measure_model

 ==============================================================
 MEASURE_MODEL: Measure the "goodness" of a model given some
                of its properties.

 Code written by Katherine Smith, 2003

    GENERAL

      score = measure_model(combined_variances, n_modes, model_evaluation_method, combined_model)

    INPUT/S

      -combined_model:
             The full combined model

      -combined_variances: (obsolete)
           The combined variances of the model.

      -n_modes:
           The total number of modes of the model.
 
      -method:
           Method of evaluating a model.

    OUTPUT/S

      -score:
           A score indicating the "goodness" of the model.

    PENDING WORK

      -Add some comments on process

    KNOWN BUG/S

      -None.

    COMMENT/S

      -Smith: add a small error to avoid zero values

    RELATED FUNCTION/S

      

    ABOUT

      -Created:     November 23rd, 2003
      -Last update: February 10th, 2004
      -Revision:    0.0.9
      -Author:      R. S. Schestowitz, University of Manchester
 ==============================================================

0001 function score = measure_model(combined_variances, n_modes, model_evaluation_method, combined_model)
0002 % ==============================================================
0003 % MEASURE_MODEL: Measure the "goodness" of a model given some
0004 %                of its properties.
0005 %
0006 % Code written by Katherine Smith, 2003
0007 %
0008 %    GENERAL
0009 %
0010 %      score = measure_model(combined_variances, n_modes, model_evaluation_method, combined_model)
0011 %
0012 %    INPUT/S
0013 %
0014 %      -combined_model:
0015 %             The full combined model
0016 %
0017 %      -combined_variances: (obsolete)
0018 %           The combined variances of the model.
0019 %
0020 %      -n_modes:
0021 %           The total number of modes of the model.
0022 %
0023 %      -method:
0024 %           Method of evaluating a model.
0025 %
0026 %    OUTPUT/S
0027 %
0028 %      -score:
0029 %           A score indicating the "goodness" of the model.
0030 %
0031 %    PENDING WORK
0032 %
0033 %      -Add some comments on process
0034 %
0035 %    KNOWN BUG/S
0036 %
0037 %      -None.
0038 %
0039 %    COMMENT/S
0040 %
0041 %      -Smith: add a small error to avoid zero values
0042 %
0043 %    RELATED FUNCTION/S
0044 %
0045 %
0046 %
0047 %    ABOUT
0048 %
0049 %      -Created:     November 23rd, 2003
0050 %      -Last update: February 10th, 2004
0051 %      -Revision:    0.0.9
0052 %      -Author:      R. S. Schestowitz, University of Manchester
0053 % ==============================================================
0054 
0055 Epsilon = 0.0001;
0056 current_method = model_evaluation_method;
0057 
0058 % size(combined_variances,2) % see number of modes
0059 if (strcmp(current_method,'determinant')),
0060           % see Kotcheff
0061     padded_variances = zeros(1,n_modes); 
0062           % a vector of zeroes the size of modes number
0063     if(size(combined_variances,2)<= n_modes),
0064       padded_variances(:,1:size(combined_variances,2)) = combined_variances;
0065     else
0066       padded_variances(:,1:n_modes) = combined_variances(:,1:n_modes);
0067     end
0068     padded_variances = padded_variances + Epsilon; 
0069           % Smith: add a small error to avoid 0
0070     score = prod(padded_variances); 
0071           % product of vector components i.e. determinant (Kotcheff)
0072 elseif (strcmp(current_method,'minimum_description_length')),
0073     msgbox('MDL: Not yet implemented');
0074 elseif (strcmp(current_method,'sum_of_log_eigenvalues')),     
0075     padded_variances = zeros(1,n_modes); 
0076           % a vector of zeroes the size of modes number
0077     if(size(combined_variances,2)<= n_modes),
0078       padded_variances(:,1:size(combined_variances,2)) = combined_variances;
0079     else
0080       padded_variances(:,1:n_modes) = combined_variances(:,1:n_modes);
0081     end
0082     padded_variances = padded_variances + 0.0001;
0083     score = sum(log(padded_variances));
0084 elseif (strcmp(current_method,'specificity')),
0085     msgbox('Specificity Evaluation: Not yet implemented'); % need ref_points and images score = find_specificity(combined_model, images, 25, ref_points_vec);
0086 elseif (strcmp(current_method,'generalisability')),  
0087     score = find_generalisability(combined_model, 25); % score = find_generalisability(combined_model........
0088 else
0089     error('Unknown model evaluation method.');
0090 end