Home > Source > Model > show_spec_v_modes.m

show_spec_v_modes

PURPOSE ^

==============================================================

SYNOPSIS ^

function [mean_specs, std_specs] = show_spec_v_modes(models, training_data, n_iters, ref_points_vec)

DESCRIPTION ^

 ==============================================================
 SHOW_SPEC_V_MODES: Shows the specificity of the models versus
                    the difference of modes.

 Code written by Katherine Smith, 2003

    GENERAL

      [mean_specs, std_specs] = show_spec_v_modes
             (models, training_data, n_iters, ref_points_vec)

    INPUT/S

      -models:
           The models to be investigated.

      -training_data:
           The training data that is used with the model.

      -n_iters:
           Number of iteration used to control complexity.

      -ref_points_vec:
           The points of the reference image
           
    OUTPUT/S

      -mean_specs:
           Mean specificity.

      -std_specs:
           Standard deviation of specificity.

    PENDING WORK

      -

    KNOWN BUG/S

      -None.

    COMMENT/S

      -

    RELATED FUNCTION/S

      

    ABOUT

      -Created:     November 23rd, 2003
      -Last update: Novermber 27th, 2003
      -Revision:    0.0.3
      -Author:      R. S. Schestowitz, University of Manchester
 ==============================================================

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function [mean_specs, std_specs] = show_spec_v_modes(models, training_data, n_iters, ref_points_vec)
0002 % ==============================================================
0003 % SHOW_SPEC_V_MODES: Shows the specificity of the models versus
0004 %                    the difference of modes.
0005 %
0006 % Code written by Katherine Smith, 2003
0007 %
0008 %    GENERAL
0009 %
0010 %      [mean_specs, std_specs] = show_spec_v_modes
0011 %             (models, training_data, n_iters, ref_points_vec)
0012 %
0013 %    INPUT/S
0014 %
0015 %      -models:
0016 %           The models to be investigated.
0017 %
0018 %      -training_data:
0019 %           The training data that is used with the model.
0020 %
0021 %      -n_iters:
0022 %           Number of iteration used to control complexity.
0023 %
0024 %      -ref_points_vec:
0025 %           The points of the reference image
0026 %
0027 %    OUTPUT/S
0028 %
0029 %      -mean_specs:
0030 %           Mean specificity.
0031 %
0032 %      -std_specs:
0033 %           Standard deviation of specificity.
0034 %
0035 %    PENDING WORK
0036 %
0037 %      -
0038 %
0039 %    KNOWN BUG/S
0040 %
0041 %      -None.
0042 %
0043 %    COMMENT/S
0044 %
0045 %      -
0046 %
0047 %    RELATED FUNCTION/S
0048 %
0049 %
0050 %
0051 %    ABOUT
0052 %
0053 %      -Created:     November 23rd, 2003
0054 %      -Last update: Novermber 27th, 2003
0055 %      -Revision:    0.0.3
0056 %      -Author:      R. S. Schestowitz, University of Manchester
0057 % ==============================================================
0058 
0059 n_models = size(models,1); 
0060            % retrieve number of models
0061 
0062 for i = 1:n_models
0063            % for each model
0064   n_modes = size(models(i).model.pcs,2); 
0065            % get number of modes in the current model
0066   for j=1:n_modes
0067            % calculate specificity using index j modes
0068     [mean_spec(i,j), std_spec(i,j)]= find_specificity(models(i).model,training_data, n_iters, ref_points_vec, 'n_modes', j);
0069   end
0070 end
0071 
0072 mean_specs = mean(mean_spec);
0073            % get the overall mean
0074 std_specs = mean(std_spec);
0075            % get the overall mean

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