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Highlights from
gapolyfitn

gapolyfitn

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06 Oct 2009 (Updated )

optimises the functional form of a multi-dimensional polynomial fit to model data

objpolyfit_MCore(Chrom, rtn_type, polyStruct)
function ObjVal = objpolyfit_MCore(Chrom, rtn_type, polyStruct)
% objpolyfit_MCore: This function implements the polynomial form
% optimisation algorithm, and uses the multicore function to distribute the
% work between multiple cores
%
% Syntax:  ObjVal = objpolyfit_MCore(Chrom, rtn_type, polyStruct)
%
% Input parameters:
%    Chrom      - Matrix containing the chromosomes of the current
%                 population. Each row corresponds to one individual's
%                 string representation.
%                 if Chrom == [], then special values will be returned
%
%    rtn_type   - if Chrom == [] and
%                 rtn_type == 1 (or []) return boundaries
%                 rtn_type == 2 return title
%                 rtn_type == 3 return value of global minimum
%
%    polyStruct - A structure containing information about the polynomial
%                 to be fitted. There should be five members:
%
%                 tVars: column matrix of the independent variables of the
%                 data to be fitted
%
%                 tData: column vector the dependent variables
%
%                 vars: the number of variables (i.e. size(tVars,2))
%
%                 maxTerms: the maximum number of term groups to be used
%
%                 maxPower: the total maximum power of any term (i.e. the
%                 maximum that the sum of all of the powers in a term can
%                 be)
%
%                 scoremethod: scalar integer denoting the scoring method
%                 to use, the folllowing options are available
%                   1 - mean squared error (mse)
%                   2 - normalised mean squared error (nmse)
%                   3 - root mean squared error (rmse)
%                   4 - normalised root mean squared error (nrmse)
%                   5 - mean absolute error (mae)
%                   6 - mean  absolute relative error  (mare)
%                   7 - coefficient of correlation (r)
%                   8 - coefficient of determination (r2)
%                   9 - coefficient of efficiency (e)
%                   10 - maximum absolute error
%                   11 - maximum absolute relative error (mxare)
%
%
% Output parameters:
%    ObjVal    - Column vector containing the objective values of the
%                individuals in the current population.
%                if called with Chrom == [], then ObjVal contains
%                rtn_type == 1, matrix with the boundaries of the function
%                rtn_type == 2, text for the title of the graphic output
%                rtn_type == 3, value of global minimum
%
%
% Author:   Richard Crozier
% Release Date: 06 OCT 2009
%

    % Compute population parameters
    [Nind,Nvar] = size(Chrom);

    if nargin == 2
        % Structure containing model data has not yet been provided, so use
        % default example data for the function sin(5xy)
        polyStruct.tVars = randMat([0.00001; 0.00001], [0.99999; 0.99999], [0; 0], 500)';
        polyStruct.tData = sin(5 .* polyStruct.tVars(1,:) .* polyStruct.tVars(2,:));
        polyStruct.vars = 2;
        polyStruct.maxTerms = 16;
        polyStruct.maxPower = 8;
    end

    Dim = polyStruct.maxTerms * (polyStruct.vars+1);

    % Check size of Chrom and do the appropriate thing
    % if Chrom is [], then define size of boundary-matrix and values
    if Nind == 0
        % return text of title for graphic output
        if rtn_type == 2
            ObjVal = ['GA POLYFIT - ' int2str(polyStruct.maxTerms), 'TERMS'];
            % return value of global minimum
        elseif rtn_type == 3
            ObjVal = 0;
            % define size of boundary-matrix and values
        else
            % lower and upper bound, identical for all n variables, this is
            % really provided for compatibility as the actiual upper bound is
            % determined by polyStruct.maxPower
            ObjVal = repmat([0 repmat(0,1,polyStruct.vars);...
                1 repmat(polyStruct.maxPower,1,polyStruct.vars)],1,polyStruct.maxTerms);
        end

        % if Dim variables, compute values of function
    elseif Nvar == Dim
        % Directory for storing temporary files
        settings.multicoreDir      = polyStruct.mcoredir;
        % Determines whether the master performs work or only coordinates
        settings.masterIsWorker    = true;
        % This is the number of function evaluations given to each worker
        % in a batch
        settings.nrOfEvalsAtOnce   = ceil(Nind / 50);
        % The maximum time a single evaluation (in this case, one
        % polynomial fit) should take, determines the timeout for a worker
        settings.maxEvalTimeSingle = 1000;
        % Determines whether a wait bar is displayed, 0 means no wait bar
        settings.useWaitbar = 0;
        % Post processing function info, we do not do any post-processing
        settings.postProcessHandle   = '';
        settings.postProcessUserData = {};

        for k = 1:size(Chrom,1)
            parameterCell{1,k} = {Chrom(k,:), polyStruct};
        end

        % avoid having another copy of data in memory
        clear polyStruct

        % Evaluate the polynomials by performing a least squares fit on each
        % one to find the best possible coefficients
        ObjVal = cell2mat(startmulticoremaster(@PolyFitScore, parameterCell, settings))';

    else
        error('size of matrix Chrom is not correct for function evaluation');
    end

end







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