find_generalisability

 ==============================================================
 FIND_GENERALISABILITY: Find the generalisability of a model.

 Code written by Katherine Smith, 2003

    GENERAL

      [mean_generalisability, std_generalisability] = 
       find_generalisability(model, n_iterations, verbose)

    INPUT/S


      -model:
           The model for which generalisability is calculated.

      -n_iteration:
           Number of iterations to control the complexity of
           this function?

      -verbose:
           Verbose flag.
           
    OUTPUT/S

      -mean_generalisability:
           The mean generalisability.

      -mean_generalisability:
           The standard deviation (?) of the generalisability values?

    PENDING WORK

      -Resolve meaning of inputs and outputs.
      -Add more comments to code.

    KNOWN BUG/S

      -None.

    COMMENT/S

      -Smith: Attempt to reconstruct an unseen example 
              and measure distance from the example
              (mean squared difference).

    RELATED FUNCTION/S

      FIND_SPECIFICITY

    ABOUT

      -Created:     November 23rd, 2003
      -Last update: Novermber 25th, 2003
      -Revision:    0.0.2
      -Author:      R. S. Schestowitz, University of Manchester
 ==============================================================

0001 function [mean_generalisability, std_generalisability] = find_generalisability(model, n_iterations, verbose)
0002 % ==============================================================
0003 % FIND_GENERALISABILITY: Find the generalisability of a model.
0004 %
0005 % Code written by Katherine Smith, 2003
0006 %
0007 %    GENERAL
0008 %
0009 %      [mean_generalisability, std_generalisability] =
0010 %       find_generalisability(model, n_iterations, verbose)
0011 %
0012 %    INPUT/S
0013 %
0014 %
0015 %      -model:
0016 %           The model for which generalisability is calculated.
0017 %
0018 %      -n_iteration:
0019 %           Number of iterations to control the complexity of
0020 %           this function?
0021 %
0022 %      -verbose:
0023 %           Verbose flag.
0024 %
0025 %    OUTPUT/S
0026 %
0027 %      -mean_generalisability:
0028 %           The mean generalisability.
0029 %
0030 %      -mean_generalisability:
0031 %           The standard deviation (?) of the generalisability values?
0032 %
0033 %    PENDING WORK
0034 %
0035 %      -Resolve meaning of inputs and outputs.
0036 %      -Add more comments to code.
0037 %
0038 %    KNOWN BUG/S
0039 %
0040 %      -None.
0041 %
0042 %    COMMENT/S
0043 %
0044 %      -Smith: Attempt to reconstruct an unseen example
0045 %              and measure distance from the example
0046 %              (mean squared difference).
0047 %
0048 %    RELATED FUNCTION/S
0049 %
0050 %      FIND_SPECIFICITY
0051 %
0052 %    ABOUT
0053 %
0054 %      -Created:     November 23rd, 2003
0055 %      -Last update: Novermber 25th, 2003
0056 %      -Revision:    0.0.2
0057 %      -Author:      R. S. Schestowitz, University of Manchester
0058 % ==============================================================
0059 
0060 % search parameters with simplex
0061 % hack in white width for now
0062 
0063 verbose='off';
0064     % RSS, added 8th December 2003 to get eval_1d_app_model_obj_fn to run
0065 
0066 white_width = 0.2;
0067 %subs = figure;
0068 for i=1:n_iterations
0069   [imagelist,example,points, his, los] = make_1d_images(1, size(model.intensity_model.pcs(:,1),1), white_width);
0070   [params, scores(i)] = fminsearch(@eval_msd,zeros(1,size(model.params,2)),optimset('Display',verbose,'TolX',0.001,'TolFun',0.001),model,example);
0071 %  scores(i)
0072 %  difference_image = abs(example - construct_model_example(params, model, 1:50));
0073 %  figure(subs),hold on,subplot(5,1,i),plot(difference_image),gca,title(['score: ' num2str(scores(i))]);
0074 end
0075 mean_generalisability = mean(scores);
0076 std_generalisability = std(scores);