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    Metadata details:
    filename: SmartSamplingLinearGaussianFilter.m
    path of file: ./repos/libDirectional/lib/external/NonlinearEstimationToolbox/Interfaces/Filters/LinearGaussianFilters
    
    the code of the file until where you have to start completion: 
classdef SmartSamplingLinearGaussianFilter < SampleBasedLinearGaussianFilter
    % Abstract base class for linear Gaussian filters that are based on the
    % point-symmetric LCD-based Gaussian sampling technique.
    %
    % SmartSamplingLinearGaussianFilter Methods:
    %   SmartSamplingLinearGaussianFilter - Class constructor.
    %   copy                              - Copy a Filter instance.
    %   copyWithName                      - Copy a Filter instance and give the copy a new name/description.
    %   getName                           - Get the filter name/description.
    %   setColor                          - Set the filter color/plotting properties.
    %   getColor                          - Get the filter color/plotting properties.
    %   setState                          - Set the system state.
    %   getState                          - Get the system state.
    %   setStateMeanAndCov                - Set the system state by means of mean and covariance matrix.
    %   getStateMeanAndCov                - Get mean and covariance matrix of the system state.
    %   getStateDim                       - Get the dimension of the system state.
    %   predict                           -
    
function setNumSamples(obj, numSamplesPrediction, numSamplesUpdate)
            % Set absolute numbers of samples used for state prediction and measurement update.
            %
            % This also overwrites a possible previous setting, where the number of
            % samples are determined by a linear factor (see setNumSamplesByFactor()).
            %
            % By default, a linear factor 10 is used for both state prediction
            % and measurement update.
            %
            % Parameters:
            %   >> numSamplesPrediction (Positive scalar or empty matrix)
            %      The new absolute number of samples used for the state prediction.
            %      Pass an empty matrix to keep the current configuration for the
            %      state prediction.
            %
            %   >> numSamplesUpdate (Positive scalar or empty matrix)
            %      The new absolute number of samples used for the measurement update.
            %      Pass an empty matrix to keep the current configuration for the
            %      measurement update. If nothing is passed, the absolute number of
            %      samples specified for the state prediction is also used for the
            %      measurement update.
            
            if ~isempty(numSamplesPrediction)
                obj.samplingPrediction.setNumSamples(numSamplesPrediction);
            end
            
            if nargin == 3
                if ~isempty(numSamplesUpdate)
                    obj.samplingUpdate.setNumSamples(numSamplesUpdate);
                end
            elseif ~isempty(numSamplesPrediction)
                obj.samplingUpdate.setNumSamples(numSamplesPrediction);
            end
        end
        
        function setNumSamplesByFactors(obj, factorPrediction, factorUpdate)
            % Set linear factors to determine the number of samples used for state prediction and measurement update.
            %
            % The actual number of samples will be computed according to
            %
            %    Number of samples = factor * dimension + 1 - mod(factor * dimension, 2)
            %
            % i.e., always an odd number of samples is used.
            %
            % This also overwrites a possible previous setting, where the number of
            % samples are determined in an absolute way (see setNumSamples()).
            %
            % By default, a linear factor of 10 is used for both state prediction
            % and measurement update.
            %
            % Parameters:
            %   >> factorPrediction (Positive scalar or empty matrix)
            %      The new linear factor to determine the number of samples used for
            %      the state prediction. Pass an empty matrix to keep the current
            %      configuration for the state prediction.
            %
            %   >> factorUpdate (Positive scalar or empty matrix)
            %      The new linear factor to determine the number of samples used for
            %      the measurement update. Pass an empty matrix to keep the current
            %      configuration for the measurement update. If nothing is passed, the
            %      linear factor specified for the state prediction is also used for
            %      the measurement update.
            
            if ~isempty(factorPrediction)
                obj.samplingPrediction.setNumSamplesByFactor(factorPrediction);
            end
            
            if nargin == 3
                if ~isempty(factorUpdate)
                    obj.samplingUpdate.setNumSamplesByFactor(factorUpdate);
                end
            elseif ~isempty(factorPrediction)
                obj.samplingUpdate.setNumSamplesByFactor(factorPrediction);
            end
        end
        
        function [numSamplesAbs, ...
                  numSamplesFactor] = getNumSamplesConfigPrediction(obj)
            % Get the number of samples configuration used for the state prediction.
            %
            % Returns:
            %   << numSamplesAbs (Positive scalar or empty matrix)
            %      Equals the absolute number of samples if set.
            %      Otherwise, an empty matrix.
            %
            %   << numSamplesFactor (Positive scalar or empty matrix)
            %      Equals the sample factor if set.
            %      Otherwise, an empty matrix.
            
            [numSamplesAbs, ...
             numSamplesFactor] = obj.samplingPrediction.getNumSamplesConfig();
        end