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