RGCmorphology/getConfusionMatrixLatexFeldmeyer.m at master · Hjorthmedh/RGCmorphology · GitHub

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% This function just generates the latex code for the confusion
% matrix in the article.

clear all, close all

rng('shuffle')

% Several cells have the same number of branch points, normally
% that is not a problem, but for TRHR there are only 9 cells, 2 of
% which have the same number of branch points (253):
% P40-TRHR-03222012-r2c2-40x.xml
% TRHR-07182012-a1r1-cell1-40x-0.xml
% Odds that in a 5-fold cross validation none of the others are
% picked for four of the folds are:
% (1/5)^6 = 6.4e-5
% When doing 1000 folds we have a 6.4% risk, and well it happened.


r = RGCclass(0);
r.lazyLoad('Feldmeyer');

% featuresUsed = { 'dendriticDensity', ...
%                  'somaArea', ...
%                  'totalDendriticLength', ...
%                  'meanTerminalSegmentLength'};

% featSet = 'Best3Set';
% featSet = 'A12Set';
% featSet = 'ManualUncorrSet';
%featSet = 'Best5';
featSet = 'Feldmeyer';

switch(featSet)

  case 'Feldmeyer'

    featuresUsed = {'branchAssymetry', 'dendriticDensity', 'numBranchPoints' };

  case 'Gulyas'

    featuresUsed = {'dendriticField', ...
                    'fractalDimensionBoxCounting', ...
                    'meanSegmentLength', ...
                    'meanSegmentTortuosity', ...
                    'totalDendriticLength' }

 case 'Best3Set'
   % Best 3 feature set for classification
   featuresUsed = { 'fractalDimensionBoxCounting', ...
                    'somaArea', ...
                    'meanTerminalSegmentLength' };

  case 'A12Set'
    % A good 12 feature set for classification??? Dont remember
    featuresUsed = {'branchAssymetry', ...
                    'dendriticDensity', ...
                    'dendriticDiameter', ...
                    'dendriticField', ...
                    'densityOfBranchPoints', ...
                    'fractalDimensionBoxCounting', ...
                    'meanBranchAngle', ...
                    'meanTerminalSegmentLength', ...
                    'numBranchPoints', ...
                    'numSegments', ...
                    'somaArea', ...
                    'totalDendriticLength' }

  case 'ManualUncorrSet'
    % Trying to pick uncorrelated features, using at correlation matrix
    featuresUsed = { 'numBranchPoints', ...
                     'meanSegmentLength', ...
                     'dendriticField', ...
                     'totalDendriticLength', ...
                     'somaArea', ...
                     'biStratificationDistance', ...
                     'meanBranchAngle', ...
                     'meanSegmentTortuosity', ...
                     'stratificationDepth' };

  case 'Best5'
    % Best 5 feature set, from exhaustive search
    featuresUsed = { 'densityOfBranchPoints', ...
                     'dendriticField', ...
                     'fractalDimensionBoxCounting', ...
                     'meanTerminalSegmentLength', ...
                     'somaArea' };


  case 'Best6'
    % Best 6 feature set, from exhaustive search
    % Redid it with more iterations (20 vs 5), and then DBP changed to NBP
    featuresUsed = { 'numberOfBranchPoints', ...
                     'dendriticField', ...
                     'fractalDimensionBoxCounting', ...
                     'meanSegmentTortuosity', ...
                     'meanTerminalSegmentLength', ...
                     'somaArea' };

  otherwise
    fprintf('Unknown featSet: %s', featSet)
    keyboard
end

r.setFeatureMat(featuresUsed);

% [CM,CMsd] = r.confusionMatrixCrossValidation(5,1000);

% disp('\nConfusion matrix with cross validation')

% % strHeader = 'C = \\left[ \\begin{array}{ccccc}\n';
% strLine = ['\\textbf{%s} & $%.2f \\pm %.2f$  &  $%.2f \\pm %.2f$  &  $%.2f \\pm %.2f$  &  $%.2f \\pm %.2f$   &  $%.2f \\pm %.2f$\\\\\n'];
% strEnd = '\\end{array} \\right]\n';

% strHeader = ['\\begin{table}\\begin{tabular}{llllll}\n' ...
%              '& \\bf{%s} & \\bf{%s} & \\bf{%s} & \\bf{%s} & \\bf{%s}\\\\\n'...
%             '\\hline\n'];
% strEnd = '\\end{tabular}\\end{table}\n';

% fprintf(strHeader, ...
%         r.RGCtypeName{find(r.RGCtypeID == 1,1)}, ...
%         r.RGCtypeName{find(r.RGCtypeID == 2,1)}, ...
%         r.RGCtypeName{find(r.RGCtypeID == 3,1)}, ...
%         r.RGCtypeName{find(r.RGCtypeID == 4,1)}, ...
%         r.RGCtypeName{find(r.RGCtypeID == 5,1)})

% for i = 1:5

%   fprintf(strLine, ...
%           r.RGCtypeName{find(r.RGCtypeID == i,1)}, ...
%           CM(i,1),CMsd(i,1), ...
%           CM(i,2),CMsd(i,2), ...
%           CM(i,3),CMsd(i,3), ...
%           CM(i,4),CMsd(i,4), ...
%           CM(i,5),CMsd(i,5))

% end

% fprintf(strEnd)

% truePos = diag(CM)'./sum(CM);

% for i = 1:5
%   fprintf('%s: %.1f %%\n', r.RGCtypeName{find(r.RGCtypeID == i,1)}, ...
%           truePos(i)*100)
% end


disp('\nConfusion matrix generated with leave one out:')

CM2 = r.confusionMatrixLeaveOneOut();
makeLatexTableHeaders(r.RGCuniqueNames, r.RGCuniqueNames, CM2, ...
                      '%d','Predicted Class','True Class')