this.registerProps(" K "," Lambda "," AlphaRelMinValue ");
        }


        function target = clone(target) {
            if nargin < 2
                error(" Must call this method from subclasses, passing the subclass instance as 'target'. ");
            end
            target.K= this.K;
            target.C= this.C;
            target.fLambda= this.fLambda;
            target.AlphaRelMinValue= this.AlphaRelMinValue;
        }


        
#if 0 //mtoc++: 'set.K'
function  K(value) {
            if (isa(value," handle ") && ~isa(value, " data.FileMatrix ")) || (~ismatrix(value) || ~isa(value," double "))
                error(" Value must be a data.FileMatrix or a double matrix. ");
            end
            this.K= value;
        }

#endif


        
#if 0 //mtoc++: 'set.Lambda'
function  Lambda(value) {
            if ~isposrealscalar(value)
                error(" Lambda must be a positive real scalar ");
            end
            this.fLambda= value;
            this.C= 1/(2*value);
        }

#endif


        
#if 0 //mtoc++: 'get.Lambda'
function value = Lambda() {
            value = this.fLambda;
        }

#endif


        
#if 0 //mtoc++: 'set.AlphaRelMinValue'
function  AlphaRelMinValue(value) {
            if ~isposrealscalar(value)
                error(" AlphaRelMinValue must be a positive real scalar ");
            end
            this.AlphaRelMinValue= value;
        }

#endif


        function  init(kernels.KernelExpansion kexp) {
            this.K= kexp.getKernelMatrix;
        }
        function [rowvecci , integersvidx , sf ] = computeKernelCoefficients(rowvec yi,initialai) {
            [ci, sf] = this.regress(yi, initialai);
            svidx = find(abs(ci) ./ max(abs(ci)) > this.AlphaRelMinValue);
            ci = ci(svidx);
            if isempty(svidx)
                ci = 0;
                svidx = 1;
                sf = StopFlag.NO_SUPPORT_VECTORS_FOUND;
            end
        }
    public: /* ( Abstract ) */

        virtual function [ci , integersf ] = regress(fxi,rowvec initialai) = 0;
};
}
}