find_specificity

 FIND_SPECIFICITY: Find the specificity of a model.

 Code written by Katherine Smith, 2003

    GENERAL

      [mean_specificity, std_specificity, all_specificities] =
       find_specificity(model, training_data, n_iterations, 
       ref_points_vec, varargin)

    INPUT/S

      -model:
           The model for which specificity is evaluated.

      -training_data:
           The training data used to evalaute the specificity
           of that model.

      -n_iterations:
           ?The number of iterations used in the evaluation process?

      -ref_points_vec:
           ?Reference points?

      -varargin:
           ?Variance argument input?
           
    OUTPUT/S

      -mean_specificity:
           The mean specificity.

      -std_specificity:
           The standard deviation of specificities.

      -all_specificities:
           All the specificity values found.

    PENDING WORK

      -Resolve meaning of inputs and outputs.

    KNOWN BUG/S

      -None.

    COMMENT/S

      -Smith: Construct examples from model and measure 
       the distance from the nearest example in training set
      -Full definition given by Rh. Davies.

    RELATED FUNCTION/S

      FIND_GENERALISABILITY

    ABOUT

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

0001 function [mean_specificity, std_specificity, all_specificities] = find_specificity(model, training_data, n_iterations, ref_points_vec, varargin)
0002 % FIND_SPECIFICITY: Find the specificity of a model.
0003 %
0004 % Code written by Katherine Smith, 2003
0005 %
0006 %    GENERAL
0007 %
0008 %      [mean_specificity, std_specificity, all_specificities] =
0009 %       find_specificity(model, training_data, n_iterations,
0010 %       ref_points_vec, varargin)
0011 %
0012 %    INPUT/S
0013 %
0014 %      -model:
0015 %           The model for which specificity is evaluated.
0016 %
0017 %      -training_data:
0018 %           The training data used to evalaute the specificity
0019 %           of that model.
0020 %
0021 %      -n_iterations:
0022 %           ?The number of iterations used in the evaluation process?
0023 %
0024 %      -ref_points_vec:
0025 %           ?Reference points?
0026 %
0027 %      -varargin:
0028 %           ?Variance argument input?
0029 %
0030 %    OUTPUT/S
0031 %
0032 %      -mean_specificity:
0033 %           The mean specificity.
0034 %
0035 %      -std_specificity:
0036 %           The standard deviation of specificities.
0037 %
0038 %      -all_specificities:
0039 %           All the specificity values found.
0040 %
0041 %    PENDING WORK
0042 %
0043 %      -Resolve meaning of inputs and outputs.
0044 %
0045 %    KNOWN BUG/S
0046 %
0047 %      -None.
0048 %
0049 %    COMMENT/S
0050 %
0051 %      -Smith: Construct examples from model and measure
0052 %       the distance from the nearest example in training set
0053 %      -Full definition given by Rh. Davies.
0054 %
0055 %    RELATED FUNCTION/S
0056 %
0057 %      FIND_GENERALISABILITY
0058 %
0059 %    ABOUT
0060 %
0061 %      -Created:     November 23rd, 2003
0062 %      -Last update: Novermber 25th, 2003
0063 %      -Revision:    0.0.2
0064 %      -Author:      R. S. Schestowitz, University of Manchester
0065 % ==============================================================
0066 
0067 args = u_packargs(varargin, 0,'n_modes', size(model.params,2));
0068 
0069 best_yet = ones(n_iterations,1)*size(training_data,1); %construct vector of as many ones as iternations and multiply these ones by the size of the training data
0070 stds = sqrt(model.variances); % get square roots of model variances
0071 stds = real(stds); % convert these to real numbers
0072 for i=1:n_iterations % for input number of iterations
0073   params = zeros(1,size(model.params,2)); 
0074          % paramters set to 0, their size being the length of second bunch of model parameters
0075   invalid_params = 1; % set initially to invalid
0076   while(invalid_params) % and iterate while no validity proven
0077     params(1:args.n_modes) = randn(1,args.n_modes).*stds(1:args.n_modes);
0078       % for all modes, set them to a number in the range of their total number * the variance in the position of number of modes
0079     [example,points] = construct_model_example(params, model, ref_points_vec);
0080      % create a few examples given the parameters
0081     if(~is_invalid(points)) % if all our valid
0082       invalid_params = 0;  % break out of loop
0083     end
0084   end
0085 
0086   for j=1:size(training_data,2)
0087     distance = msd(example, training_data(:,j)); % get mean squred distance
0088     if(distance < best_yet(i)) % and try to find the closest yet
0089       best_yet(i) = distance; 
0090     end
0091   end
0092   if(best_yet(i) > 10)
0093     disp('stop here');
0094   end
0095 end
0096 mean_specificity = mean(best_yet);
0097 std_specificity = std(best_yet);
0098 all_specificities = best_yet;